ArticleCancer imaging : the official publication of the International Cancer Imaging Society2022
Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01287741 (A Phase III, Multicenter, Open-Label Randomized Trial Comparing the Efficacy of GA101), which is not on this map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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.
A Phase III, Multicenter, Open-Label Randomized Trial Comparing the Efficacy of GA101 (RO5072759) in Combination With CHOP (G-CHOP) Versus Rituximab and CHOP (R-CHOP) in Previously Untreated Patients With CD20-Positive Diffuse Large B-Cell Lymphoma (DLBCL)
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- 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
- Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.BMC medical imaging · 2025Trial
- The impact of scan time on dynamic [Formula: see text]-FAPI-04 total-body PET parametric imaging generated by deep learning models.EJNMMI physics · 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
- Clinical features, central nervous system risk, and therapeutic outcomes in primary sinonasal diffuse large B-cell lymphoma: a retrospective cohort study of 94 patients.Translational cancer research · 2026Article
- Interpretable predictions from whole-body FDG-PET/CT using parameters associated with clinical outcome.Communications medicine · 2026Article
- The prognostic and clinical utility of circulating tumor DNA in diffuse large B-cell lymphoma: a systematic review and meta-analysis.NPJ precision oncology · 2025Review
- The radiological response of patients with advanced bone metastases to lutetium-177-labeled DOTA-ibandronic acid assessed by metabolic tumor volume.Journal of cancer research and clinical oncology · 2025Article
- Prognostic impact of metabolic tumor volume using the SUV4.0 segmentation threshold in 1,960 lymphoma patients from prospective LYSA trials.European journal of nuclear medicine and molecular imaging · 2025Article
- Prognostic Value of Multiple Manual Segmentation Methods for Diffuse Large B-Cell Lymphoma withCurrent oncology (Toronto, Ont.) · 2025Article
- Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT.BMC medicine · 2025Article
- Clinical scoring systems, molecular subtypes and baseline [Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Article
- Review
- Recent updates on central nervous system prophylaxis in patients with high-risk diffuse large B-cell lymphoma.Experimental hematology & oncology · 2024Review
- Sensitivity of an AI method for [EJNMMI research · 2023Article
- Quantitative PET-based biomarkers in lymphoma: getting ready for primetime.Nature reviews. Clinical oncology · 2023Review
- An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients.Scientific reports · 2023Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrent radiological assessments of
methodsOur aim is to enable real-time assessment of imaging-based risk factors at a large scale and we propose a fully automatic artificial intelligence (AI)-based tool to rapidly extract FDG-PET imaging metrics in DLBCL. On availability of a scan, in combination with clinical data, our approach generates clinically informative risk scores with minimal resource requirements. Overall, 1268 patients with previously untreated DLBCL from the phase III GOYA trial (NCT01287741) were included in the analysis (training: n = 846; hold-out: n = 422).
resultsOur AI-based model comprising imaging and clinical variables yielded a tangible prognostic improvement compared to clinical models without imaging metrics. We observed a risk increase for progression-free survival (PFS) with hazard ratios [HR] of 1.87 (95% CI: 1.31-2.67) vs 1.38 (95% CI: 0.98-1.96) (C-index: 0.59 vs 0.55), and a risk increase for overall survival (OS) (HR: 2.16 (95% CI: 1.37-3.40) vs 1.40 (95% CI: 0.90-2.17); C-index: 0.59 vs 0.55). The combined model defined a high-risk population with 35% and 42% increased odds of a 4-year PFS and OS event, respectively, versus the International Prognostic Index components alone. The method also identified a subpopulation with a 2-year Central Nervous System (CNS)-relapse probability of 17.1%.
conclusionOur tool enables an enhanced risk stratification compared with IPI, and the results indicate that imaging can be used to improve the prediction of central nervous system relapse in DLBCL. These findings support integration of clinically informative AI-generated imaging metrics into clinical workflows to improve identification of high-risk DLBCL patients.
trial registrationRegistered clinicaltrials.gov number: NCT01287741.
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Registered trials
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.