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BackgroundWe present a radiomics-based model for predicting response to pembrolizumab in patients with advanced rare cancers.MethodsThe study included 57 patients with advanced rare cancers who were enrolled in our phase II clinical trial of pembrolizumab. Tumor response was evaluated using Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and immune-related RECIST (irRECIST). Patients were categorized as 20 “controlled disease” (stable disease, partial response, or complete response) or 37 progressive disease). We used 3D-slicer to segment target lesions on standard-of-care, pretreatment contrast enhanced CT scans. We extracted 610 features (10 histogram-based features and 600 second-order texture features) from each volume of interest. Least absolute shrinkage and selection operator logistic regression was used to detect the most discriminatory features. Selected features were used to create a classification model, using XGBoost, for the prediction of tumor response to pembrolizumab. Leave-one-out cross-validation was performed to assess model performance.FindingsThe 10 most relevant radiomics features were selected; XGBoost-based classification successfully differentiated between controlled disease (complete response, partial response, stable disease) and progressive disease with high accuracy, sensitivity, and specificity in patients assessed by RECIST (94.7%, 97.3%, and 90%, respectively; pConclusionOur radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer.InterpretationOur radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer.

BACKGROUND Dynamic contrast-enhanced (DCE) MRI is useful for diagnosis and assessment of treatment response in breast cancer. Fast DCE MRI offers a higher sampling rate of contrast enhancement curves in comparison to conventional DCE MRI, potentially characterizing tumor perfusion kinetics more accurately for measurement of functional tumor volume (FTV) as a predictor of treatment response. PURPOSE To investigate FTV by fast DCE MRI as a predictor of neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). STUDY TYPE Prospective. POPULATION/SUBJECTS Sixty patients with biopsy-confirmed TNBC between December 2016 and September 2020. FIELD STRENGTH/SEQUENCE A 3.0 T/3D fast spoiled gradient echo-based DCE MRI ASSESSMENT: Patients underwent MRI at baseline and after four cycles (C4) of NAST, followed by definitive surgery. DCE subtraction images were analyzed in consensus by two breast radiologists with 5 (A.H.A.) and 2 (H.S.M.) years of experience. Tumor volumes (TV) were measured on early and late subtractions. Tumors were segmented on 1 and 2.5-minute early phases subtractions and FTV was determined using optimized signal enhancement thresholds. Interpolated enhancement curves from segmented voxels were used to determine optimal early phase timing. STATISTICAL TESTS Tumor volumes were compared between patients who had a pathologic complete response (pCR) and those who did not using the area under the receiver operating curve (AUC) and Mann-Whitney U test. RESULTS About 26 of 60 patients (43%) had pCR. FTV at 1 minute after injection at C4 provided the best discrimination between pCR and non-pCR, with AUC (95% confidence interval [CI]) = 0.85 (0.74,0.95) (P

Radiogenomics is a relatively new and exciting field within radiology that links different imaging features with diverse genomic events. Genomics advances provided by the Cancer Genome Atlas and the Human Genome Project have enabled us to harness and integrate this information with noninvasive imaging phenotypes to create a better 3-dimensional understanding of tumor behavior and biology. Beyond imaging-histopathology, imaging genomic linkages provide an important layer of complexity that can help in evaluating and stratifying patients into clinical trials, monitoring treatment response, and enhancing patient outcomes. This article reviews some of the important radiogenomic literatures in brain tumors.

The new World Health Organization classification of brain tumors depends on combining the histologic light microscopy features of central nervous system (CNS) tumors with canonical genetic alterations. This integrated diagnosis is redrawing the pedigree chart of brain tumors with rearrangement of tumor groups on the basis of geno-phenotypical behaviors into meaningful groups. Multiple radiogenomic studies provide a bridge between imaging features and tumor microenvironment. An overlap that can be integrated within the genophenotypical classification of CNS tumors for a better understanding of different clinically relevant entities.

