肿瘤细胞治疗研究
英文原题：Artificial intelligence for diagnosis and prognosis prediction of natural killer/T cell lymphoma using magnetic resonance imaging.
基于AI的系统在辅助准确诊断和预后预测方面显示出潜力，并可能有助于NKTCL的治疗优化。
准确诊断和预后预测有利于自然杀伤/T细胞淋巴瘤（NKTCL）的早期干预和医疗改善。基于人工智能（AI）的系统是基于鼻咽磁共振成像开发的。在独立验证数据集中，这些诊断系统在检测恶性鼻咽病变和区分NKTCL与鼻咽癌方面达到0.905-0.960的曲线下面积。与人类放射科医生相比，这些诊断系统的准确性高于住院放射科医生，并与资深放射科医生相当。该预后系统在预测NKTCL生存结局方面显示出有前景的性能，并优于若干临床模型。对于早期NKTCL患者，仅高风险组从早期放疗中获益（风险比 = 0.414 vs. 晚期放疗；95%置信区间，0.190-0.900，p = 0.022），而低风险组的无进展生存期无差异。总之，基于AI的系统在辅助准确诊断和预后预测方面显示出潜力，并可能有助于NKTCL的治疗优化。
Accurate diagnosis and prognosis prediction are conducive to early intervention and improvement of medical care for natural killer/T cell lymphoma (NKTCL). Artificial intelligence (AI)-based systems are developed based on nasopharynx magnetic resonance imaging. The diagnostic systems achieve areas under the curve of 0.905-0.960 in detecting malignant nasopharyngeal lesions and distinguishing NKTCL from nasopharyngeal carcinoma in independent validation datasets. In comparison to human radiologists, the diagnostic systems show higher accuracies than resident radiologists and comparable ones to senior radiologists. The prognostic system shows promising performance in predicting survival outcomes of NKTCL and outperforms several clinical models. For patients with early-stage NKTCL, only the high-risk group benefits from early radiotherapy (hazard ratio = 0.414 vs. late radiotherapy; 95% confidence interval, 0.190-0.900, p = 0.022), while progression-free survival does not differ in the low-risk group. In conclusion, AI-based systems show potential in assisting accurate diagnosis and prognosis prediction and may contribute to therapeutic optimization for NKTCL.
MEMBER ACCOUNT
登录成功会直接打开下一页。
