Background: Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients.
Methods: We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses.
Results: Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P = .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI = 0.48 to 0.69) and 0.65 (95% CI = 0.56 to 0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P = .019), similar false positive rate (33% vs 35%; P = .8), and increased standardized net benefit (18% vs -3.3%) compared with the Mayo model.
Conclusions: Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification.
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http://dx.doi.org/10.1093/jncics/pkae086 | DOI Listing |
Histopathology
January 2025
Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Aims: Classification and risk stratification of endometrial carcinoma (EC) has transitioned from histopathological features to molecular classification, e.g. the ProMisE classifier, identifying four prognostic subtypes: POLE mutant (POLEmut) with almost no recurrence or disease-specific death events, mismatch repair deficient (MMRd) and no specific molecular profile (NSMP), with intermediate outcome and p53 abnormal (p53abn) with poor outcomes.
View Article and Find Full Text PDFArthroplast Today
February 2025
Department of Orthopedic Surgery, UCONN Health, Farmington, CT, USA.
Background: Postoperative urinary retention (POUR), a known complication following total joint arthroplasty (TJA), remains inconsistent in its diagnostic criteria, prevalence, and risk factors. This study aims to quantify POUR rates, identify risk factors, and assess complications associated with catheterization in TJA.
Methods: A single-center cohort undergoing TJA between January 2015 and December 2022 was retrospectively reviewed.
JACC Adv
January 2025
Department of Cardiology, The Third-Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Background: Previous studies on the prevalence and prognosis of nutritional status in valvular heart disease (VHD) were primarily limited to aortic stenosis. The nutritional status of other types of VHDs remained an underexplored area.
Objectives: This study aimed to evaluate the prevalence of malnutrition risk in different types of VHD and investigate the association between malnutrition risk and adverse clinical events.
Gastro Hep Adv
September 2024
Division of Gastroenterology and Hepatology, Duke University, Durham, North Carolina.
Background And Aims: Alcohol-related liver disease is a leading cause of liver transplantation (LT) in the United States; however, alcohol relapse remains a risk, and real-world assessment of relapse prediction scores is lacking. The primary aim of this study was to assess risk factors for alcohol relapse and compare effectiveness of pre-existing risk scores (e.g.
View Article and Find Full Text PDFWorld J Gastroenterol
January 2025
Cell Biology Laboratory, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Background: Rebleeding after recovery from esophagogastric variceal bleeding (EGVB) is a severe complication that is associated with high rates of both incidence and mortality. Despite its clinical importance, recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.
Aim: To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.
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