Incremental value of PET primary lesion-based radiomics signature to conventional metabolic parameters and traditional risk factors for preoperative prediction of lymph node metastases in gastric cancer.

Abstract

Precise preoperative prediction of lymph node metastasis (LNM) is crucial for optimal diagnosis and treatment in patients with gastric cancer (GC), in which existing imaging methods have certain limitations. We hypothesized that PET primary lesion-based radiomics signature could provide incremental value to conventional metabolic parameters and traditional risk indicators in predicting LNM in patients with GC. This retrospective study was performed in 127 patients with GC who underwent preoperative PET/CT. Basic clinical data and PET conventional metabolic parameters were collected. Radiomics signature was constructed by the least absolute shrinkage and selection operator algorithm (LASSO) logistic regression. Basedon the postoperative histological results, the patients were divided into LNM group and non-lymph node metastasis (NLNM) group. Receiver-operating characteristic (ROC) was used to evaluate the discriminatory ability of Radiomics score (Rad-score) for predicting LNM and determine whether adding Rad-score to PET conventional metabolic parameters and traditional risk factors could improve the predictive value in LNM. The Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to further confirm the incremental value of Rad-score for predicting LNM in GC. The LNM group had higher Rad-score than NLNM group [(0.35 (-0.13-0.85) vs. -0.61 (-1.92-0.18), P < 0 .001)]. After adjusted for gender, age, BMI, and FBG, multivariable logistic regression analysis illustrated that Rad-score (OR: 6.38, 95% CI: 2.73-14.91, P < 0.0001) was independent risk factors for LNM in GC. Adding PET conventional parameters to traditional risk factors increased the predictive value of LNM in GC (AUC 0.751 vs 0.651, P = 0.02). Additional inclusion of Rad-score to conventional metabolic parameters and traditional risk indicators significantly improved the AUC (0.882 vs 0.751; P = 0.006). Bootstrap resampling (times = 500) was used for internal verification, 95% confidence interval (CI) was 0.802-0.948, with the sensitivity equaled to 89.5%, and positive predictive value (PPV) was 93.5%. When Rad-score was added to conventional metabolic parameters and traditional risk indicators, net reclassification improvement (NRI) was 0.293 (P = 0.0040) and integrated discrimination improvement (IDI) was 0.293 (P = 0.0045). In GC patients, PET Radiomics signature of the primary lesion-based was significantly associated with LNM and could improve the prediction of LNM above PET conventional metabolic parameters and traditional risk factors, which could provide incremental value for individual diagnosis and treatment of GC.