CHRU Nancy
Vandœuvre-lès-Nancy, France, 54500
NCT Number: NCT06931496
The investigators are interested here in the contribution of a new prostate cancer screening method and, more specifically, in the new and somewhat futuristic approach of artificial intelligence in the development of new, more accurate algorithms that make it possible to rethink the benefits of mass generalisation of prostate biopsies.
Main objective The main objective of this research is to use artificial intelligence and an associated algorithm to identify new indicators that would make it possible to avoid a prostate biopsy in patients with an initial suspicion of prostate cancer.
Trial opening soon.
Get Notified18 year and older
Male
Interventional
Not applicable
Vandœuvre-lès-Nancy, France, 54500
.1. Outline of the study The lack of accuracy of the PSA test for prostate cancer screening leads to negative biopsies. The aim of this study is to determine whether the PROSTia test, a personalised medicine test using artificial intelligence (AI) by combining PSA, digital rectal examination (DRE) and 60 other qualitative data, could reduce the number of unnecessary biopsies and to estimate its impact on the detection of clinically significant cancers.
PROSTia employs the Gradient Boosting technique to select the variables of interest and optimise model performance. This process entails the utilisation of successive decision trees to model the non-linear relationships between the input variables and cancer risk. The construction of each tree is predicated on the correction of errors identified in preceding trees, with a focus on cases that have been misclassified. This iterative process is instrumental in generating a robust and accurate model for the purpose of prostate cancer screening
2 Methodology
The result of the PROSTia test is a score on a scale of 0 to 2. A score greater than or equal to 1 is considered a positive result. A positive result means that the patient has a significant risk of developing prostate cancer in the next 12 years. The following statistical analyses are performed using a 95% confidence interval:
Sensitivity, specificity, disease prevalence, positive and negative predictive values and accuracy are expressed as percentages.
Confidence intervals for sensitivity, specificity and accuracy are Clopper-Pearson 'exact' confidence intervals.
Confidence intervals for likelihood ratios are calculated using the logarithmic method as described on page 109 of Altman et al. 2000. Confidence intervals for predictive values are standard logit confidence intervals according to Mercaldo et al. 2007, except when the predictive value is 0 or 100%, in which case a Clopper-Pearson confidence interval is reported.
Here are the assumptions that led to this number. Primary endpoint: binary (presence vs absence of disease).
Expected proportions: 50% in the study population (p2 without PROSTia) and 70% with PROSTia (p1).
Significance level (α): set at 0.05 Desired power (1-β): set at 0.8
Effect size: (p1-p2) drop rate:
The investigators plan to increase the calculated sample size to account for dropouts. calculated to account for dropouts or non-compliance with certain questionnaires (5%).
The method used to calculate the sample size is that of Woodward (1992).
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
questionnaire with 50 questions
Time frame: Immediately after to notification of prostate biopsy results.
Measuring the sensitivity of the test in comparison with the results of prostate biopsies
Time frame: Immediately after to notification of prostate biopsy results.
Measuring the specificity of the test in comparison with the results of prostate biopsies
Time frame: Immediately after to notification of prostate biopsy results.
Measuring the positive likehood ratio of the test in comparison with the results of prostate biopsies
Time frame: Immediately after to notification of prostate biopsy results.
Measuring the negative likehood ratio of the test in comparison with the results of prostate biopsies
Time frame: Immediately after to notification of prostate biopsy results.
Measuring the disease prevalence of the test in comparison with the results of prostate biopsies
Time frame: Immediately after to notification of prostate biopsy results.
Measuring the positive predictive value with the results of prostate biopsies
Time frame: Immediately after to notification of prostate biopsy results.
Measuring the negative predictive value with the results of prostate biopsies
Time frame: Immediately after to notification of prostate biopsy results.
Measuring the accuracy of the test in comparison with the results of prostate biopsies
Time frame: Immediately after to notification of prostate biopsy results.
Measuring the ability of the test to detect clinically significant cancers (ISUP ≥ 2) of the test in comparison with the results of prostate biopsies
Time frame: Immediately after to notification of prostate biopsy results.
Measuring the failure rate of the test to detect the most aggressive cancers (ISUP 4 and 5) of the test in comparison with the results of prostate biopsies
Contact information is provided by the study sponsor or research team.
Clément c Secondary investigator, MD MsC
CONTACT
Pascal Principal investigator, MD PhD
CONTACT
Central Hospital, Nancy, France
Other
Acronym: PROSTIA- BP
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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