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Purpose To evaluate the performance of a deep learning (DL) model that measures the liver segmental volume ratio (LSVR) (ie, the volumes of Couinaud segments I–III/IV–VIII) and spleen volumes from CT scans to predict cirrhosis and advanced fibrosis. Materials and Methods For this Health Insurance Portability and Accountability Act–compliant, retrospective study, two datasets were used. Dataset 1 consisted of patients with hepatitis C who underwent liver biopsy (METAVIR F0–F4, 2000–2016). Dataset 2 consisted of patients who had cirrhosis from other causes who underwent liver biopsy (Ishak 0–6, 2001–2021). Whole liver, LSVR, and spleen volumes were measured with contrast-enhanced CT by radiologists and the DL model. Areas under the receiver operating characteristic curve (AUCs) for diagnosing advanced fibrosis (≥METAVIR F2 or Ishak 3) and cirrhosis (≥METAVIR F4 or Ishak 5) were calculated. Multivariable models were built on dataset 1 and tested on datasets 1 (hold out) and 2. Results Datasets 1 and 2 consisted of 406 patients (median age, 50 years IQR, 44–56 years; 297 men) and 207 patients (median age, 50 years IQR, 41–57 years; 147 men), respectively. In dataset 1, the prediction of cirrhosis was similar between the manual versus automated measurements for spleen volume (AUC, 0.86 95% CI: 0.82, 0.9 vs 0.85 95% CI: 0.81, 0.89; significantly noninferior, P < .001) and LSVR (AUC, 0.83 95% CI: 0.78, 0.87 vs 0.79 95% CI: 0.74, 0.84; P < .001). The best performing multivariable model achieved AUCs of 0.94 (95% CI: 0.89, 0.99) and 0.79 (95% CI: 0.71, 0.87) for cirrhosis and 0.8 (95% CI: 0.69, 0.91) and 0.71 (95% CI: 0.64, 0.78) for advanced fibrosis in datasets 1 and 2, respectively. Conclusion The CT-based DL model performed similarly to radiologists. LSVR and splenic volume were predictive of advanced fibrosis and cirrhosis. Keywords: CT, Liver, Cirrhosis, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. © RSNA, 2022
Lee et al. (Wed,) studied this question.