P337 Developing Preliminary MRI-based Classifier for Perianal Fistulizing Crohn’s Disease by Using Deep Convolutional Neural Networks

Abstract

Abstract Background Early diagnosis and management of perianal fistulizing Crohn’s disease (PFCD) is critical. But there is still lack of single reliable diagnostic modality to differentiate between early PFCD and cryptoglandular anal fistula (CAF). This study aimed to evaluate the feasibility and efficacy of deep convolutional neural networks (DCNNs) for distinguishing PFCD from CAF based on pelvic magnetic resonance imaging (MRI). Methods The MRIs from 400 patients primarily diagnosed with PFCD or CAF (two hundred respectively) were retrospectively collected as datasets and used in this study. All patients had no fistula associated surgical histories. The datasets were split into training (80%), validation (10%), and test(10%). Four different DCNNs, MobileNetV2, VGG11, ResNet18 and ResNet34, were trained to classify the patients with fistula MRIs as PFCD or as CAF. Both untrained and pretrained networks were used. Pretrained networks were obtained from the Pytorch Models, an open-access repository of pretrained models for use with Pytorch. The protocol was shown in Figure 1 and was approved by the institutional review board (2022ZSLYEC-421).On the test dataset, receiver operating characteristic curves and areas under the curve (AUCs) were used to assess model performances and were compared with the performances of radiologists. Comparisons between AUCs were made by using the Delong method. Accuracy, sensitivity, and specificity were determined from the optimal threshold by the Youden index. Figure 1. Results Compared with CAF, the PFCD dataset contained more high fistulas (59.5% vs. 44.5%，P=0.003), more fistulas involved in the deep anterior space (15.5% vs. 3.0%，P<0.001), and less abscess formation (32.5% vs. 59.5%，P<0.001). Other fistula characteristics including ramification formation, supra- or infralevator extension, were not statistically different. The performances of the pretrained models were better than that of the untrained models (Figure 2). The AUC of 4 pretrained DCNN classifiers were MobileNetV2 [AUC: 0.943，95%CI（0.820~0.991）], VGG11 [AUC: 0.935, 95%CI（0.810~0.988）], ResNet 18 [AUC: 0.920, 95%CI（0.789~0.982）], ResNet 34[AUC: 0.929, 95%CI（0.801~0.986）] respectively. The performances of 4 pretrained DCNN classifiers were equivalent to that of senior radiologist, and were superior to that of junior radiologist (Figure 2, 3, 4). Figure 2. Figure 3. Figure 4. Conclusion Deep learning with DCNN in classifying PFCD or CAF on perianal MRI is feasible. Transfer learning may further improve the performance of the DCNN model. A larger sample size dataset to train the former DCCNs is conducting in our constitution and the external validation will be added in the future.