The objective of this study was to perform external validation of a previously developed prostate biopsy nomogram (the CHIBA nomogram) and to compare it with previously published nomograms developed in Japanese and overseas populations. Two different cohorts of patients were used: one from the Chiba Cancer Center (n = 392) in which transperineal 16-core biopsy was performed, and another from Chibaken Saiseikai Narashino Hospital (n = 269) in which transrectal 16-core biopsy was carried out. All patients were Japanese men with serum prostate-specific antigen levels less than 10 ng/mL. The predictive accuracy of our CHIBA nomogram and of four other published nomograms (Finne's sextant biopsy-based logistic regression model, Karakiewicz's sextant biopsy-based nomogram, Chun's 10-core biopsy-based nomogram and Kawakami's three-dimensional biopsy-based nomogram) was quantified based on area under the curve derived from receiver operating characteristic curves. Head-to-head comparison of area under the curve values demonstrated that our nomogram was significantly more accurate than all other models except Chun's (P = 0.012 vs Finne's, P = 0.000 vs Karakiewicz's, and P = 0.003 vs Kawakami's). Our nomogram appears to be more useful for the Japanese population than Western models. Moreover, external validation demonstrates that its predictive accuracy does not vary according to biopsy approach. This is the first report to demonstrate that the predictive accuracy of a nomogram is independent from the biopsy method.

Download full-text PDF

Source
http://dx.doi.org/10.1111/j.1442-2042.2009.02254.xDOI Listing

Publication Analysis

Top Keywords

external validation
12
predictive accuracy
12
biopsy-based nomogram
12
nomogram
9
head-to-head comparison
8
prostate biopsy
8
chiba nomogram
8
published nomograms
8
16-core biopsy
8
sextant biopsy-based
8

Similar Publications

Predictive model performance may deteriorate when applied to data sources that were not used for training, thus, external validation is a key step in successful model deployment. As access to patient-level external data sources is typically limited, we recently proposed a method that estimates external model performance using only external summary statistics. Here, we benchmark the proposed method on multiple tasks using five large heterogeneous US data sources, where each, in turn, plays the role of an internal source and the remaining-external.

View Article and Find Full Text PDF

Face masks can impact processing a narrative in sign language, affecting several metacognitive dimensions of understanding (i.e., perceived effort, confidence and feeling of understanding).

View Article and Find Full Text PDF

Crohn's disease (CD) is a chronic inflammatory bowel disease with an unknown etiology. Ubiquitination plays a significant role in the pathogenesis of CD. This study aimed to explore the functional roles of ubiquitination-related genes in CD.

View Article and Find Full Text PDF

The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual scoring is time-consuming and subject to variability. This study proposes a multistage deep learning model to predict the Overall Sharp Score (OSS) from hand X-ray images.

View Article and Find Full Text PDF

Rationale And Objectives: The expression of human epidermal growth factor receptor 2 (HER2) in gastric cancer is closely associated with its treatment outcomes and prognosis. This study aims to develop and validate a HER2 prediction model based on computed tomography (CT). Additionally, the study evaluates the robustness of the proposed model.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!