Local ethics committee approval was waived because data were collected solely from anthropomorphic chest phantoms (chest phantom N1, Kyoto Kagaku). Figure 1 shows the basic study design. The phantom represents an anatomically accurate life-sized model of an male human torso. The phantom features a synthetic heart, trachea, mediastinum, and lung architecture. The soft-tissue structures and the incorporated synthetic osseous structures are comparable with human tissues, providing similar absorption rates on CT. The lungs contained embedded nodules that varied in size and density. Four nodule diameters were used: 5, 8, 10, and 12 mm. Lesion attenuation simulated solid and ground-glass nodules measuring 100 HU and −630 HU, respectively (Fig. 2). Solid and ground-glass nodules were randomly assigned to different bilateral lung segments and phantoms. In total, 133 ground-glass and 133 solid nodules were placed in 55 phantoms. The absolute number of nodules placed per phantom was 8 (range, 0–8). The allocation of nodules was recorded on a standardized spreadsheet by an independent investigator not involved in image interpretation.

Each phantom was then scanned with both standard-dose and microdose CT on a 128-MDCT scanner (Somatom Definition Flash, Siemens Healthcare) featuring iterative reconstruction algorithms (IRIS, Siemens Healthcare) and an integrated detector system (Stellar, Siemens Healthcare). The following scan parameters were applied for the microdose CT scan: helical acquisition with 80 kVp and 6 mAs. To achieve maximum dose reduction, automated dose reduction features were disabled on the scanner. A high pitch factor of 2.2 was chosen for further dose minimization. FOV was kept constant at 32 cm. Slice reconstruction thickness was 1 mm. Standard-dose chest CT scans were performed with 100 kVp and 100 Quality Reference mAs. All other imaging parameters were the same used for the microdose scan.

For raw dataset processing, level three iteration was applied; axial slices were reconstructed with three different kernels (i30, i50, and i70) at 1-mm slice thickness for interpretation; multiplanar reconstructions were not performed. Radiation dose was represented by the dose-length product (DLP) calculated by the scanner for a standardized body phantom with a diameter of 32 cm and a constant scan length (z-axis) of 35 cm to image the entire phantom. Effective dose for both the standard and microdose CTs was calculated by multiplying DLP by the organ-specific weighting factor published by the International Commission on Radiologic Protection [19].

Lung cancer is the leading cause of cancer-related deaths in the United States and worldwide [20]. Screening with low-dose CT has been shown to reduce lung cancer-specific mortality through early detection [2]. Low-dose CT screening for lung cancer has been endorsed by nearly every major international medical society and other agencies including the ACR, American Cancer Society, American College of Chest Physicians, American Thoracic Society, National Comprehensive Cancer Network, U.S. Preventive Services Task Force, and Centers for Medicare & Medicaid Services. Growing acceptance of low-dose CT screening has increased interest among patients who are at high risk for lung cancer, their physicians, and insurers, many of whom offer coverage for annual low-dose CT screening examinations and anticipate growing use of this important service. Given the potential impact on a large patient population, low-dose CT screening must be properly performed and optimized.

In this context, a discussion about the costs of CAD systems is warranted, because they may contribute to the overall cost-effectiveness of low-dose CT screening. The costs of CAD systems vary widely depending on the vendor and institutional contracts. Numerous large and small enterprises offer affordable solutions, with many scanner manufacturers offering CAD solutions embedded in their workspaces. With respect to effectiveness, CAD as a second reader increases sensitivity and reduces false-negatives at lower cost than double reading by a second radiologist [21, 22]. However, increased sensitivity with CAD comes at a cost of increased reading times. Beyer and colleagues [21] found a mean increase in reading time of 45 seconds when using CAD as a second reader compared with readings without CAD. Notwithstanding the increase in time for interpretation, current European lung cancer screening guidelines recommend the general use of CAD systems to improve quality, outcomes, and cost-effectiveness [22].

Many parameters influence the accuracy and efficiency of pulmonary nodule detection on chest CT. This study compared the detection rate of solid and ground-glass pulmonary nodules of variable size using different CT dose levels and reconstruction kernels using one of two different CAD systems or MIP processing for interpretation. The data show a significant advantage of CAD1 over CAD2, with the former exhibiting higher sensitivities in both the standard-dose and microdose spectra. With a significant difference in the detection of ground-glass lesions, CAD1 outperformed CAD2. Notably, readers using MIP alone showed better detection rates than those using CAD2. CAD2 performed best with standard-dose CT and a hard lung kernel (i70), which likely reflects the most common imaging parameters for routine clinical diagnostic chest CT, but the performance of CAD2 on lower dose examinations and datasets with softer reconstruction kernels was suboptimal.

