When applying nomograms to a clinical setting it is essential to know how their predictions compare with clinicians'. Comparisons exist outside of the prostate cancer literature. We reviewed these comparisons and conducted 2 experiments comparing predictions of clinicians with prostate cancer nomograms. By using Medline, we searched studies from January 1966 to July 1999 that compared human predictions with nomogram predictions. Next, we conducted 2 experiments: (1) 17 urologists were presented with 10 case vignettes and asked to predict the 5-year recurrence-free probabilities for each patient; (2) case presentations of 63 prostate cancer patients (including full clinical histories with complete diagnostic data and surgical findings) were made to a group of 25 clinicians who were asked to predict organ-confined disease. We found 22 published studies comparing human experts with nomograms, greater than half (13 of 22) showed the nomogram performing above the level of the human expert. Our first experiment showed urologist modification of 165 nomogram predictions led to a decrease in prediction accuracy (c-index decreased from.67 to.55, P <.05). In our second experiment, clinician predictions of organ-confined disease were comparable to the nomogram (area under the receiver operating characteristic curve [AUC] 0.78 and 0.79, respectively). A mixed-model suggests the nomogram did not augment clinician prediction accuracy (doctor excess error 1.4%, P =.75, 95% confidence interval [CI]: -10.9% to 8.2%). Our data suggest that nomograms do not seem to diminish predictive accuracy and they may be of significant benefit in certain clinical decision making settings.
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http://dx.doi.org/10.1053/suro.2002.32490 | DOI Listing |
PLoS One
January 2025
Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, United Kingdom.
Introduction: Undiagnosed chronic disease has serious health consequences, and variation in rates of underdiagnosis between populations can contribute to health inequalities. We aimed to estimate the level of undiagnosed disease of 11 common conditions and its variation across sociodemographic characteristics and regions in England.
Methods: We used linked primary care, hospital and mortality data on approximately 1.
Ann Nucl Med
January 2025
Department of Biomedical Sciences, Humanitas University, Milan, Italy.
The purpose of this systematic review was to evaluate the role of PSMA PET/CT in intermediate-risk prostate cancer (PCa) patients, to determine whether it could help improve treatment strategy and prognostic stratification. A systematic literature search up to May 2024 was conducted in the PubMed, Embase and Scopus databases. Articles with mixed risk patient populations, review articles, editorials, letters, comments, or case reports were excluded.
View Article and Find Full Text PDFJ Neurooncol
January 2025
Department of Neurosurgery, Allegheny Health Network, Neuroscience Institute, Pittsburgh, PA, United States.
Langenbecks Arch Surg
January 2025
Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Purpose: Assessing surgical skills is vital for training surgeons, but creating objective, automated evaluation systems is challenging, especially in robotic surgery. Surgical procedures generally involve dissection and exposure (D/E), and their duration and proportion can be used for skill assessment. This study aimed to develop an AI model to acquire D/E parameters in robot-assisted radical prostatectomy (RARP) and verify if these parameters could distinguish between novice and expert surgeons.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Objective: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).
Materials And Methods: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development.
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