Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients.

OBJECTIVE: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage. METHODS: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. A total of 42 ovarian cancer cases were retrospectively collected to validate the scheme. A nested leave-one-out cross-validation framework was adopted for model performance assessment. RESULTS: The results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%. CONCLUSION: This study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.

Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy.

Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. Methods: For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1403 and 1595 features for the 2D and 3D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM)-based prediction models were developed and optimized for each feature set. Five-fold cross-validation was used to assess the performance of each individual model. Results: The results show that the 2D feature-based model achieved an AUC (area under the ROC curve (receiver operating characteristic)) of 0.84 ± 0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86 ± 0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91 ± 0.01. Conclusions: This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future.

Developing a Novel Image Marker to Predict the Responses of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients.

OBJECTIVE: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the patients' responses to NACT varies significantly among different subgroups. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy response prediction of the NACT at an early stage. METHODS: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. Using this cluster as the input, an SVM based classifier was developed and optimized to create a final marker, indicating the likelihood of the patient being responsive to the NACT treatment. To validate this scheme, a total of 42 ovarian cancer patients were retrospectively collected. A nested leave-one-out cross-validation was adopted for model performance assessment. RESULTS: The results demonstrate that the new method yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.745. Meanwhile, the model achieved overall accuracy of 76.2%, positive predictive value of 70%, and negative predictive value of 78.1%. CONCLUSION: This study provides meaningful information for the development of radiomics based image markers in NACT response prediction.

Peer evaluation and feedback for invasive medical procedures: a systematic review.

BACKGROUND: There is significant variability in the performance and outcomes of invasive medical procedures such as percutaneous coronary intervention, endoscopy, and bronchoscopy. Peer evaluation is a common mechanism for assessment of clinician performance and care quality, and may be ideally suited for the evaluation of medical procedures. We therefore sought to perform a systematic review to identify and characterize peer evaluation tools for practicing clinicians, assess evidence supporting the validity of peer evaluation, and describe best practices of peer evaluation programs across multiple invasive medical procedures. METHODS: A systematic search of Medline and Embase (through September 7, 2021) was conducted to identify studies of peer evaluation and feedback relating to procedures in the field of internal medicine and related subspecialties. The methodological quality of the studies was assessed. Data were extracted on peer evaluation methods, feedback structures, and the validity and reproducibility of peer evaluations, including inter-observer agreement and associations with other quality measures when available. RESULTS: Of 2,135 retrieved references, 32 studies met inclusion criteria. Of these, 21 were from the field of gastroenterology, 5 from cardiology, 3 from pulmonology, and 3 from interventional radiology. Overall, 22 studies described the development or testing of peer scoring systems and 18 reported inter-observer agreement, which was good or excellent in all but 2 studies. Only 4 studies, all from gastroenterology, tested the association of scoring systems with other quality measures, and no studies tested the impact of peer evaluation on patient outcomes. Best practices included standardized scoring systems, prospective criteria for case selection, and collaborative and non-judgmental review. CONCLUSIONS: Peer evaluation of invasive medical procedures is feasible and generally demonstrates good or excellent inter-observer agreement when performed with structured tools. Our review identifies common elements of successful interventions across specialties. However, there is limited evidence that peer-evaluated performance is linked to other quality measures or that feedback to clinicians improves patient care or outcomes. Additional research is needed to develop and test peer evaluation and feedback interventions.

Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images.

