Objective: To evaluate the performance of supplemental automated breast ultrasound (ABUS) added to mammography-based breast cancer screening for women with nondense breasts. Materials and Methods: A retrospective search of radiology databases at two tertiary institutions identified asymptomatic women with nondense breasts who underwent breast cancer screening using both digital mammography (DM) and supplemental ABUS between January 2020 and December 2023. We excluded women without sufficient follow-up data or those without an established final diagnosis, including histopathologic results. The performance measures of DM alone and ABUS combined with DM (ABUS plus DM) were compared. The primary outcome was the cancer detection rate (CDR), and the secondary outcomes were sensitivity and specificity. Subgroup analyses were performed for women with scattered fibroglandular density and almost entirely fatty breasts. Results: A total of 2,904 pairs of screening examinations were performed in 1,683 women (59 ± 10 years), detecting 26 cancers. In comparison with DM alone, ABUS plus DM showed higher CDR (9.0 vs. 7.9 per 1,000 examinations, P < 0.001), higher sensitivity (100% [26/26] vs. 88.5% [23/26], P < 0.001), and lower specificity (95.0% [2,735/2,878] vs. 97.9% [2,817/2,878], P < 0.001). In women with scattered fibroglandular density, ABUS increased the CDR from 7.4 to 8.5 per 1,000 examinations and improved the sensitivity from 87.0% [20/23] to 100% [23/23] (P < 0.001). In women with almost entirely fatty breasts, ABUS plus DM showed the same CDR (16.4 per 1,000 examinations) and sensitivity (100% [3/3]) as DM alone. Three cancers (11.5% [3/26]), all of which were stage T1N0, were detected only by supplemental ABUS. Conclusion Supplemental ABUS improved cancer detection and sensitivity in women with nondense breasts, with the benefits primarily observed in those with scattered fibroglandular density.

Regenerative therapy in the foot and ankle region has attracted attention, with magnetic resonance imaging (MRI) playing an increasingly important role. Biochemical MRI techniques, such as T2 mapping, T2* mapping, delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), and sodium imaging, are being assessed as promising tools for cartilage assessment. These methods can quantitatively analyze the water content, collagen structure, and glycosaminoglycan content in cartilage, providing valuable information for assessing the quality of regenerated cartilage. Quantitative analysis using the signal-to-noise ratio has been attempted in a ligament evaluation. Recent studies have shown that T2* mapping can quantify ankle instability and correlate with the clinical findings. Various techniques, such as dynamic contrast-enhanced MRI, diffusion tensor imaging, T2 mapping, and T2* mapping, are being assessed for a tendon assessment, particularly in the Achilles tendon. These methods can be used to analyze quantitatively the vascular regeneration and healing processes of microstructure. Although some of these advanced MRI techniques are still in the research stage, they are expected to play a crucial role in non-invasively evaluating the effects of regenerative therapies in the foot and ankle field. Future research will optimize the MRI protocols specific to each tissue type and validate the clinical efficacy. This review describes the latest MRI techniques for evaluating the regeneration of ankle cartilage, ligaments, and tendons and introduces future research directions.

Purpose: This study devised triple tension band wirings (TTBW) fixation in patients with comminuted patella fractures to compare the clinical result of TTBW with that of tension band wiring (TBW). Materials and Methods: This study was conducted on 91 patients who had undergone surgery diagnosed with acute patella fracture from January 2011 to December 2016. The study included 51 double TBW patients (Group 1) and 40 patients with TTBW (Group 2). Results: Five out of 51 cases had a loss of reduction and fixation failure in Group 1, and no failure of fracture formation healing occurred in Group 2. Nonunion was noted in one case in Group 1 and no case in Group 2. Eight K-wire migration cases were observed in Group 1, which was not observed in Group 2. Six patients in Group 1 underwent revisional surgery. No patients in Group 2 had a reoperation. As a result of a one-year follow-up after the operation, the mean range of motion of the knee joint in groups 1 and 2 was 128.3°±11.3° and 127.9°±10.8°, respectively. The Lysholm’s scores for groups 1 and 2 were 90.8±4.2 and 90.3±3.8 points, respectively, which was not statistically significant. Conclusion TTBW is a helpful technique for the surgical treatment of comminuted patella fractures. The TTBW method has less reoperation due to nonunion and fixation failure. After a one-year followup, the clinical results were similar to the conventional TBW method.

