Disease volumes as a marker for patient response in malignant pleural mesothelioma

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1 31. Ceresoli GL, Cappuzzo F, Gregorc V et al.. Gefitinib in patients with brain metastases from non-small-cell lung cancer: a prospective trial. Ann Oncol 2004; 15: Hotta K, Kiura K, Ueoka H et al.. Effect of gefitinib ( Iressa, ZD1839) on brain metastases in patients with advanced non-small-cell lung cancer. Lung Cancer 2004; 46: Namba Y, Kijima T, Yokota S et al.. Gefitinib in patients with brain metastases from non-small-cell lung cancer: review of 15 clinical cases. Clin Lung Cancer 2004; 6: Chiu CH, Tsai CM, Chen YM et al.. Gefitinib is active in patients with brain metastases from non-small cell lung cancer and response is related to skin toxicity. Lung Cancer 2005; 47: Wu C, Li YL, Wang ZM et al.. Gefitinib as palliative therapy for lung adenocarcinoma metastatic to the brain. Lung Cancer 2007; 57: Kim ST, Uhm JE, Lee J et al.. Randomized phase II study of gefitinib versus erlotinib in patients with advanced non-small cell lung cancer who failed previous chemotherapy. Lung Cancer 2012; 75: Annals of Oncology 24: , 2013 doi: /annonc/mds535 Published online 8 November 2012 Disease volumes as a marker for patient response in malignant pleural mesothelioma Z. E. Labby 1,A.K.Nowak 2,3, J. J. Dignam 4, C. Straus 1, H. L. Kindler 5 & S. G. Armato III 1 * 1 Department of Radiology, The University of Chicago, Chicago, USA; 2 Department of Medical Oncology, Sir Charles Gairdner Hospital, Nedlands; 3 School of Medicine and Pharmacology, University of Western Australia, Crawley, Western Australia, Australia; Departments of 4 Health Studies; 5 Medicine, The University of Chicago, Chicago, USA Received 12 July 2012; revised 7 September 2012; accepted 12 September 2012 Background: The goal of this study was to create a comprehensive model for malignant pleural mesothelioma patient survival utilizing continuous, time-varying estimates of disease volume from computed tomography (CT) imaging in conjunction with clinical covariates. Patients and methods: Serial CT scans were obtained during the course of clinically standard chemotherapy for 81 patients. The pleural disease volume was segmented for each of the 281 CT scans, and relative changes in disease volume from the baseline scan were tracked over the course of serial follow-up imaging. A prognostic model was built using time-varying disease volume measurements in conjunction with clinical covariates. Results: Over the course of treatment, disease volume decreased by an average of 19%, and median patient survival was 12.6 months from baseline. In a multivariate survival model, changes in disease volume were significantly associated with patient survival along with disease histology, Eastern Cooperative Oncology Group performance status, and presence of dyspnea. Conclusions: Analysis of the trajectories of disease volumes during chemotherapy for patients with mesothelioma indicates that increasing disease volume was significantly and independently associated with poor patient prognosis in both univariate and multivariate survival models. Key words: chest CT, malignant pleural mesothelioma, therapy response assessment introduction Any image-based response evaluation method has two components; the first describes a protocol for making measurements, and the second describes how to classify patients into response categories once those measurements are available. Tumor response assessment with medical images has focused on reducing the dimensionality of the first component and discretization of the second component, beginning with the World Health Organization [1] bi-dimensional *Correspondence to: Dr S. G. Armato III, Department of Radiology, The University of Chicago, 5841 S Maryland Ave., MC 2026, Chicago, IL 60637, USA. Tel: ; Fax: ; s-armato@uchicago.edu measurement technique that used the product of two linear measurements as a quasi-two-dimensional metric to assess tumor response across serial scans. Progressive disease (PD) was considered an increase of 25% from the minimum of previous tumor measurements, and partial response (PR) was considered a decrease of 50% or more from the baseline tumor measurement. Tumors not meeting the criteria for either PD or PR were classified as stable disease. The Response Evaluation Criteria In Solid Tumors, or RECIST, criteria were developed to simplify this measurement process to a single longest tumor diameter [2], and the threshold criteria were derived from a geometrical relationship between the crosssectional area and the diameter of a sphere, leading to the current 30%/+20% RECIST classification criteria [3, 4]. The The Author Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please journals.permissions@oup.com.

