Physician-guided deep learning model for assessing thymic epithelial tumor volume

Abstract

Purpose: The Response Evaluation Criteria in Solid Tumors (RECIST) relies solely on one-dimensional measurements to evaluate tumor response to treatments. However, thymic epithelial tumors (TETs), which frequently metastasize to the pleural cavity, exhibit a curvilinear morphology that complicates accurate measurement. To address this, we developed a physician-guided deep learning model and performed a retrospective study based on a patient cohort derived from clinical trials, aiming at efficient and reproducible volumetric assessments of TETs. Approach: We used 231 computed tomography scans comprising 572 TETs from 81 patients. Tumors within the scans were identified and manually outlined to develop a ground truth that was used to measure model performance. TETs were characterized by their general location within the chest cavity: lung parenchyma, pleura, or mediastinum. Model performance was quantified on an unseen test set of 61 scans by mask Dice similarity coefficient (DSC), tumor DSC, absolute volume difference, and relative volume difference. Results: and a mean relative volume difference of 22%. Conclusion: We have successfully developed a robust annotation workflow and AI segmentation model for analyzing advanced TETs. The model has been integrated into the Picture Archiving and Communication System alongside RECIST measurements to enhance outcome assessments for patients with metastatic TETs.

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