基于机器学习的组织学切片病理特征和临床特征作为弥漫性大b细胞淋巴瘤的一种新的预后指标

IF 2.9 4区医学 Q2 PATHOLOGY

Pathology, research and practice Pub Date : 2025-06-04 DOI:10.1016/j.prp.2025.156071

Zheng Li , Jiajie Shi , Xiaolin Wu , Zhongze Cui , Beichen Liu , Yueping Liu

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引用次数: 0

摘要

目的：本研究建立并验证了基于临床和组织病理学特征的深度学习模型，用于预测弥漫性大b细胞淋巴瘤（DLBCL）的预后。方法对194例DLBCL患者的194张整片影像进行分析。使用CellProfiler提取苏木精-伊红染色切片的临床特征和组织病理学特征。对这些特征进行了分析和验证。通过Cox回归分析、受试者工作特征曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估这些特征的预后价值。结果采用全自动化方法提取了1120个数字特征。临床病理形态图的Harrell’s一致性指数明显高于基于病理评分的形态图（0.791 vs. 0.750）。临床病理形态图在预测总生存期（OS）方面具有较高的准确性。1年和2年生存期病理评分nomogram AUC均显著高于临床病理nomogram AUC(1年生存期：0.892 vs 0.810；2年OS: 0.824 vs. 0.764)。尽管如此，临床病理形态图比简单形态图预测3年OS的能力更强（AUC: 0.812比0.759）。DCA证实临床病理图是长期OS的更好预测指标，改善了临床决策。结论基于临床和组织病理学特征的nomogram预后方法是预测DLBCL患者OS的一种新颖、无创、便捷的方法，可以预测患者对治疗的反应。

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Machine learning-based histopathological features of histological slides and clinical characteristics as a novel prognostic indicator in diffuse large B-cell lymphoma

Objective

This study developed and validated a deep learning model based on clinical and histopathological features for predicting the outcomes of diffuse large B-cell lymphoma (DLBCL).

Methods

This study analyzed 194 whole slide images from 194 patients with DLBCL. Clinical characteristics and histopathological features of hematoxylin-eosin-stained sections were extracted using CellProfiler. These features were analyzed and validated. The prognostic value of these features was evaluated by Cox regression analysis, the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).

Results

A total of 1120 digital features were extracted using a fully automated process. Harrell’s concordance index of the clinicopathologic nomogram was significantly higher than that of the Pathomics score based nomogram (0.791 vs. 0.750). The clinicopathologic nomogram had higher accuracy in predicting overall survival (OS). The AUC of the Pathomics score based nomogram for 1-year and 2-year OS was significantly higher than that of the clinicopathologic nomogram (1-year OS: 0.892 vs. 0.810; 2-year OS: 0.824 vs. 0.764). Nonetheless, the clinicopathologic nomogram had a stronger ability to predict 3-year OS than the simple nomogram (AUC: 0.812 vs. 0.759). DCA confirmed that the clinicopathologic nomogram was a better predictor of long-term OS, improving clinical decision-making.

Conclusion

The nomogram based on clinical and histopathological features is a novel, non-invasive, and convenient method to predict OS in patients with DLBCL and can potentially predict responses to treatment.

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来源期刊

Pathology, research and practice 医学-病理学

CiteScore

5.00

自引率

3.60%

发文量

405

审稿时长

24 days

期刊介绍： Pathology, Research and Practice provides accessible coverage of the most recent developments across the entire field of pathology: Reviews focus on recent progress in pathology, while Comments look at interesting current problems and at hypotheses for future developments in pathology. Original Papers present novel findings on all aspects of general, anatomic and molecular pathology. Rapid Communications inform readers on preliminary findings that may be relevant for further studies and need to be communicated quickly. Teaching Cases look at new aspects or special diagnostic problems of diseases and at case reports relevant for the pathologist''s practice.