Lymphocytic Reaction Combined with MRI Deep Learning Radiomics in Risk Stratification of Distant Metastases in Rectal Cancer.
Authors
Affiliations (2)
Affiliations (2)
- Department of Radiology, Tianjin Union Medical Center, The First Affiliated Hospital of Nankai University, Tianjin 300121, China.
- Department of Pathology, Tianjin Union Medical Center, The First Affiliated Hospital of Nankai University, Tianjin 300121, China.
Abstract
<b>Objectives:</b> Our aim was to develop an optimal model for predicting distant metastasis (DM) in rectal cancer (RC) via integrating tumor lymphocytic reaction (LR) with deep learning radiomic (DLR) features. <b>Methods:</b> A total of 190 patients with RC were divided into a training cohort (<i>n</i> = 133) and a validation cohort (<i>n</i> = 57) in a 7:3 ratio. Preoperative radiomics (Rad), deep transfer learning (DTL), and DLR features were extracted from T<sub>2</sub>WI scans, and clinicopathological variables were collected. All models were constructed using the support vector machine (SVM) algorithm, and their performance was evaluated using the area under the receiver operating characteristic curve (AUC). A 3-year follow-up was conducted to analyze 3-year distant-metastasis-free survival (DMFS) outcomes and DM risk. <b>Results:</b> Multivariate analysis confirmed LR as an independent predictor of 3-year DMFS (<i>p</i> < 0.05). The fusion model integrating LR and DLR exhibited preliminary predictive performance, with AUC values of 0.911 and 0.884 in the training and validation cohorts, respectively. Subgroup observational analyses showed apparent DMFS differences associated with adjuvant chemotherapy exposure among low-risk patients, while such survival patterns were not evident in the high-risk subgroup defined by the nomogram cut-off of 0.3. A moderate negative correlation was detected between DLR signatures and LR (<i>r</i> = -0.44, <i>p</i> < 0.001), providing preliminary immune-related clues for interpreting deep learning radiomic biomarkers. <b>Conclusions:</b> The multimodal fusion nomogram combining pathological LR and DLR signatures shows encouraging preliminary predictive performance for 3-year DMFS risk in patients with RC. This postoperative multimodal tool may provide a preliminary reference for clinicians to implement individualized postoperative surveillance for patients with RC, and the inverse association between DLR and LR helps reveal the immune-related background of imaging signatures.