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DOI:10.2214/AJR.05.0039
AJR 2006; 187:65-72
© American Roentgen Ray Society


Original Research

Serial Therapy-Induced Changes in Tumor Shape in Cervical Cancer and Their Impact on Assessing Tumor Volume and Treatment Response

Nina A. Mayr1, William T. C. Yuh2, Toshiaki Taoka3, Jian Z. Wang1, Dee H. Wu4, Joseph F. Montebello1, Sanford L. Meeks5, Arnold C. Paulino6, Vincent A. Magnotta3, Mustafa Adli7, Joel I. Sorosky8, Michael V. Knopp2 and John M. Buatti7

1 Department of Radiation Medicine, Division of Radiation Oncology, Arthur G. James Hospital and Solove Research Institute, The Ohio State University, College of Medicine, 300 W 10th Ave., Rm. 080, Columbus, OH 43210.
2 Department of Radiology, The Ohio State University, Columbus, OH.
3 Magnetic Resonance Imaging Center, Department of Radiology, The University of Iowa, Iowa City, IA.
4 Department of Radiological Sciences, Oklahoma University Health Sciences Center, Oklahoma City, OK.
5 Department of Radiation Oncology, M. D. Anderson Cancer Center, Orlando, FL.
6 Department of Radiation Oncology, Baylor College of Medicine, Houston, TX.
7 Department of Radiation Oncology, The University of Iowa College of Medicine, Iowa City, IA.
8 Department of Obstetrics and Gynecology, University of Connecticut, Hartford, CT.

Received January 9, 2005; accepted after revision April 27, 2005.

 
Supported by NIH RO1 CA 71906.

Address correspondence to N. A. Mayr (nina.mayr{at}osumc.edu).


Abstract
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
 
OBJECTIVE. The purpose of this study was to evaluate the patterns and distribution of tumor shape and its temporal change during radiation therapy (RT) in cervical cancer and the effect of tumor configuration changes on the correlation between region of interest (ROI)-based and diameter-based MRI tumor measurement.

MATERIALS AND METHODS. Serial MRI examinations (T1-weighted and T2-weighted images) were performed in 60 patients (age range, 29-75 years; mean, 53.3 years) with advanced cervical cancer (stages IB2-IVB/recurrent) who were treated with RT at four time points: start of RT, during RT (at 2-2.5 and at 4-5 weeks of RT), and post-RT. Tumor configuration was classified qualitatively into oval, lobulated, and complex based on MR film review. Two methods of tumor volume measurement were compared: ellipsoid computation of three orthogonal diameters (diameter based) and ROI volumetry by delineating the entire tumor volume on the MR workstation (ROI based). Temporal changes of tumor shape and the respective tumor volumes measured by the two methods were analyzed using linear regression analysis.

RESULTS. Most tumors (70%) had a non-oval (lobulated and complex) shape before RT and became increasingly irregular during and after RT: 84% at 2-2.5 weeks of RT (p = 0.037), 86% (p = 0.025) at 4-5 weeks, and 96% post-RT (p = 0.010), compared with 70% pre-RT. Diameter-based and ROI-based measurement correlated well before RT (r = 0.89) but not during RT (r = 0.68 at 2-2.5 weeks, r = 0.67 at 4-5 weeks of RT).

CONCLUSION. Most cervical cancers are not oval in shape pretherapy, and they become increasingly irregular during and after therapy because of nonconcentric tumor shrinkage. ROI-based volumetry, which can optimally measure irregular volumes, may provide better response assessment during treatment than diameter-based measurement.

Keywords: cervical cancer • response configuration • tumor configuration • tumor response • tumor shape • tumor size measurement • tumor volume


Introduction
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
 
Imaging-based tumor volume measurements before, during, and after therapy have become essential components of cancer management [1-14]. Before treatment, the delineation and volume measurement of the initial tumor are critical for staging, assessment of disease prognosis, and treatment planning. During and after therapy, information on tumor shrinkage or regression has been an important parameter for assessing the therapeutic response to cytotoxic therapy (chemotherapy or radiation therapy) and contributes critically to the decision-making process in cancer care. In clinical trials, imaging-based volume measurement of the tumor and its temporal regression rate are widely used as quantitative therapeutic response criteria in the development of new cancer therapeutics and treatment regimens [1, 2, 15].