2 RECIST measurement technique and classification criteria are currently used across many diseases [5], although the standard measurement technique for mesothelioma is now defined by modified RECIST [6] and mesothelioma-specific classification criteria have been investigated [7, 8]. With advances in medical imaging technology, there has been a more recent drive to use full three-dimensional volume measurements for response assessment [4, 9, 10] and to track measurements over time using continuous rather than discretized response [3, 11]. Especially for diseases such as mesothelioma, where the morphological characteristics do not correspond to the spherical extrapolation of the RECIST classification criteria, disease volumes are a logical choice for tumor burden assessment. While the past history of response assessment has moved away from volumetric quantification for reasons such as ease of manual measurement, the techniques used for response assessment were always intended to identify tumor burden changes (i.e. volumetric changes), and the purpose of this study was to return to the direct evaluation of tumor volume for response assessment. Numerous studies have investigated the use of tumor volume and tumor volume-related measurements for response assessment in patients with malignant pleural mesothelioma (MPM) using magnetic resonance imaging, computed tomography (CT), and fluorodeoxyglucose positron emission tomography (FDG-PET) imaging [12 18]. The main challenge in these volumetric studies is the segmentation of the complete tumor volume. Using FDG- PET imaging, the segmentation of MPM tumor volume is greatly facilitated by the FDG avidity of the tumor [15, 16, 19]. The segmentation of MPM in CT images is more problematic predominantly due to the similar appearance of neighboring structures on CT [20] and has required semiautomated tools [17, 18]. Frauenfelder et al. [17] used a linear shape-based interpolation technique, requiring manual contours on every fourth or fifth slice. Interobserver agreement of volumetric response classification was found to be much higher than that of manual modified RECIST response classification (general κ =0.9 versus κ =0.33, respectively) [17]. Liu et al. [18]utilizeda combination of semiautomated techniques for volumetric MPM segmentation, and their analysis revealed changes in tumor volume (dichotomized as tumor growth versus tumor decrease) to be significantly associated with patient survival. The goal of this study was to create a comprehensive model for MPM patient survival utilizing time-changing estimates of disease volume from CT imaging in conjunction with clinical covariates such as patient age, sex, performance status (PS), white blood cell count, blood platelet count, histological cellular subtype, presence of chest pain, and weight loss [12, 21 25]. Continuous, time-varying measurements of disease volume were hypothesized to be a significant predictor of MPM patient survival, both as a single covariate and in conjunction with other clinical covariates. patients and methods patient cohort Imaging and clinical data from 81 patients were obtained from a prospective study involving FDG-PET and CT imaging of MPM [26]. All patients were over 18 years old with histologically or cytologically confirmed MPM and had not received prior chemotherapy or radiotherapy. Patient accrual occurred from late 2003 to 2010, and the original study was approved by the local institutional Human Research Ethics Committee at Sir Charles Gairdner Hospital in Perth, Western Australia, with patients providing written informed consent. The subsequent analysis of the HIPAA (Health Insurance Portability and Accountability Act)-compliant data was approved by both the originating institution s Human Research Ethics Committee and the Institutional Review Board at The University of Chicago, where the analyses for the present study were carried out. The original study was not a treatment study, so patients were treated as clinically indicated. Combination chemotherapy consisted of cisplatin or carboplatin with gemcitabine or pemetrexed (Eli Lilly, Indianapolois, IN). Palliative radiotherapy was used when indicated, and a single patient had undergone previous pleurectomy/decortication (Table 1). imaging Patients were imaged using helical CT, with a baseline scan performed up to 1 month before the first cycle of chemotherapy and follow-up scans performed throughout their treatment