DOI:10.2214/AJR.07.3472
AJR 2008; 191:1057-1069
© American Roentgen Ray Society
Pulmonary Nodules: Detection, Assessment, and CAD
Francis Girvin1 and
Jane P. Ko
1 Both authors: Department of Radiology, Thoracic Imaging, New York University
Medical Center, 560 1st Ave., New York, NY 10016.
Received November 29, 2007;
accepted after revision May 4, 2008.
Address correspondence to F. Girvin.
Abstract
OBJECTIVE. The imaging of pulmonary nodules is an evolving and
dynamic field. In this review, we discuss the detection and multitechnique
characterization of pulmonary nodules, emphasizing the impact of technological
advances on both noninvasive and invasive evaluation and surveillance. The
potential contribution of MRI, evolving imaging-guided techniques, and
computer applications are also discussed.
CONCLUSION. Advances in MDCT and PET and the potential contribution
of fast-imaging MRI sequences and computer applications should continue to
improve our evaluation of the solitary pulmonary nodule.
Keywords: chest imaging • computer-aided diagnosis (CAD) • CT • lung • MRI • PET/CT • pulmonary nodules
Introduction
Pulmonary nodules are a common incidental finding on imaging studies,
particularly MDCT. Advances in CT and PET have improved characterization of
nodules, helping to differentiate benign from malignant lesions noninvasively.
Many nodules, however, remain indeterminate and require either temporal
characterization to confirm stability or invasive assessment for a definitive
diagnosis. In this review, we discuss the role of imaging in the detection and
characterization of pulmonary nodules, emphasizing the impact of advances in
CT technology on management strategies. Advances in computer-aided diagnosis
(CAD) in terms of both nodule detection and evaluation are also discussed.
Etiology of the Solitary Pulmonary Nodule
A pulmonary nodule is defined as a "round opacity, at least
moderately well marginated and no greater than 3 cm in maximum diameter"
[1]. Solitary pulmonary nodules
(SPNs) may be caused by a variety of benign and malignant disorders
[2–5].
CT is significantly more sensitive than standard radiography for nodule
detection, and with the increasing use of MDCT, small nodules less than 1 cm
are detected with increasing frequency. As a result, small benign lesions that
would otherwise have been invisible on radiographs are now detected. Henschke
et al. [6], in the Early Lung
Cancer Action Project (ELCAP), reported a detection rate for noncalcified
nodules three times greater with low-dose CT compared with chest radiography
[6]. Of those patients with
nodules, 11% were eventually diagnosed as lung cancer, the majority as stage I
tumors.
Chest Radiography
Although less sensitive and specific than chest CT
[6,
7], radiography often reveals
nodules of the chest. Ketai et al.
[7] reported that 77% of
nodules smaller than 7 mm visualized on a chest radiograph are calcified. Very
small nodules that are visible on radiographs therefore have a higher
probability of representing calcified granulomas.
The detection of a pulmonary nodule on radiography is limited by the number
of overlapping structures and low contrast of the nodule on radiography in
comparison with CT [8]. Missed
nodules on the frontal radiograph include those at the apices and lung bases
as well as centrally located lesions
[8–10]
(Fig. 1A,
1B,
1C). The failure to diagnose a
nodule can relate to an inadequate or incomplete visual survey or to
interpretative failures
[11–13].
A systematic approach toward the interpretation of the radiograph improves the
detection of abnormalities.
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Fig. 1A —Missed lesion on radiograph in 56-year-old man with large
cell neuroendocrine carcinoma. On radiograph, sizable lesion was not detected
overlapping left first costochondral cartilage. Asymmetric density in this
region is clue to nodule in this region.
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For chest radiography, dual-energy and temporal-subtraction radiography
show significant potential for enhanced detection of subtle and often
overlooked lung lesions on radiographs
[14–16].
Dual-energy chest radiography exploits the difference in the energy-dependent
attenuation between bone and soft tissues to produce tissue-selective images.
By more clearly depicting calcification, the technique greatly aids in
characterizing pulmonary nodules as benign
[17,
18]. By reducing anatomic
noise from overlying bones, the technique also has improved sensitivity for
noncalcified lung nodules
[19–21].
Temporal-subtraction technology enables easier visualization of areas that
have changed between radiographs obtained at different time points
[16,
22,
23].
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Fig. 2C —73-year-old man with hamartoma. Histopathology slide from
asymptomatic 62-year-old man with hamartoma shows chondroid tissue correlating
with calcifications in this entity (H and E, x40) (Courtesy of Nonaka D, New
York, NY)
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Fig. 3A —62-year-old woman who presented with growth of subsolid
nodule representing adenocarcinoma with pleural invasion. CT image through
nodules in right middle lobe shows two nodules that are subsolid, with one
nearly entirely ground-glass (lateral) and the other part solid, part
ground-glass in attenuation (medial).
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Fig. 3B —62-year-old woman who presented with growth of subsolid
nodule representing adenocarcinoma with pleural invasion. In CT image obtained
3 years before A, lateral nodule is evident, yet smaller and more
medial nodule is not evident.
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MDCt
Technique
The introduction of MDCT has minimized misregistration artifacts and
improved spatial and temporal resolution, thereby improving nodule detection
and characterization. IV contrast administration is not routinely required.
However, it may prove useful in cases in which the suspected nodule is located
adjacent to the mediastinum or hila or if there is a suspicion for an
arteriovenous malformation (AVM). Routine reconstructions typically are
composed of 5-mm sections with a nontargeted field of view. A targeted field
of view with thin sections (1–1.5 mm) through an area of interest,
however, greatly improves spatial resolution and hence nodule assessment
[24].
Detection on MDCT
Despite being more sensitive than radiography, nodules are overlooked on
MDCT because of their central location (either within bronchi or adjacent to
vessels), their small size and faint attenuation, lower lobe location, or
location adjacent to other parenchymal abnormalities such as inflammatory
lesions
[25–30].
Postprocessing techniques such as maximum intensity projection (MIP), volume
rendering, and cine viewing of data sets have been shown to improve nodule
detection
[31–34].
Characterization by MDCT
Morphologic features including shape, margin, cavitation, and attenuation
are helpful for identifying those nodules that are more likely to represent
malignancy.
Margin—Irregular or spiculated margins are highly suggestive
of bronchogenic carcinoma [4,
35,
36]. Lobulation because of
differential growth within nodules is associated with both primary and secon
dary malignancies; however, it has also been described in benign lesions, such
as hamartomas or granulomas
[4]. Smooth borders and the
presence of a pleural tail are seen in a range of benign and malignant
entities and are therefore of little practical assistance
[3,
4,
37]. Zerhouni et al.
[5] showed that 41 of 130
nodules with smooth edges were malignant.
CT halo sign—An ill-defined rim of ground-glass attenuation
has been described as the CT halo sign and correlated pathologically with
perinodular hemorrhage, tumor infiltration, or nonhemorrhagic inflammation.
Although nonspecific, the most common cause of a CT halo sign is infection,
most notably invasive aspergillosis. Bronchoalveolar cell carcinoma is
reported to be the most common solitary nodule demonstrating the CT halo sign
in an immunocompetent patient
[38].
Density and internal characteristics— Common benign patterns
of calcification include laminated, central, diffuse, and popcorn
calcifications (Fig. 2A,
2B,
2C). Stippled or eccentric
calcifications are associated with malignant causes, occurring in 13.4% of
cases [35]. In a study by
Grewal and Austin [39],
intratumoral calcifications were seen on CT in 10% of 500 patients with lung
cancer, tending to occur in larger and more central cancers. Macroscopic fat
within a nodular density has been associated with benign causes such as
hamartomas and reported in up to 50% of these lesions
[40]. Cavitation within a
nodule is seen in necrotic tumors as well as infectious and inflammatory
lesions. Bronchoalveolar cell carcinoma can also show small internal lucencies
from lepidic growth of tumor cells with patent bronchi
[4].
