The deep learning radiomics nomogram helps to evaluate the lymph node status in cervical adenocarcinoma/adenosquamous carcinoma

The accurate assessment of lymph node metastasis (LNM) can facilitate clinical decision-making on radiotherapy or radical hysterectomy (RH) in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC). This study aims to develop a deep learning radiomics nomogram (DLRN) to preoperatively evaluate LNM in cervical AC/ASC. A total of 652 patients from a multicenter were enrolled and randomly allocated into primary, internal, and external validation cohorts. The radiomics features were extracted from axial T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (CE-T1WI). The DL features from T2WI, DWI, and CE-T1WI were exported from Resnet 34, which was pretrained by 14 million natural images of the ImageNet dataset. The radscore (RS) and DL score (DLS) were independently obtained after repeatability test, Pearson correlation coefficient (PCC), minimum redundancy maximum relevance (MRMR), and least absolute shrinkage and selection operator (LASSO) algorithm performed on the radiomics and DL feature sets. The DLRN was then developed by integrating the RS, DLS, and independent clinicopathological factors for evaluating the LNM in cervical AC/ASC. The nomogram of DLRN-integrated FIGO stage, menopause, RS, and DLS achieved AUCs of 0.79 (95% CI, 0.74-0.83), 0.87 (95% CI, 0.81-0.92), and 0.86 (95% CI, 0.79-0.91) in the primary, internal, and external validation cohorts. Compared with the RS, DLS, and clinical models, DLRN had a significant higher AUC for evaluating LNM (all P < 0.005). The nomogram of DLRN can accurately evaluate LNM in cervical AC/ASC.