For clinical practice and clinical trials, the diameter-based tumor size measurement has been the standard method to assess the initial tumor volume and tumor regression [1, 16]. This method is used under the assumption that the configuration of the tumor closely approximates an ellipsoid (oval or round) shape [17]. Tumor volume is typically estimated with one maximal diameter of the tumor, as outlined in the Response Evaluation Criteria in Solid Tumors (RECIST) [15], the most widely used tumor measurement standard, or as two [16] or three orthogonal tumor diameters, derived from imaging studies or by clinical palpation. With the advancement and availability of commercially available computer software for quantitative analysis in cross-sectional imaging, including MR and CT scanners, the entire tumor, regardless of its shape, can now be identified and traced as a region of interest (ROI) on each imaging slice, and the 3D ROI-based quantitative measurement of tumor volume can now be more readily performed in clinical and community settings [3, 4, 18].

Despite the increasing importance of imaging-based tumor volume and tumor regression in cancer care, the optimal method for adequate volume measurement for specific tumors and specific therapy regimens has not been defined and remains controversial [1, 2, 17, 19-21]. The optimal method likely depends on the overall configuration of the tumor before therapy and the configuration changes it undergoes during and after therapy.

Advanced cervical cancer is an excellent model to study this question because it is typically not amenable to surgical resection, therefore treated with cytotoxic therapy [22-25], and shows measurable tumor regression during and after therapy [3, 14, 26]. These sequential tumor volumes and serial tumor configurations can be assessed noninvasively by MRI [27-29]. MRI is an excellent imaging technique for the 3D delineation of cervical cancer [27-29]. In addition, tumor volume and tumor regression rate have prognostic significance for clinical management in cervical cancer [3, 14, 22-25].

The purpose of this study was to evaluate the patterns and distribution of tumor shape before radiation therapy (RT) in cervical cancer, the temporal changes in tumor configuration coinciding with tumor regression during and after RT, and the effect of the tumor configuration changes on the correlation of tumor volume measurement between the diameter-based and the ROI volumetry method.


Materials and Methods
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
 
Patient Population
Both qualitative analysis of tumor configuration and quantitative analyses of tumor volume were performed in 60 patients with biopsy-proven advanced cervical cancer who were treated with RT. Fifty-three patients had squamous cell carcinoma and seven had adenocarcinoma. Twelve patients were assessed with International Federation of Gynecology and Obstetrics (FIGO) stage IB2, two with stage IIA, 12 with stage IIB, one with stage IIIA, 25 with stage IIIB, 5 with stage IVA, one with stage IVB, and two with locally recurrent disease. Median age at diagnosis was 53.3 years (range, 29-75 years). Pretreatment evaluations consisted of routine workup following FIGO guidelines. The patient population in this report was part of a study approved by the institutional review board to study MRI in cervical cancer.

Therapy consisted of a combination of external beam RT with 24 MeV photons delivering a pelvic dose ranging from 39.6 to 70 Gy including field reductions. External beam radiation was combined with a standard low-dose rate of brachytherapy. No changes in the patients' therapy were made based on the findings of the MRI examinations.

MRI Protocol
The imaging protocol included serial MRI studies at four well-defined time points: at the beginning of the RT, early during RT (at an RT dose of 20-25 Gy at 2-2.5 weeks of RT), midway during RT (at 45-50 Gy at 4-5 weeks of RT), and at the follow-up visit (1-2 months after the completion of all therapy). Four of the 60 patients did not have the early RT MRI at 2-2.5 weeks, and four patients did not have the post-RT follow-up MRI because of intervening morbidity, refusal, or scheduling conflicts. Because all patients with fewer than four MRI studies had three studies, it was decided that the information gained from the inclusion of these patients outweighed the disadvantage of not having completely uniform data in all cases. This resulted in a total of 232 MRI studies in the 60 patients.

The MRI examinations were obtained using a standard body coil with a 1.5-T superconductive scanner including Signa (GE Healthcare) and Vision (Siemens Medical Solutions) scanners. The change in platforms was related to a change in imaging systems at our institution. Imaging included sagittal 5-mm (4-mm thickness with 1-mm gap) conventional fast spin-echo T2-weighted images (TEeff/TR, 104/4,000; echo-train length, 10; number of excitations [NEX], 2) and axial 7-mm (5-mm thickness with 2-mm gap) T2-weighted and T1-weighted images (TE/TR, 16/600; NEX, 2).

