Fully‐automated detection of small bowel carcinoid tumors in CT scans using deep learning

Abstract

AbstractBackgroundSmall 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.PurposeWe 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.MethodsThirty‐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 18F‐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.ResultsThe 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.ConclusionsThe 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.