Generation and Validation of Predictive Models for Localized Prostate Cancer Treated With External Radiotherapy.
Condition: Predictive Cancer Model; Localized Prostate Carcinoma
MeSH terms: Prostatic Neoplasms
Study type: Observational

Summary: 
      The generation of predictive models in radiotherapy has seen a significant increase. In 2017,
      Raymond published the largest systematic review of predictive prognostic models for
      biochemical relapse (BR), metastasis-free survival, and overall survival in patients with
      localized prostate cancer treated with radiotherapy (14), attempting to identify whether they
      were adequately developed and validated.

      He found 72 unique predictive models for external radiotherapy: 22 corresponding to BR risk,
      20 corresponding to Cancer-Specific Survival, 10 corresponding to Overall Survival, and 20
      for Disease/Metastasis-Free Survival detection. In his analysis, he highlighted a significant
      variation in the quality of these predictive models, understanding that they were developed
      prior to the existence of TRIPOD guidelines.

      In this regard, he pointed out that 54% of these models did not report their accuracy, and
      61% of the models lacked validation (either internal or external). He also noted that they
      had limited follow-up (only 65% had follow-up beyond 5 years), that the treatment doses in
      these models were lower than current standards, and that the radiation techniques were
      different from current practices. Although in his final assessment, Raymond maintains that
      predictive models provide more certainty in predicting oncological outcomes than professional
      assessments, he considers it vital to validate these models for each population that wants to
      use them (the vast majority of these models are based on U.S. populations) or, even better,
      to generate predictive models specific to the local population while adhering to the TRIPOD
      guidelines.

      Probably due to the lack of validation in our patients for existing predictive models and/or
      the absence of predictive models originating from our population, in our routine clinical
      practice (Multidisciplinary Oncology Committees), phisycians do not apply any predictive
      models to patients diagnosed with localized prostate cancer.
    

Detailed description: 
      The general objective of the study is to develop a predictive model for oncological outcomes
      and bladder and rectal toxicities based on the analysis of patients with localized prostate
      cancer who have received external radiotherapy, useful for medical decision-making. This
      objective is divided into three specific objectives:

      1) Estimate a predictive model for oncological outcomes, such as the probability of
      Biochemical Recurrence (BR), Disease-Free or Metastasis-Free Time (DFT), Overall Survival
      (OS), and Cancer-Specific Survival (CSS).

      2) Estimate a predictive model for bladder and rectal toxicities in patients with localized
      prostate cancer who have received external radiotherapy.

      3) Analyze the feasibility and impact of implementing an individualized decision-making model
      based on this model, and its implementation in oncology committees and in initial
      (informative) visits in radiation oncology.

        -  Hypotheses**:

             1. The developed predictive model will have adequate predictive values.

             2. Validate and compare the model with other shared decision-making methods in
                localized prostate cancer in Radiation Oncology Services.

             3. Professionals and patients will show appropriate satisfaction with the use of this
                type of model for shared decision-making with the patient in radiation oncology.

        -  Method**:

      This study is divided into phases for its execution:

        1. Retrospective analysis of 400 patients diagnosed with localized prostate cancer and
           treated with external radiotherapy between January 2014 and December 2019. Sub-analyses
           of the population will be conducted to evaluate different oncological outcomes and the
           produced toxicities, with particular emphasis on distinguishing differences that
           technological variables may justify.

        2. Generation and validation of a predictive model for treatment oncological outcomes and
           bladder and rectal toxicities. The development of the model will follow the TRIPOD
           guidelines.

        3. Analysis of the model's implementation in patient decision-making, for which a validated
           questionnaire will be applied to measure satisfaction with the decision made in patients
           diagnosed with prostate neoplasia (15). The predictive model will be applied through a
           pilot study involving 30 patients who will be presented with treatment options based on
           different outcome scenarios. The questionnaire will be administered after the first
           visit to radiation oncology to assess the use of predictive models as part of the
           information provided. This questionnaire will evaluate improvements in the patient's
           knowledge of prostate cancer, clarity of personal values, decision efficacy, and
           reduction of uncertainty regarding the decision made.

             -  Statistical Analysis**:

      Phase 1: The annual incidence rate will be calculated using the actuarial method for
      oncological outcomes, estimating the annual number of cases divided by the sum of the total
      person-years at risk per 100 treated patients. Rates will be calculated as crude and
      age-standardized rates, stratified by relevant sociodemographic and clinical characteristics.
      Rates will be summarized as cumulative incidences over time using a Kaplan-Meier estimator.
      Individual associations between predictive factors and complication outcomes will be
      calculated using a bivariate binomial logistic prediction model. The optimal prediction time
      will be estimated by comparing changes in odds at different time-to-event cutoff points.

      Phase 2: The risk prediction model will be constructed using penalized logistic regression
      techniques to optimize predictive accuracy for the occurrence of oncological outcomes. Model
      development will be conducted on a subset of training data using k-fold validation, and
      diagnostic prediction will be calculated using the recalibrated algorithm. The predictive
      model's performance will be evaluated by calculating the area under the receiver operating
      characteristic curve and other measures of accuracy for various cutoff points (percentage of
      patients at maximum risk, maximization of positive and negative predictive value, and
      F-score).

      Phase 3: The predictive model will be applied to 30 new patients. Mean satisfaction with the
      provided information will be estimated using questionnaire results. Changes in uncertainty
      before and after receiving information will be assessed using paired-sample t-tests.
    

Intervention name(s): EXTERNAL RADIOTHERAPY
Intervention type(s): Radiation
Intervention description(s): EXTERNAL RADIOTHERAPY
Observational model: Cohort
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        Patients diagnosed with prostate cancer through prostate biopsy and presented in the
        Oncology Committees of some of the hospitals in the healthcare region of Vallès Occidental,
        which may include Parc Tauli Hospital (Sabadell), Terrassa Hospital (Terrassa), General
        Hospital of Catalonia (Sant Cugat del Vallès), and San Joan de Deu Hospital (Manresa).
        These patients have been treated with external radiotherapy in the Radiation Oncology
        Department of Terrassa Hospital or General Hospital of Catalonia and are currently under
        active surveillance with in-person semi-annual visits and PSA monitoring.