Self-supervised network predicting neoadjuvant chemoradiotherapy response to locally advanced rectal cancer patients.
Authors
Affiliations (6)
Affiliations (6)
- Image Processing Center, Beihang University, Beijing 102206, China.
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Unior Medical College, Shenzhen, 518116, China.
- Department of Oncology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, 400016, China; Molecular Medicine Diagnostic and Testing Center, Chongqing Medical University, 400016, China.
- Department of Oncology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China. Electronic address: [email protected].
- Image Processing Center, Beihang University, Beijing 102206, China; The State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China. Electronic address: [email protected].
Abstract
Radiographic imaging is a non-invasive technique of considerable importance for evaluating tumor treatment response. However, redundancy in CT data and the lack of labeled data make it challenging to accurately assess the response of locally advanced rectal cancer (LARC) patients to neoadjuvant chemoradiotherapy (nCRT) using current imaging indicators. In this study, we propose a novel learning framework to automatically predict the response of LARC patients to nCRT. Specifically, we develop a deep learning network called the Expand Intensive Attention Network (EIA-Net), which enhances the network's feature extraction capability through cascaded 3D convolutions and coordinate attention. Instance-oriented collaborative self-supervised learning (IOC-SSL) is proposed to leverage unlabeled data for training, reducing the reliance on labeled data. In a dataset consisting of 1,575 volumes, the proposed method achieves an AUC score of 0.8562. The dataset includes two distinct parts: the self-supervised dataset containing 1,394 volumes and the supervised dataset comprising 195 volumes. Analysis of the lifetime predictions reveals that patients with pathological complete response (pCR) predicted by EIA-Net exhibit better overall survival (OS) compared to non-pCR patients with LARC. The retrospective study demonstrates that imaging-based pCR prediction for patients with low rectal cancer can assist clinicians in making informed decisions regarding the need for Miles operation, thereby improving the likelihood of anal preservation, with an AUC of 0.8222. These results underscore the potential of our method to enhance clinical decision-making, offering a promising tool for personalized treatment and improved patient outcomes in LARC management.