Subsolid Nodules
Nodules containing a component of ground-glass attenuation are termed
"subsolid" and include pure ground-glass, as well mixed solid and
ground-glass lesions (part-solid) (Fig.
3A,
3B). In the ELCAP study, 44 of
233 (19%) instances of positive results on the baseline screening were
subsolid. In this study, Henschke et al.
[41] also reported rates of
malignancy for solid and subsolid nodules as 7% and 34%, respectively, with
part-solid nodules having rates of 63% and pure ground-glass nodules, 18%.
Malignancies typically associated with these subsolid nodules are those
that form the spectrum of primary lung adenocarcinoma and its potential
precursors, ranging from a premalignant entity termed "atypical
adenomatous hyperplasia" to low-grade broncho alveolar cell carcinoma
and invasive adenocarcinoma
[41–47]
(Fig. 4). Like bronchoalveolar
cell carcinoma, atypical adenomatous hyperplasia has been reported as a
ground-glass nodule with a round or oval shape and distinct borders
[48–52].
Subsolid nodules greater than 1 cm are more likely to represent
bronchoalveolar cell carcinoma rather than atypical adenomatous hyperplasia
[48,
53]. Ground-glass nodules less
than 1 cm may represent either atypical adenomatous hyperplasia or possibly an
early form of bronchoalveolar cell carcinoma.
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Fig. 4 —Chart shows spectrum of adenocarcinoma. Noguchi pathologic
subtypes are on left with correlating appearance of subsolid nodules on CT on
right. Subsolid nodules of pure ground-glass attenuation (top)
correlate with more indolent constituents and predominantly solid attenuation
with more aggressive forms of adenocarcinoma. BAC = bronchoalveolar carcinoma,
VDT = volume doubling time.
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The spectrum of bronchoalveolar cell carcinoma and invasive adenocarcinoma
has been pathologically graded by Noguchi and Shimosato
[42] into types A through F,
representing less to more aggressive entities. The correlation of CT with
entities categorized according to this system has revealed that the presence
of solid portions on CT in a ground-glass nodule is concerning for higher
grades of adenocarcinoma [43,
47]
(Fig. 4). On the other hand,
Ohta et al. [54] looked at 87
resected lung specimens that showed ground-glass opacity on CT, including 47
lesions that were pure ground-glass, and found the frequency of invasion of
metastasis was low in pure ground-glass opacities.
Nodule Size and Measurement
The risk of malignancy is strongly cor related with nodule size
[55]. The clinical context,
however, is paramount because even small nodules less than 5 mm may be
significant in a patient with a known malignancy. Ginsberg and colleagues
[56], for example, in a study
of oncology patients undergoing video-assisted thoracoscopic resection of
nodules, showed that nodules 5 mm or smaller were malignant in 115 of 275
(42%) patients with cancer. Nodule measurements, however, are subject to
significant inter- and intraobserver variation, which can lead to erroneous
growth estimations
[57–59].
The use of automated or semiautomated measurement methods has been reported to
reduce the impact of observer variation
[58–66].
Because nodule growth is a 3D process, the use of 3D volume measurement
methods may provide a more accurate and reproducible assessment of size and
growth than axial measurements. Even 3D techniques, however, are susceptible
to precision error [67,
68]. Goodman et al.
[68], for example, looked at
the reproducibility of lung nodule volumes in patients scanned three times in
the same session and found an interscan volumetric variation of ± 20%.
In another study, Boll and colleagues
[69] used cardiac gating and
showed that small nodules near the heart show as much as 34% volume change
during the cardiac cycle. Thus, although promising, automated volume
techniques are not without their limitations.
Further Characterization by MDCT: Temporal Assessment, CT-Guided Biopsy, Nodule Enhancement and Dual-Energy Evaluation
Nodule follow-up—A widely applied growth expression is the
volume doubling time, the time for a nodule to double in volume. Volume
doubling times between 20 and 400 days have been reported with bronchogenic
carcinoma
[70–72].
Volume doubling times less than 20–30 days are suggestive of infections
but have been associated with lymphoma or rapidly growing metastases. Volume
doubling times greater than 400 days have been most commonly associated with
benign lesions such as hamartomas and granulomas. On the basis of these data
derived from radiographic assessment of nodule size, it has been suggested
that a nodule stable for at least 2 years is a reliable indicator of benignity
[70]. This criterion, however,
does not apply to subsolid nodules because low-grade adenocarcinoma and bron
choalveolar cell carcinoma have been identified as having doubling times
approaching 1,346 days
[43].
A majority of the current knowledge pertaining to the significance of small
nodules has been obtained from low-dose CT screening trials for lung cancer in
which patients had substantial smoking histories. The knowledge gained has
contributed to the statement issued by the Fleischner Society pertaining to
the surveillance of small incidentally detected indeterminate nodules and more
recently to the American College of Chest Physicians evidence-based clinical
practice guidelines [73,
74]. In the Fleischner Society
statement, the authors concluded that even in smokers, the likelihood that
nodules smaller than 4 mm represent lethal cancers is very low (less than 1%).
For nodules in the 8-mm range the likelihood is higher, approximately
10%–20%.
The investigators in the ELCAP study reported that if their 378 nodules
that were less than 5 mm were reimaged before 1 year, all imaging would have
been unproductive, and therefore nothing was lost by omitting these studies
and having the first repeat scan at 1 year
[75]. Therefore, the
Fleischner Society guidelines have stratified patients into low- and high-risk
groups and have recommended that routine follow-up is not required for
low-risk patients with very small nodules measuring 4 mm or less. The authors
also suggest that follow-up can be shortened to 12 months rather than 2 years
for certain lesions, depending on individual patient risk and nodule size
(Table 1). It should be
stressed that these guidelines should be applied with knowledge of the
clinical scenario. For example, patients who are known or suspected to have
malignancy, or in whom the finding of lung nodules may reflect active
infection, may require more frequent imaging or intervention. In addition,
these guidelines are not applicable to subsolid nodules.
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Fig. 5A —81-year-old man with history of bronchiectasis and atypical
mycobacterial infection with right upper lobe nodule representing poorly
differentiated non-small-cell lung cancer. Chest CT shows 2-cm irregularly
marginated nodule in right upper lobe, partially occluding posterior
subsegmental division of posterior segmental bronchus.
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Fig. 5B —81-year-old man with history of bronchiectasis and atypical
mycobacterial infection with right upper lobe nodule representing poorly
differentiated non-small-cell lung cancer. CT-guided transbronchial biopsy
displayed on bone window provides better visualization of bronchoscope and
forceps tip just proximal to lesion.
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Fig. 5C —81-year-old man with history of bronchiectasis and atypical
mycobacterial infection with right upper lobe nodule representing poorly
differentiated non-small-cell lung cancer. Images of virtual bronchoscopy
including endoluminal view of occluded subsegmental bronchus (F).
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Fig. 5D —81-year-old man with history of bronchiectasis and atypical
mycobacterial infection with right upper lobe nodule representing poorly
differentiated non-small-cell lung cancer. Images of virtual bronchoscopy
including endoluminal view of occluded subsegmental bronchus (F).
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Fig. 5E —81-year-old man with history of bronchiectasis and atypical
mycobacterial infection with right upper lobe nodule representing poorly
differentiated non-small-cell lung cancer. Images of virtual bronchoscopy
including endoluminal view of occluded subsegmental bronchus (F).
