ArticleEuropean journal of nuclear medicine and molecular imaging2021
Weakly supervised deep learning for determining the prognostic value of
Article in European journal of nuclear medicine and molecular imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- Mapping the research landscape of PET/CT in lymphoma: insights from a bibliometric analysis.Frontiers in oncology · 2025Pooled it
- Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Case Report: Clinical characteristics and outcomes in natural killer (NK)/T-cell lymphomas with cardiac involvement: a case series and literature review.Frontiers in cardiovascular medicine · 2026Article
- Identification of benign and malignant breast nodules on ultrasound: comparison of multiple deep learning models and model interpretation.Frontiers in oncology · 2025Article
- The Role of Artificial Intelligence and Radiomics in the Management of Lymphomas by PET/CT: The Clairvoyance in Clinic.Cancer management and research · 2025Review
- The role ofFrontiers in oncology · 2025Article
- Review
- Review
- Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.Cell reports. Medicine · 2024Article
- Deep learning for [The Lancet. Digital health · 2024Article
- Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study.European journal of nuclear medicine and molecular imaging · 2023Article
- An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients.Scientific reports · 2023Article
- Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients.BMC bioinformatics · 2023Article
- Review
- Multimodality imaging evaluation of primary testicular extranodal natural killer/T-cell lymphoma: two case reports.Frontiers in medicine · 2023Article
- Detection and localization of hyperfunctioning parathyroid glands on [Radiology and oncology · 2022Article
- Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation.European journal of nuclear medicine and molecular imaging · 2022Review
- Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer.EJNMMI physics · 2022Article
- Artificial intelligence for nuclear medicine in oncology.Annals of nuclear medicine · 2022Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment
methodsOne hundred and sixty-seven patients with ENKTL who underwent pretreatment
resultsPSI achieved area under the curve scores of 0.9858 and 0.9946 (training set) and 0.8750 and 0.7344 (test set) in the prediction of progression-free survival (PFS) with the WSDL and CDL methods, respectively. PSI threshold of 1.0 could significantly differentiate the prognosis. In the test set, WSDL and CDL achieved prediction sensitivity, specificity, and accuracy of 87.50% and 62.50%, 83.33% and 83.33%, and 85.00% and 75.00%, respectively. Multivariate analysis confirmed PSI to be an independent significant predictor of PFS in both the methods.
conclusionThe WSDL-based framework was more effective for extracting
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Registered trials
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