Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA.
Digestive Disease Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA.
Med Phys. 2023 Dec;50(12):7865-7878. doi: 10.1002/mp.16391. Epub 2023 Apr 5.
Small bowel carcinoid tumor is a rare neoplasm and increasing in incidence. Patients with small bowel carcinoid tumors often experience long delays in diagnosis due to the vague symptoms, slow growth of tumors, and lack of clinician awareness. Computed tomography (CT) is the most common imaging study for diagnosis of small bowel carcinoid tumor. It is often used with positron emission tomography (PET) to capture anatomical and functional aspects of carcinoid tumors and thus to increase the sensitivity.
We compared three different kinds of methods for the automatic detection of small bowel carcinoid tumors on CT scans, which is the first to the best of our knowledge.
Thirty-three preoperative CT scans of 33 unique patients with surgically-proven carcinoid tumors within the small bowel were collected. Ground-truth segmentation of tumors was drawn on CT scans by referring to available F-DOPA PET scans and the corresponding radiology report. These scans were split into the trainval set (n = 24) and the test positive set (n= 9). Additionally, 22 CT scans of 22 unique patients who had no evidence of the tumor were collected to comprise the test negative set. We compared three different kinds of detection methods, which are detection network, patch-based classification, and segmentation-based methods. We also investigated the usefulness of small bowel segmentation for reduction of false positives (FPs) for each method. Free-response receiver operating characteristic (FROC) curves and receiver operating characteristic (ROC) curves were used for lesion- and patient-level evaluations, respectively. Statistical analyses comparing the FROC and ROC curves were also performed.
The detection network method performed the best among the compared methods. For lesion-level detection, the detection network method, without the small bowel segmentation-based filtering, achieved sensitivity values of (60.8%, 81.1%, 82.4%, 86.5%) at per-scan FP rates of (1, 2, 4 ,8), respectively. The use of the small bowel segmentation did not improve the performance ( ). For patient-level detection, again the detection network method, but with the small bowel segmentation-based filtering, achieved the highest AUC of 0.86 with a sensitivity of 78% and specificity of 82% at the Youden point.
The carcinoid tumors in this patient population were very small and potentially difficult to diagnose. The presented method showed reasonable sensitivity at small numbers of FPs for lesion-level detection. It also achieved a promising AUC for patient-level detection. The method may have clinical application in patients with this rare and difficult to detect disease.
小肠类癌肿瘤是一种罕见的肿瘤，发病率正在上升。由于症状模糊、肿瘤生长缓慢以及临床医生意识不足，小肠类癌肿瘤患者的诊断往往存在较长的延误。计算机断层扫描（CT）是诊断小肠类癌肿瘤最常用的影像学检查方法。它通常与正电子发射断层扫描（PET）一起使用，以捕捉类癌肿瘤的解剖和功能方面，从而提高敏感性。
我们比较了三种不同的方法用于自动检测 CT 扫描中的小肠类癌肿瘤，这在我们所知的范围内是首次。
收集了 33 例经手术证实的小肠类癌肿瘤患者的 33 例术前 CT 扫描。通过参考可用的 F-DOPA PET 扫描和相应的放射学报告，在 CT 扫描上对肿瘤进行了地面真实分割。这些扫描分为训练验证集（n=24）和测试阳性集（n=9）。此外，还收集了 22 例无肿瘤证据的独特患者的 22 例 CT 扫描，组成测试阴性集。我们比较了三种不同的检测方法，即检测网络、基于补丁的分类和基于分割的方法。我们还研究了小肠分割对于减少每种方法的假阳性（FP）的有用性。自由反应接收器操作特性（FROC）曲线和接收器操作特性（ROC）曲线分别用于病变级和患者级评估。还对 FROC 和 ROC 曲线进行了统计分析。
在比较的方法中，检测网络方法表现最好。对于病变级检测，在每扫描 FP 率为（1、2、4、8）时，检测网络方法（不使用小肠分割过滤）的灵敏度值分别为（60.8%、81.1%、82.4%、86.5%）。使用小肠分割并没有提高性能（）。对于患者级检测，再次使用检测网络方法，但使用小肠分割过滤，在 Youden 点处获得了最高 AUC 为 0.86，灵敏度为 78%，特异性为 82%。
该患者人群中的类癌肿瘤非常小，诊断可能很困难。所提出的方法在病变级检测中具有较小的 FP 数量时具有合理的灵敏度。它还为患者级检测实现了有希望的 AUC。该方法可能在这种罕见且难以检测的疾病的患者中具有临床应用价值。