Qualitative analysis of tumor configuration and its temporal changes—In this retrospective review, the hard copy films of the serial MR studies were qualitatively evaluated by three reviewers, two MRI radiologists (14 and 10 years of experience, respectively) and one radiation oncologist (10 years of experience in MRI for imaging-based radiation therapy planning and MRI). The review occurred over approximately 5 months in multiple sessions.

The tumor was defined as an abnormal area with intermediate to high signal intensity on T2-weighted images with respect to the surrounding cervical stroma and uterus, and lower than the fluid signal in the urinary bladder [13, 27, 29] (Figs. 1A, 1B, and 1C). The reviewers reviewed all serial MR studies of each of the 60 patients and qualitatively classified the tumor configuration into three categories: oval, lobulated, and complex shape (Figs. 1A, 1B, and 1C). The oval category was defined as a smooth configuration with a broad well-defined border without lobulations, closely approximating a round or oval shape. The lobulated category was defined as a smooth lobulated (single or multiple) well-defined border and without infiltrating strands. The complex category was defined as an irregular configuration with infiltrating borders or strands extending into surrounding healthy tissue. Discrepancies among reviewers were resolved by consensus or majority of opinions. The results of the qualitative evaluation of tumor shape at the four different time points were tabulated and analyzed for the distribution pattern of the three tumor configuration categories and the temporal changes of tumor shape before, during, and after RT.


Figure 1
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Fig. 1A Classification of tumor configuration. Tumors (arrows) were classified into oval configuration characterized by smooth round or oval shape with broad well-defined border without lobulations (A); lobulated configuration with smooth margin and single or multiple lobulated projections, but without infiltrating strands (B); and complex configuration characterized by irregular shape with infiltrating borders or strands extending into surrounding healthy tissue (C). MR images are fast spin-echo T2-weighted sagittal images.

 

Figure 2
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Fig. 1B Classification of tumor configuration. Tumors (arrows) were classified into oval configuration characterized by smooth round or oval shape with broad well-defined border without lobulations (A); lobulated configuration with smooth margin and single or multiple lobulated projections, but without infiltrating strands (B); and complex configuration characterized by irregular shape with infiltrating borders or strands extending into surrounding healthy tissue (C). MR images are fast spin-echo T2-weighted sagittal images.

 

Figure 3
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Fig. 1C Classification of tumor configuration. Tumors (arrows) were classified into oval configuration characterized by smooth round or oval shape with broad well-defined border without lobulations (A); lobulated configuration with smooth margin and single or multiple lobulated projections, but without infiltrating strands (B); and complex configuration characterized by irregular shape with infiltrating borders or strands extending into surrounding healthy tissue (C). MR images are fast spin-echo T2-weighted sagittal images.

 
Quantitative analysis of volume measurement methods at four time points—Two methods of volume calculation for each of the 60 patients at the four different time points were analyzed and correlated: the reference standard method of diameter-based calculation and the ROI-based 3D volumetry method, including the entire tumor region identified and traced on the MR workstation on all T2-weighted sagittal imaging slices throughout the tumor. The diameter-based calculation was computed by measuring the largest tumor diameter in each orthogonal plane on film hard copies. The longitudinal diameter (d1, along the long axis of the endometrial cavity) and the anteroposterior diameter (d2, orthogonal to the longitudinal diameter) were measured on the sagittal images, and the lateral diameter (d3) was measured on the axial images. Diameter-based tumor volume was calculated from the diameters as an ellipsoid (volume = d1 x d2 x d3 x {pi} / 6). For the ROI-based 3D volumetry, the tumor area or ROI on each imaging slice on the sagittal T2-weighted image of each MR study was first delineated, and the 3D ROI-based volumes were then calculated by the summation of all tumor areas and multiplication by the slice profile.

Statistical analysis—The correlation between the serial tumor volumes derived from the diameter-based and ROI-based measurement methods at each of the four measurement time points was performed for the 60 patients using linear regression analysis. The correlation coefficients between the two measurement methods were analyzed with respect to the distribution of the tumor configurations in each of the four measurement time points (before, early during, mid-way during, and after RT).


