ArticleCancers2021
Deep-Learning Assessed Muscular Hypodensity Independently Predicts Mortality in DLBCL Patients Younger Than 60 Years.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01659099 (Randomized Phase III Study Using a Pet-driven Strategy and Comparing GA101 OR Rituximab Associated to a Chemotherapy Delivered Every 14 Days), which is not on this map. Cited by 6 papers, 4 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Randomized Phase III Study Using a Pet-driven Strategy and Comparing GA101 OR Rituximab Associated to a Chemotherapy Delivered Every 14 Days (ACVBP or CHOP) in DLBCL CD20+ Lymphoma Untreated Patients From 18 to 60 Presenting With 1 or More Adverse Prognostic Factors of the Age-adjusted IPI
Who cites it
6 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Pre-Therapeutic Sarcopenia among Cancer Patients: An Up-to-Date Meta-Analysis of Prevalence and Predictive Value during Cancer Treatment.Nutrients · 2023Pooled it
- Prevalence and effect on survival of pre-treatment sarcopenia in patients with hematological malignancies: a meta-analysis.Frontiers in oncology · 2023Pooled it
- Prevalence and prognostic value of baseline sarcopenia in hematologic malignancies: a systematic review.Frontiers in oncology · 2023Pooled it
- Article
- Pectoralis major muscle quantification by cardiac MRI is a strong predictor of major events in HF.Heart and vessels · 2022Article
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24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMuscle depletion (MD) assessed by computed tomography (CT) has been shown to be a predictive marker in solid tumors, but has not been assessed in non-Hodgkin's lymphomas. Despite software improvements, MD measurement remains highly time-consuming and cannot be used in clinical practice.
methodsThis study reports the development of a Deep-Learning automatic segmentation algorithm (DLASA) to measure MD, and investigate its predictive value in a cohort of 656 diffuse large B cell lymphoma (DLBCL) patients included in the GAINED phase III prospective trial (NCT01659099).
resultsAfter training on a series of 190 patients, the DLASA achieved a Dice coefficient of 0.97 ± 0.03. In the cohort, the median skeletal muscle index was 50.2 cm
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.