MR-DELTAnet: A Longitudinal MRI-Transformer Model Predicting Pathological Complete Response and Revealing Immune Microenvironment via scRNA-seq in Locally Advanced Rectal Cancer.
Authors
Affiliations (10)
Affiliations (10)
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, No.26, Yuancun Er Heng Road, Tianhe District, Guangzhou, Guangdong, 510655, China.
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, No.26, Yuancun Er Heng Road, Tianhe District, Guangzhou, Guangdong, 510655, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, No.26, Yuancun Er Heng Road, Tianhe District, Guangzhou, Guangdong, 510655, China.
- Department of Coloproctology, The Sixth Affiliated Hospital, Sun Yat-sen University, No.26, Yuancun Er Heng Road, Tianhe District, Guangzhou, Guangdong, 510655, China.
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, No.105, Jiuyi North Road, Xinluo District, Longyan City, Fujian, 364028, China.
- Department of Radiology, Zhongshan City People's Hospital, No.2 Sunwen East Road, Zhongshan City, Guangdong, 528403, China.
- Department of Radiology, Jiangsu Provincial Hospital of Chinese Medicine, No.155 Hanzhong Road, Qinhuai District, Nanjing, JiangSu, 210029, China.
- BGI Research, No. 9 Yunhua Road, Meisha Street, Yantian District, Shenzhen, Guangdong, 518085, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No.106, Zhongshan 2nd Road, Guangzhou, Guangdong, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, No.106, Zhongshan 2nd Road, Guangzhou, Guangdong, 510080, China.
Abstract
Accurate tumor response assessment to neoadjuvant chemoradiotherapy (NCRT) is crucial for personalized treatment strategies in locally advanced rectal cancer (LARC). However, reliable non-invasive assessment tool remains clinically lacking. To fill this unmet need, MR-DELTAnet, a longitudinal MRI-based Transformer framework that integrates Delta-Efficient Latent-Temporal Attention, is constructed to predict pathological complete response (pCR) to NCRT in locally advanced rectal cancer patients. In a multicenter retrospective cohort of 1,026 LARC patients between July 2012 and July 2023, MR-DELTAnet demonstrated robust discriminative performance across independent datasets, with the area under the curves (AUC) of 0.93 (95% CI 0.90-0.96), 0.88 (95% CI 0.82-0.94) and 0.90 (95% CI 0.79-1.00) and in training (n═633), internal validation (n═212) and external validation (n═181) sets, respectively. Risk-stratification by MR-DELTAnet prediction scores reveals significant survival differences: low-score patients exhibit prolonged disease-free and overall survival versus high-score patients (log-rank p<0.05). Applying the model to an independent single-cell RNA sequencing cohort (n═26) discloses biologically distinct immune microenvironments: high-score tumors are myeloid-rich and immunosuppressive, whereas low-score tumors harbor cytotoxic T-cell-dominant. Clinically, MR-DELTAnet provides an accurate, non-invasive tool for preoperative identification of pCR likelihood and biological phenotype, thereby potentially informing individualized treatment strategies for LARC management.