Dataset Information

Based on biomedical index data: Risk prediction model for prostate cancer.

ABSTRACT:

Abstract

To explore the influencing factors of prostate cancer occurrence, set up risk prediction model, require reference for the preliminary diagnosis of clinical doctors, this model searched database through the data of prostate cancer patients and prostate hyperplasia patients National Clinical Medical Science Data Center.With the help of Stata SE 12.0 and SPSS 25.0 software, the biases between groups were balanced by propensity score matching. Based on the matched data, the relevant factors were further screened by stepwise logistic regression analysis, the key variable and artificial neural network model are established. The prediction accuracy of the model is evaluated by combining the probability of test set with the area under receiver operating characteristic curve (ROC).After 1:2 PSM, 339 pairs were matched successfully. There are 159 cases in testing groups and 407 cases in training groups. And the regression model was P = 1 / (1 + e (0.122 ∗ age + 0.083 ∗ Apo lipoprotein C3 + 0.371 ∗ total prostate specific antigen (tPSA) -0.227 ∗ Apo lipoprotein C2-6.093 ∗ free calcium (iCa) + 0.428 ∗ Apo lipoprotein E-1.246 ∗ triglyceride-1.919 ∗ HDL cholesterol + 0.083 ∗ creatine kinase isoenzyme [CKMB])). The logistic regression model performed very well (ROC, 0.963; 95% confidence interval, 0.951 to 0.978) and artificial neural network model (ROC, 0.983; 95% confidence interval, 0.964 to 0.997). High degree of Apo lipoprotein E (Apo E) (Odds Ratio, [OR], 1.535) in blood test is a risk factor and high triglyceride (TG) (OR, 0.288) is a protective factor.It takes the biochemical examination of the case as variables to establish a risk prediction model, which can initially reflect the risk of prostate cancer and bring some references for diagnosis and treatment.

SUBMITTER: Guo H

PROVIDER: S-EPMC8084031 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundWe compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user.MethodsTen North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach.ResultsAmong 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history.ConclusionDevelopers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

Project description:BackgroundPlant-based diets are associated with multiple health benefits and a favorable environmental impact. For prostate cancer, previous studies suggest a beneficial role of specific plant-based foods (e.g., tomatoes) and a potentially harmful role of specific animal-based foods (e.g., meat, dairy). However, less is known about plant-based dietary patterns.ObjectivesWe sought to examine the relation between plant-based diet indices and prostate cancer risk, including clinically relevant disease.MethodsThis was a prospective cohort study including 47,239 men in the Health Professionals Follow-Up Study (1986-2014). Overall and healthful plant-based diet indices were calculated from FFQs. Cox proportional hazards models were used to estimate HRs and 95% CIs to examine the risk of incident prostate cancer (total and by clinical category), among men ages <65 and ≥65 y.ResultsOf the 47,239 men, 6655 men were diagnosed with prostate cancer over follow-up, including 515 with advanced-stage disease at diagnosis, 956 with lethal disease (metastasis or death), and 806 prostate cancer deaths. Greater overall plant-based consumption was associated with a significantly lower risk of fatal prostate cancer (HR: 0.81; 95% CI: 0.64, 1.01; P-trend = 0.04). In men aged <65, a higher plant-based diet index was associated with a lower risk of advanced, lethal, and fatal prostate cancer. Moreover, greater consumption of a healthful plant-based diet was associated with lower risks of total (HR: 0.84; 95% CI: 0.73, 0.98; P-trend = 0.046) and lethal prostate cancer (HR: 0.56; 95% CI: 0.34, 0.94; P-trend = 0.03) at age <65. There were no associations between overall or healthful plant-based diet indices with prostate cancer among men ≥65 y. Fewer than 1% of participants followed a strict vegetarian or vegan diet.ConclusionsThis prospective study provides supportive evidence that greater consumption of healthful plant-based foods is associated with a lower risk of aggressive forms of prostate cancer, with stronger benefit among men aged <65 y.

Project description:Importance:Multiparametric magnetic resonance imaging (MRI) in conjunction with MRI-transrectal ultrasound (TRUS) fusion-guided biopsies have improved the detection of prostate cancer. It is unclear whether MRI itself adds additional value to multivariable prediction models based on clinical parameters. Objective:To determine whether an MRI-based prediction model can reduce unnecessary biopsies in patients with suspected prostate cancer. Design, Setting, and Participants:Patients underwent MRI, MRI-TRUS fusion-guided biopsy, and 12-core systematic biopsy in 1 session. The development cohort used to derive the prediction model consisted of 400 patients from 1 institution enrolled between May 14, 2015, and August 31, 2016, and the validation cohort included 251 patients from 2 independent institutions who underwent biopsies between April 1, 2013, and June 30, 2016, at 1 institution and between July 1, 2015, and October 31, 2016, at the other institution. The MRI model included MRI-derived parameters in addition to clinical variables. Area under the curve of receiver operating characteristic curves and decision curve analysis were performed. Main Outcomes and Measures:Risk of clinically significant prostate cancer on biopsy, defined as a Gleason score of 3?+?4 or higher in at least 1 biopsy core. Results:Overall, 193 (48.3%) of the 400 patients in the development cohort (mean [SD] age at biopsy, 64.3 [7.1] years) and 96 (38.2%) of the 251 patients in the validation cohort (mean [SD] age at biopsy, 64.9 [7.2] years) had clinically significant prostate cancer, defined as a Gleason score greater than or equal to 3?+?4. By applying the model to the external validation cohort, the area under the curve increased from 64% to 84% compared with the baseline model (P?<?.001). At a risk threshold of 20%, the MRI model had a lower false-positive rate than the baseline model (46% [95% CI, 32%-66%] vs 92% [95% CI, 70%-100%]), with only a small reduction in the true-positive rate (89% [95% CI, 85%-96%] vs 99% [95% CI, 89%-100%]). Eighteen of 100 fewer biopsies could have been performed, with no increase in the number of patients with missed clinically significant prostate cancers. Conclusions and Relevance:The inclusion of MRI-derived parameters in a risk model could reduce the number of unnecessary biopsies while maintaining a high rate of diagnosis of clinically significant prostate cancers.

Dataset Information

Based on biomedical index data: Risk prediction model for prostate cancer.

Abstract

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets