Deep learning‐based radiomics model can predict extranodal soft tissue metastasis in gastric cancer

Abstract Background The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet. Purpose This multicenter study aimed to develop a deep learning‐based radiomics model to preoperatively identify ESTM and evaluate its prognostic value. Methods A total of 959 GC patients were enrolled from two centers and split into a training cohort ( N = 551) and a test cohort ( N = 236) for ESTM evaluation. Additionally, an external survival cohort ( N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand‐crafted radiomic model, a clinical model, and a combined model. C‐index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS). Results The combined model showed good discrimination of the ESTM [C‐indices (95% confidence interval, CI): 0.770 (0.729–0.812) and 0.761 (0.718–0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification ( p < 0.05 ). Moreover, the model score showed strong consistency with the OS [C‐indices (95%CI): 0.723 (0.658–0.789, p < 0.0001 ) in the internal survival cohort and 0.715 (0.650–0.779, p < 0.0001 ) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis ( p < 0.05 ) that was missed by preoperative diagnosis. Conclusions The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.