基于CT的放射学临床机器学习模型,用于预测结直肠周围转移中细胞减少的完整性
Samuel Pau1, Timothy Eglinton2, Alan Wang3
1Waikato Clinical Campus, Faculty of Medical and Health Sciences, University of Auckland, Hamilton, New Zealand.
概括
一个新的放射性临床模型准确地预测了结直肠周周转移 (CPM) 手术前细胞减退 (CC) 的完整性. 这种工具有助于手术决策,用于细胞还原性手术 (CRS) 加上高热性腹腔内化疗 (HIPEC).
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 机器学习 机器学习
- 手术瘤学手术瘤学
背景情况:
- 细胞还原 (CC) 的完整性对于细胞还原性手术 (CRS) 以及结直肠周周转移 (CPM) 的高热内皮质化疗 (HIPEC) 后的患者结果至关重要.
- 准确的手术前预测CC仍然具有挑战性,影响治疗计划和患者选择.
研究的目的:
- 开发和验证一个放射性临床机器学习模型,用于预测CPM患者的手术前细胞还原完整性.
- 评估模型的性能与仅临床和仅放射性方法相比.
主要方法:
- 对83名接受CPM的CRS±HIPEC患者的回顾性分析.
- 使用PyRadiomics从术前对比度增强CT扫描中提取放射性特征.
- 开发一个机器学习管道 (LASSO,随机森林,GBC),结合临床和放射性变量,使用嵌套的五倍交叉验证进行验证.
主要成果:
- 放射性临床模型在预测不完整的细胞减少 (iCC) 中表现优越 (AUROC 0.90,AUPRC 0.69),超过仅临床 (AUROC 0.82-0.86) 和放射性 (AUROC 0.69-0.75) 模型.
- iCC的独立预测因素包括高放射性腹膜癌指数 (PCI),腹部上部疾病,缺乏新辅助化疗和正常的CEA水平.
- 预测iCC的关键放射性特征与瘤形态和异质性有关 (例如,低球性,高直径,高区域).
结论:
- 一个基于CT的放射性临床模型可以准确地在手术前预测CPM中的CC.
- 这种概念验证模型需要多中心的外部验证,以支持其融入CRS±HIPEC的临床决策.
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