BACKGROUND: Pancreatic cancer is one of the most aggressive cancers with approximate 10% five-year survival rate. To reduce mortality rate, accurate detection and diagnose of suspicious pancreatic tumors at an early stage plays an important role. OBJECTIVE: To develop and test a new radiomics-based computer-aided diagnosis (CAD) scheme of computed tomography (CT) images to detect and classify suspicious pancreatic tumors. METHODS: A retrospective dataset consisting of 77 patients who had suspicious pancreatic tumors detected on CT images was assembled in which 33 tumors are malignant. A CAD scheme was developed using the following 5 steps namely, (1) apply an image pre-processing algorithm to filter and reduce image noise, (2) use a deep learning model to detect and segment pancreas region, (3) apply a modified region growing algorithm to segment tumor region, (4) compute and select optimal radiomics features, and (5) train and test a support vector machine (SVM) model to classify the detected pancreatic tumor using a leave-one-case-out cross-validation method. RESULTS: By using the area under receiver operating characteristic (ROC) curve (AUC) as an evaluation index, SVM model yields AUC = 0.750 with 95% confidence interval [0.624, 0.885] to classify pancreatic tumors. CONCLUSIONS: Study results indicate that radiomics features computed from CT images contain useful information associated with risk of tumor malignancy. This study also built a foundation to support further effort to develop and optimize CAD schemes with more advanced image processing and machine learning methods to more accurately and robustly detect and classify pancreatic tumors in future.

Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients.

BACKGROUND AND OBJECTIVE: In diagnosis of cervical cancer patients, lymph node (LN) metastasis is a highly important indicator for the following treatment management. Although CT/PET (i.e., computed tomography/positron emission tomography) examination is the most effective approach for this detection, it is limited by the high cost and low accessibility, especially for the rural areas in the U.S.A. or other developing countries. To address this challenge, this investigation aims to develop and test a novel radiomics-based CT image marker to detect lymph node metastasis for cervical cancer patients. METHODS: A total of 1,763 radiomics features were first computed from the segmented primary cervical tumor depicted on one CT image with the maximal tumor region. Next, a principal component analysis algorithm was applied on the initial feature pool to determine an optimal feature cluster. Then, based on this optimal cluster, the prediction models (i.e., logistic regression or support vector machine) were trained and optimized to generate an image marker to detect LN metastasis. In this study, a retrospective dataset containing 127 cervical cancer patients were established to build and test the model. The model was trained using a leave-one-case-out (LOCO) cross-validation strategy and image marker performance was evaluated using the area under receiver operation characteristic (ROC) curve (AUC). RESULTS: The results indicate that the SVM based imaging marker achieved an AUC value of 0.841 ± 0.035. When setting an operating threshold of 0.5 on model-generated prediction scores, the imaging marker yielded a positive and negative predictive value (PPV and NPV) of 0.762 and 0.765 respectively, while the total accuracy is 76.4%. CONCLUSIONS: This study initially verified the feasibility of utilizing CT image and radiomics technology to develop a low-cost image marker to detect LN metastasis for assisting stratification of cervical cancer patients.

Relapse Prevention with Tyrosine Kinase Inhibitors after Allogeneic Transplantation for Philadelphia Chromosome-Positive Acute Lymphoblast Leukemia: A Systematic Review.

Relapse after stem cell transplantation for Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia (ALL) remains a significant challenge. In this systematic review, we compare survival outcomes of second-generation tyrosine kinase inhibitors (TKIs) nilotinib and dasatinib with first-generation TKI imatinib when these agents are used after allogeneic hematopoietic stem cell transplantation (allo-HSCT) in Ph+ ALL. In addition, we review the literature on TKI use to prevent relapse in patients who proceed to allo-HSCT beyond first complete response (>CR1). We performed database searches (inception to January 2018) using PubMed, Cochrane Library, and Embase. After exclusions, 17 articles were included in this analysis. Imatinib was used post-transplant either prophylactically or preemptively in 12 studies, 7 prospective studies and 5 retrospective studies. Overall survival (OS) for most prospective studies at 1.5 to 3 and 5 years ranged between 62% to 92% and 74.5% to 86.7%. Disease-free survival at 1.5 to 5 years was 60.4% to 92%. Additionally, imatinib failed to show survival benefit in patients who were >CR1 at the time of allo-HSCT. The cumulative OS for most retrospective studies using imatinib at 1 to 2 and 3 to 5 years was 42% to 100% and 33% to 40% respectively. Event-free survival at 1 to 2 and 3 to 5 years was 33.3% to 67% and 20% to 31% respectively. Dasatinib was used as maintenance treatment in 3 retrospective studies (n = 34). The OS for patients with Ph+ ALL using dasatinib as maintenance regimen after allo-HSCT at 1.4 to 3 years was 87% to 100% and disease-free survival at 1.4 to 3 years was 89% to 100%. Ninety-three percent of patients with minimal residual disease (MRD) positive status after allo-HSCT became MRD negative. Three prospective studies used nilotinib. In 2 studies where investigators studied patients with advanced chronic myeloid leukemia and Ph+ ALL, the cumulative OS and event-free survival at 7.5 months to 2 years were 69% to 84% and 56% to 84%, respectively. In the third study (n = 5) in patients with Ph+ ALL, nilotinib use resulted in OS at 5 years of 60%. Our review showed that use of TKIs (all generations) after allo-HSCT for patients in CR1 improved OS when given as a prophylactic or preemptive regimen. Limited data suggest that second-generation TKIs (ie, dasatinib) have a better OS, especially in patients with MRD-positive status. Imatinib did not improve OS in patients who were >CR1 at the time of allo-HSCT; for this population, no data were available with newer generation TKIs. The evaluation of survival benefit with newer generation TKIs and their efficacy in patients in >CR1 needs further study in large randomized clinical trials.

