自主监督的人工智能在诊断时预测由原发性皮肤状细胞癌的复发,转移和疾病特异性死亡
Nicolas Coudray1,2, Michelle C Juarez3, Maressa C Criscito3
1Applied Bioinformatics Laboratories, New York University School of Medicine, New York, NY, USA.
Research square
|January 3, 2024
概括
一个新的深度学习模型从初始活检中预测皮肤状细胞癌 (cSCC) 的不良结果. 这种人工智能工具可以识别高风险的患者,以便制定更好的治疗和监测策略.
科学领域:
- 在瘤学瘤学.
- 人工智能在医学中的应用
- 皮肤病理学 皮肤病理学
背景情况:
- 初级皮肤状细胞癌 (cSCC) 导致显著的死亡率,需要在诊断时改善风险分层.
- 准确预测不良结果 (PO),包括复发,转移和疾病特异性死亡 (DSD),对于及时的临床干预至关重要.
- 目前对T2阶段cSCCs的风险评估存在局限性,特别是T2a/T2分类.
结论:
- 这种人工智能驱动的方法提高了对T2/T2acSCC风险的评估能力,解决了当前临床实践中的关键差距.
- 早期识别高风险患者可以进行更密集的监测,诊断工作,并考虑早期辅助治疗.
- 实施该工具可以改善患者管理,并可能提高cSCC患者的存活率.
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