ArticleDiagnostics (Basel, Switzerland)2022
Deep Learning Approach to Automatize TMTV Calculations Regardless of Segmentation Methodology for Major FDG-Avid Lymphomas.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 21 citations in OpenAlex.
- A convolutional neural network for fully automated total metabolic tumor volume delineation in patients with aggressive Non-Hodgkin lymphoma.European journal of nuclear medicine and molecular imaging · 2026Article
- A Lesion-adaptive Segmentation Approach for Tumor Delineation on FDG PET/CT in Diffuse Large B-cell Lymphoma Patients.European journal of nuclear medicine and molecular imaging · 2026Article
- Review
- Review
- International Benchmark for Total Metabolic Tumor Volume Measurement in BaselineJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Quantitative PET-based biomarkers in lymphoma: getting ready for primetime.Nature reviews. Clinical oncology · 2023Review
- Advances in positron emission tomography and radiomics.Hematological oncology · 2023Review
- A Deep Learning-Aided Automated Method for Calculating Metabolic Tumor Volume in Diffuse Large B-Cell Lymphoma.Cancers · 2022Article
- Recent advances in deep learning for lymphoma segmentation: Clinical applications and challenges.Digital healthReview
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Authors and funding
14 authors at 10 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The total metabolic tumor volume (TMTV) is a new prognostic factor in lymphomas that could benefit from automation with deep learning convolutional neural networks (CNN). Manual TMTV segmentations of 1218 baseline 18FDG-PET/CT have been used for training. A 3D V-NET model has been trained to generate segmentations with soft dice loss. Ground truth segmentation has been generated using a combination of different thresholds (TMTVprob), applied to the manual region of interest (Otsu, relative 41% and SUV 2.5 and 4 cutoffs). In total, 407 and 405 PET/CT were used for test and validation datasets, respectively. The training was completed in 93 h. In comparison with the TMTVprob, mean dice reached 0.84 in the training set, 0.84 in the validation set and 0.76 in the test set. The median dice scores for each TMTV methodology were 0.77, 0.70 and 0.90 for 41%, 2.5 and 4 cutoff, respectively. Differences in the median TMTV between manual and predicted TMTV were 32, 147 and 5 mL. Spearman's correlations between manual and predicted TMTV were 0.92, 0.95 and 0.98. This generic deep learning model to compute TMTV in lymphomas can drastically reduce computation time of TMTV.
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