10067 Background: Metastatic melanoma pt outcomes have been revolutionized by targeted therapy (TT) and immune checkpoint blockade (ICB), which are now being evaluated in the neoadjuvant (neoadj) setting. While tumor-based biomarkers may help predict response, predictors of response obtained by less invasive strategies could greatly benefit pt care and allow real-time treatment response monitoring. Radiomic signatures derived from computerized tomography (CT) images have recently been shown to predict response to ICB in stage IV pts. However, the association of radiomic features with pathological response following neoadj therapy has not been assessed. We sought to determine if radiomic assessment predicts pCR in pts receiving neoadj TT and ICB. Methods: We collected data for a cohort of melanoma pts with locoregional metastases who were treated with neoadj TT (n = 33) or ICB (n = 30). Pts received systemic therapy for 8-10 weeks prior to planned surgical resection. Responses were evaluated radiographically (RECIST 1.1) and via pathological assessment (evaluating for pathologic complete response; (pCR) versus < pCR). Thirty two pts (19 ICB; 13 TT) were included in the radiomics analysis based on the availability of appropriate CT imaging. A total of 310 unique radiomic features (10 histogram-based and 300 second-order texture features) were calculated from each extracted volume of interest (VOI). Feature extraction was performed on baseline and initial on-treatment pre-operative CT scans. Features associated with pCR were assessed using a feature selection approach based on Least Absolute Shrinkage and Selection Operator (LASSO). Selected features were used to build a classification model for prediction of pCR to ICB or TT. Leave-One-Out Cross-Validation was performed to evaluate the robustness of the estimates. Results: Out of 310 radiomic features, three features measured at baseline were able to predict a pCR to neoadj ICB or TT with sensitivity, specificity and accuracy of 100%, though these signatures were non-overlapping. In the on-treatment pre-operative scans, 3 distinct features (also non-overlapping and distinct from the predictive pre-treatment signatures) also predicted pCR to ICB and TT with 100% sensitivity, specificity and accuracy. Conclusions: Radiomic signatures in baseline and on-treatment CT scans accurately predict pCR in melanoma pts with locoregional metastases treated with neoadj TT or ICB. These provocative findings warrant further investigation in larger, independent cohorts.

66 Background: To predict responders versus non-responders to Pembrolizumab, an anti PD-1 monoclonal antibody, in patients with advanced rare cancers. Methods: The study included 58 patients with advanced rare cancers (eg. squamous cell carcinoma of the skin, adrenocortical carcinoma, carcinoma of unknown primary, and paraganglioma) who were enrolled in a phase 2 trial of Pembrolizumab. Tumor response was evaluated using Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. Patients were categorized into: 21 responders (stable disease, partial response, and complete response) and 37 non-Responders (progressive disease). Target lesion(s) obtain from standard-of-care, pre-treatment contrast enhanced CT scans were segmented using 3D slicer v4.8.1. A total of 610 features (10 histogram-based and 600 second-order texture features) were calculated from each extracted volume of interest (VOI). Radiomic features were obtained using a feature selection approach based on Least Absolute Shrinkage and Selection Operator (LASSO). Selected features were used to build a classification model, using XGboost, for prediction of tumor response to Pembrolizumab. To evaluate the robustness of the estimates, Leave-One-Out Cross-Validation (LOOCV) was performed. Results: A total of 10 radiomic features were selected; the XGboost-based classification robustly differentiated between responders vs non-responders (area under the curve, sensitivity and specificity were 99%, 100%, and 95%, respectively [p

Purpose: Radiomics is the extraction of multidimensional imaging features, which when correlated with genomics, is termed radiogenomics. However, radiogenomic biological validation is not sufficiently described in the literature. We seek to establish causality between differential gene expression status and MRI-extracted radiomic-features in glioblastoma. Experimental Design: Radiogenomic predictions and validation were done using the Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data glioblastoma patients (n = 93) and orthotopic xenografts (OX; n = 40). Tumor phenotypes were segmented, and radiomic-features extracted using the developed radiome-sequencing pipeline. Patients and animals were dichotomized on the basis of Periostin (POSTN) expression levels. RNA and protein levels confirmed RNAi-mediated POSTN knockdown in OX. Total RNA of tumor cells isolated from mouse brains (knockdown and control) was used for microarray-based expression profiling. Radiomic-features were utilized to predict POSTN expression status in patient, mouse, and interspecies. Results: Our robust pipeline consists of segmentation, radiomic-feature extraction, feature normalization/selection, and predictive modeling. The combination of skull stripping, brain-tissue focused normalization, and patient-specific normalization are unique to this study, providing comparable cross-platform, cross-institution radiomic features. POSTN expression status was not associated with qualitative or volumetric MRI parameters. Radiomic features significantly predicted POSTN expression status in patients (AUC: 76.56%; sensitivity/specificity: 73.91/78.26%) and OX (AUC: 92.26%; sensitivity/specificity: 92.86%/91.67%). Furthermore, radiomic features in OX were significantly associated with patients with similar POSTN expression levels (AUC: 93.36%; sensitivity/specificity: 82.61%/95.74%; P = 02.021E−15). Conclusions: We determined causality between radiomic texture features and POSTN expression levels in a preclinical model with clinical validation. Our biologically validated radiomic pipeline also showed the potential application for human–mouse matched coclinical trials.