Results
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
 
Qualitative Analysis of Tumor Configuration and Its Temporal Changes
The shapes of most tumors (70%) were not oval or ellipsoid before RT. At the pretherapy time point, the configuration patterns of the tumors were approximately equally distributed: 30% oval, 35% lobulated, and 35% complex (Fig. 2). However, during RT, the tumor configuration became increasingly irregular (Figs. 2, 3A, 3B, and 3C). Non-oval (lobulated and complex) shapes increased in number significantly early during RT at 2-2.5 weeks of RT (84%, p = 0.037), at 4-5 weeks of RT (86%, p = 0.025), and post-RT (96%, p = 0.010) compared with the pre-RT baseline of 70%.


Figure 4
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Fig. 2 Distribution of tumor configurations at different time points. At radiation therapy (RT) start, all tumors are equally distributed into three configuration categories. Number of tumors with oval shape declines continuously during and after RT. Number of tumors with complex configuration shows sharp and persistent increase during and after RT. Number of tumors with lobulated configuration increases early during RT and then declines in favor of complex configuration.

 

Figure 5
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Fig. 3A Temporal changes of tumor configuration are shown on serial MR studies (fast spin-echo T2-weighted sagittal images) obtained in 49-year-old woman with stage IIB squamous cell carcinoma of cervix. Imaging before radiation therapy (RT) shows relatively well-circumscribed tumor contour (arrows) that was classified as lobulated.

 

Figure 6
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Fig. 3B Temporal changes of tumor configuration are shown on serial MR studies (fast spin-echo T2-weighted sagittal images) obtained in 49-year-old woman with stage IIB squamous cell carcinoma of cervix. During course of RT, at 21.6 Gy/2.2 weeks (B) and at 45 Gy/5 weeks (C), tumor (arrows) becomes increasingly irregular and is classified as complex in configuration.

 

Figure 7
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Fig. 3C Temporal changes of tumor configuration are shown on serial MR studies (fast spin-echo T2-weighted sagittal images) obtained in 49-year-old woman with stage IIB squamous cell carcinoma of cervix. During course of RT, at 21.6 Gy/2.2 weeks (B) and at 45 Gy/5 weeks (C), tumor (arrows) becomes increasingly irregular and is classified as complex in configuration.

 

When each of the three morphologic patterns was examined individually, the lobulated configuration increased in number early during the course of RT (at 2-2.5 weeks) and later (at 4-5 weeks) declined in number in favor of the complex configuration, indicating that most tumors became increasingly irregular in shape during and after therapy. The complex configuration increased in number rapidly and persistently during and after RT (to 43% at 2-2.5 weeks, 56% at 4-5 weeks, and 64% post-RT) (Fig. 2). The oval shape continuously declined in number during and after RT in favor of the more irregular configurations.

Quantitative Analysis of Volume Measurement Methods at Four Time Points
Figures 4A, 4B, 4C, 4D, and 5 summarize the tumor volumes derived from diameter-based and ROI-based measurements at the four time points. The tumor volume showed a trend to decrease with time (Fig. 5). The volume derived by the diameter-based method was overall larger than that by the ROI-based method. Compared with the ROI-based method, the median tumor volume measured by the diameter-based method exceeded the ROI-based volume by 28% (69 cm3 vs 54 cm3) pre-RT, by 61% (50 cm3 vs 31 cm3) at 2-2.5 weeks of RT, and by 29% (9 cm3 vs 7 cm3) at 4-5 weeks of RT. This difference was greatest at 2-2.5 weeks during RT and decreased at 4-5 weeks and post-RT, when the overall tumor volume rapidly declined.


Figure 8
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Fig. 4A Correlation between diameter-based and region-of-interest (ROI)-based methods. Scattergrams of volume measurement derived with diameter-based (x-axis) and ROI-based (y-axis) methods are shown at four measurement time points, at radiation therapy (RT) start (A), at 20-25 Gy/2-2.5 weeks (B), at 45-50 Gy/4-5 weeks (C), and post-RT (follow-up at 1-2 months) (D). Measurements derived by the two methods correlate well before (A) and after (D) RT (r = 0.89 and r = 0.88, respectively) but poorly during RT at 2-2.5 weeks (B) and at 4-5 weeks (C) (r = 0.68 and r = 0.67, respectively). Poor correlation during therapy is likely related to increasing irregularity of tumor volumes that are still sufficiently large during therapy to have impacted correlation. Later, post-RT, as tumor volume decreased further, impact of tumor irregularity is small in magnitude and correlation of diameter-based with 3D ROI-based measurement improves.

