American Association for Cancer Research
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Supplementary Table 4 from Integrative Prognostic Machine-Learning Models in Mantle Cell Lymphoma

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journal contribution
posted on 2023-07-11, 13:20 authored by Holly Ann Hill, Preetesh Jain, Chi Young Ok, Koji Sasaki, Han Chen, Michael L. Wang, Ken Chen

Hyperparameters for XGBoost Models



Patients with mantle cell lymphoma (MCL), an incurable B-cell malignancy, benefit from accurate pretreatment disease stratification. We curated an extensive database of 862 patients diagnosed between 2014 and 2022. A machine learning (ML) gradient-boosted model incorporated baseline features from clinicopathological, cytogenetic, and genomic data with high predictive power discriminating between patients with indolent or responsive MCL and those with aggressive disease (AUC ROC = 0.83). Additionally, we utilized the gradient-boosted framework as a robust feature selection method for multivariate logistic and survival modeling. The best ML models incorporated features from clinical and genomic data types highlighting the need for correlative molecular studies in precision oncology. As proof of concept, we launched our most accurate and practical models using an application interface, which has potential for clinical implementation. We designated the 20-feature ML model-based index the “integrative MIPI” or iMIPI and a similar 10-feature ML index the “integrative simplified MIPI” or iMIPI-s. The top 10 baseline prognostic features represented in the iMIPI-s are: Lactase Dehydrogenase (LDH), Ki-67%, platelet count, bone marrow involvement percentage, hemoglobin levels, the total number of observed somatic mutations, TP53 mutational status, ECOG performance level, beta-2 microglobulin, and morphology. Our findings emphasize that prognostic applications and indices should include molecular features, especially TP53 mutational status. This work demonstrates the clinical utility of complex ML models and provides further evidence for existing prognostic markers in MCL.