BACKGROUND: Radiomic texture analysis (TA) from standard MRI imaging may be able to discriminate between responders versus non-responders in glioblastoma patients treated with pembrolizumab immunotherapy. METHODS: 14 patients (5 males; mean age 58 years; range: 32–72 years), with pathologically-proven recurrent GBM, enrolled in a pembrolizumab clinical trial, were retrospectively evaluated. Immunotherapy Response Assessment in Neuro-Oncology(iRANO) were performed. Patients were categorized based on: 1) best response or 2) overall response (OR) using the iRANO status at the last scan time in the trial. Patients with progressive disease (PD) were classified as non-responders, while patients with partial response (PR) or stable disease (SD) were classified as responders. T2-FLAIR (edema/invasion) and post-contrast T1WI (enhancing tumor) of baseline scans were co-registered and segmented (3D Slicer, v.4.3.1) to create a volume of interest for Radiomic TA. A total of 4880 texture features were extracted. Feature selection was performed using Lasso regularization. For classification and predictive model building, gbtree booster of XGboost with Leave-One-Out Cross-Validation (LOOCV) was used on the selected texture features to build a binary logistic regression model and classify the patients into respective groups RESULTS: Using the best response classification, 10 patients were classified as non-responders and four patients classified as responders (1 SD; 3 PR). Using 13 radiomic features, these patients could be classified into their respective responding groups with a sensitivity, specificity and accuracy of 100%, p-value=0.0089. Based on OR, 12 patients were classified as non-responders and two as responders (2 SD). Seven features were able to differentiate the responding patients with a sensitivity, specificity and accuracy of 100%, p-value=0.0089. CONCLUSION: Radiomic TA was able to discriminate and predict those GBM patients that are responders versus non-responders to pembrolizumab with high robustness. Of note, given the small number of patients in this cohort, a larger cohort of patients is needed to minimize overfitting.

Treatment response and survival after bevacizumab failure remains poor in patients with glioblastoma. Several recent publications examining glioblastoma patients treated with bevacizumab have described specific radiographic patterns of disease progression as correlating with outcome. This study aims to scrutinize these previously reported radiographic prognostic models in an independent data set to inspect their reproducibility and potential for clinical utility. Sixty four patients treated at MD Anderson matched predetermined inclusion criteria. Patients were categorized based on previously published data by: (1) Nowosielski et al. into: T2-diffuse, cT1 Flare-up, non-responders and T2 circumscribed groups (2) Modified Pope et al. criteria into: local, diffuse and distant groups and (3) Bahr et al. into groups with or without new diffusion-restricted and/or pre-contrast T1-hyperintense lesions. When classified according to Nowosielski et al. criteria, the cT1 Flare-up group had the longest overall survival (OS) from bevacizumab initiation, with non-responders having the worst outcomes. The T2 diffuse group had the longest progression free survival (PFS) from start of bevacizumab. When classified by modified Pope at al. criteria, most patients did not experience a shift in tumor pattern from the pattern at baseline, while the PFS and OS in patients with local-to-local and local-to-diffuse/distant patterns of progression were similar. Patients developing restricted diffusion on bevacizumab had worse OS. Diffuse patterns of progression in patients treated with bevacizumab are rare and not associated with worse outcomes compared to other radiographic subgroups. Emergence of restricted diffusion during bevacizumab treatment was a radiographic marker of worse OS.

Normal tissue toxicity is an important consideration in the continued development of more effective external beam radiotherapy (EBRT) regimens for head and neck tumors. The ability to detect EBRT-induced changes in mandibular bone vascularity represents a crucial step in decreasing potential toxicity. To date, no imaging modality has been shown to detect changes in bone vascularity in real time during treatment. Based on our institutional experience with multi-parametric MRI, we hypothesized that DCE-MRI can provide in-treatment information regarding EBRT-induced changes in mandibular vascularity. Thirty-two patients undergoing EBRT treatment for head and neck cancer were prospectively imaged prior to, mid-course and following treatment. DCE-MRI scans were co-registered to dosimetric maps to correlate EBRT dose and change in mandibular bone vascularity as measured by Ktrans and Ve. DCE-MRI was able to detect dose-dependent changes in both Ktrans and Ve in a subset of patients. One patient who developed ORN during the study period demonstrated decreases in Ktrans and Ve following treatment completion. We demonstrate, in a prospective imaging trial, that DCE-MRI can detect dose-dependent alterations in mandibular bone vascularity during chemoradiotherapy, providing biomarkers that are physiological correlates of acute of acute mandibular vascular injury and recovery temporal kinetics.