 

Figure 9
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Fig. 4B Correlation between diameter-based and region-of-interest (ROI)-based methods. Scattergrams of volume measurement derived with diameter-based (x-axis) and ROI-based (y-axis) methods are shown at four measurement time points, at radiation therapy (RT) start (A), at 20-25 Gy/2-2.5 weeks (B), at 45-50 Gy/4-5 weeks (C), and post-RT (follow-up at 1-2 months) (D). Measurements derived by the two methods correlate well before (A) and after (D) RT (r = 0.89 and r = 0.88, respectively) but poorly during RT at 2-2.5 weeks (B) and at 4-5 weeks (C) (r = 0.68 and r = 0.67, respectively). Poor correlation during therapy is likely related to increasing irregularity of tumor volumes that are still sufficiently large during therapy to have impacted correlation. Later, post-RT, as tumor volume decreased further, impact of tumor irregularity is small in magnitude and correlation of diameter-based with 3D ROI-based measurement improves.

 

Figure 10
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Fig. 4C Correlation between diameter-based and region-of-interest (ROI)-based methods. Scattergrams of volume measurement derived with diameter-based (x-axis) and ROI-based (y-axis) methods are shown at four measurement time points, at radiation therapy (RT) start (A), at 20-25 Gy/2-2.5 weeks (B), at 45-50 Gy/4-5 weeks (C), and post-RT (follow-up at 1-2 months) (D). Measurements derived by the two methods correlate well before (A) and after (D) RT (r = 0.89 and r = 0.88, respectively) but poorly during RT at 2-2.5 weeks (B) and at 4-5 weeks (C) (r = 0.68 and r = 0.67, respectively). Poor correlation during therapy is likely related to increasing irregularity of tumor volumes that are still sufficiently large during therapy to have impacted correlation. Later, post-RT, as tumor volume decreased further, impact of tumor irregularity is small in magnitude and correlation of diameter-based with 3D ROI-based measurement improves.

 

Figure 11
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Fig. 4D Correlation between diameter-based and region-of-interest (ROI)-based methods. Scattergrams of volume measurement derived with diameter-based (x-axis) and ROI-based (y-axis) methods are shown at four measurement time points, at radiation therapy (RT) start (A), at 20-25 Gy/2-2.5 weeks (B), at 45-50 Gy/4-5 weeks (C), and post-RT (follow-up at 1-2 months) (D). Measurements derived by the two methods correlate well before (A) and after (D) RT (r = 0.89 and r = 0.88, respectively) but poorly during RT at 2-2.5 weeks (B) and at 4-5 weeks (C) (r = 0.68 and r = 0.67, respectively). Poor correlation during therapy is likely related to increasing irregularity of tumor volumes that are still sufficiently large during therapy to have impacted correlation. Later, post-RT, as tumor volume decreased further, impact of tumor irregularity is small in magnitude and correlation of diameter-based with 3D ROI-based measurement improves.

 

Figure 12
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Fig. 5 Temporal change of median tumor volume between ROI-based and diameter-based methods. Sequential median tumor volume before, during, and after radiation therapy (RT) shows that tumors appear larger when measured with diameter-based method (solid line) compared with 3D ROI method (dashed line). This is likely related to overestimation of tumor volume with diameter-based method because maximal orthogonal diameters used for ellipsoid computation cannot adequately account for deviation of tumor shape from presumed oval or ellipsoid tumor shape.

 
The diameter-based tumor volume measurement had the best correlation with the 3D ROI-based measurement at the pretherapy time point (r = 0.89, Figs. 4A, 4B, 4C, and 4D) when most of the tumors did not have the complex configuration (Fig. 2). The correlation between diameter-based and ROI-based measurement decreased during RT (r = 0.68 at 2-2.5 weeks of RT, r = 0.67 at 4-5 weeks of RT) when overall tumor irregularity increased (Figs. 2, 3A, 3B, and 3C). Despite the irregular configurations (Fig. 2), the correlation between the measurement methods improved (r = 0.88) again after RT when tumor volumes decreased to a minimum (Figs. 4A, 4B, 4C, 4D, and 5).


Discussion
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
 
Tumor imaging is now an essential part of cancer management in oncology, and increasingly detailed quantitative information on tumor volume and tumor response to cytotoxic therapy is readily available in clinical practice to guide clinical therapy decisions and clinical trials assessing new therapy regimens [1, 2, 15]. Therefore, the appropriate method and its accuracy for tumor size measurement to provide this essential information is critical for cancer management.

