Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma.
Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma.
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DOI：
10.1158/2767-9764.crc-23-0083
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发表时间：
2023-08
期刊：
影响因子：
--
通讯作者：
Chen, Ken
中科院分区：
文献类型：
--
作者：
Hill, Holly A.;Jain, Preetesh;Ok, Chi Young;Sasaki, Koji;Chen, Han;Wang, Michael L.;Chen, Ken
套细胞淋巴瘤（MCL）是一种无法治愈的B细胞恶性肿瘤，其患者可从准确的治疗前疾病分层中获益。我们策划了一个广泛的数据库，其中包含2014年至2022年间诊断的862名患者。机器学习（ML）梯度增强模型结合了来自临床病理学、细胞遗传学和基因组数据的基线特征，具有区分惰性或反应性MCL患者和侵袭性疾病患者的高预测能力（AUC ROC = 0.83）。此外，我们利用梯度提升框架作为多变量逻辑和生存建模的鲁棒特征选择方法。最好的ML模型结合了临床和基因组数据类型的特征，突出了在精确肿瘤学中进行相关分子研究的必要性。作为概念验证，我们使用具有临床应用潜力的应用程序界面推出了最准确和实用的模型。我们将基于20个特征的ML模型的索引命名为“综合MIPI”或iMIPI，将类似的10个特征的ML索引命名为“综合简化MIPI”或iMIPI-s。iMIPI-s中代表的前10个基线预后特征是：乳糖酶脱氢酶（LDH）、Ki-67%、血小板计数、骨髓受累百分比、血红蛋白水平、观察到的体细胞突变总数、TP 53突变状态、东部肿瘤协作组性能水平、β-2微球蛋白和形态学。我们的研究结果强调，预后的应用和指标应包括分子特征，特别是TP 53突变状态。这项工作证明了复杂ML模型的临床实用性，并为MCL中现有的预后标志物提供了进一步的证据。我们的模型是第一个将动态算法与多种临床和分子特征相结合的模型，可以准确预测大型患者队列中的MCL疾病结局。
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 clinicopathologic, 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). In addition, 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, Eastern Cooperative Oncology Group 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. Our model is the first to integrate a dynamic algorithm with multiple clinical and molecular features, allowing for accurate predictions of MCL disease outcomes in a large patient cohort.
影响因子：
2.8
作者：
Chen, Xijie;Wang, Wenhui;Chen, Junguo;Xu, Liang;He, Xiaosheng;Lan, Ping;Hu, Jiancong;Lian, Lei
通讯作者：
Lian, Lei