使用深度学习模型评估的新辅助化疗后可行的瘤细胞密度反映了骨髓瘤的预后
Kengo Kawaguchi1,2, Kazuki Miyama1,3, Makoto Endo4
1Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
NPJ precision oncology
|January 22, 2024
概括
深度学习模型可以评估可行的瘤细胞密度,以预测经过新辅助化疗后的骨髓瘤预后. 较高的可活性瘤细胞密度表明预后更差,改善了患者的分层.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 目前的骨髓瘤预后依赖于经新辅助化疗 (NAC) 后的手动缩评估.
- 手动死亡率缺乏可复制性,无法捕捉单个细胞的反应.
- 对于接受NAC.的骨髓瘤患者,需要客观的预后标记.
研究的目的:
- 为了评估深度学习模型 (DLM) 评估的可行的瘤细胞密度是否预测骨髓瘤预后.
- 将基于DLM的预后分层与传统方法进行比较.
- 确定可行的瘤细胞密度和患者生存结果之间的相关性.
主要方法:
- 一个DLM被训练在患者样本中识别和量化可行的瘤细胞.
- 包括71名接受NAC治疗的骨髓瘤患者.
- 患者被分为高和低可活性瘤细胞密度组;进行生存分析 (DSS,MFS).
主要成果:
- DLM成功计算了可行的瘤细胞密度.
- 高可活性瘤细胞密度组的患者的疾病特异性存活率 (DSS) 和无转移存活率 (MFS) 显著降低.
- 通过DLM评估的可活性密度有效地将患者分为不同的预后组.
结论:
- 深度学习评估的可活性瘤细胞密度是骨髓瘤的可重现和准确的预后指标.
- 这种人工智能驱动的方法为接受新辅助化疗的骨肉瘤患者提供了精确的预后分层.
- 通过DLM进行可行的瘤细胞密度评估可以提高临床决策和治疗规划.
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