However, the ideal method for the measurement of tumor volume and tumor shrinkage remains controversial [1, 2, 17, 19-21]. Different measurement techniques have been used in many tumors, including cervical cancer. For imaging-based tumor volume assessment, both the initial tumor volume and the regression rate of tumors have been traditionally estimated from measurements of one, two, or three diameters of the tumor. These reference standard diameter-based tumor measurements have been reported to be equivalent to the more complex 3D ROI volumetry that delineates the entire tumor region three dimensionally [7, 13, 21, 30]. However, this concept has been challenged by studies showing discordance between diameter-based and 3D volumetry measurements [17, 20, 31]. Others have reported that the 3D ROI-based tumor volume regression measurement is superior as a predictor of tumor control or survival [3, 4, 18].

The advanced cervical cancer chosen for this project is an excellent model to study the pattern of initial tumor shape and its temporal configuration changes with MRI for a number of reasons: First, advanced cervical cancer is typically not amenable to surgical resection, and therefore tumor shape can be sequentially studied during and after RT. Second, cervical cancer shows measurable tumor regression during or after RT on MRI, ideal for assessing temporal changes in tumor shape induced by cytotoxic therapy [3, 4, 13, 32]. Third, initial tumor volume and tumor regression rate are clinically significant prognostic and predictive factors in cervical cancer [3, 14, 22-25]. Fourth, there are four well-defined time points for MRI based on prognostic significance and clinical management [3, 13, 14, 26]. Fifth, MRI is an excellent imaging technique for the delineation of cervical cancer [27-29]. Finally, MRI is noninvasive and easily available in the common clinical setting.

Although the diameter-based tumor volume measurement has been formally adopted by the World Health Organization (WHO) criteria [16] and RECIST guidelines [15] as an end point for the assessment of therapy response in cancer, its value and interpretation have to be considered carefully in the context of individual tumors. RECIST, the most widely used current guideline, relies on a one-dimensional measurement using the longest tumor diameter.

The estimation of tumor volume with the diameter-based method relies on the assumption that the configuration of the tumor is ellipsoid (oval or round) and remains ellipsoid throughout and after therapy. It is thus assumed that the shrinkage during and after therapy occurs in a concentric fashion along all dimensions of the tumor.

The ideal measurement method likely depends on the overall configuration and geometric properties of the tumor before, during, and after therapy. However, to our knowledge, very little information is available regarding the shape of tumors at pretherapy baseline and temporal changes in tumor configuration induced by the cytotoxic effects during and after therapy. Furthermore, it is unknown how these factors influence the accuracy of a specific method for measurement of tumor volume and tumor regression for therapy response assessment. Our study was therefore intended to address these issues.

Our results show that, contrary to the general perception, most of the cervical cancers (70%) did not have a shape closely approximating the ellipsoid (oval or round) configuration before treatment. Furthermore, during treatment the tumor shape became increasingly irregular, and the likelihood of the tumor to maintain an oval shape declined rapidly during and after therapy (Fig. 2). As the tumors gradually underwent radiation-induced cell killing and volume reduction, the tumor volume appeared to shrink in an irregular nonconcentric fashion. This explains why the tumor shape deteriorated from the initial compact well-circumscribed shapes to irregular fragmented configurations (Figs. 2, 3A, 3B, and 3C). Because only the maximal diameter in three orthogonal planes is measured, tumor measurements derived from the diameter-based method are expected, as shown in our results, to be larger than those measured by the ROI-based volumetry method.

As a result of the increasingly irregular tumor shape, the correlation between diameter-based and ROI-based measurements worsened as the tumor configurations further deviated from the ideal oval or ellipsoid shape (Figs. 4B and 4C). This observation was particularly true during treatment, when the tumor size became smaller and simultaneously configuration became more irregular (i.e., when the degree of deviation from the ideal ellipsoid shape increased as the ratio of irregularity per unit of tumor volume increased). This degree of deviation from ellipsoid explains why, at the second and third measurement time points during RT (at 2-2.5 weeks and at 4-5 weeks), the correlation of diameter-based with ROI-based volume was poorest (r = 0.68 and r = 0.67, respectively). Unfortunately, these early tumor response measurements during therapy, which can provide most critical predictive information for therapy response, prognosis, and early decision making, are those most adversely affected by the compromised accuracy of diameter-based tumor measurement.