When we compared the performance of both CAD systems, an inverse relationship between nodule size and sensitivity for CAD2 was evident. Our findings indicate that CAD2 sensitivity decreases with increasing lesion diameter (Table 2). Since both CAD systems were tested with the same scans, this issue is most likely attributable to the software algorithm applied in CAD2. CAD detection sensitivity has been reported to typically decrease with decreasing nodule size [23, 24]. In a study by Yuan et al. [25], the CAD system tested missed seven large (> 10 mm) nodules out of 27 lesions. Yuan and colleagues were able to verify that these large nodules were missed because of immediate continuity with normal anatomic structures such as fissures, pleura, or lung vessels. We hypothesize that the CAD2 system used in this study suffered from similar segmentation errors.

Radiation dose minimization places further demands on CAD systems. Systems cannot only perform well with standard-dose CT; current imaging guidelines and expectations necessitate increasing performance in a low-dose imaging range. In our study, CAD1 and the MIP reconstructions met these demands (Table 1). Comparing the sensitivity of the two CAD systems related to the image reconstruction kernel, microdose CT performed better in combination with the soft-tissue kernel (i30) on both systems. Inversely, the performance of both CADs applied on microdose CT decreased with i50 and i70 kernels. This trend is likely a result of variations in the signal-to-noise ratio. As a result of minimizing tube voltage and tube current in obtaining microdose CT, image noise increases and is directly related to the applied folding kernel with higher (harder) kernels producing inferior signal-to-noise ratios compared with standard-dose CT. Using soft-tissue reconstruction kernels can improve the accuracy of nodule detection on microdose CT examinations when used in conjunction with CAD or MIP processing. In a study by Ebner et al. [6], readers favored soft-tissue kernels over standard lung kernels for microdose CT. In contrast, standard-dose chest CT was more accurate with a hard kernel (i70). The present data outline that soft-tissue kernels increase the sensitivity and consequently increase the false-positive rate in microdose examinations. Despite the influence of a satisfaction-of-search effect in phantoms containing two lesions, our findings suggest that MIP reconstructions might deliver similar sensitivities in a screening population and thus be able to serve as a substitute for CAD systems. Furthermore, MIP reconstructions are feasible without requiring more expensive CAD software solutions. The two CAD systems we studied exhibited distinct differences in their false-positive rates in a phantom-based analysis. The false-positive results of CAD2 were more than 10 times higher than those of CAD1. Although CAD2 delivered markedly higher false-positive results, pooled sensitivity remained low. These discrepant results are not dose- or kernel-dependent and may be due to differences in software algorithms. Similar to the dose-kernel relationship, false-positive findings were minimized in standard-dose CT scans to which CAD1 was applied.

The current study shares inherent limitations related to the nature of phantom experimentation, such as lack of anatomic detail, interindividual demographic variation, and imaging artifacts. These limitations were minimized as much as possible by using a lifelike replica of a human torso, but the results will require validation in prospective trials with human subjects. An additional consideration in using a phantom is that the artificial lung parenchyma and synthetic nodules result in images with sharp contrast compared with the air-attenuating background, which may explain the excellent performance of all stand-alone reading devices, especially CAD1. Another point to consider is that the CAD software of both vendors is primarily designed to detect solid lung lesions. Although the vendors claim that their systems can also detect subsolid lesions, a lack of optimization for ground-glass nodules may explain the inferior performance of CAD2. Finally, a general limitation for CAD systems is the high false-positive rate (CAD1, maximum of eight false-positives per study; CAD2, maximum of 64.7 false-positives per study), which resulted in reduced specificity. Furthermore, we recognize that both dose and CT interpretation in a phantom study may not accurately reflect conditions encountered in clinical practice. The actual performance of CAD and MIP systems in a clinical setting may vary to a substantial degree not only because of the inherent differences of each system but also because of the nodule prevalence in a screening cohort. The present evaluation showed a high frequency of lung nodules (mean, 4.84 per patient); only five phantoms held no lesions, which may have introduced a selection bias.

The presented results suggest that lung cancer screening with microdose (submillisievert) CT is feasible for detection of solid and ground-glass nodules of varying size and has potential for eventual implementation in clinical lung cancer screening programs. We also determined that microdose CT requires soft-tissue reconstruction kernels for both MIP and CAD systems to achieve acceptable sensitivities, whereas CAD applications in standard-dose CT work most reliably with hard lung kernels. In our study, using a CAD system or MIP reconstructions best facilitated detection of solid and ground-glass nodules on CT. Because the CAD systems we evaluated did not perform similarly, we conclude that sensitivities of CAD algorithms can be improved with respect to nodule size and subsolid features. In settings where CAD is not available, MIP reconstructions remain a valuable adjunct to the interpretation of chest CT scans for increasing sensitivity, with the advantage of significantly lower false-positive results.