Fully automated 3D multi-modal deep learning model for preoperative T-stage prediction of colorectal cancer using <sup>18</sup>F-FDG PET/CT.
Authors
Affiliations (10)
Affiliations (10)
- Department of Electronic Engineering, School of Electronic Engineering, Xidian University, Xi'an, Shaanxi Province, China.
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
- GDMPA Key Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. [email protected].
- GDMPA Key Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. [email protected].
- Department of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China. [email protected].
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. [email protected].
- GDMPA Key Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. [email protected].
- Department of Electronic Engineering, School of Electronic Engineering, Xidian University, Xi'an, Shaanxi Province, China. [email protected].
Abstract
This study aimed to develop a fully automated 3D multi-modal deep learning model using preoperative <sup>18</sup>F-FDG PET/CT to predict the T-stage of colorectal cancer (CRC) and evaluate its clinical utility. A retrospective cohort of 474 CRC patients was included, with 400 patients for internal cohort and 74 patients for external cohort. Patients were classified into early T-stage (T1-T2) and advanced T-stage (T3-T4) groups. Automatic segmentation of the volume of interest (VOI) was achieved based on TotalSegmentator. A 3D ResNet18-based deep learning model integrated with a cross-multi-head attention mechanism was developed. Five models (CT + PET + Clinic (CPC), CT + PET (CP), PET (P), CT (C), Clinic) and two radiologists' assessment were compared. Performance was evaluated using Area Under the Curve (AUC). Grad-CAM was employed to provide visual interpretability of decision-critical regions. The automated segmentation achieved Dice scores of 0.884 (CT) and 0.888 (PET). The CPC and CP models achieved superior performance, with AUCs of 0.869 and 0.869 in the internal validation cohort, respectively, outperforming single-modality models (P: 0.832; C: 0.809; Clinic: 0.728) and the radiologists (AUC: 0.627, P < 0.05 for all models vs. radiologists, except for the Clinical model). External validation exhibited a similar trend, with AUCs of 0.814, 0.812, 0.763, 0.714, 0.663 and 0.704, respectively. Grad-CAM visualization highlighted tumor-centric regions for early T-stage and peri-tumoral tissue infiltration for advanced T-stage. The fully automated multimodal, fusing PET/CT with cross-multi-head-attention, improved T-stage prediction in CRC, surpassing the single-modality models and radiologists, offering a time-efficient tool to aid clinical decision-making.