Dahm Isabel C, Kolb Manuel, Altmann Sebastian, Nikolaou Konstantin, Gatidis Sergios, Othman Ahmed E, Hering Alessa, Moltz Jan H, Peisen Felix
Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany.
Department of Radiology, Te Whatu Ora Waikato, Hamilton 3240, New Zealand.
Cancers (Basel). 2024 Nov 29;16(23):4009. doi: 10.3390/cancers16234009.
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.
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.
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).
The automated diameter measurement of preselected target lesions in follow-up CT is reliable and can potentially help to accelerate RECIST evaluation.
在纵向计算机断层扫描（CT）研究中，与由三位放射科医生进行的实体瘤反应评估标准（RECIST 1.1）手动评估相比，评估一种定制的卷积神经网络（CNN）算法在完全自动化病变跟踪、分割以及RECIST 1.1评估方面的性能。
在一项回顾性阅片研究中，对IV期黑色素瘤患者（n = 58）的基线和随访CT进行了研究。三位放射科医生对转移病灶进行手动测量。生成完全自动化的分割结果，并根据分割结果计算直径和体积，随后进行RECIST 1.1评估。我们测量了：（1）手动直径测量中读者内和读者间的变异性；（2）手动和自动直径测量之间的一致性，以及由此得出的RECIST 1.1类别；（3）与自动体积测量相比，由自动直径测量得出的RECIST 1.1类别之间的一致性。
在基线和随访时总共测量了114个目标病灶。直径测量的读者内和读者间可靠性的组内相关系数（ICC）极佳，所有读者均>0.90。将所有三位读者的平均手动直径测量得出的时间点反应类别与自动直径测量得出的类别进行比较时，一致性为中度至几乎完美（Cohen's k 0.67 - 0.76）。手动和自动体积时间点反应之间的一致性较高（Fleiss' k 0.66 - 0.68），自动直径和体积时间点反应之间的一致性较高至几乎完美（Cohen's k 0.81）。
随访CT中预选目标病灶的自动直径测量是可靠的，并且可能有助于加快RECIST评估。
