Otgonbaatar C, et al. Korean J Radiol. 2022 Sep;23:e68. https://doi.org/10.3348/kjr.2022.0127

Cho HS, et al. Korean J Radiol. 2013 Jul-Aug;14(4):607-615. http://dx.doi.org/10.3348/kjr.2013.14.4.607

Immunoglobulin G4 (IgG4)-related sclerosing disease is rare and is known to involve various organs. We present a case of histologically proven IgG4-related sclerosing disease of the small bowel with imaging findings on computed tomography (CT) and small bowel series. CT showed irregular wall thickening, loss of mural stratification and aneurysmal dilatation of the distal ileum. Small bowel series showed aneurysmal dilatations, interloop adhesion with traction and abrupt angulation.

Primary liposarcoma of the liver is extremely rare. We report here on a case of primary well-differentiated liposarcoma in the left hepatic lobe of a 63-year-old woman. Abdominal ultrasonography showed a well-defined, echogenic, round mass. Abdominal computed tomography (CT) and magnetic resonance (MR) images showed an almost fatty, lobulated mass with a few, random distributed vascular structures and a small area of nodular enhancement. The resected tumor appeared as a well-defined, round, tan-yellow mass. Histological analysis showed a well-differentiated liposarcoma.

Background: Approximately 20–50% of prostate cancer patients experience biochemical recurrence (BCR) after radical prostatectomy (RP). Among them, cancer recurrence occurs in about 20–30%. Thus, we aim to reveal the utility of machine learning algorithms for the prediction of early BCR after RP. Methods: A total of 104 prostate cancer patients who underwent magnetic resonance imaging and RP were evaluated. Four well-known machine learning algorithms (i.e., k-nearest neighbors (KNN), multilayer perceptron (MLP), decision tree (DT), and auto-encoder) were applied to build a prediction model for early BCR using preoperative clinical and imaging and postoperative pathologic data. The sensitivity, specificity, and accuracy for detection of early BCR of each algorithm were evaluated. Area under the receiver operating characteristics (AUROC) analyses were conducted. Results: A prediction model using an auto-encoder showed the highest prediction ability of early BCR after RP using all data as input (AUC = 0.638) and only preoperative clinical and imaging data (AUC = 0.656), followed by MLP (AUC = 0.607 and 0.598), KNN (AUC = 0.596 and 0.571), and DT (AUC = 0.534 and 0.495). Conclusion: The auto-encoder-based prediction system has the potential for accurate detection of early BCR and could be useful for long-term follow-up planning in prostate cancer patients after RP.