目的:确定如何实现人工智能结节算法,肺癌预测卷积神经网络(LCP-CNN)在偶发结节检测时,会影响进一步的调查和管理,使用一系列良性和恶性的阈值评分.
方法:进行了一项观察性回顾性研究,以评估5-15mm之间的结节(158个良性,32恶性)在CT扫描中检测到,作为常规练习的一部分进行。将LCP-CNN应用于基线CT扫描,产生百分比分数,以及为每个阈值组确定的后续成像和管理。我们假设5%低风险阈值组只需要一次随访,0.56%的极低风险阈值组无需随访,80%的高风险阈值组需要加快干预.
结果:158个良性结节的LCP-CNN评分在0.1%至70.8%之间,中位数为5.5%(IQR1.4-18.0),而32个癌结节的LCP-CNN评分在10.1%至98.7%之间,中位数为59.0%(IQR37.1-83.9)。可以避免0.56-5%组(n=37)和21/21CT扫描<0.56%组(n=13)的24/61CT扫描,从而导致整体减少18.6%(45/242)良性队列中的CT扫描。在80%组(n=10)中,在5例癌症患者中,对恶性结节的加速干预可使时间延迟减少3.6个月.
结论:我们显示了人工智能的潜力,可以减少对低评分良性结节的后续扫描和干预的需要,同时可能加速高评分癌结节的调查和治疗。
OBJECTIVE: To determine how implementation of an artificial intelligence nodule algorithm, the Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), at the point of incidental nodule detection would have influenced further investigation and management using a series of threshold scores at both the benign and malignant end of the spectrum.
METHODS: An observational retrospective study was performed in the assessment of nodules between 5-15 mm (158 benign, 32 malignant) detected on CT scans, which were performed as part of routine practice. The LCP-CNN was applied to the baseline CT scan producing a percentage score, and subsequent imaging and management determined for each threshold group. We hypothesized that the 5% low risk threshold group requires only one follow-up, the 0.56% very low risk threshold group requires no follow-up and the 80% high risk threshold group warrants expedited intervention.
RESULTS: The 158 benign nodules had an LCP-CNN score between 0.1 and 70.8%, median 5.5% (IQR 1.4-18.0), whilst the 32 cancer nodules had an LCP-CNN score between 10.1 and 98.7%, median 59.0% (IQR 37.1-83.9). 24/61 CT scans in the 0.56-5% group (n = 37) and 21/21 CT scans <0.56% group (n = 13) could be obviated resulting in an overall reduction of 18.6% (45/242) CT scans in the benign cohort. In the 80% group (n = 10), expedited intervention of malignant nodules could result in a 3.6-month reduction in time delay in 5 cancer patients.
CONCLUSIONS: We show the potential of artificial intelligence to reduce the need for follow-up scans and intervention in low-scoring benign nodules, whilst potentially accelerating the investigation and treatment of high-scoring cancer nodules.