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Fig. 5F —81-year-old man with history of bronchiectasis and atypical
mycobacterial infection with right upper lobe nodule representing poorly
differentiated non-small-cell lung cancer. Images of virtual bronchoscopy
including endoluminal view of occluded subsegmental bronchus (F).
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Fig. 5G —81-year-old man with history of bronchiectasis and atypical
mycobacterial infection with right upper lobe nodule representing poorly
differentiated non-small-cell lung cancer. Bronchoscopic correlation image
shows infiltrative endobronchial lesion.
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CT-guided biopsy—Typically, sampling is performed on nodules
with a higher probability of malignancy, such as those of larger size and with
aggressive features. Sampling methods include transthoracic needle aspiration
and biopsy (TTNAB), transbronchial needle aspiration and biopsy (TBNA), and
minimally invasive video-assisted surgical methods. Nodules that are ideal for
percutaneous sampling should be accessible without crossing major vascular
structures and fissures [76,
77]. TBNA enables biopsy of
lesions typically centrally located and involving the airways, with yields of
19% and 62% reported [78]. The
use of thinner bronchoscopes is increasing the number of nodule candidates for
TBNA [79,
80]. Exciting new imaging
technology has enabled the development of virtual CT bronchoscopy as well as
imaging-guided techniques including direct CT-guided bronchoscopy (Fig.
5A,
5B,
5C,
5D,
5E,
5F,
5G) and electromagnetic-guided
bronchoscopy, for sampling of small peripheral lesions while minimizing the
use of fluoroscopy or CT fluoroscopy. The electromagnetic navigation system
uses a bronchoscopic probe sensor placed within an electromagnetic field
created around the chest. Real-time position and orientation information is
generated and superimposed on previously acquired thin-section CT images
during the procedure to enable navigation to the lesion
[80–82].
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Fig. 6A —76-year-old man with renal cell carcinoma metastasis assessed
with nodule enhancement study. Patient presented with incidental nodule on
chest radiograph that prompted CT evaluation. CT image displayed on lung
windows shows solitary 7-mm nonspecific lung nodule.
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Fig. 6B —76-year-old man with renal cell carcinoma metastasis assessed
with nodule enhancement study. Patient presented with incidental nodule on
chest radiograph that prompted CT evaluation. Unenhanced CT image shows nodule
that measures 56 HU.
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Fig. 6C —76-year-old man with renal cell carcinoma metastasis assessed
with nodule enhancement study. Patient presented with incidental nodule on
chest radiograph that prompted CT evaluation. On CT image displayed on lung
windows, at 2 minutes nodule measures 109 HU with peak enhancement of 53 HU.
Subsequent workup revealed occult renal cell cancer and excision of lung
nodule confirmed renal cell carcinoma metastasis to lung.
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Fig. 7C —81-year-old woman with adenocarcinoma of lung and
false-negative nodule enhancement study. On contrast-enhanced CT image, peak
nodule attenuation is 39 HU at 2 minutes, representing peak enhancement of 13
HU. Lesion was not suitable for nodule enhancement study, given large size and
obvious central necrosis.
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CT nodule enhancement—Nodules greater than 7 mm and less
than 3 cm are amenable to nodule enhancement studies
[83,
84]. One method validated by a
multicenter trial entails acquiring thin-section CT images through a nodule
before and 1, 2, 3, and 4 minutes after the administration of IV contrast
material at 2 mL/s (Fig. 6A,
6B,
6C). Absence of significant
enhancement of 15 HU or less is strongly predictive of benignity, whereas
those nodules with greater degrees of enhancement may reflect either
inflammatory or malignant processes. As shown by Swensen et al.
[83], such a technique had 98%
sensitivity for malignancy, although a 58% specificity for benignity. The
relatively lower sensitivity for benignity was related to difficulty in
differentiating active inflammation from malignancy. This technique is not
suitable for calcified lesions or lesions greater than 3 cm with a higher
chance of necrosis and therefore areas that may fail to enhance (Fig.
7A,
7B,
7C). Peak attenuation of
nodules has been correlated positively with microvessel density and vascular
endothelial growth factor (VEGF) staining on pathology with malignant
etiologies having higher VEGF expression than benign entities
[85,
86].
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Fig. 8A —61-year-old woman with lung cancer with bronchoalveolar cell
carcinoma components and negative PET/CT. CT image 1.0-mm section through
right upper lobe subsolid nodule shows small solid component within
lesion.
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Fig. 8F —61-year-old woman with lung cancer with bronchoalveolar cell
carcinoma components and negative PET/CT. Histopathology slide shows lepidic
growth pattern typical of bronchoalveolar cell carcinoma (H and E, x200).
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Fig. 9C —54-year-old woman with lung cancer and lung metastases.
Three-dimensional maximum-intensity-projection, T1-weighted, volumetric
interpolated breath-hold examination image provides better depiction of
small-to-intermediate size lesions compared with HASTE image in this
patient.
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Dual-energy CT—This technique is now feasible, given the
introduction of dual-source CT technology. Simultaneous 80 kV and 140 kV
images can be obtained and the varying behavior of different tissue composites
when exposed to the two different x-ray spectra enables identification of
areas of fat, calcium, bone, soft tissue, and iodinated contrast uptake
[87–89].
Post-processing techniques can be performed that create virtual unenhanced
images from a contrast-enhanced data set, and the virtual unenhanced images
can be subtracted from the contrast-enhanced images to identify areas of
enhancement. Although still in their infancy, such techniques are promising
for measuring lung nodule and tumor perfusion.
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Fig. 10C —Display images from a computer-aided diagnosis (CAD) device.
Nodule marking multiplanar reformation CT image. Round CAD marks (red
circles) can be displayed and either deleted or accepted after
interpreter evaluates CT images and places own marks (green
squares).
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Nodule Evaluation with PET
Metabolic activity within lung nodules may be assessed with PET using the
glucose analog 8F-FDG. FDG uptake in tissues reflects metabolic
activity and perfusion. PET for diagnosing a nodule as malignant has been
shown to have a sensitivity and specificity of 96% and 88%, respectively
[90,
91]. As is the case with
nodule enhancement, the lower positive predictive value relates to the
false-positives created by infectious and inflammatory causes
[90,
92,
93]. The negative predictive
value and sensitivity of PET are lowered by its decreased spatial resolution.
Therefore, for lesions less than 1 cm, the utility of PET is less, although
with improvement in technology the evaluation of nodules approximately 7 mm is
possible [94]. In addition,
tumors of lower metabolic activity have been associated with false-negative
PET studies, such as carcinoid tumors and bronchoalveolar cell carcinoma (Fig.
8A,
8B,
8C,
8D,
8E,
8F).
MR Assessment of the Pulmonary Nodule
To date, MRI has had a limited role in the evaluation of lung nodules
because of its limited spatial resolution compared with MDCT, high
susceptibility differences between airspaces and the pulmonary interstitium,
and the presence of respiratory and cardiac motion on sequences with low
temporal resolution. However, as the technique evolves there is immense
opportunity for nodule characterization, particularly with fast acquisition
sequences with high temporal resolution.
A number of factors favor HASTE as the sequence of choice for MRI of the
lungs. Most neoplastic tissues show high T2 relaxivity and consequent high
signal intensity relative to surrounding air-filled, low-signal, pulmonary
parenchyma. Furthermore, vessels are depicted as flow voids without any
apparent signal. Schroeder and colleagues
[95] showed a sensitivity of
95.7% for lung nodules between 6 and 10 mm using cardiac-gated axial and
coronal HASTE sequences with a 5-mm slice thickness. Sensitivity, however,
dropped to 73% for lesions less than 3 mm. In another study by Vogt et al.
