Multimodal prognostic model for de novo metastatic nasopharyngeal carcinoma after first-line immunochemotherapy.

Background And Purpose: Immunochemotherapy has been recommended as a first-line treatment for patients with de novo metastatic nasopharyngeal carcinoma (dmNPC), but there is a lack of effective ways to predict survival benefits. The objective of this study was to utilize multimodal data to construct a prognostic model for patients undergoing immunochemotherapy, to facilitate subsequent treatment decisions.

Material And Methods: A total of 268 patients with dmNPC who received first-line programmed death-1(PD-1) inhibitor in combination with a chemotherapy regimen were enrolled.

MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images.

Combining molecular classification with clinicopathologic methods improves risk assessment and chooses therapies for endometrial cancer (EC). Detecting mismatch repair (MMR) deficiencies in EC is crucial for screening Lynch syndrome and identifying immunotherapy candidates. An affordable and accessible tool is urgently needed to determine MMR status in EC patients.

Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.

Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of medical care for natural killer/T cell lymphoma (NKTCL). Artificial intelligence (AI)-based systems are developed based on nasopharynx magnetic resonance imaging. The diagnostic systems achieve areas under the curve of 0.

A deep learning-based semiautomated workflow for triaging follow-up MR scans in treated nasopharyngeal carcinoma.

It is imperative to optimally utilize virtues and obviate defects of fully automated analysis and expert knowledge in new paradigms of healthcare. We present a deep learning-based semiautomated workflow (RAINMAN) with 12,809 follow-up scans among 2,172 patients with treated nasopharyngeal carcinoma from three centers (ChiCTR.org.

Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (NPC) into distinct survival subgroups.

Objectives: To explore and evaluate the feasibility of radiomics in stratifying nasopharyngeal carcinoma (NPC) into distinct survival subgroups through multi-modalities MRI.

Methods: A total of 658 patients (training cohort: 424; validation cohort: 234) with non-metastatic NPC were enrolled in the retrospective analysis. Each slice was considered as a sample and 4863 radiomics features on the tumor region were extracted from T1-weighted, T2-weighted, and contrast-enhanced T1-weighted MRI.