人工智能（AI）在检测中等收入国家国家筛查项目中间期癌的效果。
The efficacy of artificial intelligence (AI) in detecting interval cancers in the national screening program of a middle-income country.
机构信息
Maltepe University Hospital, Feyzullah cad 39, Maltepe, 34843, Istanbul, Turkey.
Acibadem University, School of Medicine, 34752, Istanbul, Turkey; Acibadem Altunizade Hospital, Tophanelioglu cad 13, Altunizade, 34662, Istanbul, Turkey.
出版信息
Clin Radiol. 2024 Jul;79(7):e885-e891. doi: 10.1016/j.crad.2024.03.012. Epub 2024 Mar 29.
AIM
We aimed to investigate the efficiency and accuracy of an artificial intelligence (AI) algorithm for detecting interval cancers in a middle-income country's national screening program.
MATERIAL AND METHODS
A total of 2,129,486 mammograms reported as BIRADS 1 and 2 were matched with the national cancer registry for interval cancers (IC). The IC group consisted of 442 cases, of which 36 were excluded due to having mammograms incompatible with the AI system. A control group of 446 women with two negative consequent mammograms was defined as time-proven normal and constituted the normal group. The cancer risk scores of both groups were determined from 1 to 10 with the AI system. The sensitivity and specificity values of the AI system were defined in terms of IC detection. The IC group was divided into subgroups with six-month intervals according to their time from screening to diagnosis: 0-6 months, 6-12 months, 12-18 months, and 18-24 months. The diagnostic performance of the AI system for all patients was evaluated using receiver operating characteristics (ROC) curve analysis. The diagnostic performance of the AI system for major and minor findings that expert readers determined was re-evaluated.
RESULTS
AI labeled 53% of ICs with the highest score of 10. The sensitivity of AI in detecting ICs was 53.7% and 38.5% at specificities of 90% and 95%, respectively. Area under the curve (AUC) of AI in detecting major signs was 0.93 (95% CI: 0.90-0.95) with a sensitivity of 81.6% and 72.4% at specificities of 90% and 95%, respectively (95% CI: 0.73-0.88 and 95% CI: 0.60-0.82 respectively) and minor signs was 0.87 (95% CI: 0.87-0.92) with a sensitivity of 70% and 53% at a specificity of 90% and 95%, respectively (95% CI: 0.65-0.82 and 95% CI: 0.52-0.71 respectively). In subgroup analysis for time to diagnosis, the AUC value of the AI system was higher in the 0-6 month period than in later periods.
CONCLUSION
This study showed the potential of AI in detecting ICs in initial mammograms and reducing human errors and undetected cancers.
目的
我们旨在研究一种人工智能（AI）算法在中低收入国家国家筛查计划中检测间期癌的效率和准确性。
材料与方法
共有 2129486 例报告为 BIRADS 1 和 2 的乳腺 X 线照片与国家癌症登记处的间期癌（IC）相匹配。IC 组由 442 例病例组成，其中 36 例因与 AI 系统不兼容而被排除在外。定义随后两次阴性乳腺 X 线照片的 446 名女性为时间证实正常的对照组，并构成正常组。使用 AI 系统从 1 到 10 为两组确定癌症风险评分。AI 系统的灵敏度和特异性值定义为 IC 检测。根据从筛查到诊断的时间将 IC 组分为 6 个月的亚组：0-6 个月、6-12 个月、12-18 个月和 18-24 个月。使用接收者操作特征（ROC）曲线分析评估 AI 系统对所有患者的诊断性能。重新评估 AI 系统对专家读者确定的主要和次要发现的诊断性能。
结果
AI 将 53%的 IC 标记为最高评分 10。AI 检测 IC 的灵敏度分别为特异性为 90%和 95%时为 53.7%和 38.5%。AI 检测主要征象的曲线下面积（AUC）为 0.93（95%CI：0.90-0.95），特异性为 90%和 95%时的灵敏度分别为 81.6%和 72.4%（95%CI：0.73-0.88 和 95%CI：0.60-0.82），次要征象的 AUC 为 0.87（95%CI：0.87-0.92），特异性为 90%和 95%时的灵敏度分别为 70%和 53%（95%CI：0.65-0.82 和 95%CI：0.52-0.71）。在诊断时间的亚组分析中，AI 系统的 AUC 值在 0-6 个月期间高于后期。
结论
本研究表明 AI 在检测初始乳腺 X 线照片中的 IC 并减少人为错误和未检测到的癌症方面具有潜力。
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