[96], HASTE sequences had a
sensitivity of 94.9% for nodules between 5 and 10 mm in diameter (Fig.
9A,
9B,
9C).
Bruegel and colleagues [97]
recently compared the value of different turbo spin-echo (TSE) and 3D
gradient-echo (volume interpolated breath-hold [VIBE]) MRI sequences of the
lung for detecting pulmonary metastases in 28 patients with 225 lesions.
Although they found HASTE images to have the lowest rate of physiologic motion
artifacts, these sequences performed less well in lesion detection compared
with T2-weighted TSE. The authors suggest using a breath-hold TSE sequence for
imaging of the lung. The addition of respiratory gating also poses a specific
problem for lung imaging because image acquisition is triggered at end
expiration when lung volumes are low, which may impair lesion conspicuity.
Although MDCT has superior sensitivity for small nodules in the 1–3 mm
range, the significance of very small incidental nodules in low-risk patients
is questionable. In younger patients without risk factors, MR could
potentially provide a useful alternative to MDCT for follow-up of a known
lesion measuring greater than 5 mm.
As a parallel to CT nodule enhancement, the enhancement characteristics of
nodules on MR have also been investigated
[98–102].
Analysis has primarily measured signal intensity variables such as maximal
enhancement ratio and slope of contrast uptake. Ohno et al.
[98] looked at maximum
relative enhancement ratios using a 3D gradient-echo sequence and were able to
differentiate malignant and active infection nodules from noninfectious benign
nodules, with sensitivity, specificity, and accuracy of 100%, 70%, and 95%,
respectively. More recently, Kono et al.
[100] in a large cohort of
421 patients with lung nodules showed an early peak pattern of enhancement
with lung cancer and active infection. As with CT enhancement and PET, the
differentiation of malignant and active infectious nodules remains an obstacle
that requires further evaluation. Some authors propose that a clinical
preselection could be applied, exclud ing patients with symptoms of acute
pulmonary infection or active inflammation. In time, MR may play a more
practical clinical role in the morphologic evaluation of nodules and also in
the temporal characterization of nodules greater than 5 mm, thus eliminating
the need for ionizing radiation.
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Fig. 11B —51-year-old man with gastric cancer and lung metastasis. CT
image at 10-month follow-up shows 2-cm nodule, which shows importance of
clinical context, even regarding very small lesions.
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CAD and Nodule Assessment
The potential impact of CAD in thoracic radiology is immense. CAD
techniques aim to provide a method of assisting interpretation by means of
computerized image analysis. CAD schemes have been reported to improve reader
detection of lung nodules on radiographs in large-scale observer tests for
both radiologists and radiologists in training
[103–105].
The CAD schemes so far have primarily concentrated on the frontal radiograph,
although more recently investigation has addressed the lateral radiograph
[106].
A large amount of CAD research has been devoted to nodule detection on CT
[30,
65,
107–123]
(Fig. 10A,
10B,
10C,
10D). The evolution in
computer-assisted technology has been in part driven by the increasing use of
MDCT, rendering smaller abnormalities apparent, yet generating a larger amount
of image data for review. CAD can potentially take advantage of the benefits
of thin-section images, although the amount of image data that the radiologist
addresses is kept within reason so that the usual approach to interpretation
is not hindered.
Sensitivity and specificity vary widely among CAD systems relative to the
diversity of algorithms, CT input, and varying populations of nodules in which
CAD has been studied. The need for increased sensitivity, however, is offset
by the desire to minimize the number of false-positive detections, which are
often significant in number, particularly when lower size criteria for nodules
are used [116]. Clinical use
of CAD will likely be hindered unless false-positive detections are
minimized.
CAD has been developed primarily to serve as a second reader. A number of
studies lend support to this idea of CAD as a second interpreter, providing
improved radiologist sensitivity for nodule detection with the assistance of
CAD [112,
113,
118,
122]. CAD has been shown to
identify clinically significant nodules that were overlooked by radiologists.
Armato et al. [30], for
example, found that CAD detected 84% of 38 missed lung cancers. The
ground-glass-containing nodule, however, remains problematic, and the
algorithms available so far have focused on CAD for detection of the solid
nodule.
The application of CAD is not only limited to lesion detection. CAD also
may potentially assist the radiologist in terms of the estimation of
malignancy [121,
124]. CAD systems that
integrate both CT and PET information may also improve characterization of
lung nodules in the future. Nie and colleagues
[125] studied a semiautomatic
computer-aided method using features from both PET and CT scans. The scheme
was able to differentiate benign from malignant nodules better than those
based on either PET or CT data alone. CAD methods can aid the assessment of
nodule size and volume, attenuation, and enhancement characteristics by
performing global analysis of high-resolution MDCT data of the entire nodule
while minimizing the need for user intervention.
Computer techniques can also decrease the tedium involved in the temporal
characterization of nodules, particularly when the nodules are multiple and
imaged at many time points. Such techniques can automatically identify the
corresponding CT sections for a particular nodule, decreasing reader
interpretation time [65,
126–129].
The use of CAD to improve textural characterization may in time prove
beneficial for assessing textural change within subsolid lesions in which
increased solid elements may indicate transformation to a more malignant
histologic grade [4,
130].
For CAD technology to be fully used in all aspects of nodule evaluation,
the integration of multifunctional CAD platforms into PACS is necessary to
enable easy accessibility for the user during reader interpretation.
Management of the Pulmonary Nodule
The management of a patient with an SPN requires a case-by-case evaluation
of both radiologic features and clinical factors (Fig.
11A,
11B). Consideration of patient
age, smoking history, and history of malignancy is particularly important. A
close liaison between the radiologists, interventional radiologists, and
referring clinicians is essential in this regard. The availability of prior
imaging studies is extremely useful for solid nodules because stability over
time may lead to consideration of a conservative approach given the lower
likelihood of malignancy.
Larger nodules greater than 7 mm are amenable to noninvasive and invasive
characterization. For solid nodules, lack of enhancement and low metabolic
activity are reassuring for a benign lesion, although low-grade neoplasia,
such as carcinoid, remains a consideration. Subsolid nodules are unsuitable
for nodule enhancement studies and have been associated with low metabolic
activity on PET. Alternatively, positive nodule enhancement studies and PET
scans should also be interpreted in the clinical context because of the
overlap with inflammatory conditions. Biopsy can often confirm that a nodule
is malignant or infectious, although the diagnosis of a noninfectious benign
lesion is less successfully achieved. Lastly, for those incidentally detected
small 8-mm or less nodules, the Fleischner Society has provided useful
guidelines for monitoring small nodules that have a lower probability for
malignancy, particularly those 4 mm or smaller in size.
In conclusion, management of the pulmonary nodule requires the expertise
and collaboration of a range of specialists including the referring clinician,
diagnostic and interventional radiologists, the bronchoscopist, surgeon, and
pathologist. The clinical context is important in terms of stratifying an
individual's risk factors and guiding subsequent management. MDCT and PET
improve the detection and surveillance of nodules and have enabled physiologic
information to be obtained. Advances in these techniques and the potential
contribution of fast MRI sequences and computer applications should continue
to impact our evaluation of the SPN.
Acknowledgments
We thank Emilio Vega for his technical assistance.
