Lymphoma development constitutes one of the most serious clinico-pathological manifestations of patients with Sjögren's Syndrome (SS). Over the last decades the risk for lymphomagenesis in SS patients has been studied aiming to identify novel biomarkers and risk factors predicting lymphoma development in this patient population. Objective: The current study aims to explore whether genetic susceptibility profiles of SS patients along with known clinical, serological and histological risk factors enhance the accuracy of predicting lymphoma development in this patient population. Methods: The potential predicting role of both genetic variants, clinical and laboratory risk factors were investigated through a Machine Learning-based (ML) framework which encapsulates ensemble classifiers. Results: Ensemble methods empower the classification accuracy with approaches which are sensitive to minor perturbations in the training phase. The evaluation of the proposed methodology based on a 10-fold stratified cross validation procedure yielded considerable results in terms of balanced accuracy (GB: 0.7780 ± 0.1514, RF Gini: 0.7626 ± 0.1787, RF Entropy: 0.7590 ± 0.1837). Conclusions: The initial clinical, serological, histological and genetic findings at an early diagnosis have been exploited in an attempt to establish predictive tools in clinical practice and further enhance our understanding towards lymphoma development in SS.

中文翻译：

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在一组干燥综合征患者中利用基于机器学习的方法中的遗传变异和临床发现预测淋巴瘤的发展

淋巴瘤的发展是干燥综合征 (SS) 患者最严重的临床病理表现之一。在过去的几十年中，已经研究了 SS 患者发生淋巴瘤的风险，旨在确定预测该患者群体中淋巴瘤发展的新生物标志物和风险因素。目的：本研究旨在探讨 SS 患者的遗传易感性谱以及已知的临床、血清学和组织学危险因素是否提高了预测该患者群体中淋巴瘤发展的准确性。方法：通过封装集成分类器的基于机器学习 (ML) 的框架研究遗传变异、临床和实验室风险因素的潜在预测作用。结果：集成方法通过对训练阶段的微小扰动敏感的方法来提高分类准确性。基于 10 倍分层交叉验证程序对所提出的方法的评估在平衡精度方面产生了可观的结果（GB：0.7780 ± 0.1514，RF Gini：0.7626 ± 0.1787，RF Entropy：0.7590 ± 0.1837）。结论：已利用早期诊断时的初始临床、血清学、组织学和遗传学发现，试图在临床实践中建立预测工具，并进一步加深我们对 SS 淋巴瘤发展的理解。基于 10 倍分层交叉验证程序对所提出的方法的评估在平衡精度方面产生了可观的结果（GB：0.7780 ± 0.1514，RF Gini：0.7626 ± 0.1787，RF Entropy：0.7590 ± 0.1837）。结论：已利用早期诊断时的初始临床、血清学、组织学和遗传学发现，试图在临床实践中建立预测工具，并进一步加深我们对 SS 淋巴瘤发展的理解。基于 10 倍分层交叉验证程序对所提出的方法的评估在平衡精度方面产生了可观的结果（GB：0.7780 ± 0.1514，RF Gini：0.7626 ± 0.1787，RF Entropy：0.7590 ± 0.1837）。结论：已利用早期诊断时的初始临床、血清学、组织学和遗传学发现，试图在临床实践中建立预测工具，并进一步加深我们对 SS 淋巴瘤发展的理解。