ArticleCancers2021
A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial.
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 3 registered trials, which are not on this map. Cited by 6 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.
Efficacy of Maintenance Therapy With Rituximab After Induction Chemotherapy (R-CHOP vs. R-FC) for Elderly Patients With Mantle Cell Lymphoma Not Suitable for Autologous Stem Cell Transplantation
Efficacy of 6x R-CHOP Followed by Myeloablative Radiochemotherapy and Autologous Stem Cell Transplantation vs. 3 x (R-CHOP/R-DHAP) Followed by a High Dose ARA-C Containing Myeloablative Regimen and Autologous Stem Cell Transplantation
A Phase III Multicenter, Randomized Study With Lenalidomide Maintenance vs Observation After Induction Regimen Containing Rituximab Followed by High Dose Chemotherapy and ASCT as First Line Treatment in Adult Patients With Advanced Mantle Cell Lymphoma
Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Construction and validation of a risk prediction model for complications in patients with acute leukemia based on machine learning.Scientific reports · 2025Article
- Predicting one-year overall survival in patients with AITL using machine learning algorithms: a multicenter study.Scientific reports · 2025Article
- MRI Radiomics-Based Machine Learning Models for Ki67 Expression and Gleason Grade Group Prediction in Prostate Cancer.Cancers · 2023Article
- Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma.Cancer research communications · 2023Article
- Prognostic Stratification of Diffuse Large B-cell Lymphoma Using Clinico-genomic Models: Validation and Improvement of the LymForest-25 Model.HemaSphere · 2022Article
- Machine Learning-based Correlation Study between Perioperative Immunonutritional Index and Postoperative Anastomotic Leakage in Patients with Gastric Cancer.International journal of medical sciences · 2022Article
Corrections and comments
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Authors and funding
29 authors at 20 institutions in 4 countries.
Funding
Abstract
backgroundMulticenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313).
methodsWe developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0-9.6; High→Int, HR: 2.3, 95% CI: 1.5-4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential.
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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.