ArticleCancer medicine2020
Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy.
Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 34 citations in OpenAlex.
- Developing a Histology-Based Artificial Intelligence Biomarker to Predict Adjuvant Chemotherapy Benefit in Pancreatic Cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- Deep Learning in Otolaryngology: A Narrative Review.JAMA otolaryngology-- head & neck surgery · 2026Review
- Prognostic value of tumour-stroma ratio in nasopharyngeal carcinoma: a two-center retrospective study.Radiation oncology (London, England) · 2025Article
- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Integrative radiopathomics model for predicting progression-free survival in patients with nonmetastatic nasopharyngeal carcinoma.Journal of cancer research and clinical oncology · 2024Article
- Deep learning in cancer genomics and histopathology.Genome medicine · 2024Review
- Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.Frontiers in oncology · 2024Article
- AI-Based Risk Score from Tumour-Infiltrating Lymphocyte Predicts Locoregional-Free Survival in Nasopharyngeal Carcinoma.Cancers · 2023Article
- Comparison of deep learning-based recurrence-free survival with random survival forest and Cox proportional hazard models in Stage-I NSCLC patients.Cancer medicine · 2023Article
- Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma.Journal of clinical medicine · 2023Review
- Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Deep Learning for Predicting Distant Metastasis in Patients with Nasopharyngeal Carcinoma Based on Pre-Radiotherapy Magnetic Resonance Imaging.Combinatorial chemistry & high throughput screening · 2023Article
- Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme.Frontiers in medicine · 2023Article
- A Survey on Deep Learning for Precision Oncology.Diagnostics (Basel, Switzerland) · 2022Review
- Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis.BMC cancer · 2022Article
- Development and Validation of a DeepSurv Nomogram to Predict Survival Outcomes and Guide Personalized Adjuvant Chemotherapy in Non-Small Cell Lung Cancer.Frontiers in oncology · 2022Article
- Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management - A Systematic Review.Cancer management and research · 2022Review
- The role of deep learning-based survival model in improving survival prediction of patients with glioblastoma.Cancer medicine · 2021Article
- A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging.Diagnostics (Basel, Switzerland) · 2021Review
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo explore the prognostic value and the role for treatment decision of pathological microscopic features in patients with nasopharyngeal carcinoma (NPC) using the method of deep learning.
methodsThe pathological microscopic features were extracted using the software QuPath (version 0.1.3. Queen's University) in the training cohort (Guangzhou training cohort, n = 843). We used the neural network DeepSurv to analyze the pathological microscopic features (DSPMF) and then classified patients into high-risk and low-risk groups through the time-dependent receiver operating characteristic (ROC). The prognosis accuracy of the pathological feature was validated in a validation cohort (n = 212). The primary endpoint was progression-free survival (PFS).
resultsWe found 429 pathological microscopic features in the H&E image. Patients with high-risk scores in the training cohort had shorter 5-year PFS (HR 10.03, 6.06-16.61; P < .0001). The DSPMF (C-index: 0.723) had the higher C-index than the EBV DNA (C-index: 0.612) copies and the N stage (C-index: 0.593). Furthermore, induction chemotherapy (ICT) plus concomitant chemoradiotherapy (CCRT) had better 5-year PFS to those received CCRT (P < .0001) in the high-risk group.
conclusionThe DSPMF is a reliable prognostic tool for survival risk in patients with NPC and might be able to guide the treatment decision.
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