可解释的人工智能在辅助治疗反应预测局部先进的直肠癌后新辅助化疗放射治疗:一个前性,多中心,人与模型的相互作用研究研究
Xiaolin Pang1, Xiaobo Chen2, Guangdong Zeng1
1Department of Radiation Oncology, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; Biomedical Innovation Center, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China; State Key Laboratory of Metabolic Dysregulation & Prevention and Treatment of Esophageal Cancer, Tianjian Laboratory of Advanced Biomedical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China.
这项研究介绍了RAPIDS-II,这是一种用于预测局部晚期直肠癌 (LARC) 患者病理完整反应 (pCR) 的AI工具. 它有助于临床医生做出治疗决策,提高准确性,特别是对于经验较少的放射科医生来说.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 在局部晚期直肠癌 (LARC) 中,对病理完整反应 (pCR) 的术前评估对于直肠的保存至关重要.
- 目前用于pCR预测的AI辅助面临着未来验证和解释性方面的挑战.
研究的目的:
- 开发和验证可解释的AI模型 (RAPIDS-II) 用于在LARC患者的手术前PCR评估.
- 评估RAPIDS-II在预测pCR方面的表现及其对临床决策的影响.
主要方法:
- 一个深度残留收缩网络 (DRSN) 在MRI扫描中的放射性特征上受过训练.
- 一个多模式模型,RAPIDS-II,将DRSN的Radscore与临床病理因素集成在一起.
- 模型性能被追溯验证,在测试组中,并在多中心试验中进行前性验证.
主要成果:
- 在前验证队列中,RAPIDS-II表现出强大的pCR预测性能,AUC为0.795.
- 人工智能工具显著提高了放射科医生的视觉评估准确性,特别是对于初级临床医生.
- 莎普利添加式扩展证实了Radscore作为RAPIDS-II预测的主要贡献者.
结论:
- 可解释的RAPIDS-II模型在LARC的pCR评估中显示出强的表现.
- RAPIDS-II有可能帮助临床医生定制个性化的新辅助疗法策略.
- 人工智能工具对于经验较少的放射科医生来说尤其有益,可以增强治疗计划.
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