ArticleJournal of clinical oncology : official journal of the American Society of Clinical Oncology2024
Automated Lugano Metabolic Response Assessment in
Article in Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 11 papers.
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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.
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)
A Phase IB/II Study Evaluating The Safety, Tolerability and Anti-Tumor Activity of Polatuzumab Vedotin in Combination With Rituximab (R) or Obinutuzumab (G) Plus Bendamustine (B) in Relapsed or Refractory Follicular or Diffuse Large B-Cell Lymphoma
An Open-Label, Multicenter, Phase I/II Trial Evaluating the Safety, Efficacy, and Pharmacokinetics of Escalating Doses of Mosunetuzumab (BTCT4465A) as a Single Agent and Combined With Atezolizumab in Patients With Relapsed or Refractory B-Cell Non-Hodgkin's Lymphoma and Chronic Lymphocytic Leukemia
Who cites it
11 citing papers in PubMed.
- Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.BMC medical imaging · 2025Trial
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Multimodality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma.International journal of radiation oncology, biology, physics · 2026Article
- coPERCIST: AI-assisted PET-CT response assessment.European journal of nuclear medicine and molecular imaging · 2026Article
- Multi-view deep learning for automated lymphoma staging fromEJNMMI research · 2026Article
- Impact of reader experience on the time-saving potential of a novel computer-aided tool for Ann Arbor staging of pediatric hodgkin lymphoma.Frontiers in nuclear medicine · 2026Article
- Advanced stage classical Hodgkin lymphoma patients with a positive interim-PET (PET-2) Deauville score 5 after 2 ABVD cycles: a pooled analysis of three multicenter trials.Blood cancer journal · 2025Article
- Assessing large language models for Lugano classification of malignant lymphoma in Japanese FDG-PET reports.EJNMMI reports · 2025Article
- [Advancements in artificial intelligence for the precise diagnosis and treatment of hematological malignancies].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2025Review
- Review
- Clinical scoring systems, molecular subtypes and baseline [Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeArtificial intelligence can reduce the time used by physicians on radiological assessments. For
methodsHere, we present a deep learning-based algorithm for fully automated treatment response assessments according to the Lugano 2014 classification. The proposed four-stage method, trained on a multicountry clinical trial (ClinicalTrials.gov identifier: NCT01287741) and tested in three independent multicenter and multicountry test sets on different non-Hodgkin lymphoma subtypes and different lines of treatment (ClinicalTrials.gov identifiers NCT02257567, NCT02500407; 20% holdout in ClinicalTrials.gov identifier NCT01287741), outputs the detected lesions at baseline and follow-up to enable focused radiologist review.
resultsThe method's response assessment achieved high agreement with the adjudicated radiologic responses (eg, agreement for overall response assessment of 93%, 87%, and 85% in ClinicalTrials.gov identifiers NCT01287741, NCT02500407, and NCT02257567, respectively) similar to inter-radiologist agreement and was strongly prognostic of outcomes with a trend toward higher accuracy for death risk than adjudicated radiologic responses (hazard ratio for end of treatment by-model CMR of 0.123, 0.054, and 0.205 in ClinicalTrials.gov identifiers NCT01287741, NCT02500407, and NCT02257567, compared with, respectively, 0.226, 0.292, and 0.272 for CMR by the adjudicated responses). Furthermore, a radiologist review of the algorithm's assessments was conducted. The radiologist median review time was 1.38 minutes/assessment, and no statistically significant differences were observed in the level of agreement of the radiologist with the model's response compared with the level of agreement of the radiologist with the adjudicated responses.
conclusionThese results suggest that the proposed method can be incorporated into radiologic response assessment workflows in cancer imaging for significant time savings and with performance similar to trained medical experts.
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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.