Dataset Information

The first patient-reported outcomes from the Utrecht Prostate Cohort (UPC): the first platform facilitating 'trials within cohorts' (TwiCs) for the evaluation of interventions for prostate cancer.

ABSTRACT:

Purpose

To describe the development and first outcomes of the Utrecht Prostate Cohort (UPC): the first 'trials within cohorts' (TwiCs) platform for prostate cancer (PCa).

Methods

All non-metastasized, histologically proven PCa patients who are planned to receive standard of care are eligible for inclusion in UPC. Patients provide informed consent for the collection of clinical and technical patient data, physician-reported outcomes, and patient-reported outcomes (PROs) up to 10 years post-treatment. Additionally, patients may provide broad consent for future randomization for experimental-intervention trials (TwiCs). Changes in PROs (EPIC-26 questionnaire domains) of the participants who received standard of care were analyzed using Wilcoxon signed-rank tests.

Results

In two years, 626 patients were enrolled, 503 (80.4%) of whom provided broad consent for future randomization. Among these, 293 (46.8%) patients underwent magnetic resonance-guided adaptive radiotherapy (MRgRT), 116 (18.5%) CT-guided external beam radiation therapy (EBRT), 109 (17.4%) robot-assisted radical prostatectomy (RARP), and 65 (10.4%) patients opted for active surveillance. Patients treated with MRgRT and CT-guided EBRT showed a transient but significant decline in urinary irritative/obstructive and bowel domain scores at 1-month follow-up. RARP patients showed a significant deterioration of urinary incontinence domain scores between baseline and all follow-up moments and significant improvement of urinary irritative/obstructive domain scores between baseline and 9- and 12-month follow-up. All radical treatment groups showed a significant decline in sexual domain scores during follow-up. Active surveillance patients showed no significant deterioration over time in all domains.

Conclusion

The first results from the UPC study show distinct differences in PROs between treatment options for PCa.

Registration no

NCT04228211.

SUBMITTER: Teunissen FR

PROVIDER: S-EPMC9427931 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

The first patient-reported outcomes from the Utrecht Prostate Cohort (UPC): the first platform facilitating 'trials within cohorts' (TwiCs) for the evaluation of interventions for prostate cancer.

Teunissen Frederik R FR Willigenburg Thomas T Meijer Richard P RP van Melick Harm H E HHE Verkooijen Helena M HM van der Voort van Zyp Jochem R N JRN

World journal of urology 20220721 9

<h4>Purpose</h4>To describe the development and first outcomes of the Utrecht Prostate Cohort (UPC): the first 'trials within cohorts' (TwiCs) platform for prostate cancer (PCa).<h4>Methods</h4>All non-metastasized, histologically proven PCa patients who are planned to receive standard of care are eligible for inclusion in UPC. Patients provide informed consent for the collection of clinical and technical patient data, physician-reported outcomes, and patient-reported outcomes (PROs) up to 10 ye ...[more]

PMID: 35861861

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Project description:Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.

Dataset Information

The first patient-reported outcomes from the Utrecht Prostate Cohort (UPC): the first platform facilitating 'trials within cohorts' (TwiCs) for the evaluation of interventions for prostate cancer.

Purpose

Methods

Results

Conclusion

Registration no

Publications

The first patient-reported outcomes from the Utrecht Prostate Cohort (UPC): the first platform facilitating 'trials within cohorts' (TwiCs) for the evaluation of interventions for prostate cancer.

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