机器学习算法的系统文献审查使用治疗前放射成像用于质瘤分子亚型预测
Jan Lost1,2, Tej Verma1, Leon Jekel1
1From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut.
AJNR. American journal of neuroradiology
|September 28, 2023
概括
机器学习算法显示出从MRI扫描中预测质瘤分子亚型的前景,实现高精度. 然而,有限的外部验证和显著的偏差风险阻碍了即时的临床应用.
科学领域:
- 神经瘤学神经瘤学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 质瘤分子亚型是患者生存和治疗决策的关键预后指标.
- 目前基于病理学的分子诊断是侵入性的,并且由于瘤异质性限制了新辅助疗法选择.
结论:
- 虽然人工智能驱动的MRI成像显示出非侵入性质瘤亚型的潜力,但由于外部验证不足和当前算法固有的偏差,广泛的临床采用受到挑战.
- 专注于强大的外部验证和偏差缓解的进一步研究对于将这些预测技术转化为常规临床实践至关重要.
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