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Background: To develop a predictive model to identify atypical ductal hyperplasia (ADH) that was underestimated by US-guided core needle biopsy (CNB) and to evaluate the risk factors for underestimation for ADH with intraductal papilloma diagnosed by CNB.
Methods: In this retrospective study, 300 CNB-diagnosed ADH lesions in 291 consecutive women between January 2014 and July 2023 were included and divided into training set (n = 181), internal validation set (n = 54), and external validation set (n = 65). The review included clinical, pathological, and US features, as well as final outcomes. Multivariate logistic regression was employed to establish predictive model and to evaluate risk factors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis, and utility (patient stratification into low and high-risk groups). Model was validated both internally and externally by calculating its performance on validation sets.
Results: The upgrade rate to malignancy was 51.0%. Predictors included in the model were age, the pathological pattern of ADH with intraductal papilloma or ADH alone, Ki-67 positivity, and imaging-pathological discordance. The AUC was 0.915 (95% CI: 0.858, 0.955) in the training set, 0.906 (95% CI: 0.785, 0.972) in the internal validation set, and 0.934 (95% CI: 0.836, 0.983) in the external validation set. Using a cutoff value of 0.11, 38.3% of nonmalignant lesions in the training set were stratified into low-risk group with an upgrade rate of 4.1%. Similar results were obtained in the validation sets. For ADH with intraductal papilloma, age and imaging-pathological discordance were the independent risk factors for malignancy upgrading.
Conclusions: The model established to predict ADH upgrading can help in individualized risk management. If predictors of non-upgraded ADH lesions can be confirmed with larger studies, more than one-third of non-malignant lesions are expected to be candidates for non-excision.
Trial Registration: This is a retrospective study.
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http://dx.doi.org/10.1186/s12880-025-01707-z | DOI Listing |
J Am Coll Cardiol
August 2025
Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, California, USA. Electronic address:
Background: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intelligence has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.
Objectives: The purpose of this study was to develop and validate open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.
J Chem Inf Model
September 2025
Key Laboratory of Micro-nano Sensing and IoT of Wenzhou, Wenzhou Institute of Hangzhou Dianzi University, Wenzhou 325038, China.
Transcription factors (TFs) are essential proteins that regulate gene expression by specifically binding to transcription factor binding sites (TFBSs) within DNA sequences. Their ability to precisely control the transcription process is crucial for understanding gene regulatory networks, uncovering disease mechanisms, and designing synthetic biology tools. Accurate TFBS prediction, therefore, holds significant importance in advancing these areas of research.
View Article and Find Full Text PDFDisabil Rehabil
September 2025
Department of Occupational Therapy, Yonsei University Graduate School, Seoul, South Korea.
Purpose: This study aimed to develop a tailored International Classification of Functioning, Disability and Health (ICF) Core Set for driving rehabilitation in South Korea, addressing the functional needs of individuals with disabilities and the gaps in the current rehabilitation system.
Materials And Methods: An initial item pool was created based on focus group interviews with 13 individuals with disabilities who use assistive driving technologies. This was followed by two Delphi rounds with 12 occupational therapy experts.
J Robot Surg
September 2025
Department of Oncology, Shengli Oilfield Central Hospital, Dongying, China.
A major cause of cancer death, colorectal cancer is becoming more common in younger people. The comparative effectiveness of robotic versus laparoscopic total mesorectal excision (TME) as surgical interventions for mid-low rectal cancer following neoadjuvant chemoradiotherapy (nCRT) remains uncertain. To systematically evaluate oncological, perioperative, and survival outcomes of robotic versus laparoscopic surgery for mid-low rectal cancer following nCRT.
View Article and Find Full Text PDFISA Trans
August 2025
College of Automotive Engineering, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun City, Jilin Province 130000, China. Electronic address:
In this paper, an event-triggered fuzzy control algorithm is proposed for the unmanned surface vessel (USV) and unmanned aerial vehicle (UAV) cooperative plant to achieve the high-precision landing mission. In the guidance module, an L virtual ship-L virtual aerial vehicle (LVS-LVA) guidance principle is developed to generate the reasonable reference signals for the USV-UAV plant under the landing mission. The proposed guidance principle incorporates a rolling kinematic compensation mechanism based on the 4-degree-of-freedom model of USV, specifically designed to counteract wave-induced rolling disturbances during UAV landing operations on unstable marine platforms.
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