Extrahepatic duct duplication is an extremely rare congenital anomaly. Hilar cholangiocarcinoma with extrahepatic bile duct duplication was reported; however, intraductal papillary neoplasm of the bile duct (IPNB) with extrahepatic bile duct duplication has not been reported to the best of our knowledge. We report a rare case of IPNB with extrahepatic bile duct duplication of a 64-year-old female. The patient underwent extended right hepatectomy, and the results of a subsequence histopathological examination were consistent with an IPNB with extrahepatic bile duct duplication. We report this rare case with radiologic imaging findings and a brief review of the current literature.

Extrahepatic duct duplication is an extremely rare congenital anomaly. Hilar cholangiocarcinoma with extrahepatic bile duct duplication was reported; however, intraductal papillary neoplasm of the bile duct (IPNB) with extrahepatic bile duct duplication has not been reported to the best of our knowledge. We report a rare case of IPNB with extrahepatic bile duct duplication of a 64-year-old female. The patient underwent extended right hepatectomy, and the results of a subsequence histopathological examination were consistent with an IPNB with extrahepatic bile duct duplication. We report this rare case with radiologic imaging findings and a brief review of the current literature.

Large vessel vasculitis is characterized by chronic inflammation within the aortic wall and its major branches. The inflammation is considered to occur as a result of immune dysregulation. Hematologic malignancy is one of the rare causes of secondary vasculitis. Herein, we report a rare case of large vessel vasculitis associated with acute myeloid leukemia mimicking primary vasculitis.

We report a recent case in which ciprofloxacin-resistant Shigella flexneri was isolated from a 23-yr-old female patient with a history of travel to India. Prior to her admission to our internal medicine department, she experienced symptoms of high fever and generalized weakness from continuous watery diarrhea that developed midway during the trip. S. flexneri was isolated from the stool culture. Despite initial treatment with ciprofloxacin, the stool cultures continued to show S. flexneri growth. In the susceptibility test for antibiotics of the quinolone family, the isolate showed resistance to ciprofloxacin (minimum inhibitory concentration [MIC], 8 microg/mL), norfloxacin (MIC, 32 microg/mL), ofloxacin (MIC, 8 microg/mL), nalidixic acid (MIC, 256 microg/mL), and intermediate resistance to levofloxacin (MIC, 4 microg/mL). In molecular studies for quinolone resistance related genes, plasmid borne-quinolone resistance genes such as qnrA, qnrB, qnrS, aac(6')-Ib-cr, qepA, and oqxAB were not detected. Two mutations were observed in gyrA (248C-->T, 259G-->A) and 1 mutation in parC (239G-->T). The molecular characteristics of the isolated S. flexneri showed that the isolate was more similar to the strains isolated from the dysentery outbreak in India than those isolated from Korea.
Anti-Bacterial Agents/pharmacology ; Bacterial Proteins/genetics/metabolism ; Drug Resistance, Bacterial/drug effects ; Dysentery, Bacillary/microbiology ; Feces/microbiology ; Female ; Humans ; India ; Mutation ; Quinolones/*pharmacology ; Shigella flexneri/drug effects/*isolation & purification/metabolism ; Travel ; Young Adult

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.