regimen (typically after the first cycle, then every two cycles thereafter). CT staging was carried out by a thoracic radiologist or a medical oncologist experienced in mesothelioma imaging according to the Union for International Cancer Control tumornode-metastasis (TNM) staging system (2002). Pathologic staging was not carried out. A total of 281 thoracic CT scans were used in this study, with a median of four scans per patient. Ten patients had only a baseline scan with one follow-up scan, while 30 patients had three scans total, 34 patients had four scans total, and seven patients had five scans total. The median interval between scans was 49 days. One hundred ninety-seven scans had been performed on General Electric scanners (HiSpeed CT/i, n = 105; LightSpeed Pro 16, n = 2; or LightSpeed VCT, n = 90), and 84 scans had been performed on Philips Brilliance 64-slice scanners. At least 135 of the scans had been performed with contrast media. Slice thickness and reconstruction kernels were matched across scans for each patient. Slice thicknesses for the different scans used in this study then were 0.63 mm (n = 6), 1 mm (n = 18), 1.25 mm (n = 36), 2.5 mm (n = 97), or 5 mm (n = 124). In-plane voxel dimensions ranged from 0.54 to 0.87 mm, and all reconstructed axial images had an in-plane matrix size of 512 by 512 pixels. The kvp setting for the scans was predominantly 120 kvp (n = 273), with 100 kvp (n = 2), and 140 kvp (n = 6) also used. Reconstruction kernels fell into two broad categories, with lung kernels (Philips L and General Electric lung kernels) used for 185 scans and standard kernels (Philips B and General Electric chest, soft, and standard kernels) used for the remaining 96 scans. disease segmentation Annals of Oncology For each scan, the pleural disease was segmented using a semiautomated method [27] modified with a semiautomated shape-based interpolation [28] for hemithorax segmentation that requires seeding of the approximate hemithorax boundary on selected CT sections. The semiautomated hemithorax segmentation is shown in Figure 1. After the pleural disease was identified as the region between the semiautomated hemithoracic segmentation and the automated lung segmentation as outlined in Sensakovic et al. [27], a watershed segmentation [29] was used to split the disease segmentation into three-dimensional regions based on morphology and spatial proximity; this process allowed manual editing to remove entire three-dimensional subregions that may have been erroneously included in the disease segmentation. Segmentation editing was performed without knowledge of patient outcome by an observer (ZEL) trained in thoracic anatomy. Pleural 1000 Labby et al. Volume 24 No. 4 April 2013

3 Table 1. Description of the patient cohort used in this study disease segmentation is shown in Figure 1. To calculate pleural disease volume for each scan, a pixel-counting technique was used [30]. Sex (n) Male 68 Female 13 Age at diagnosis (years) Median 66 Range Chemotherapy (n) Carboplatin/pemetrexed 7 Cisplatin/pemetrexed 42 Cisplatin/gemcitabine 32 Histology (n) Epithelioid 60 Sarcomatoid 7 Biphasic 14 T stage (n) T1 19 T2 22 T3 25 T4 15 N stage (n) N0 25 N1 3 N2 40 N3 13 M stage (n) M0 70 M1 11 IMIG stage (n) I 13 II 5 III 36 IV 27 Known asbestos exposure (n) Yes 75 No 6 Chest pain (n) Yes 50 No 31 Shortness of breath (n) Yes 67 No 14 ECOG performance status (n) Talc pleurodesis (n) Yes 30 No 51 Weight (kg) Median 75 Range Height (cm) Median 172 Range Smoking status (n) Never 36 Past 39 Present 6 Pleurectomy/decortication (n) Yes 1 No 80 statistical analysis Pleural disease volume measurements were modeled using scaled logarithmic transforms of relative changes from baseline, known as the specific growth rate (SGR) [11]. The definition of the SGR metric is ln VðtÞ Vðt SGRðtÞ ¼ 0 Þ ð1þ ðt t 0 Þ where V(t) denotes the volume measurement at an arbitrary time point and t 0 indicates the time of baseline scanning (times were modeled as fractional years). Patient survival was measured from the pretreatment baseline scan until either patient death or administrative censoring. Clinical covariates available for each patient included sex, histology, weight, height, body surface area, TNM staging, International Mesothelioma Interest Group (IMIG) staging, smoking status, asbestos exposure, presence of chest pain, presence of dyspnea, weight loss prior to diagnosis, Eastern Cooperative Oncology Group (ECOG) PS, talc