Precise segmentation of rectal cancer tumors on routine MRI is critical for accurate clinical staging and downstream computational analyses. While deep learning-based segmentation algorithms have shown much promise in automating the otherwise tedious, subjective, and costly process of manual segmentation, they require significant amounts of manually annotated data for training. To address these limitations of deep learning-based segmentation models, we present a novel deep learning framework that incorporates human-in-the-loop (HITL) refinement to automatically delineate rectal tumors on multi-plane pre-treatment MR imaging. When evaluated on multiple holdout validation cohorts including a clinical trial dataset, the post-HITL segmentation model significantly outperformed the pre-HITL model with median dice similarity coefficient of 0.763 and Hausdorff distance of 28.4mm in comparison to 0.601 and 31.8mm, respectively. HITL refinement learning also significantly accelerated the manual annotation process by 20 minutes. HITL learning represents a feasible, effective, and efficient solution to semi-automated tumor segmentation on routine rectal cancer MRI scans.
Radiomic analysis has shown significant potential for predicting treatment response to neoadjuvant therapy in rectal cancers via routine MRI, though primarily based off a single acquisition plane or single region of interest. To exploit intuitive clinical and biological aspects of tumor extent on MRI, we present a novel multi-plane, multi-region radiomics framework to more comprehensively characterize and interrogate treatment response on MRI. Our framework was evaluated on a cohort of 71 T2-weighted axial and coronal MRIs from patients diagnosed with rectal cancer and who underwent chemoradiation. 2D radiomic features were extracted from three regions of interest (tumor, fat proximal to tumor, and perirectal fat) across axial and coronal planes, with a two-stage feature selection scheme designed to identify descriptors associated with pathologic complete response. When evaluated via a quadratic discriminant analysis classifier, our multi-plane, multi-region radiomics model outperformed single-plane or single-region feature sets with an area under the ROC curve (AUC) of 0.80 ± 0.03 in discovery and AUC=0.65 in hold-out validation. Uniquely, the optimal feature set comprised descriptors from across multiple planes (axial, coronal) as well as multiple regions (tumor, proximal fat, perirectal fat). Our multi-plane, multi-region radiomics framework may thus enable more comprehensive phenotyping of treatment response on MRI, potentially finding application for improved personalization of therapeutic and surgical interventions in rectal cancers.
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