Objectives: To evaluate the performance of a custom-made convolutional neural network (CNN) algorithm for fully automated lesion tracking and segmentation, as well as RECIST 1.1 evaluation, in longitudinal computed tomography (CT) studies compared to a manual Response Evaluation Criteria in Solid Tumors (RECIST 1.1) evaluation performed by three radiologists. Methods: Baseline and follow-up CTs of patients with stage IV melanoma (n = 58) was investigated in a retrospective reading study. Three radiologists performed manual measurements of metastatic lesions. Fully automated segmentations were generated, and diameters and volumes were computed from the segmentation results, with subsequent RECIST 1.1 evaluation. We measured (1) the intra- and inter-reader variability in the manual diameter measurements, (2) the agreement between manual and automated diameter measurements, as well as the resulting RECIST 1.1 categories, and (3) the agreement between the RECIST 1.1 categories derived from automated diameter measurement compared to automated volume measurements. Results: In total, 114 target lesions were measured at baseline and follow-up. The intraclass correlation coefficients (ICCs) for the intra- and inter-reader reliability of the diameter measurements were excellent, being >0.90 for all readers. There was moderate to almost perfect agreement when comparing the timepoint response category derived from the mean manual diameter measurements from all three readers with those derived from automated diameter measurements (Cohen’s k 0.67–0.76). The agreement between the manual and automated volumetric timepoint responses was substantial (Fleiss’ k 0.66–0.68) and that between the automated diameter and volume timepoint responses was substantial to almost perfect (Cohen’s k 0.81). Conclusions: The automated diameter measurement of preselected target lesions in follow-up CT is reliable and can potentially help to accelerate RECIST evaluation.
目的：评估定制卷积神经网络（CNN）算法在纵向计算机断层扫描（CT）研究中，对病灶进行全自动追踪、分割及RECIST 1.1评估的性能，并与三位放射科医师手动执行的实体瘤疗效评价标准（RECIST 1.1）评估结果进行比较。方法：在一项回顾性阅片研究中，对58例IV期黑色素瘤患者的基线和随访CT图像进行分析。三位放射科医师对转移性病灶进行手动测量。研究同时生成全自动分割结果，基于分割结果计算病灶直径和体积，并进行后续RECIST 1.1评估。我们测量了：（1）手动直径测量的观察者内及观察者间变异度；（2）手动与自动直径测量结果之间的一致性，以及由此得出的RECIST 1.1分类的一致性；（3）基于自动直径测量与基于自动体积测量所得的RECIST 1.1分类之间的一致性。结果：在基线和随访中共对114个靶病灶进行了测量。直径测量的观察者内及观察者间信度的组内相关系数（ICCs）均表现优异，所有阅片者的ICC值均大于0.90。将三位阅片者手动直径测量均值所得的时间点疗效分类与自动直径测量所得分类进行比较时，一致性为中度至几乎完全一致（Cohen's k 0.67–0.76）。手动与自动体积测量时间点疗效分类之间的一致性为高度一致（Fleiss' k 0.66–0.68），而自动直径与自动体积测量时间点疗效分类之间的一致性为高度一致至几乎完全一致（Cohen's k 0.81）。结论：在随访CT中对预选靶病灶进行自动直径测量是可靠的，并有望帮助加速RECIST评估流程。
肿瘤(癌症)患者之家