pleurodesis status before chemotherapy, blood hemoglobin levels, white cell count, platelet count, and age at diagnosis. All covariates were fixed at their baseline values in the survival models. In concordance with prior prognostic models for mesothelioma [24, 25], some covariates were dichotomized as follows: ECOG PS level 0 versus level 1 or 2, epithelioid histology versus other, N stage N0 versus other, IMIG stages 1, 2, or 3 versus stage 4, blood platelet count 400 versus >400 per nl, blood hemoglobin 140 versus >140 g per liter, and blood white cell count 8.3 versus >8.3 per nl. Survival modeling was carried out using Cox proportional hazards (PH) models [31, 32] with potentially time-varying covariates [33], and models were constructed using a forward selection process [34]. Survival model performance was assessed using Heagerty s C τ [35], which is scaled from 0 to 1 since it is derived from receiver operating characteristic analysis. C τ is especially useful for survival models with time-varying covariates; C τ =0.5 indicates no prognostic ability, and C τ =1.0 indicates perfect prognostic ability. To obtain estimates of model predictive performance for novel cases [34], two methods were used. First, a leave-one-out cross-validation (LOOCV) was used in which each of the 81 patients was omitted one at a time. A multivariate survival model that consisted of the terms included in the fullcohort model was fit to the remaining 80 patients, and the fit model then was applied to the left-out patient. The process was repeated until each of the 81 patients had been excluded from the training cohort. A value of C τ was calculated from these LOOCV-based survival predictions, called C τ cv. The second validation procedure required repeated random subsampling of the patient cohort and was intended to generate a confidence interval (CI) for model performance. For each subsampling iteration, a model was trained on two-thirds of the patient cohort (n = 54, randomly selected) and tested on the remaining one-third (n = 27) of the patient cohort. The final multivariate survival model derived from the full patient cohort was fit to the training cohort, and the fit model was applied to the testing cohort. The repeated random subsampling simulation consisted of 1000 iterations, and the mean performance metric value will be referred to as C τ sub. All analyses were carried out using Revolution R Enterprise (version 4.3, based on R version 2.12; Revolution Analytics, Palo Alto, CA) [36]. results patients and overall survival Table 1 presents patient characteristics for the 81 MPM patients included in this study. Median survival for the entire Volume 24 No. 4 April 2013 doi: /annonc/mds

4 Annals of Oncology Figure 2. Overall survival for the patient cohort in this study. univariate survival analysis Covariates predictive for survival in univariate Cox PH models at the α =0.10 level are shown in Table 2. All clinical covariates found to be predictive for survival in this study have been reported in previous prognostic models for patients with mesothelioma. Continuous, time-varying measurements of the pleural disease volume on serial CT scans [modeled according to equation (1)] may now be included among covariates that Table 2. Factors predictive for survival (from baseline imaging) in univariate Cox PH models, including hazard ratios and 95% confidence intervals (CI) Variable Hazard ratio 95% CI P-value Figure 1. (A) Semiautomated hemithorax segmentation. This axial section did not contain a seeded contour, so the dashed contour is an interpolation from other manual axial contours. (B) Pleural disease segmentation from the semiautomated hemithorax segmentation. Because the hemithorax contour only needed to encompass the identifiable pleural disease, manual editing of pleural disease segmentations was generally minimal (restricted to the removal of the partial volume artifacts adjacent to the aortic arch in this axial section). patient cohort was 12.6 months ( percentiles, months; range months). Of the 81 patients, there were 76 observed deaths, while the remaining 5 patients were censored after a median duration of 35 months. The overall Kaplan Meier survival curve is shown in Figure 2. Across all patients, the mean pleural disease volume at baseline was 1511 ± 1065 (range ml). At the first follow-up scan, the mean disease volume had reduced to 1397 ml, with geometric mean change from baseline of 11%. By the end of treatment, the geometric mean change in disease volume from baseline was 19%. The semiautomated techniques used in this study (hemithoracic segmentation and final pleural disease contour editing) required min of active user intervention per case on average. N stage N0 1 N M stage M0 1 M Dyspnea No 1 Yes Weight loss Continuous (in kg) Talc pleurodesis No 1 Yes ECOG performance status or Histology Epithelioid 1 Other Blood platelet count 400 per nl 1 >400 per nl Disease volume Continuous, SGR Disease volume was modeled as the continuous specific growth rate (SGR) from baseline 1002 Labby et al. Volume 24 No. 4 April 2013

