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.cn, Chi-CTR2200056595). A boost of diagnostic performance and reduced workload was observed in RAINMAN compared with the original manual interpretations (internal vs. external: sensitivity, 2.5% [p = 0.500] vs. 3.2% [p = 0.031]; specificity, 2.9% [p < 0.001] vs. 0.3% [p = 0.302]; workload reduction, 79.3% vs. 76.2%). The workflow also yielded a triaging performance of 83.6%, with increases of 1.5% in sensitivity (p = 1.000) and 0.6%-1.3% (all p < 0.05) in specificity compared to three radiologists in the reader study. The semiautomated workflow shows its unique superiority in reducing radiologist's workload by eliminating negative scans while retaining the diagnostic performance of radiologists.

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

The original version of this article, published on 14 March 2019, unfortunately contained a mistake.

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. Consensus clustering and manual aggregation were performed on the training cohort to generate a baseline model and classification reference used to train a support vector machine classifier. The risk of each patient was defined as the maximum risk among the slices. Each patient in the validation cohort was assigned to the risk model using the trained classifier. Harrell's concordance index (C-index) was used to measure the prognosis performance, and differences between subgroups were compared using the log-rank test. RESULTS: The training cohort was clustered into four groups with distinct survival patterns. Each patient was assigned to one of the four groups according to the estimated risk. Our method gave a performance (C-index = 0.827, p < .004 and C-index = 0.814, p < .002) better than the T-stage (C-index = 0.815, p = .002 and C-index = 0.803, p = .024), competitive to and more stable than the TNM staging system (C-index = 0.842, p = .003 and C-index = 0.765, p = .050) in the training cohort and the validation cohort. CONCLUSIONS: Through investigating a large one-institutional cohort, the quantitative multi-modalities MRI image phenotypes reveal distinct survival subtypes. KEY POINTS: • Radiomics phenotype of MRI revealed the subtype of nasopharyngeal carcinoma (NPC) patients with distinct survival patterns. • The slice-wise analysis method on MRI helps to stratify patients and provides superior prognostic performance over the TNM staging method. • Risk estimation using the highest risk among slices performed better than using the majority risk in prognosis.