Elena Ivanova, Alexey Fayzullin, Victor Grinin, Dmitry Zhavoronkov, Dmitry Ermilov, Maxim Balyasin, Anna Timakova, Alesia Bakulina, Yusif Osmanov, Ekaterina Rudenko, Alexander Arutyunyan, Ruslan Parchiev, Nina Shved, Marina Astaeva, Aleksey Lychagin, Tatiana Demura, Peter Timashev
{"title":"利用核仁突出的机器学习分析预测肾癌的分级和患者生存","authors":"Elena Ivanova, Alexey Fayzullin, Victor Grinin, Dmitry Zhavoronkov, Dmitry Ermilov, Maxim Balyasin, Anna Timakova, Alesia Bakulina, Yusif Osmanov, Ekaterina Rudenko, Alexander Arutyunyan, Ruslan Parchiev, Nina Shved, Marina Astaeva, Aleksey Lychagin, Tatiana Demura, Peter Timashev","doi":"10.1002/cam4.71196","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>These findings suggest that incorporating refined criteria into the WHO/ISUP system could enhance its prognostic accuracy in future revisions.</p>\n </section>\n </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"14 17","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cam4.71196","citationCount":"0","resultStr":"{\"title\":\"Predicting Grade and Patient Survival in Renal Cancer Using Machine Learning Analysis of Nucleolar Prominence\",\"authors\":\"Elena Ivanova, Alexey Fayzullin, Victor Grinin, Dmitry Zhavoronkov, Dmitry Ermilov, Maxim Balyasin, Anna Timakova, Alesia Bakulina, Yusif Osmanov, Ekaterina Rudenko, Alexander Arutyunyan, Ruslan Parchiev, Nina Shved, Marina Astaeva, Aleksey Lychagin, Tatiana Demura, Peter Timashev\",\"doi\":\"10.1002/cam4.71196\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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. 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Predicting Grade and Patient Survival in Renal Cancer Using Machine Learning Analysis of Nucleolar Prominence
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.
期刊介绍：
Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas:
Clinical Cancer Research
Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations
Cancer Biology:
Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery.
Cancer Prevention:
Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach.
Bioinformatics:
Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers.
Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.
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