References
- Austin JH, Muller NL, Friedman PJ, et al. Glossary of terms for CT
of the lungs: recommendations of the Nomenclature Committee of the Fleischner
Society. Radiology 1996;200
: 327–331[Free Full Text]
- Osada H, Takahashi T. Molecular pathogenesis of lung cancer [in
Japanese]. Nippon Rinsho 2000;58
:1012
–1016[Medline]
- Oshiro Y, Kusumoto M, Moriyama N, et al. Intrapulmonary lymph
nodes: thin-section CT features of 19 nodules. J Comput Assist
Tomogr 2002; 26:553
–557[CrossRef][Medline]
- Zwirewich CV, Vedal S, Miller RR, Müller NL. Solitary
pulmonary nodule: high-resolution CT and radiologic–pathologic
correlation. Radiology 1991;179
: 469–476[Abstract/Free Full Text]
- Zerhouni EA, Stitik FP, Siegelman SS, et al. CT of the pulmonary
nodule: a cooperative study. Radiology1986; 160:319
–327[Abstract/Free Full Text]
- Henschke CI, McCauley DI, Yankelevitz DF, et al. Early lung cancer
action project: overall design and findings from baseline screening.
Lancet 1999; 354:99
–105[CrossRef][Medline]
- Ketai L, Malby M, Jordan K, Meholic A, Locken J. Small nodules
detected on chest radiography: does size predict calcification?
Chest 2000; 118:610
–614[CrossRef][Medline]
- Shah PK, Austin JH, White CS, et al. Missed non-small cell lung
cancer: radiographic findings of potentially resectable lesions evident only
in retrospect. Radiology 2003;226
: 235–241[Abstract/Free Full Text]
- Austin JH, Romney BM, Goldsmith LS. Missed bronchogenic carcinoma:
radiographic findings in 27 patients with a potentially resectable lesion
evident in retrospect. Radiology 1992;182
: 115–122[Abstract/Free Full Text]
- Samei E, Flynn MJ, Peterson E, Eyler WR. Subtle lung nodules:
influence of local anatomic variations on detection.
Radiology 2003;228
: 76–84[Abstract/Free Full Text]
- Berbaum KS, Franken EA Jr, Dorfman DD, et al. Role of faulty visual
search in the satisfaction of search effect in chest radiography.
Acad Radiol 1998;5
: 9–19[CrossRef][Medline]
- Kundel HL, Nodine CF, Krupinski EA. Searching for lung nodules:
visual dwell indicates locations of false-positive and false-negative
decisions. Invest Radiol 1989;24
: 472–478[Medline]
- Samuel S, Kundel H, Nodine C, Toto L. Mechanism of satisfaction of
search: eye position recordings in the reading of chest radiographs.
Radiology 1995;194
: 895–902[Abstract/Free Full Text]
- MacMahon H. Improvement in detection of pulmonary nodules: digital
image processing and computer-aided diagnosis.
RadioGraphics 2000;20
:1169
–1177[Free Full Text]
- Abe H, MacMahon H, Shiraishi J, Li Q, Engelmann R, Doi K.
Computer-aided diagnosis in chest radiology. Semin Ultrasound CT
MR 2004; 25:432
–437[CrossRef][Medline]
- McAdams HP, Samei E, Dobbins J 3rd, Tourassi GD, Ravin CE. Recent
advances in chest radiography. Radiology2006; 241:663
–683[Abstract/Free Full Text]
- Fischbach F, Freund T, Rottgen R, Engert U, Felix R, Ricke J.
Dual-energy chest radiography with a flat-panel digital detector: revealing
calcified chest abnormalities. AJR 2003;181
:1519
–1524[Abstract/Free Full Text]
- Kuhlman JE, Collins J, Brooks GN, Yandow DR, Broderick LS.
Dual-energy subtraction chest radiography: what to look for beyond calcified
nodules. RadioGraphics 2006;26
: 79–92[Abstract/Free Full Text]
- Ricke J, Fischbach F, Freund T, et al. Clinical results of
CsI-detector-based dual-exposure dual energy in chest radiography.
Eur Radiol 2003;13
:2577
–2582[CrossRef][Medline]
- Uemura M, Miyagawa M, Yasuhara Y, et al. Clinical evaluation of
pulmonary nodules with dual-exposure dual-energy subtraction chest
radiography. Radiat Med 2005;23
: 391–397[Medline]
- Tagashira H, Arakawa K, Yoshimoto M, Mochizuki T, Murase K.
Detectability of lung nodules using flat panel detector with dual energy
subtraction by two shot method: evaluation by ROC method. Eur J
Radiol 2007; 64:279
–284[CrossRef][Medline]
- Johkoh T, Kozuka T, Tomiyama N, et al. Temporal subtraction for
detection of solitary pulmonary nodules on chest radiographs: evaluation of a
commercially available computer-aided diagnosis system.
Radiology 2002;223
: 806–811[Abstract/Free Full Text]
- Kakeda S, Kamada K, Hatakeyama Y, et al. Effect of temporal
subtraction technique on interpretation time and diagnostic accuracy of chest
radiography. AJR 2006;187
:1253
–1259[Abstract/Free Full Text]
- Swensen SJ, Morin RL, Aughenbaugh GL, Leimer DW. CT reconstruction
algorithm selection in the evaluation of solitary pulmonary nodules.
J Comput Assist Tomogr 1995;19
: 932–935[Medline]
- White CS, Romney BM, Mason AC, Austin JH, Miller BH, Protopapas Z.
Primary carcinoma of the lung overlooked at CT: analysis of findings in 14
patients. Radiology 1996;199
: 109–115[Abstract/Free Full Text]
- Li F, Arimura H, Suzuki K, et al. Computer-aided detection of
peripheral lung cancers missed at CT: ROC analyses without and with
localization. Radiology 2005;237
: 684–690[Abstract/Free Full Text]
- Gurney JW. Missed lung cancer at CT: imaging findings in nine
patients. Radiology 1996;199
: 117–122[Abstract/Free Full Text]
- Kakinuma R, Ohmatsu H, Kaneko M, et al. Detection failures in
spiral CT screening for lung cancer: analysis of CT findings.
Radiology 1999;212
: 61–66[Abstract/Free Full Text]
- Henschke CI, Naidich DP, Yankelevitz DF, et al. Early lung cancer
action project: initial findings on repeat screenings.
Cancer 2001; 92:153
–159[CrossRef][Medline]
- Armato SG 3rd, Li F, Giger ML, MacMahon H, Sone S, Doi K. Lung
cancer: performance of automated lung nodule detection applied to cancers
missed in a CT screening program. Radiology2002; 225:685
–692[Abstract/Free Full Text]
- Gruden JF, Ouanounou S, Tigges S, Norris SD, Klausner TS.
Incremental benefit of maximum-intensity-projection images on observer
detection of small pulmonary nodules revealed by MDCT.
AJR 2002; 179:149
–157[Abstract/Free Full Text]
- Valencia R, Denecke T, Lehmkuhl L, Fischbach F, Felix R, Knollmann
F. Value of axial and coronal maximum intensity projection (MIP) images in the
detection of pulmonary nodules by multislice spiral CT: comparison with axial
1-mm and 5-mm slices. Eur Radiol 2006;16
: 325–332[CrossRef][Medline]
- Peloschek P, Sailer J, Weber M, Herold CJ, Prokop M,
Schaefer-Prokop C. Pulmonary nodules: sensitivity of maximum intensity
projection versus that of volume rendering of 3D MDCT data.
Radiology 2007;243
: 561–569[Abstract/Free Full Text]
- Tillich M, Kammerhuber F, Reittner P, Riepl T, Stoeffler G, Szolar
DH. Detection of pulmonary nodules with helical CT: comparison of cine and
film-based viewing. AJR 1997;169
:1611
–1614[Abstract/Free Full Text]
- Siegelman SS, Khouri NF, Leo FP, Fishman EK, Braverman RM, Zerhouni
EA. Solitary pulmonary nodules: CT assessment.
