嫌色细胞
嗜酸细胞瘤
肾细胞癌
医学
肾嗜酸细胞瘤
病态的
金标准(测试)
肾切除术
放射科
病理
肾
机器学习
清除单元格
计算机科学
内科学
作者
Sabrina H. Rossi,Izzy Newsham,Sara Pita,Kevin Brennan,Gahee Park,Christopher Smith,Radoslaw P. Lach,Thomas J. Mitchell,Junfan Huang,Anne Babbage,Anne Y. Warren,John T. Leppert,Grant D. Stewart,Olivier Gevaert,Charles E. Massie,Shamith A. Samarajiwa
出处
期刊:Science Advances
[American Association for the Advancement of Science (AAAS)]
日期:2022-09-30
卷期号:8 (39)
标识
DOI:10.1126/sciadv.abn9828
摘要
Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples ( N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.
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