Yoon Sung Hyun, Oh Dong Yul, Kim Hyo Jin, Jang Sowon, Kim Minseon, Kim Jihang, Lee Kyung Won, Lee Kyong Joon, Kim Junghoon
Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Monitor Corporation, Seoul, Republic of Korea.
Quant Imaging Med Surg. 2024 Feb 1;14(2):1493-1506. doi: 10.21037/qims-23-1174. Epub 2024 Jan 2.
Detecting new pulmonary metastases by comparing serial computed tomography (CT) scans is crucial, but a repetitive and time-consuming task that burdens the radiologists' workload. This study aimed to evaluate the usefulness of a nodule-matching algorithm with deep learning-based computer-aided detection (DL-CAD) in diagnosing new pulmonary metastases on cancer surveillance CT scans.
Among patients who underwent pulmonary metastasectomy between 2014 and 2018, 65 new pulmonary metastases missed by interpreting radiologists on cancer surveillance CT (Time 2) were identified after a retrospective comparison with the previous CT (Time 1). First, DL-CAD detected nodules in Time 1 and Time 2 CT images. All nodules detected at Time 2 were initially considered metastasis candidates. Second, the nodule-matching algorithm was used to assess the correlation between the nodules from the two CT scans and to classify the nodules at Time 2 as "new" or "pre-existing". Pre-existing nodules were excluded from metastasis candidates. We evaluated the performance of DL-CAD with the nodule-matching algorithm, based on its sensitivity, false-metastasis candidates per scan, and positive predictive value (PPV).
A total of 475 lesions were detected by DL-CAD at Time 2. Following a radiologist review, the lesions were categorized as metastases (n=54), benign nodules (n=392), and non-nodules (n=29). Upon comparison of nodules at Time 1 and 2 using the nodule-matching algorithm, all metastases were classified as new nodules without any matching errors. Out of 421 benign lesions, 202 (48.0%) were identified as pre-existing and subsequently excluded from the pool of metastasis candidates through the nodule-matching algorithm. As a result, false-metastasis candidates per CT scan decreased by 47.9% (from 7.1 to 3.7, P<0.001) and the PPV increased from 11.4% to 19.8% (P<0.001), while maintaining sensitivity.
The nodule-matching algorithm improves the diagnostic performance of DL-CAD for new pulmonary metastases, by lowering the number of false-metastasis candidates without compromising sensitivity.
通过比较系列计算机断层扫描（CT）来检测新的肺转移瘤至关重要，但这是一项重复性且耗时的任务，增加了放射科医生的工作量。本研究旨在评估基于深度学习的计算机辅助检测（DL-CAD）的结节匹配算法在癌症监测CT扫描中诊断新肺转移瘤的实用性。
在2014年至2018年间接受肺转移瘤切除术的患者中，通过与之前的CT（时间1）进行回顾性比较，确定了65个放射科医生在癌症监测CT（时间2）解读时漏诊的新肺转移瘤。首先，DL-CAD在时间1和时间2的CT图像中检测结节。时间2检测到的所有结节最初都被视为转移瘤候选者。其次，使用结节匹配算法评估两次CT扫描结节之间的相关性，并将时间2的结节分类为“新的”或“先前存在的”。先前存在的结节被排除在转移瘤候选者之外。我们基于其敏感性、每次扫描的假转移瘤候选者数量和阳性预测值（PPV）评估了DL-CAD与结节匹配算法的性能。
DL-CAD在时间2共检测到475个病灶。经过放射科医生复查后，这些病灶被分类为转移瘤（n = 54）、良性结节（n = 392）和非结节（n = 29）。使用结节匹配算法比较时间1和时间2的结节时，所有转移瘤均被分类为新结节，无任何匹配错误。在421个良性病变中，202个（48.0%）被确定为先前存在的，随后通过结节匹配算法从转移瘤候选者中排除。结果，每次CT扫描的假转移瘤候选者数量减少了47.9%（从7.1降至3.7，P < 0.001），PPV从11.4%提高到19.8%（P < 0.001），同时保持了敏感性。
结节匹配算法通过在不影响敏感性的情况下减少假转移瘤候选者数量，提高了DL-CAD对新肺转移瘤的诊断性能。
