使用机器学习预测前列腺癌中的HER2过度表达:对个性化治疗的含义
Xuantong Huang1,2, Zhen Jiang1,3, Xun Wang1
1Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China.
Frontiers in oncology
|January 29, 2026
概括
结合放射学和临床数据的新模型准确地预测了前列腺癌中人类表皮生长因子受体2 (HER2) 的过度表达. 这有助于个性化治疗患有HER2阳性疾病的患者.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 医疗成像医学成像
背景情况:
- 人体表皮生长因子受体2 (HER2) 过度表达与晚期前列腺癌 (PCa) 有关.
- 目前对PCa中的HER2的研究主要集中在分子病理学上,对成像生物标志物的研究有限.
- 需要使用非侵入性方法来预测PCa中的HER2状态.
研究的目的:
- 在前列腺癌中开发HER2过度表达的预测模型.
- 从MRI中提取和利用高通量放射学特征.
- 将放射学与临床特征相结合,以提高预测准确度.
主要方法:
- 对201名接受了急性前列腺切除和HER2免疫组织化学治疗的患者进行了回顾性分析.
- 从T2加权成像 (T2WI) 和明显扩散系数 (ADC) 地图中提取多式辐射特征.
- 使用LASSO回归和交叉验证的预测模型的开发和评估,评估歧视和临床实用性.
主要成果:
- 综合ISUP等级,PSA和Radscore的组合模型在测试组中实现了0.841的AUC.
- 这种模型的表现明显优于临床模型 (AUC = 0.580) 并比仅使用Radscore模型 (AUC = 0.838) 略有改善.
- 该模型以0.78的准确性,0.77的灵敏性和0.79的特异性表现出一致的区分能力,同时具有强大的临床适用性.
结论:
- 一种联合的放射学和临床模型有效地预测了前列腺癌中的HER2过度表达.
- 这种预测模型有潜力指导 HER2 过度表达的 PCa 的个性化治疗策略.
- 整合成像生物标志物为PCa中HER2状态的非侵入性评估提供了一个有希望的途径.
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