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PURPOSE: To explore the predictive potential of serial computed tomography (CT) radiology reports for pancreatic cancer survival using natural language processing (NLP). METHODS: Deep-transfer-learning–based NLP models were retrospectively trained and tested with serial, free-text CT reports, and survival information of consecutive patients diagnosed with pancreatic cancer in a Korean tertiary hospital was extracted. Randomly selected patients with pancreatic cancer and their serial CT reports from an independent tertiary hospital in the United States were included in the external testing data set. The concordance index (c-index) of predicted survival and actual survival, and area under the receiver operating characteristic curve (AUROC) for predicting 1-year survival were calculated. RESULTS: Between January 2004 and June 2021, 2,677 patients with 12,255 CT reports and 670 patients with 3,058 CT reports were allocated to training and internal testing data sets, respectively. ClinicalBERT (Bidirectional Encoder Representations from Transformers) model trained on the single, first CT reports showed a c-index of 0.653 and AUROC of 0.722 in predicting the overall survival of patients with pancreatic cancer. ClinicalBERT trained on up to 15 consecutive reports from the initial report showed an improved c-index of 0.811 and AUROC of 0.911. On the external testing set with 273 patients with 1,947 CT reports, the AUROC was 0.888, indicating the generalizability of our model. Further analyses showed our model's contextual interpretation beyond specific phrases. CONCLUSION: Deep-transfer-learning–based NLP model of serial CT reports can predict the survival of patients with pancreatic cancer. Clinical decisions can be supported by the developed model, with survival information extracted solely from serial radiology reports. Survival of patients with pancreatic cancer can be predicted solely by leveraging an NLP model on serial CT reports. [ABSTRACT FROM AUTHOR]

Purpose: The present study aimed to investigate the frequency and extent of compensatory common bile duct (CBD) dilatation after cholecystectomy, assess the time between cholecystectomy and CBD dilatation, and identify potentially useful CT findings suggestive of obstructive CBD dilatation., Materials and Methods: This retrospective study included 121 patients without biliary obstruction who underwent multiple CT scans before and after cholecystectomy at a single center between 2009 and 2011. The maximum short-axis diameters of the CBD and intrahepatic duct (IHD) were measured on each CT scan. In addition, the clinical and CT findings of 11 patients who were initially excluded from the study because of CBD stones or periampullary tumors were examined to identify distinguishing features between obstructive and non-obstructive CBD dilatation after cholecystectomy., Results: The mean (standard deviation) short-axis maximum CBD diameter of 121 patients was 5.6 (± 1.9) mm in the axial plane before cholecystectomy but increased to 7.9 (± 2.6) mm after cholecystectomy ( p < 0.001). Of the 106 patients with a pre-cholecystectomy axial CBD diameter of < 8 mm, 39 (36.8%) showed CBD dilatation of ≥ 8 mm after cholecystectomy. Six of the 17 patients with longterm (> 2 years) serial follow-up CT scans (35.3%) eventually showed a significant (> 1.5-fold) increase in the axial CBD diameter, all within two years after cholecystectomy. Of the 121 patients without obstruction or related symptoms, only one patient (0.1%) showed IHD dilatation > 3 mm after cholecystectomy. In contrast, all 11 patients with CBD obstruction had abdominal pain and abnormal laboratory indices, and 81.8% (9/11) had significant dilatation of the IHD and CBD., Conclusion: Compensatory non-obstructive CBD dilatation commonly occurs after cholecystectomy to a similar extent as obstructive dilatation. However, the presence of relevant symptoms, significant IHD dilatation, or further CBD dilatation 2-3 years after cholecystectomy should raise suspicion of CBD obstruction., Competing Interests: Conflicts of Interest: The authors have no potential conflicts of interest to disclose., (Copyrights © 2024 The Korean Society of Radiology.)