5 Table 3. Factors predictive for survival in the final multivariate Cox PH model, including hazard ratios and 95% confidence intervals (CI) Variable Hazard ratio 95% CI P-value Disease volume Continuous, SGR Histology Epithelioid 1 Other Dyspnea No 1 Yes ECOG performance status or Disease volume was modeled as the continuous specific growth rate (SGR) from baseline. are significantly associated with patient survival. The model indicates that larger disease SGR values are associated with a larger hazard; therefore, patients with disease growth have worse prognosis than patients with disease shrinkage, and patients with substantial growth have worse prognosis than patients with minimal growth. multivariate survival analysis The final multivariate survival model included the covariates disease volume SGR (hazard ratio [HR] = 1.31, P = ), histology (HR = 2.28, P =0.0029), dyspnea (HR = 3.20, P =0.0020), and ECOG PS (HR = 1.69, P =0.029; Table 3). No two-way interactions between covariates in this final multivariate model were significant. The performance of the final multivariate model on the full patient cohort (after training on the full patient cohort) was calculated as C τ = From the LOOCV, the survival model performance from the final multivariate model was C τ cv = The 1000 random subsampling iterations yielded a mean model performance of C τ sub =0.660 with a 95% CI of discussion The goal of this study was to create a survival model for MPM patients using continuous, time-varying, image-based measurements of pleural disease volume modeled as specific growth rate from baseline. Disease volume extracted from semiautomated pleural disease segmentations proved to be a significant predictor of patient survival in conjunction with clinical covariates. Two essential aspects of this study were the ability to model changes in disease volume through time and the modeling of continuous (not discretized) changes in disease volume. In the only other study using changes in CT disease volume as a prognostic covariate for patients with MPM [18], analysis was limited to a single follow-up time point, and changes in disease volume from baseline were discretized as volume decrease or volume increase. In the current study, all available follow-up time points were used, and changes in disease volume were treated continuously. In the work by Liu et al., hypothetical Figure 3. Disease volume trajectories for three hypothetical patients. patients #2 and #3 in Figure 3 would be interpreted identically after the first follow-up volume quantification, and subsequent updates to the volume trajectory would be ignored. In the present study, however, the distinct disease volume trajectories across all time points for all three patients in Figure 3 would be considered as continuous changes from baseline. In the present patient cohort, fitting a model similar to Liu et al. in which the only covariate was discrete volume change after the first followup scan yielded C τ =0.521 (compare with C τ =0.690 for the full time-varying model in this study). While the use of MPM tumor volume as a prognostic covariate was motivated over a decade ago [37] and other studies have investigated correlations between discrete changes in MPM disease volume and patient survival [16, 18], this study is the first to model changing disease volumes using the SGR metric and combine clinical covariates with continuous, time-varying disease volumes in a multivariate survival model. Although not reported in the results, a model based only on disease volume yields C τ =0.610, while C τ =0.690 for the final multivariate model that included baseline clinical covariates. Therefore, time-dependent disease volume changes and clinical covariates at baseline are both crucial components of the final multivariate survival