Although only 30% of the tumors had an oval shape before treatment, the pretherapy time point had the best correlation between the diameter- and ROI-based measurements. We believe the reason for the better correlation is related to the relatively larger tumor size with relatively smoother border and therefore less complexity of shape per unit of tumor volume as compared with the configurations during therapy. The overall concept that temporal tumor geometry changes influence the accuracy of response measurement, and our observations, are supported by a recent study by Mazumdar et al. [17]. This theoretic simulation study showed that simulated sequential variations in tumor shape result in statistically significant variability of the response assessment when diameter-based measurements are used.

At the posttherapy measurement time point, the overall discrepancy between the diameter-based and ROI-based methods was improved despite the predominance of irregular shapes. This is likely explained by the fact that most tumor volumes are very small and close to zero at that time, and discrepancies of volume between the measurement methods are therefore lower in magnitude.

Although the ROI-based quantitative volume measurement is a more involved and time-consuming method than the diameter-based measurement, and its practical application in a busy clinical setting has been a challenge, this technology is now becoming more readily available and user-friendly for general practice and ideally will become near-completely automated in the future. Regardless of the type or complexity in tumor shape, the ROI-based volumetry measurement includes all tumor components identified on all images throughout the lesion. Therefore, tumor-specific or therapy-induced deviations from the ideal ellipsoid shape do not compromise its accuracy.

Our study has several limitations. The patient population was accrued over a relatively long time span in cervical cancer patients, a relatively uncommon cancer. At the time of the study, new pulse sequences with improved spatial resolution and volume acquisition techniques as well as phase array coils were not available. If this study were repeated today, the results might be different because of the improved lesion delineation provided by phase array coils and newer advanced imaging techniques.

The review was a simultaneous one by three observers with discrepancies resolved in consensus, and the MR studies of each patient were reviewed consecutively. Thus, we do not have interobserver variability data for this study, and this subject would be important to evaluate in future research. The studies for each patient were reviewed consecutively because this is similar to the actual clinical situation in which the prior MR examination is usually available on review of a subsequent study. Although this consecutive review is similar to the typical clinical situation, the fact that the studies were not randomly distributed, without knowledge of which serial MRI examination belonged to which patient, may represent a limitation of our study.

Our study does not have histologic validation of the imaging findings. This is a challenge we are facing in the sequential imaging of cervical cancer and in many other unresectable cancers that are imaged serially while undergoing cytotoxic therapy. Tumors, which are amenable to serial imaging, are typically not resectable; therefore, no tumor specimen is available for precise imaging-pathology correlation. Conversely, tumors, which can be resected and are thus amenable to imaging-pathology correlation, cannot be imaged serially because the tumor is resected.

Despite this dilemma, a European study by Burghardt et al. [27] provides strong evidence of a high correlation between MRI- and pathology-based tumor volumetry in cervical cancer (stages I and IIB tumors, which are all treated surgically in Europe). Although this surgical-pathologic validation of pretherapy MRI with histologic tumor volume is strong [27], no absolute pathologic proof indicates this is also the case for tumor margin delineation and tumor volume assessment during therapy. However, a recent biopsy study by Hatano et al. [13] in patients with posttherapy MRI has shown excellent correlation between areas of increased signal intensity and residual tumor and between areas of hypointensity and absence of tumor.

For cancer care, ultimately more important than pathology correlation will be the clinical validity of the MRI-based measurement methodology for patient outcome. The methodology of measurement that is most sensitive in assessing response, predicting outcome, or both, and is the most effective in providing a window of opportunity to adjust treatment regimens in those with poor response to optimize care, will be the most useful.

In conclusion, our limited data suggest that most cervical cancers are not oval or ellipsoid in shape before, during, and after RT. Diameter-based tumor measurement therefore may not be the most ideal method to provide the critical information needed for prognosis and response assessment in cancer management and in the development of new cancer therapeutics. The measurement of the initial tumor volume and its shrinkage must account for the deviation from the presumed ellipsoid configuration before treatment, and particularly for the increasing irregularity induced by therapy. The ROI-based method has the advantage of not being limited to oval-shaped tumors and can compensate for therapy-induced irregularities in tumor configuration, providing superior volumetric measurement.


References
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Abstract
Introduction
Materials and Methods
Results
Discussion
References
 

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