Iron and Ferritin Deposition in the Ovarian Tissues of the Yellow Fever Mosquito (Diptera: Culicidae).

Dengue, yellow fever, and Zika are viruses transmitted by yellow fever mosquito, Aedes aegypti [Linnaeus (Diptera: Culicidae)], to thousands of people each year. Mosquitoes transmit these viruses while consuming a blood meal that is required for oogenesis. Iron, an essential nutrient from the blood meal, is required for egg development. Mosquitoes receive a high iron load in the meal; although iron can be toxic, these animals have developed mechanisms for dealing with this load. Our previous research has shown iron from the blood meal is absorbed in the gut and transported by ferritin, the main iron transport and storage protein, to the ovaries. We now report the distribution of iron and ferritin in ovarian tissues before blood feeding and 24 and 72 h post-blood meal. Ovarian iron is observed in specific locations. Timing post-blood feeding influences the location and distribution of the ferritin heavy-chain homolog, light-chain homolog 1, and light-chain homolog 2 in ovaries. Understanding iron deposition in ovarian tissues is important to the potential use of interference in iron metabolism as a vector control strategy for reducing mosquito fecundity, decreasing mosquito populations, and thereby reducing transmission rates of vector-borne diseases.

Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker.

This study aimed to investigate the feasibility of integrating image features computed from both spatial and frequency domain to better describe the tumor heterogeneity for precise prediction of tumor response to postsurgical chemotherapy in patients with advanced-stage ovarian cancer. A computer-aided scheme was applied to first compute 133 features from five categories namely, shape and density, fast Fourier transform, discrete cosine transform (DCT), wavelet, and gray level difference method. An optimal feature cluster was then determined by the scheme using the particle swarm optimization algorithm aiming to achieve an enhanced discrimination power that was unattainable with the single features. The scheme was tested using a balanced dataset (responders and non-responders defined using 6 month PFS) retrospectively collected from 120 ovarian cancer patients. By evaluating the performance of the individual features among the five categories, the DCT features achieved the highest predicting accuracy than the features in other groups. By comparison, a quantitative image marker generated from the optimal feature cluster yielded the area under ROC curve (AUC) of 0.86, while the top performing single feature only had an AUC of 0.74. Furthermore, it was observed that the features computed from the frequency domain were as important as those computed from the spatial domain. In conclusion, this study demonstrates the potential of our proposed new quantitative image marker fused with the features computed from both spatial and frequency domain for a reliable prediction of tumor response to postsurgical chemotherapy.

Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks.

