Pseudoprogression (PsP) is a diagnostic clinical dilemma in cancer. In this study, we retrospectively analyse glioblastoma patients, and using their dynamic susceptibility contrast and dynamic contrast-enhanced perfusion MRI images we build a classifier using radiomic features obtained from both Ktrans and rCBV maps coupled with support vector machines. We achieve an accuracy of 90.82% (area under the curve (AUC) = 89.10%, sensitivity = 91.36%, 67 specificity = 88.24%, p = 0.017) in differentiating between pseudoprogression (PsP) and progressive disease (PD). The diagnostic performances of the models built using radiomic features from Ktrans and rCBV separately were equally high (Ktrans: AUC = 94%, 69 p = 0.012; rCBV: AUC = 89.8%, p = 0.004). Thus, this MR perfusion-based radiomic model demonstrates high accuracy, sensitivity and specificity in discriminating PsP from PD, thus provides a reliable alternative for noninvasive identification of PsP versus PD at the time of clinical/radiologic question. This study also illustrates the successful application of radiomic analysis as an advanced processing step on different MR perfusion maps.
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. Radiogenomic predictions and validation were done using the Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data glioblastoma patients ( = 93) and orthotopic xenografts (OX; = 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 ( expression levels. RNA and protein levels confirmed RNAi-mediated 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 expression status in patient, mouse, and interspecies. 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. expression status was not associated with qualitative or volumetric MRI parameters. Radiomic features significantly predicted 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 expression levels (AUC: 93.36%; sensitivity/specificity: 82.61%/95.74%; = 02.021E-15). We determined causality between radiomic texture features and 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.
Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to improve targeting and evaluation of responses to therapy in this disease are needed. Here, we integrate quantitative magnetic resonance imaging (MRI) data with biologically-based mathematical modeling to accurately predict the response of TNBC to neoadjuvant systemic therapy (NAST) on an individual basis. Specifically, 56 TNBC patients enrolled in the ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) and then paclitaxel for NAST, where dynamic contrast-enhanced MRI and diffusion-weighted MRI were acquired before treatment and after two and four cycles of A/C. A biologically-based model was established to characterize tumor cell movement, proliferation, and treatment-induced cell death. Two evaluation frameworks were investigated using: 1) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and 2) images acquired before, after two cycles, and after four cycles of A/C for calibration and predicting response following NAST. For Framework 1, the concordance correlation coefficients between the predicted and measured patient-specific, post-A/C changes in tumor cellularity and volume were 0.95 and 0.94, respectively. For Framework 2, the biologically-based model achieved an area under the receiver operator characteristic curve of 0.89 (sensitivity/specificity = 0.72/0.95) for differentiating pathological complete response (pCR) from non-pCR, which is statistically superior (P < 0.05) to the value of 0.78 (sensitivity/specificity = 0.72/0.79) achieved by tumor volume measured after four cycles of A/C. Overall, this model successfully captured patient-specific, spatiotemporal dynamics of TNBC response to NAST, providing highly accurate predictions of NAST response.
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; p<0.001) and in patients assessed by irRECIST (94.7%, 93.9%, and 95.8%, respectively; p<0.001). Additionally, the common features of the RECIST and irRECIST groups also highly predicted pembrolizumab response with accuracy, sensitivity, specificity, and p value of 94.7%, 97%, 90%, p<0.001% and 96%, 96%, 95%, p<0.001, respectively.ConclusionOur 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.
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.
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