Radiology 1986;160
: 307–312[Abstract/Free Full Text]
- Swensen SJ, Silverstein MD, Ilstrup DM, Schleck CD, Edell ES. The
probability of malignancy in solitary pulmonary nodules: application to small
radiologically indeterminate nodules. Arch Intern Med1997; 157:849
–855[Abstract/Free Full Text]
- Marchbank ND, Wilson AG, Joseph AE. Ultrasound features of folded
lung. Clin Radiol 1996;51
: 433–437[CrossRef][Medline]
- Lee YR, Choi YW, Lee KJ, Jeon SC, Park CK, Heo JN. CT halo sign:
the spectrum of pulmonary diseases. Br J Radiol2005; 78:862
–865[Abstract/Free Full Text]
- Grewal RG, Austin JH. CT demonstration of calcification in
carcinoma of the lung. J Comput Assist Tomogr1994; 18:867
–871[Medline]
- Siegelman SS, Khouri NF, Scott WW Jr, et al. Pulmonary hamartoma:
CT findings. Radiology 1986;160
: 313–317[Abstract/Free Full Text]
- Henschke CI, Yankelevitz DF, Mirtcheva R, McGuinness G, McCauley D,
Miettinen OS. CT screening for lung cancer: frequency and significance of
part-solid and nonsolid nodules. AJR2002; 178:1053
–1057[Abstract/Free Full Text]
- Noguchi M, Shimosato Y. The development and progression of
adenocarcinoma of the lung. Cancer Treat Res1995; 72:131
–142[Medline]
- Aoki T, Nakata H, Watanabe H, et al. Evolution of peripheral lung
adenocarcinomas: CT findings correlated with histology and tumor doubling
time. AJR 2000;174
: 763–768[Abstract/Free Full Text]
- Aoki T, Tomoda Y, Watanabe H, et al. Peripheral lung
adenocarcinoma: correlation of thin-section CT findings with histologic
prognostic factors and survival. Radiology2001; 220:803
–809[Abstract/Free Full Text]
- Takashima S, Maruyama Y, Hasegawa M, et al. Prognostic significance
of high-resolution CT findings in small peripheral adenocarcinoma of the lung:
a retrospective study on 64 patients. Lung Cancer2002; 36:289
–295[CrossRef][Medline]
- Takashima S, Maruyama Y, Hasegawa M, et al. CT findings and
progression of small peripheral lung neoplasms having a replacement growth
pattern. AJR 2003;180
: 817–826[Abstract/Free Full Text]
- Yang ZG, Sone S, Takashima S, et al. High-resolution CT analysis of
small peripheral lung adenocarcinomas revealed on screening helical CT.
AJR 2001; 176:1399
–1407[Abstract/Free Full Text]
- Kawakami S, Sone S, Takashima S, et al. Atypical adenomatous
hyperplasia of the lung: correlation between high-resolution CT findings and
histopathologic features. Eur Radiol2001; 11:811
–814[CrossRef][Medline]
- Takashima S, Maruyama Y, Hasegawa M, Saito A, Haniuda M, Kadoya M.
High-resolution CT features: prognostic significance in peripheral lung
adenocarcinoma with bronchioloalveolar carcinoma components.
Respiration 2003;70
: 36–42[CrossRef][Medline]
- Nomori H, Ohtsuka T, Naruke T, Suemasu K. Differentiating between
atypical adenomatous hyperplasia and bronchioloalveolar carcinoma using the
computed tomography number histogram. Ann Thorac Surg2003; 76:867
–871[Abstract/Free Full Text]
- Nakata M, Sawada S, Saeki H, et al. Prospective study of
thoracoscopic limited resection for ground-glass opacity selected by computed
tomography. Ann Thorac Surg 2003;75
: 1601–1605;
discussion 1605–1606[Abstract/Free Full Text]
- Park CM, Goo JM, Lee HJ, et al. CT findings of atypical adenomatous
hyperplasia in the lung. Korean J Radiol2006; 7:80
–86[Medline]
- Ohtsuka T, Watanabe K, Kaji M, Naruke T, Suemasu K. A
clinicopathological study of resected pulmonary nodules with focal pure
ground-glass opacity. Eur J Cardiothorac Surg2006; 30:160
–163[Abstract/Free Full Text]
- Ohta Y, Shimizu Y, Kobayashi T, et al. Pathologic and biological
assessment of lung tumors showing ground-glass opacity. Ann Thorac
Surg 2006; 81:1194
–1197[Abstract/Free Full Text]
- Midthun DE, Swensen SJ, Jett JR, Hartman TE. O-127 evaluation of
nodules detected by screening for lung cancer with low dose spiral computed
tomography. (abstr) Proceedings of the 10th World Conference on
Lung Cancer 2003; 41:S40
- Ginsberg MS, Griff SK, Go BD, Yoo HH, Schwartz LH, Panicek DM.
Pulmonary nodules resected at video-assisted thoracoscopic surgery: etiology
in 426 patients. Radiology 1999;213
: 277–282[Abstract/Free Full Text]
- Erasmus JJ, Gladish GW, Broemeling L, et al. Interobserver and
intraobserver variability in measurement of non-small-cell carcinoma lung
lesions: implications for assessment of tumor response. J Clin
Oncol 2003; 21:2574
–2582[Abstract/Free Full Text]
- Revel MP, Bissery A, Bienvenu M, Aycard L, Lefort C, Frija G. Are
two-dimensional CT measurements of small noncalcified pulmonary nodules
reliable? Radiology 2004;231
: 453–458[Abstract/Free Full Text]
- Schwartz LH, Ginsberg MS, DeCorato D, et al. Evaluation of tumor
measurements in oncology: use of film-based and electronic techniques.
J Clin Oncol 2000;18
:2179
–2184[Abstract/Free Full Text]
- Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, Henschke CI. Small
pulmonary nodules: volumetrically determined growth rates based on CT
evaluation. Radiology 2000;217
: 251–256[Abstract/Free Full Text]
- Yankelevitz DF, Gupta R, Zhao B, Henschke CI. Small pulmonary
nodules: evaluation with repeat CT—preliminary experience.
Radiology 1999;212
: 561–566[Abstract/Free Full Text]
- Revel MP, Lefort C, Bissery A, et al. Pulmonary nodules:
preliminary experience with three-dimensional evaluation.
Radiology 2004;231
: 459–466[Abstract/Free Full Text]
- Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI.
Three-dimensional segmentation and growth-rate estimation of small pulmonary
nodules in helical CT images. IEEE Trans Med Imaging2003; 22:1259
–1274[CrossRef][Medline]
- Kostis WJ, Yankelevitz DF, Reeves AP, Fluture SC, Henschke CI.
Small pulmonary nodules: reproducibility of three-dimensional volumetric
measurement and estimation of time to follow-up CT.
Radiology 2004;231
: 446–452[Abstract/Free Full Text]
- Ko JP, Betke M. Chest CT: automated nodule detection and assessment
of change over time—preliminary experience.
Radiology 2001;218
: 267–273[Abstract/Free Full Text]
- Ko JP, Rusinek H, Jacobs E, et al. Volume measurement of small
pulmonary nodules on chest CT: a phantom study.
Radiology 2003;228
: 864–870[Abstract/Free Full Text]
- Wormanns D, Kohl G, Klotz E, et al. Volumetric measurements of
pulmonary nodules at multirow detector CT: in vivo reproducibility.