The tumor-stroma ratio (TSR) reflected on hematoxylin and eosin (H&E)-stained histological images is a potential prognostic factor for survival. Automatic image processing techniques that allow for high-throughput and precise discrimination of tumor epithelium and stroma are required to elevate the prognostic significance of the TSR. As a variant of deep learning techniques, transfer learning leverages nature-images features learned by deep convolutional neural networks (CNNs) to relieve the requirement of deep CNNs for immense sample size when handling biomedical classification problems. Herein we studied different transfer learning strategies for accurately distinguishing epithelial and stromal regions of H&E-stained histological images acquired from either breast or ovarian cancer tissue. We compared the performance of important deep CNNs as either a feature extractor or as an architecture for fine-tuning with target images. Moreover, we addressed the current contradictory issue about whether the higher-level features would generalize worse than lower-level ones because they are more specific to the source-image domain. Under our experimental setting, the transfer learning approach achieved an accuracy of 90.2 (vs. 91.1 for fine tuning) with GoogLeNet, suggesting the feasibility of using it in assisting pathology-based binary classification problems. Our results also show that the superiority of the lower-level or the higher-level features over the other ones was determined by the architecture of deep CNNs.

Uterine Didelphys with Bilateral Cervical Agenesis in a 15-Year-Old Girl.

BACKGROUND: Isolated uterine didelphys requires no treatment in contrast to cervical agenesis, which requires a hysterectomy. Because of this, correct diagnosis of Müllerian anomalies is paramount for making recommendations for patient care. CASE: A 15-year-old girl presented to clinic with pelvic pain and primary amenorrhea. Uterine didelphys with bilateral cervical agenesis was diagnosed using imaging. Hysterectomy was recommended and diagnosis was confirmed at surgery and according to anatomic pathology. SUMMARY AND CONCLUSION: Our patient with uterine didelphys with bilateral cervical agenesis presented a diagnostic challenge, because, to our knowledge, it has never been reported before in the literature. Her pattern of anomalies had significant implications for future fertility. Radiology exam was vital to confirming this diagnosis in a young, virginal female patient.

Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy.

RATIONALE AND OBJECTIVES: The study aimed to investigate the role of applying quantitative image features computed from computed tomography (CT) images for early prediction of tumor response to chemotherapy in the clinical trials for treating ovarian cancer patients. MATERIALS AND METHODS: A dataset involving 91 patients was retrospectively assembled. Each patient had two sets of pre- and post-therapy CT images. A computer-aided detection scheme was applied to segment metastatic tumors previously tracked by radiologists on CT images and computed image features. Two initial feature pools were built using image features computed from pre-therapy CT images only and image feature difference computed from both pre- and post-therapy images. A feature selection method was applied to select optimal features, and an equal-weighted fusion method was used to generate a new quantitative imaging marker from each pool to predict 6-month progression-free survival. The prediction accuracy between quantitative imaging markers and the Response Evaluation Criteria in Solid Tumors (RECIST) criteria was also compared. RESULTS: The highest areas under the receiver operating characteristic curve are 0.684 ± 0.056 and 0.771 ± 0.050 when using a single image feature computed from pre-therapy CT images and feature difference computed from pre- and post-therapy CT images, respectively. Using two corresponding fusion-based image markers, the areas under the receiver operating characteristic curve significantly increased to 0.810 ± 0.045 and 0.829 ± 0.043 (P < 0.05), respectively. Overall prediction accuracy levels are 71.4%, 80.2%, and 74.7% when using two imaging markers and RECIST, respectively. CONCLUSIONS: This study demonstrated the feasibility of predicting patients' response to chemotherapy using quantitative imaging markers computed from pre-therapy CT images. However, using image feature difference computed between pre- and post-therapy CT images yielded higher prediction accuracy.

A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images.

Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognosis. In order to overcome limitation of using only one subjectively selected CT image slice to estimate size of fat areas, this study aims to develop and test a computer-aided detection (CAD) scheme based on deep learning technique to automatically segment subcutaneous fat areas (SFA) and visceral fat areas (VFA) depicting on volumetric CT images. A retrospectively collected CT image dataset was divided into two independent training and testing groups. The proposed CAD framework consisted of two steps with two convolution neural networks (CNNs) namely, Selection-CNN and Segmentation-CNN. The first CNN was trained using 2,240 CT slices to select abdominal CT slices depicting SFA and VFA. The second CNN was trained with 84,000pixel patches and applied to the selected CT slices to identify fat-related pixels and assign them into SFA and VFA classes. Comparing to the manual CT slice selection and fat pixel segmentation results, the accuracy of CT slice selection using the Selection-CNN yielded 95.8%, while the accuracy of fat pixel segmentation using the Segmentation-CNN was 96.8%. This study demonstrated the feasibility of applying a new deep learning based CAD scheme to automatically recognize abdominal section of human body from CT scans and segment SFA and VFA from volumetric CT data with high accuracy or agreement with the manual segmentation results.

Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome.

BACKGROUND: To investigate the feasibility of automated segmentation of visceral and subcutaneous fat areas from computed tomography (CT) images of ovarian cancer patients and applying the computed adiposity-related image features to predict chemotherapy outcome. METHODS: A computerized image processing scheme was developed to segment visceral and subcutaneous fat areas, and compute adiposity-related image features. Then, logistic regression models were applied to analyze association between the scheme-generated assessment scores and progression-free survival (PFS) of patients using a leave-one-case-out cross-validation method and a dataset involving 32 patients. RESULTS: The correlation coefficients between automated and radiologist's manual segmentation of visceral and subcutaneous fat areas were 0.76 and 0.89, respectively. The scheme-generated prediction scores using adiposity-related radiographic image features significantly associated with patients' PFS (p < 0.01). CONCLUSION: Using a computerized scheme enables to more efficiently and robustly segment visceral and subcutaneous fat areas. The computed adiposity-related image features also have potential to improve accuracy in predicting chemotherapy outcome.

Quantitative measurement of adiposity using CT images to predict the benefit of bevacizumab-based chemotherapy in epithelial ovarian cancer patients.

The present study aims to quantitatively measure adiposity-related image features and to test the feasibility of applying multivariate statistical data analysis-based prediction models to generate a novel clinical marker and predict the benefit of epithelial ovarian cancer (EOC) patients with and without maintenance bevacizumab-based chemotherapy. A dataset involving computed tomography (CT) images acquired from 59 patients diagnosed with advanced EOC was retrospectively collected. Among them, 32 patients received maintenance bevacizumab following primary chemotherapy, while 27 did not. A computer-aided detection scheme was developed to automatically segment visceral and subcutaneous fat areas depicted on CT images of abdominal sections, and 7 adiposity-related image features were computed. Upon combining these features with the measured body mass index, multivariate data analyses were performed using three statistical models (multiple linear, logistic and Cox proportional hazards regressions) to analyze the association between the model-generated prediction results and the treatment outcome, including progression-free survival (PFS) and overall survival (OS) of the patients. The results demonstrated that applying all three prediction models yielded a significant association between the adiposity-related image features and patients' PFS or OS in the group of the patients who received maintenance bevacizumab (P<0.010), while there was no significant difference when these prediction models were applied to predict both PFS and OS in the group of patients that did not receive maintenance bevacizumab. Therefore, the present study demonstrated that the use of a quantitative adiposity-related image feature-based statistical model may generate a novel clinical marker to predict who will benefit among EOC patients receiving maintenance bevacizumab-based chemotherapy.

A New Approach to Evaluate Drug Treatment Response of Ovarian Cancer Patients Based on Deformable Image Registration.

Although Response Evaluation Criteria in Solid Tumors (RECIST) is the current clinical guideline to assess size change of solid tumors after therapeutic treatment, it has a relatively lower association to the clinical outcome of progression free survival (PFS) of the patients. In this paper, we presented a new approach to assess responses of ovarian cancer patients to new chemotherapy drugs in clinical trials. We first developed and applied a multi-resolution B-spline based deformable image registration method to register two sets of computed tomography (CT) image data acquired pre- and post-treatment. The B-spline difference maps generated from the co-registered CT images highlight the regions related to the volumetric growth or shrinkage of the metastatic tumors, and density changes related to variation of necrosis inside the solid tumors. Using a testing dataset involving 19 ovarian cancer patients, we compared patients' response to the treatment using the new image registration method and RECIST guideline. The results demonstrated that using the image registration method yielded higher association with the six-month PFS outcomes of the patients than using RECIST. The image registration results also provided a solid foundation of developing new computerized quantitative image feature analysis schemes in the future studies.

Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis.

BACKGROUND: In current clinical trials of treating ovarian cancer patients, how to accurately predict patients' response to the chemotherapy at an early stage remains an important and unsolved challenge. PURPOSE: To investigate feasibility of applying a new quantitative image analysis method for predicting early response of ovarian cancer patients to chemotherapy in clinical trials. MATERIAL AND METHODS: A dataset of 30 patients was retrospectively selected in this study, among which 12 were responders with 6-month progression-free survival (PFS) and 18 were non-responders. A computer-aided detection scheme was developed to segment tumors depicted on two sets of CT images acquired pre-treatment and 4-6 weeks post treatment. The scheme computed changes of three image features related to the tumor volume, density, and density variance. We analyzed performance of using each image feature and applying a decision tree to predict patients' 6-month PFS. The prediction accuracy of using quantitative image features was also compared with the clinical record based on the Response Evaluation Criteria in Solid Tumors (RECIST) guideline. RESULTS: The areas under receiver operating characteristic curve (AUC) were 0.773 ± 0.086, 0.680 ± 0.109, and 0.668 ± 0.101, when using each of three features, respectively. AUC value increased to 0.831 ± 0.078 when combining these features together. The decision-tree classifier achieved a higher predicting accuracy (76.7%) than using RECIST guideline (60.0%). CONCLUSION: This study demonstrated the potential of using a quantitative image feature analysis method to improve accuracy of predicting early response of ovarian cancer patients to the chemotherapy in clinical trials.

Measurements of adiposity as clinical biomarkers for first-line bevacizumab-based chemotherapy in epithelial ovarian cancer.

OBJECTIVE: There is a lack of reliable indicators to predict who will benefit most from anti-angiogenic therapy, such as bevacizumab. Recognizing obesity is associated with increased levels of VEGF, the main target of bevacizumab, we sought to assess if adiposity, measured in terms of BMI, subcutaneous fat area (SFA), and visceral fat area (VFA) was prognostic. METHODS: Reviewed 46 patients with advanced EOC who received primary treatment with bevacizumab-based chemotherapy (N=21) or chemotherapy alone (N=25) for whom complete records, CT prior to the first cycle of chemo, and serum were available. CT was used to measure SFA and VFA by radiologists blinded to outcomes. ELISA was used to measure serum levels of VEGF and angiopoietin-2 in the bevacizumab group. RESULTS: BMI, SFA, and VFA were dichotomized using the median and categorized as "high" or "low". In the bevacizumab group median PFS was shorter for patients with high BMI (9.8 vs. 24.7months, p=0.03), while in the chemotherapy group median PFS was similar between high and low BMI (17.6 vs. 11.9months, p=0.19). In the bevacizumab group patients with a high BMI had higher median levels of VEGF and angiopoietin-2, 371.9 vs. 191.4pg/ml (p=0.05) and 45.9 vs. 16.6pg/ml (p=0.09) respectively. On multivariate analysis neither BMI, SFA, nor VFA were associated with PFS (p=0.13, p=0.86, p=0.16 respectively) or OS (p=0.14, p=0.93, p=0.28 respectively) in the chemotherapy group. However, in the bevacizumab group BMI was significantly associated with PFS (p=0.02); accounting for confounders adjusted HR for high vs. low BMI was 5.16 (95% CI 1.31-20.24). Additionally in the bevacizumab group SFA was significantly associated with OS (p=0.03); accounting for confounders adjusted HR for high vs. low SFA was 3.58 (95% CI 1.12-11.43). CONCLUSION: Results provide the first evidence in EOC that patients with high levels of adiposity may not derive benefit from bevacizumab and that measurements of adiposity are likely to be a useful biomarker.