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Background Magnetic resonance imaging (MRI) images are crucial unstructured data for prognostic evaluation in nasopharyngeal carcinoma (NPC). We developed and validated a prognostic system based on the MRI features and clinical data of locoregionally advanced NPC (LA-NPC) patients to distinguish low-risk patients with LA-NPC, for whom concurrent chemoradiotherapy (CCRT) is sufficient. Methods This multicenter, retrospective study included 3444 patients with LA-NPC from January 1, 2010, to January 31, 2017. A three-dimensional convolutional neural network was used to learn the image features from pretreatment MRI images. An eXtreme Gradient Boosting model was trained with the MRI features and clinical data to assign an overall score to each patient. Comprehensive evaluations were implemented to assess the performance of the predictive system. We applied the overall score to distinguish high-risk patients from low-risk patients. The clinical benefit of induction chemotherapy (IC) was analyzed in each risk group by survival curves. Results We constructed a prognostic system displaying a concordance index of 0.776 (95% CI = 0.746-0.806) for the internal validation cohort and 0.757 (95% CI = 0.695-0.819), 0.719 (95% CI = 0.650-0.789) and 0.746 (95% CI = 0.699-0.793) for the three external validation cohorts, which presented a statistically significant improvement compared to the conventional tumor-node-metastasis (TNM) staging system. In the high-risk group, patients who received IC plus CCRT had better outcomes than patients who received CCRT alone, while there was no statistically significant difference in the low-risk group. Conclusions The proposed framework can capture more complex and heterogeneous information to predict the prognosis of patients with LA-NPC and potentially contribute to clinical decision making.

Background Vulvar squamous cell carcinoma (VSCC) is a relatively rare gynecologic cancer. Unlike cervical squamous cell carcinoma (CSCC), in which nearly all cases are caused by HPV infection, most VSCCs are HPV-independent. Patients with VSCC also have worse overall survival (OS) than those with CSCC. Unlike CSCC, the risk factors of VSCC have not been extensively studied. Here, we investigated the prognostic values of clinicopathological parameters as well as biomarkers in patients with VSCC. Methods In total, 69 cases of VSCC accessions were selected for analysis between April 2010 and October 2020. The risk factors of VSCC were screened using Cox models to establish nomograms for predicting survival outcomes. Results Following the multivariate COX model for OS, independent predictors including advanced age (hazard ratio [HR] 5.899, p = 0.009), HPV positivity (HR 0.092, p = 0.016), high Ki-67 index (HR 7.899, p = 0.006), PD-L1-positivity (HR 4.736, p = 0.077), and CD8 + tumor-infiltrating lymphocytes (TILs) (HR 0.214, p = 0.024) were included in the nomogram for OS; multivariate COX model for progression-free survival (PFS) was used to screen prognostic factors including advanced age (HR 2.902, p = 0.058), lymph node metastasis (HR 5.038, p = 0.056), HPV positivity (HR 0.116, p = 0.011), high Ki-67 index (HR 3.680, p = 0.042), PD-L1-positivity (HR 5.311, p = 0.045), and CD8 + TILs (HR 0.236, p = 0.014) to establish the PFS nomogram model. Based on the C-index (0.754 for OS and 0.754 for PFS) from our VSCC cohort and the corrected C-index (0.699 for OS and 0.683 for PFS) from an internal validation cohort, the nomograms demonstrated good predictive and discriminative ability. Kaplan-Meier curves also supported the excellent performance of the nomograms. Conclusion Our prognostic nomograms suggested that (1) shorter OS and PFS were associated with PD-L1-positivity, high Ki-67 index, and low CD8 + TILs; (2) HPV-independent tumors were associated with poorer survival outcome, and mutant p53 status showed no prognostic significance.