A High-resolution T2WI-based Deep Learning Model for Preoperative Discrimination Between T2 and T3 Rectal Cancer: A
Yanmei Lin1, Ying Liu2, Xiao Zhang3
1Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China (Y.L., Y.L., F.H.); Department of Radiology, the Second Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China (Y.L.).
Rationale And Objectives:
To construct a deep learning model (DL) based on high-resolution T2-weighted images for preoperative differentiation between T2 and T3 stage rectal cancer (RC), and to compare its performance with experienced radiologists.
Methods:
This retrospective study included 281 patients with pathologically confirmed RC from four centers (January 2017-December 2022). A DenseNet model was developed using 255 patients from three centers (training:validation ratio=8:2) and externally tested on 26 patients from a fourth center. Two experienced radiologists independently assessed T staging. Diagnostic performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).
Results:
The DL model outperformed radiologists in differentiating T2 and T3 stages across all datasets. In the training set, the DL model achieved an AUC of 0.810, compared to 0.578 and 0.625 for radiologists A and B, respectively. In the external test set, the DL model maintained superior diagnostic performance (AUC=0.715) compared to radiologist A (AUC=0.549) and radiologist B (AUC=0.493). The DL model demonstrated higher accuracy for T2 staging (0.625-0.787) and T3 staging (0.611-0.814) compared to radiologists (0.373-0.526 for T2; 0.611-0.783 for T3), who showed a tendency to over-stage T2 tumors. Inter-observer agreement between radiologists was moderate (kappa=0.451).
Conclusion:
The DenseNet-based DL model demonstrated superior accuracy and diagnostic efficiency than radiologists in preoperative differentiation between T2 and T3 stages RC. This automated approach could potentially improve staging accuracy and support clinical decision-making in RC treatment planning.