Purpose: To evaluate the results of intramedullary nailing with sliding restriction and dynamization methods in treating intertrochanteric fractures. Materials and Methods: From August 2016 to March 2019, patients aged 65 years and older who underwent intramedullary nailing in treating intertrochanteric fractures were enrolled in this study. The radiological and clinical results were analyzed in 49 patients who had undergone lag screw sliding re-striction and dynamization of the distal interlocking screw method. Results: Forty-seven patients achieved union without complications (95.9%). The mean union period was 6.5 weeks (range, 6-9 weeks). Complications occurred in two patients (4.1%), including the cut through of the lag screw in one patient and varus deformity of more than 10° in the other. The preinjury mean Koval grade was 2.8 (range, 1-7). The mean was 3.3 (range, 1-7) at the final follow-up, and the mean difference was 0.5 (range, 0-2). Conclusion Intramedullary nailing with a sliding restriction and dynamization method for treating in-tertrochanteric fractures achieved union. The reduction achieved during surgery was maintained with good clinical results. This method is a safe and effective treatment technique for femoral intertrochanteric fractures.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

Background: s/Aims: Artificial intelligence (AI) technology has been used to assess surgery quality, educate, and evaluate surgical performance using video recordings in the minimally invasive surgery era. Much attention has been paid to automating surgical workflow analysis from surgical videos for an effective evaluation to achieve the assessment and evaluation. This study aimed to design a deep learning model to automatically identify surgical phases using laparoscopic cholecystectomy videos and automatically assess the accuracy of recognizing surgical phases. Methods: One hundred and twenty cholecystectomy videos from a public dataset (Cholec80) and 40 laparoscopic cholecystectomy videos recorded between July 2022 and December 2022 at a single institution were collected. These datasets were split into training and testing datasets for the AI model at a 2:1 ratio. Test scenarios were constructed according to structural characteristics of the trained model. No pre- or post-processing of input data or inference output was performed to accurately analyze the effect of the label on model training. Results: A total of 98,234 frames were extracted from 40 cases as test data. The overall accuracy of the model was 91.2%. The most accurate phase was Calot’s triangle dissection (F1 score: 0.9421), whereas the least accurate phase was clipping and cutting (F1 score:0.7761). Conclusions Our AI model identified phases of laparoscopic cholecystectomy with a high accuracy.

The first edition of ‘A Standardized Pathology Report for Gastric Cancer’ was initiated by the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists and published 17 years ago. Since then, significant advances have been made in the pathologic diagnosis, molecular genetics, and management of gastric cancer (GC). To reflect those changes, a committee for publishing a second edition of the report was formed within the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists. This second edition consists of two parts: standard data elements and conditional data elements.The standard data elements contain the basic pathologic findings and items necessary to predict the prognosis of GC patients, and they are adequate for routine surgical pathology service. Other diagnostic and prognostic factors relevant to adjuvant therapy, including molecular biomarkers, are classified as conditional data elements to allow each pathologist to selectively choose items appropriate to the environment in their institution. We trust that the standardized pathology report will be helpful for GC diagnosis and facilitate large-scale multidisciplinary collaborative studies.

The first edition of ‘A Standardized Pathology Report for Gastric Cancer’ was initiated by the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists and published 17 years ago. Since then, significant advances have been made in the pathologic diagnosis, molecular genetics, and management of gastric cancer (GC). To reflect those changes, a committee for publishing a second edition of the report was formed within the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists. This second edition consists of two parts: standard data elements and conditional data elements. The standard data elements contain the basic pathologic findings and items necessary to predict the prognosis of GC patients, and they are adequate for routine surgical pathology service. Other diagnostic and prognostic factors relevant to adjuvant therapy, including molecular biomarkers, are classified as conditional data elements to allow each pathologist to selectively choose items appropriate to the environment in their institution. We trust that the standardized pathology report will be helpful for GC diagnosis and facilitate large-scale multidisciplinary collaborative studies.