Application of Deep Learning as a Noninvasive Tool to Determine Pathological Diagnosis of Enlarged Cervical Lymph Nodes with PET/CT

Abstract Objective: To construct a deep-learning convolution neural network (DL-CNN) system for pathological diagnosis of cervical lymph nodes by using computed tomography (CT), fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography (PET), and fused PET/CT images.Materials and methods: A total of 1020 cross-sectional images for each imaging modality was obtained from 211 patients (153 patients with lymphomas and 116 patients with metastases) with enlarged cervical lymph nodes from January 2014 to June 2018. All eligible images were distributed randomly into the training, validation, and testing cohorts with ratios of 70%, 15%, and 15%. We applied eight DL-CNN algorithms with pretrained bases from ImageNet dataset on CT, PET, and fused PET/CT imaging datasets to differentiate lymphomatous nodes from metastatic nodes, respectively. Attention heatmaps of PET and fused PET/CT images generated by class activation mapping (CAM) were used in visualization of class specific regions recognized by the prediction model with best performance. Results: The accuracy of eight deep learning algorithms with pretrained base ranged from 0.650 to 0.981 on PET testing cohort, and from 0.738 to 0.981 on fused PET/CT testing cohort. The VGG16 model on PET images and DenseNet121 model on fused PET/CT images had the best diagnostic performance among all eight algorithms with sensitivity and specificity of 1.000 and 0.963. Class-specific discriminative subregions were highlighted by attention maps for clinical review.Conclusion: A DL-CNN system was developed for classifying metastatic and lymphomatous involvement with favorable diagnostic performance on PET and PET/CT images in patients with enlarged cervical lymph nodes. The further clinical practice of this system may improve quality of the following therapeutic interventions and optimize patients’ outcomes.