Eur Radiol 2004;14
: 86–92[CrossRef][Medline]
- Goodman LR, Gulsun M, Washington L, Nagy PG, Piacsek KL. Inherent
variability of CT lung nodule measurements in vivo using semiautomated
volumetric measurements. AJR 2006;186
: 989–994[Abstract/Free Full Text]
- Boll DT, Gilkeson RC, Fleiter TR, Blackham KA, Duerk JL, Lewin JS.
Volumetric assessment of pulmonary nodules with ECG-gated MDCT.
AJR 2004; 183:1217
–1223[Abstract/Free Full Text]
- Nathan MH, Collins VP, Adams RA. Differentiation of benign and
malignant pulmonary nodules by growth rate. Radiology1962; 79:221
–232[Medline]
- Collins VP, Loeffler RK, Tivey H. Observations on growth rates of
human tumors. Am J Roentgenol Radium Ther Nucl Med1956; 76:988
–1000[Medline]
- Steele JD, Buell P. Asymptomatic solitary pulmonary nodules: host
survival, tumor size, and growth rate. J Thorac Cardiovasc
Surg 1973; 65:140
–151[Medline]
- MacMahon H, Austin JH, Gamsu G, et al. Guidelines for management of
small pulmonary nodules detected on CT scans: a statement from the Fleischner
Society. Radiology 2005;237
: 395–400[Abstract/Free Full Text]
- Gould MK, Fletcher J, Iannettoni MD, et al. Evaluation of patients
with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical
practice guidelines (2nd edition). Chest2007; 132[3 suppl]:108S
–130S[CrossRef][Medline]
- Henschke CI, Yankelevitz DF, Naidich DP, et al. CT screening for
lung cancer: suspiciousness of nodules according to size on baseline scans.
Radiology 2004;231
: 164–168[Abstract/Free Full Text]
- Moore EH. Technical aspects of needle aspiration lung biopsy: a
personal perspective. Radiology 1998;208
: 303–318[Free Full Text]
- Yankelevitz DF, Vazquez M, Henschke CI. Special techniques in
transthoracic needle biopsy of pulmonary nodules. Radiol Clin North
Am 2000; 38:267
–279[CrossRef][Medline]
- Makris D, Scherpereel A, Leroy S, et al. Electromagnetic navigation
diagnostic bronchoscopy for small peripheral lung lesions. Eur
Respir J 2007; 29:1187
–1192[Abstract/Free Full Text]
- Yung RC. Tissue diagnosis of suspected lung cancer: selecting
between bronchoscopy, transthoracic needle aspiration, and resectional biopsy.
Respir Care Clin N Am 2003;9
: 51–76[CrossRef][Medline]
- Shinagawa N, Yamazaki K, Onodera Y, et al. CT-guided transbronchial
biopsy using an ultrathin bronchoscope with virtual bronchoscopic navigation.
Chest 2004; 125:1138
–1143[CrossRef][Medline]
- Gildea TR, Mazzone PJ, Karnak D, Meziane M, Mehta AC.
Electromagnetic navigation diagnostic bronchoscopy: a prospective study.
Am J Respir Crit Care Med 2006;174
: 982–989[Abstract/Free Full Text]
- Schwarz Y, Greif J, Becker HD, Ernst A, Mehta A. Real-time
electromagnetic navigation bronchoscopy to peripheral lung lesions using
overlaid CT images: the first human study. Chest2006; 129:988
–994[CrossRef][Medline]
- Swensen SJ, Viggiano RW, Midthun DE, et al. Lung nodule enhancement
at CT: multicenter study. Radiology 2000;214
: 73–80[Abstract/Free Full Text]
- Swensen SJ, Morin RL, Schueler BA, et al. Solitary pulmonary
nodule: CT evaluation of enhancement with iodinated contrast material—a
preliminary report. Radiology 1992;182
: 343–347[Abstract/Free Full Text]
- Yi CA, Lee KS, Kim EA, et al. Solitary pulmonary nodules: dynamic
enhanced multi-detector row CT study and comparison with vascular endothelial
growth factor and microvessel density. Radiology2004; 233:191
–199[Abstract/Free Full Text]
- Jeong YJ, Lee KS, Jeong SY, et al. Solitary pulmonary nodule:
characterization with combined wash-in and washout features at dynamic
multi-detector row CT. Radiology 2005;237
: 675–683[Abstract/Free Full Text]
- Johnson TR, Krauss B, Sedlmair M, et al. Material differentiation
by dual energy CT: initial experience. Eur Radiol2007; 17:1510
–1517[CrossRef][Medline]
- Mendler MH, Bouillet P, Le Sidaner A, et al. Dual-energy CT in the
diagnosis and quantification of fatty liver: limited clinical value in
comparison to ultrasound scan and single-energy CT, with special reference to
iron overload. J Hepatol 1998;28
: 785–794[CrossRef][Medline]
- Wang B, Gao Z, Zou Q, Li L. Quantitative diagnosis of fatty liver
with dual-energy CT: an experimental study in rabbits. Acta
Radiol 2003; 44:92
–97[CrossRef][Medline]
- Patz EF Jr. 1999 plenary session: Friday imaging
symposium—evaluation of focal pulmonary abnormalities with FDG PET.
RadioGraphics 2000;20
:1182
–1185[Free Full Text]
- Patz EF Jr, Lowe VJ, Hoffman JM, et al. Focal pulmonary
abnormalities: evaluation with F-18 fluorodeoxyglucose PET scanning.
Radiology 1993;188
: 487–490[Abstract/Free Full Text]
- Mortensen J, Enevoldsen H, Friberg L, et al. Preliminary findings
of a prospective study of FDG-PET in patients with possible lung cancer.
Clin Positron Imaging 2000;3
: 158[CrossRef][Medline]
- Talwar A, Mayerhoff R, London D, Shah R, Stanek A, Epstein M.
False-positive PET scan in a patient with lipoid pneumonia simulating lung
cancer. Clin Nucl Med 2004;29
: 426–428[CrossRef][Medline]
- Herder GJ, Golding RP, Hoekstra OS, et al. The performance of
(18)F-fluorodeoxyglucose positron emission tomography in small solitary
pulmonary nodules. Eur J Nucl Med Mol Imaging2004; 31:1231
–1236[Medline]
- Schroeder T, Ruehm SG, Debatin JF, Ladd ME, Barkhausen J, Goehde
SC. Detection of pulmonary nodules using a 2D HASTE MR sequence: comparison
with MDCT. AJR 2005;185
: 979–984[Abstract/Free Full Text]
- Vogt FM, Herborn CU, Hunold P, et al. HASTE MRI versus chest
radiography in the detection of pulmonary nodules: comparison with MDCT.
AJR 2004; 183:71
–78[Abstract/Free Full Text]
- Bruegel M, Gaa J, Woertler K, et al. MRI of the lung: value of
different turbo spin-echo, singleshot turbo spin-echo, and 3D gradient-echo
pulse sequences for the detection of pulmonary metastases. J Magn
Reson Imaging 2007; 25:73
–81[CrossRef][Medline]
- Ohno Y, Hatabu H, Takenaka D, Adachi S, Kono M, Sugimura K.
Solitary pulmonary nodules: potential role of dynamic MR imaging in
management—initial experience. Radiology2002; 224:503
–511[Abstract/Free Full Text]
- Hittmair K, Eckersberger F, Klepetko W, Helbich T, Herold CJ.