model. The indication that non-epithelioid histology, presence of dyspnea, and non-zero ECOG PS are associated with poor prognosis is consistent with previous prognostic models for MPM patients [22]. These factors are routinely available as part of the diagnostic workup of patients with MPM. The presence of dyspnea and ECOG PS can change over time and could be treated as time-varying in the multivariate prognostic model; however, these data are not typically available after the baseline assessment of each patient. The modeling of disease volume SGR as a continuous and time-varying quantity has clear advantages. First, any attempt to discretize disease volume changes into two or more classes will result in somewhat arbitrary class distinctions. Liu et al., for example, discretized response into two classes: disease growth or shrinkage. As another example, Frauenfelder et al. [17] used an extrapolation of the three-class RECIST classification criteria to volumetric changes based on a spherical geometry. The modeling of changes in disease volume as time-varying is more complicated but allows Volume 24 No. 4 April 2013 doi: /annonc/mds

6 arbitrary volumetric trajectories to be modeled for each patient. In many published modeling studies, tumor measurements are acquired at exactly two time points, baseline and follow-up. Follow-up typically is defined after a set number of chemotherapy cycles, but what happens before or after that event? If a patient gets four follow-up scans during treatment, the researchers have updated information about how the patient s disease volume has changed at multiple time points since baseline. The modeling approach used in this study takes advantage of the updated information, whereas fixed follow-up models cannot. Pleural disease segmentation is not necessarily the same as segmenting specifically mesothelioma tumor. For cases in which pleural effusion is present and spatially mixed with tumor, the separation of tumor from effusion can be difficult [27]. Pleural effusion regions were manually excluded only if they were localized and spatially independent of tumor regions. True tumor volume may have more prognostic value than pleural disease volume, although both pleural effusion and tumor negatively impact patients quality of life. The feasibility of pleural disease segmentation in the clinical workflow is unclear, and therefore, the routine clinical use of disease volumes for MPM must be investigated. Pleural disease/tumor segmentation may be avoided by models that incorporate changing lung volume as a surrogate for tumor volume change. Increasing the patient cohort may reveal associations between new covariates and survival, potentially improving the performance of the survival model. Validation of the model in a larger independent patient cohort remains as a future study. Finally, the patients in this study were treated with cytotoxic chemotherapy; treatment-specific models could be developed for individual patient cohorts, since the model derived in this study may not be directly applicable to patient cohorts receiving biologically different treatments. In summary, continuous volumes derived from semiautomatic segmentations of pleural disease in patients with MPM are prognostically significant. Disease volume (specifically the specific growth rate ) is allowed to change over time, allowing for survival prediction from arbitrary disease volume trajectories. In a final multivariate model, disease histology, dyspnea, and ECOG PS were also significant predictors of patient survival. funding This work was supported by The University of Chicago Comprehensive Cancer Center; the Raine Medical Research Foundation; the US National Institutes of Health (grant numbers T32EB and R01CA102085); the Simmons Mesothelioma Foundation; the Kazan Law Firm s Charitable Foundation; the National Health and Medical Research Council, Australia; and the Cancer Council Western Australia. disclosure The authors would like to acknowledge the following possible conflict of interest: S.G. Armato III receives royalties from computer-aided diagnosis technology licensed through The University of Chicago. All remaining authors have declared no conflicts of interest. references Annals of Oncology 1. Miller AB, Hoogstraten B, Staquet M, Winkler A. Reporting results of cancer treatment. Cancer 1981; 47(1): Therasse P, Arbuck SG, Eisenhauer EA et al.. 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