The value of 18F-FDG PET/CT-based radiomics in predicting perineural invasion and outcome in non-metastatic colorectal cancer

Purpose Perineural invasion (PNI) has been recognized as an important prognosis factor in patients with colorectal cancer (CRC). The purpose of this retrospective study was to investigate the value of F-18-FDG PET/CT-based radiomics integrating clinical information, PET/CT features, and metabolic parameters for preoperatively predicting PNI and outcome in non-metastatic CRC and establish an easy-to-use nomogram. Methods A total of 131 patients with non-metastatic CRC who undergo PET/CT scan were retrospectively enrolled. Univariate analysis was used to compare the differences between PNI-present and PNI-absent groups. Multivariate logistic regression was performed to select the independent predictors for PNI status. Akaike information criterion (AIC) was used to select the best prediction models for PNI status. CT radiomics signatures (RSs) and PET-RSs were selected by maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) regression and radiomics scores (Rad-scores) were calculated for each patient. The prediction models with or without Rad-score were established. According to the nomogram, nomogram scores (Nomo-scores) were calculated for each patient. The performance of different models was assessed with the area under the curve (AUC), specificity, and sensitivity. The clinical usefulness was assessed by decision curve (DCA). Multivariate Cox regression was used to selected independent predictors of progression-free survival (PFS). Results Among all the clinical information, PET/CT features, and metabolic parameters, CEA, lymph node metastatic on PET/CT (N stage), and total lesion glycolysis (TLG) were independent predictors for PNI (p < 0.05). Six CT-RSs and 12 PET-RSs were selected as the most valuable factors to predict PNI. The Rad-score calculated with these RSs was significantly different between PNI-present and PNI-absent groups (p < 0.001). The AUC of the constructed model was 0.90 (95%CI: 0.83-0.97) in the training cohort and 0.80 (95%CI: 0.65-0.95) in the test cohort. The nomogram's predicting sensitivity was 0.84 and the specificity was 0.83 in the training cohort. The clinical model's predicting sensitivity and specificity were 0.66 and 0.85 in the training cohort, respectively. Besides, DCA showed that patients with non-metastatic CRC could get more benefit with our model. The results also indicated that N stage, PNI status, and the Nomo-score were independent predictors of PFS in patients with non-metastatic CRC. Conclusion The nomogram, integrating clinical data, PET/CT features, metabolic parameters, and radiomics, performs well in predicting PNI status and is associated with the outcome in patients with non-metastatic CRC.