ArticleTranslational oncology2021
CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma.
Article in Translational oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Risk Stratification in Diffuse Large B-Cell Lymphoma: A Systematic Review of Classification Models and Predictive Performances.Medical sciences (Basel, Switzerland) · 2025Pooled it
- Predicting Primary Refractory Diffuse Large B-Cell Lymphoma: A Comparison of Machine Learning Models with the IPI Score.Journal of clinical medicine · 2026Article
- Development and validation of a radiomics nomogram for predicting outcomes following combined radiotherapy and modern chemotherapy in early-stage extranodal nasal-type NK/T-cell lymphoma: a multicenter study.Annals of hematology · 2026Article
- Predicting overall survival after initial chemotherapy for diffuse large B-cell lymphoma using CT nomogram analysis.BMC medical imaging · 2026Article
- Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis.Discover oncology · 2025Review
- Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.BMC medical imaging · 2025Article
- A proof of concept study ofScientific reports · 2025Article
- Radiomics based onQuantitative imaging in medicine and surgery · 2025Article
- PET-based radiomic feature based on the cross-combination method for predicting the mid-term efficacy and prognosis in high-risk diffuse large B-cell lymphoma patients.Frontiers in oncology · 2024Article
- Prognostic value of genetic alterations andAmerican journal of cancer research · 2023Article
- Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma.Cancers · 2022Article
- Radiomics and machine learning for the diagnosis of pediatric cervical non-tuberculous mycobacterial lymphadenitis.Scientific reports · 2022Article
- A Multi-Center Study of CT-Based Neck Nodal Radiomics for Predicting an Adaptive Radiotherapy Trigger of Ill-Fitted Thermoplastic Masks in Patients with Nasopharyngeal Carcinoma.Life (Basel, Switzerland) · 2022Article
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9 authors.
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Abstract
Biomarkers which can identify Diffuse Large B-Cell Lymphoma (DLBCL) likely to be refractory to first-line therapy are essential for selecting this population prior to therapy initiation to offer alternate therapeutic options that can improve prognosis. We tested the ability of a CT-based radiomics approach with machine learning to predict Primary Treatment Failure (PTF)-DLBCL from initial imaging evaluation. Twenty-six refractory patients were matched to 26 non-refractory patients, yielding 180 lymph nodes for analysis. Manual 3D delineation of the total node volume was performed by two independent readers to test the reproducibility. Then, 1218 hand-crafted radiomic features were extracted. The Random Forests machine learning approach was used as a classifier for constructing the prediction models. Seventy percent of the nodes were randomly assigned to a training set and the remaining 30% were assigned to an independent test set. The final model was tested on the dataset from the 2 readers, showing a mean accuracy, sensitivity and specificity of 73%, 62% and 82%, respectively, for distinguishing between refractory and non-refractory patients. The area under the receiver operating characteristic curve (AUC) was 0.83 and 0.79 for the two readers. We conclude that machine learning CT-based radiomics analysis is able to identify a priori PTF-DLBCL with a good accuracy.
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