Enhanced CT-Based Delta-Radiomics: Predicting Lymphovascular and Perineural Invasion in Rectal Cancer Preoperatively

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Abstract
To construct and validate a multi-phase contrast-enhanced computed tomography delta-radiomics signature for preoperatively predicting lymphovascular invasion (LVI) and perineural invasion (PNI) in patients with rectal cancer (RC). This study retrospectively enrolled 519 patients with RC between January 2017 and December 2022, with patients assigned to the training (n = 363) or validation (n = 156) sets. Radiomic features were extracted from routine scanning (A0), the arterial phase (A1), and the venous phase (A2). Delta-1 and Delta-2 radiomic signatures were derived by subtracting radiomic features acquired from A0 images from those of A2 and A1, respectively. Subsequently, Delta-3 and Delta-4 radiomic features were obtained by performing image subtraction between the A0 images and A2 and A1 images, then extracting the radiomic features from the resulting residual images. A delta-radiomics model was constructed using the Least Absolute Shrinkage and Selection Operator method. Model performance was evaluated using receiver operating characteristic, calibration, and decision curves. Delta-1–Delta-4 models exhibited moderate predictive performance for LVI and PNI in patients with RC, with area under the curve (AUC) values of 0.73, 0.73, 0.67, and 0.68, respectively. The combined model (C-Delta-12) showed the best predictive performance (AUC, 0.81; accuracy, 0.76; sensitivity, 0.86; specificity, 0.65). Calibration curves confirmed high goodness of fit, and decision curve analysis confirmed the clinical value. Integrating delta-radiomics signature and clinical predictors into a radiomics prediction model enables accurate and non-invasive risk assessments of PNI and LVI in RC. Stratifying patients based on their PNI and LVI status may facilitate more individualised treatment.
Funding Information
  • Jinhua Science and Technology Bureau (2024-3-109)

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