Evaluation of solitary pulmonary nodules with dynamic contrast-enhanced MR
imaging: a promising technique. Magn Reson Imaging1995; 13:923
–933[CrossRef][Medline]
- Kono R, Fujimoto K, Terasaki H, et al. Dynamic MRI of solitary
pulmonary nodules: comparison of enhancement patterns of malignant and benign
small peripheral lung lesions. AJR 2007;188
: 26–36[Abstract/Free Full Text]
- Schaefer JF, Schneider V, Vollmar J, et al. Solitary pulmonary
nodules: association between signal characteristics in dynamic contrast
enhanced MRI and tumor angiogenesis. Lung Cancer2006; 53:39
–49[CrossRef][Medline]
- Schaefer JF, Vollmar J, Schick F, et al. Solitary pulmonary
nodules: dynamic contrast-enhanced MR imaging–perfusion differences in
malignant and benign lesions. Radiology2004; 232:544
–553[Abstract/Free Full Text]
- MacMahon H, Engelmann R, Behlen FM, et al. Computer-aided diagnosis
of pulmonary nodules: results of a large-scale observer test.
Radiology 1999;213
: 723–726[Abstract/Free Full Text]
- Schilham AMR, van Ginneken B, Loog M. A computer-aided diagnosis
system for detection of lung nodules in chest radiographs with an evaluation
on a public database. Med Image Anal2006; 10:258
- Abe H, MacMahon H, Engelmann R, et al. Computer-aided diagnosis in
chest radiography: results of large-scale observer tests at the
1996–2001 RSNA scientific assemblies.
RadioGraphics 2003;23
: 255–265[Abstract/Free Full Text]
- Shiraishi J, Li F, Doi K. Computer-aided diagnosis for improved
detection of lung nodules by use of posterior–anterior and lateral chest
radiographs. Acad Radiol 2007;14
: 28–37[CrossRef][Medline]
- Giger ML, Bae KT, MacMahon H. Computerized detection of pulmonary
nodules in computed tomography images. Invest Radiol1994; 29:459
–465[CrossRef][Medline]
- Armato SG 3rd, Giger ML, Moran CJ, Blackburn JT, Doi K, MacMahon H.
Computerized detection of pulmonary nodules on CT scans.
RadioGraphics 1999;19
:1303
–1311[Abstract/Free Full Text]
- Brown MS, McNitt-Gray MF, Goldin JG, Suh RD, Sayre JW, Aberle DR.
Patient-specific models for lung nodule detection and surveillance in CT
images. IEEE Trans Med Imaging 2001;20
:1242
–1250[CrossRef][Medline]
- Wormanns D, Fiebich M, Saidi M, Diederich S, Heindel W. Automatic
detection of pulmonary nodules at spiral CT: clinical application of a
computer-aided diagnosis system. Eur Radiol2002; 12:1052
–1057[CrossRef][Medline]
- Gurcan MN, Sahiner B, Petrick N, et al. Lung nodule detection on
thoracic computed tomography images: preliminary evaluation of a
computer-aided diagnosis system. Med Phys2002; 29:2552
–2558[CrossRef][Medline]
- Brown MS, Goldin JG, Suh RD, McNitt-Gray MF, Sayre JW, Aberle DR.
Lung micronodules: automated method for detection at thin-section
CT—initial experience. Radiology2003; 226:256
–262[Abstract/Free Full Text]
- Awai K, Murao K, Ozawa A, et al. Pulmonary nodules at chest CT:
effect of computer-aided diagnosis on radiologists' detection performance.
Radiology 2004;230
: 347–352[Abstract/Free Full Text]
- Li Q, Li F, Suzuki K, et al. Computer-aided diagnosis in thoracic
CT. Semin Ultrasound CT MR 2005;26
: 357–363[CrossRef][Medline]
- Kim JS, Kim JH, Cho G, Bae KT. Automated detection of pulmonary
nodules on CT images: effect of section thickness and reconstruction
interval—initial results. Radiology2005; 236:295
–299[Abstract/Free Full Text]
- Bae KT, Kim JS, Na YH, Kim KG, Kim JH. Pulmonary nodules: automated
detection on CT images with morphologic matching algorithm—preliminary
results. Radiology 2005;236
: 286–293[Abstract/Free Full Text]
- Rubin GD, Lyo JK, Paik DS, et al. Pulmonary nodules on
multi-detector row CT scans: performance comparison of radiologists and
computer-aided detection. Radiology 2005;234
: 274–283[Abstract/Free Full Text]
- Marten K, Engelke C, Seyfarth T, Grillhosl A, Obenauer S, Rummeny
EJ. Computer-aided detection of pulmonary nodules: influence of nodule
characteristics on detection performance. Clin Radiol2005; 60:196
–206[CrossRef][Medline]
- Yuan R, Vos PM, Cooperberg PL, et al. Computer-aided detection in
screening CT for pulmonary nodules. AJR2006; 186:1280
–1287[Abstract/Free Full Text]
- Way TW, Hadjiiski LM, Sahiner B, et al. Computer-aided diagnosis of
pulmonary nodules on CT scans: segmentation and classification using 3D active
contours. Med Phys 2006;33
:2323
–2337[CrossRef][Medline]
- Awai K, Murao K, Ozawa A, et al. Pulmonary nodules: estimation of
malignancy at thin-section helical CT—effect of computer-aided diagnosis
on performance of radiologists. Radiology2006; 239:276
–284[Abstract/Free Full Text]
- Das M, Muhlenbruch G, Mahnken AH, et al. Small pulmonary nodules:
effect of two computer-aided detection systems on radiologist performance.
Radiology 2006;241
: 564–571[Abstract/Free Full Text]
- Beigelman-Aubry C, Raffy P, Yang W, Castellino RA, Grenier PA.
Computer-aided detection of solid lung nodules on follow-up MDCT screening:
evaluation of detection, tracking, and reading time.
AJR 2007; 189:948
–955[Abstract/Free Full Text]
- Li F, Aoyama M, Shiraishi J, et al. Radiologists' performance for
differentiating benign from malignant lung nodules on high-resolution CT using
computer-estimated likelihood of malignancy. AJR2004; 183:1209
–1215[Abstract/Free Full Text]
- Nie Y, Li Q, Li F, Pu Y, Appelbaum D, Doi K. Integrating PET and CT
information to improve diagnostic accuracy for lung nodules: a semiautomatic
computer-aided method. J Nucl Med 2006;47
:1075
–1080[Abstract/Free Full Text]
- Betke M, Hong H, Ko JP. Automated 3D registration of lung surfaces
in computed tomography scans. (abstr) Medical Image Computing and
Computer-Assisted Intervention (MICCAI) Proceedings. Utrecht,
Netherlands: Springer-Verlag, 2001:725
–733
- Shi J, Sahiner B, Chan HP, et al. Pulmonary nodule registration in
serial CT scans based on rib anatomy and nodule template matching.
Med Phys 2007; 34:1336
–1347[CrossRef][Medline]
- Betke M, Hong H, Thomas D, Prince C, Ko JP. Landmark detection in
the chest and registration of lung surfaces with an application to nodule
registration. Med Image Anal 2003;7
: 265–281[CrossRef][Medline]
- Sun S, Rubin GD, Paik D, Steiner RM, Zhuge F, Napel S. Registration
of lung nodules using a semi-rigid model: method and preliminary results.
Med Phys 2007; 34:613
–626[CrossRef][Medline]
- Suzuki K, Kusumoto M, Watanabe S, Tsuchiya R, Asamura H. Radiologic
classification of small adenocarcinoma of the lung:
radiologic–pathologic correlation and its prognostic impact.
Ann Thorac Surg 2006;81
: 413–419[Abstract/Free Full Text]
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Abstract
Introduction