BACKGROUND: Treatment-related imaging changes are often difficult to distinguish from true tumor progression. Treatment-related changes or pseudoprogression (PsP) subsequently subside or stabilize without any further treatment, whereas progressive tumor requires a more aggressive approach in patient management. Pseudoprogression can mimic true progression radiographically and may potentially alter the physician's judgment about the recurrent disease. Hence, it can predispose a patient to overtreatment or be categorized as a non-responder and exclude him from the clinical trials. This study aims at assessing the potential of radiomics to discriminate PsP from progressive disease (PD) in glioblastoma (GBM) patients. METHODS: We retrospectively evaluated 304 GBM patients with new or increased enhancement on conventional MRI after treatment, of which it was uncertain for PsP versus PD. 149 patients had the histopathological evidence of PD and 27 of PsP. Remaining 128 patients were categorized into PD and PsP based on RANO criteria performed by a board-certified radiologist. Volumetrics using 3D slicer 4.3.1 and radiomics texture analysis were performed of the enhancing lesion(s) in question. RESULTS: Using the MRMR feature selection method, we identified 100 significant features that were used to build a SVM model. Five texture features (E, CS, SA, MP, CP) were found to be most predictive of pseudoprogression. On Leave One Out Cross-Validation (LOOCV), sensitivity, specificity and accuracy were 97%, 72%, and 90%, respectively. Using 70% of the patient data for training and 30% for validation, an AUC of 94% was achieved, with the sensitivity of 97% and specificity of 75%. CONCLUSION: 3D radiomic texture features of conventional MRI successfully discriminated pseudoprogression from true progression in a large cohort of GBM patients. Citation Format: Srishti Abrol, Aikaterini Kotrotsou, Ahmed Hassan, Nabil Elshafeey, Tagwa Idris, Naveen Manohar, Anand Agarwal, Islam Hassan, Kamel Salek, Nikdokht Farid, Carrie McDonald, Shiao-Pei Weathers, Naeim Bahrami, Samuel Bergamaschi, Ahmed Elakkad, Kristin Alfaro-Munoz, Fanny Moron, Jason Huse, Jeffrey Weinberg, Sherise Ferguson, Evangelos Kogias, Amy Heimberger, Raymond Sawaya, Ashok Kumar, John de Groot, Meng Law, Pascal Zinn, Rivka R. Colen. Radiomics discriminates pseudo-progression from true progression in glioblastoma patients: A large-scale multi-institutional study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3040.

2015 Background: Treatment-related imaging changes are often difficult to distinguish from true tumor progression. Treatment-related changes or pseudoprogression (PsP) subsequently subside or stabilize without any further treatment, whereas progressive tumor requires a more aggressive approach in patient management. Pseudoprogression can mimic true progression radiographically and may potentially alter the physician’s judgment about the residual disease. Hence, it can predispose a patient to overtreatment or be categorized as a non-responder and exclude him from the clinical trials. This study aims at assessing the potential of radiomics to discriminate PsP from progressive disease (PD) in glioblastoma (GBM) patients. Methods: We retrospectively evaluated 304 GBM patients with new or increased enhancement on conventional MRI after treatment, of which it was uncertain for PsP versus PD. 149 patients had the histopathological evidence of PD and 27 of PsP. Remaining 128 patients were categorized into PD and PsP based on RANO criteria performed by a board-certified radiologist. Volumetrics using 3D slicer 4.3.1 and radiomics texture analysis were performed of the enhancing lesion(s) in question. Results: Using the MRMR feature selection method, we identified 100 significant features that were used to build a SVM model. Five texture features (E, CS, SA, MP, CP) were found to be most predictive of pseudoprogression. On Leave One Out Cross-Validation (LOOCV), sensitivity, specificity and accuracy were 97%, 72%, and 90%, respectively. Conclusions: 3D radiomic texture features of conventional MRI successfully discriminated pseudoprogression from true progression in a large cohort of GBM patients.