利用核仁突出度的机器学习分析预测肾癌的分级和患者生存率。

Predicting Grade and Patient Survival in Renal Cancer Using Machine Learning Analysis of Nucleolar Prominence.

作者信息

Ivanova Elena, Fayzullin Alexey, Grinin Victor, Zhavoronkov Dmitry, Ermilov Dmitry, Balyasin Maxim, Timakova Anna, Bakulina Alesia, Osmanov Yusif, Rudenko Ekaterina, Arutyunyan Alexander, Parchiev Ruslan, Shved Nina, Astaeva Marina, Lychagin Aleksey, Demura Tatiana, Timashev Peter

机构信息

Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.

B.V.Petrovsky Russian Research Center of Surgery, Moscow, Russia.

出版信息

Cancer Med. 2025 Sep;14(17):e71196. doi: 10.1002/cam4.71196.

DOI:10.1002/cam4.71196

PMID:40926367

Abstract

BACKGROUND

Patients with clear cell renal cell carcinoma (ccRCC) often undergo organ resection, with treatment strategies based on recurrence risk. Current metastatic potential assessments rely on the WHO/ISUP grading system, which is subject to interobserver variability.

METHODS

We developed an artificial intelligence (AI) model to classify cells according to contemporary grading rules and evaluated the prognostic significance of tumor cell profiles, particularly focusing on cells with prominent nucleoli.

RESULTS

The model accurately distinguished low (G1/G2) and high (G3/G4) grades, achieving an area under the ROC curve of 0.79. Survival analysis identified four tissue patterns defined by total cell density and the proportion of cells with prominent nucleoli. The relative abundance of such cells had greater prognostic value than their mere presence, correlating with survival times ranging from 2.2 to over 6 years. Additionally, we confirmed that dystrophic changes and focal necrosis are linked to shorter survival.

CONCLUSION

These findings suggest that incorporating refined criteria into the WHO/ISUP system could enhance its prognostic accuracy in future revisions.

摘要

背景

透明细胞肾细胞癌（ccRCC）患者常接受器官切除，治疗策略基于复发风险。目前的转移潜能评估依赖于世界卫生组织/国际泌尿病理学会（WHO/ISUP）分级系统，该系统存在观察者间差异。

方法

我们开发了一种人工智能（AI）模型，根据当代分级规则对细胞进行分类，并评估肿瘤细胞图谱的预后意义，特别关注有明显核仁的细胞。

结果

该模型准确区分了低（G1/G2）级和高（G3/G4）级，受试者工作特征曲线下面积达到0.79。生存分析确定了由总细胞密度和有明显核仁的细胞比例定义的四种组织模式。此类细胞的相对丰度比其单纯存在具有更大的预后价值，与2.2至6年以上的生存时间相关。此外，我们证实营养不良性改变和局灶性坏死与较短的生存期有关。