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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.)

BACKGROUND. In LI-RADS version 2018, observations showing at least one of five targetoid appearances in different sequences or postcontrast phases are categorized LR-M, indicating likely non-hepatocellular carcinoma (HCC) malignancy. OBJECTIVE. The purpose of this study was to evaluate interobserver agreement for LI-RADS targetoid appearances among a large number of radiologists of varying experience and the diagnostic performance of targetoid appearances for differentiating HCC from non-HCC malignancy. METHODS. This retrospective study included 100 patients (76 men, 24 women; mean age, 58 ± 9 [SD] years) at high risk of HCC who underwent gadoxetic acid-enhanced MRI within 30 days before hepatic tumor resection (25 randomly included patients with non-HCC malignancy [13, intrahepatic cholangiocarcinoma; 12, combined HCC-cholangiocarcinoma]; 75 matched patients with HCC). Eight radiologists (four more experienced [8-15 years]; four less experienced [1-5 years]) from seven institutions independently assessed observations for the five targetoid appearances and LI-RADS categorization. Interobserver agreement and diagnostic performance for non-HCC malignancy were evaluated. RESULTS. Interobserver agreement was poor for peripheral washout (κ = 0.20); moderate for targetoid transitional phase or hepatobiliary phase appearance (κ = 0.33), delayed central enhancement (κ = 0.37), and targetoid restriction (κ = 0.43); and substantial for rim arterial phase hyperenhancement (κ = 0.61). Agreement was fair for at least one targetoid appearance (κ = 0.36) and moderate for at least two, three, or four targetoid appearances (κ = 0.43-0.51). Agreement for individual targetoid appearances was not significantly different between more experienced and less experienced readers other than for targetoid restriction (κ = 0.63 vs 0.43; p = .001). Agreement for at least one targetoid appearance was fair among more experienced (κ = 0.29) and less experienced (κ = 0.37) reviewers. Agreement for at least two, three, or four targetoid appearances was moderate to substantial among more experienced reviewers (κ = 0.45-0.63) and moderate among less experienced reviewers (κ = 0.42-0.56). Existing LR-M criteria of at least one targetoid appearance had median accuracy for non-HCC malignancy of 62%, sensitivity of 84%, and specificity of 54%. For all reviewers, accuracy was highest when at least three (median accuracy, 79%; sensitivity, 68%; specificity, 82%) or four (median accuracy, 80%; sensitivity, 54%; specificity, 88%) targetoid appearances were required. CONCLUSION. Targetoid appearances and LR-M categorization exhibited considerable interobserver variation among both more and less experienced reviewers. CLINICAL IMPACT. Requiring multiple targetoid appearances for LR-M categorization improved interobserver agreement and diagnostic accuracy for non-HCC malignancy.

The field of medical image analysis has been attracted to deep learning. Various deep learning-based techniques have been introduced to aid diagnosis in the CT image of the patient. The auxiliary model for diagnosis that we proposed is to detect colorectal tumors in the CT image. The model is combined with two contrary networks of 'Detection Transformer" and 'Hourglass". Furthermore., to improve the performance of the model., we propose an efficient connection method for two contrary models by using intermediate prediction information. A total of 3.,509 patients (193.,567 CT images) were applied to the experiment and our model outperforms the conventional models in colorectal tumor detection. Clinical Relevance - The proposed model in this paper automatically detects colorectal tumors and provides the bounding box in the CT images. Colorectal tumor is one of the common diseases. In addition, the mortality rate is so high that in-time treatment is required. The model we present here has a sensitivity (or recall) of 84.73 % for tumor detection and a precision of 88.25 % in the patient CT data. The in-slice performance of the tumor detection shows an IoU of 0.56, a sensitivity of 0.67, and a precision of 0.68.

Purpose: To construct a standard dataset of contrast-enhanced CT images of liver tumors to test the performance and safety of artificial intelligence (AI)-based algorithms for clinical decision support systems (CDSSs)., Materials and Methods: A consensus group of medical experts in gastrointestinal radiology from four national tertiary institutions discussed the conditions to be included in a standard dataset. Seventy-five cases of hepatocellular carcinoma, 75 cases of metastasis, and 30-50 cases of benign lesions were retrieved from each institution, and the final dataset consisted of 300 cases of hepatocellular carcinoma, 300 cases of metastasis, and 183 cases of benign lesions. Only pathologically confirmed cases of hepatocellular carcinomas and metastases were enrolled. The medical experts retrieved the medical records of the patients and manually labeled the CT images. The CT images were saved as Digital Imaging and Communications in Medicine (DICOM) files., Results: The medical experts in gastrointestinal radiology constructed the standard dataset of contrast-enhanced CT images for 783 cases of liver tumors. The performance and safety of the AI algorithm can be evaluated by calculating the sensitivity and specificity for detecting and characterizing the lesions., Conclusion: The constructed standard dataset can be utilized for evaluating the machine-learningbased AI algorithm for CDSS., Competing Interests: Conflicts of Interest: The authors have no potential conflicts of interest to disclose., (Copyrights © 2021 The Korean Society of Radiology.)

Objective: The purpose of this study was to examine the associations between preoperative Liver Imaging Reporting and Data System (LI-RADS) categories and prognosis after curative resection of single hepatic malignancies in patients with chronic liver disease., Materials and Methods: Between January 2008 and December 2010, 225 patients with chronic liver disease underwent resection of single hepatic malignant tumors (218 hepatocellular carcinomas, three cholangiocarcinomas, four biphenotypic carcinomas) after undergoing gadoxetic acid-enhanced MRI. Two radiologists retrospectively categorized the tumors into LI-RADS categories. Differences in disease-free survival duration between categories were analyzed by the Kaplan-Meier method with the log-rank test., Results: Reviewer 1 categorized two (0.9%) patients as having LR-3, 53 (23.6%) LR-4, 159 (70.7%) LR-5, and 11 (4.9%) LR-M lesions. The corresponding numbers for reviewer 2 were six (2.7%) LR-3, 30 (13.3%) LR-4, 178 (79.1%) LR-5, and 11 (4.9%) LR-M. The 2-year cumulative recurrence or death rates were 15.1% for lesions categorized LR-3 or LR-4 by reviewer 1, 31.7% for LR-5, and 60% for LR-M. For lesions categorized by reviewer 2 the corresponding rates were 20.6% for LR-3 or LR-4, 29% for LR-5, and 54.5% for LR-M. Disease-free survival was significantly worse among patients with lesions categorized as LR-M than for lesions categorized as LR-3 or LR-4 or as LR-5 (p < 0.01 for both reviewers). Disease-free survival did not significantly differ between patients with LR-3 or LR-4 and those with LR-5 lesions (reviewer 1, p = 0.301; reviewer 2, p = 0.291)., Conclusion: Patients with tumors preoperatively categorized as LR-M may have a worse prognosis than those with tumors categorized LR-3, LR-4, or LR-5 after curative resection of single hepatic malignancy.