Predicting distant metastases in soft-tissue sarcomas from PET-CT scans using constrained hierarchical multi-modality feature learning. (7th December 2021)

• Record Type: Journal Article
• Authors: Peng, Yige; Bi, Lei; Kumar, Ashnil; Fulham, Michael; Feng, Dagan; Kim, Jinman
• Is Part Of: Physics in medicine & biology. Volume 66:Number 24(2021)
• Publication Date: 2021-12-07
• Subjects: distant metastases -- PET-CT -- radiomics -- soft-tissue sarcomas -- convolutional neural networks; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac3d17
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Objective. Positron emission tomography-computed tomography (PET-CT) is regarded as the imaging modality of choice for the management of soft-tissue sarcomas (STSs). Distant metastases (DM) are the leading cause of death in STS patients and early detection is important to effectively manage tumors with surgery, radiotherapy and chemotherapy. In this study, we aim to early detect DM in patients with STS using their PET-CT data. Approach. We derive a new convolutional neural network method for early DM detection. The novelty of our method is the introduction of a constrained hierarchical multi-modality feature learning approach to integrate functional imaging (PET) features with anatomical imaging (CT) features. In addition, we removed the reliance on manual input, e.g. tumor delineation, for extracting imaging features. Main results. Our experimental results on a well-established benchmark PET-CT dataset show that our method achieved the highest accuracy (0.896) and AUC (0.903) scores when compared to the state-of-the-art methods (unpaired student's t-test p -value < 0.05). Significance. Our method could be an effective and supportive tool to aid physicians in tumor quantification and in identifying image biomarkers for cancer treatment.