Improving Detection of Intrahepatic Cholangiocarcinoma with a Contrast-enhanced US–based Deep Learning Model

Purpose To develop a deep learning (DL) model based on contrast-enhanced US (CEUS) to help radiologists diagnose intrahepatic cholangiocarcinoma (iCCA). Materials and Methods In this retrospective study (July 2017-December 2023), CEUS examinations from 49 centers were used to train and validate a DL model using four algorithms (BNInception, MobileNet-v2, ResNet-50, and VGG-19). External test set A, collected from two independent centers, was used to evaluate model performance. External test set B, collected from 51 centers, was used to compare the DL model's performance on iCCA diagnosis with that of three CEUS radiologists and one MRI radiologist and to assess the effect of DL-assisted interpretation on radiologist performance. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). Results A total of 1148 CEUS examinations were divided into training (<i>n</i> = 804) and validation (<i>n</i> = 344) sets. External test sets A and B included 153 (mean age, 55.52 years ± 11.12 [SD]; 120 male patients) and 240 (mean age, 55.03 years ± 11.25; 184 male patients) CEUS examinations, respectively. Among the four evaluated algorithms, ResNet-50 achieved the best performance (AUC, 0.92) and robustness (coefficient of variation, 5.1) in external test set A. In external test set B, the DL model achieved a higher AUC than did the junior (0.91 vs 0.72, <i>P</i> < .01) and midlevel (0.91 vs 0.78, <i>P</i> < .01) CEUS radiologists and performance similar to that of the senior CEUS radiologist (0.91 vs 0.87, <i>P</i> = .32) and senior MRI radiologist (0.91 vs 0.89, <i>P</i> = .56). With DL assistance, diagnostic performance of the junior and midlevel CEUS radiologists improved significantly (from 0.72 to 0.89 [<i>P</i> < .01] and from 0.78 to 0.90 [<i>P</i> < .01], respectively), reaching performance similar to that of the senior CEUS radiologist (<i>P</i> = .50 for junior radiologist and <i>P</i> = .94 for midlevel radiologist). Conclusion A CEUS-based DL model demonstrated diagnostic performance similar to that of a senior CEUS radiologist and improved the performance of junior and midlevel CEUS radiologists. <b>Keywords:</b> Applications-Ultrasound, Deep Learning, Ultrasound-Contrast, Abdomen/GI, Liver, Oncology ClinicalTrials.gov identifier no. NCT04682886 <i>Supplemental material is available for this article.</i> © RSNA, 2025.