An observational study in Solid Tumor Cancer, sponsored by Latin American Cooperative Oncology Group. Not yet recruiting at 2 sites in Brazil. Open to participants aged 65 Years and older. Per ClinicalTrials.gov, last updated 2026-09-04.
Sponsored by Latin American Cooperative Oncology Group · Observational
This study aims to develop, train, and validate a machine learning-based prediction model (PROTEGER) to provide treatment decision recommendations for older adults diagnosed with solid tumor cancers. The study has a two-phase observational design: a retrospective cohort using anonymized data from an oncogeriatric telecommittee to train the predictive model, followed by a prospective multicenter cohort across Chile, Peru, and Brazil. Information from Comprehensive Geriatric Assessments (CGA), treatment decisions, and 3- and 6-month clinical outcomes will be collected to evaluate and validate the decision-support platform's performance in assisting oncology teams.
Cancer incidence increases significantly with age, and up to 70% of cancer mortality occurs in patients aged 65 years or older. Despite this, older patients are frequently undertreated due to the high risk of secondary toxicity, which is associated with quality of life deterioration, increased hospitalizations, and higher mortality. Comprehensive Geriatric Assessment (CGA) has proven to be an effective tool to identify vulnerability, reduce chemotherapy-related toxicity, and tailor interventions. However, the lack of geriatricians, especially in Latin American public health systems, creates significant barriers to accessing CGA-guided oncological care.
To overcome these barriers, the PROTEGER program proposes an innovative digital health solution by developing and validating a machine learning-based clinical decision support system (CDSS) for oncogeriatric care. The study is an observational, multicenter, bidirectional cohort study conducted in two phases:
Phase 1: Retrospective Training Phase This phase uses anonymized clinical data (2021-2023) from the Oncogeriatric Tele-Committee of the Chilean Ministry of Health's Digital Hospital. Data from older patients with solid tumors who underwent a CGA will be used to train and test predictive models using machine learning techniques (e.g., Gradient Boosting Trees and Random Forest) following the CRISP-DM methodology. The predictive model aims to learn the Committee's treatment recommendation patterns based on patient functionality, comorbidities, and geriatric syndromes.
Phase 2: Prospective Validation Phase A prospective, multicenter cohort will be enrolled across healthcare centers in Chile, Peru, and Brazil. Eligible patients (aged 65+ with a solid tumor diagnosis) who undergo routine CGA and oncological care will be followed for 6 months. Data regarding baseline characteristics, treatment decisions (made by local oncology teams blinded to the AI model's recommendation), dose reductions, treatment discontinuation, disease progression, quality of life (EORTC QLQ-C30 and ELD14), and survival will be collected.
Study Objectives:
The primary objective is to develop, train, and clinically validate the PROTEGER machine learning predictive model to provide an accurate treatment recommendation (e.g., standard treatment, dose-adjusted treatment, or supportive care only) capable of assisting clinical decision-making by oncology teams. A secondary objective involves the design and development of an intuitive graphical user interface capable of being used by healthcare providers and patients for data management and result interpretation.
All predictive models will be evaluated using standard metrics, such as the Area Under the ROC Curve (AUC) and the C-statistic, to determine their discriminatory capacity in a real-world clinical setting.
Latin American Cooperative Oncology Group is the lead sponsor of 48 studies on the registry; 10 are open to participants now.
Counted across the registry records on this site, refreshed daily.
The study population consists of older adults (aged 65 years and older) diagnosed with solid tumor cancers who receive care in public and private health institutions in South America (Chile, Brazil, and Peru). The retrospective cohort includes anonymized historical data from patients evaluated by the Oncogeriatric Tele-Committee of the Chilean Ministry of Health. The prospective cohort comprises consecutive patients seen in routine oncological care who undergo a Comprehensive Geriatric Assessment (CGA) and are managed by local oncogeriatric teams.
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Exclusion Criteria:
- Patients who are unable or unwilling to consent to providing information will be excluded.
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Anonymized data from cases submitted to the Oncogeriatrics Telecommittee from 2021 to the end of 2023, obtained from the Chilean Ministry of Health's Digital Hospital database, along with patient survival data, will be used to train and validate the predictive model using machine learning techniques.
Older adults with cancer receive a comprehensive geriatric assessment at their respective centers and are introduced to an oncogeriatric team. They will share their baseline characteristics, the results of their CGA, clinical data related to cancer treatment, and 3- and 6-month follow-up for the development and validation of a predictive model using machine learning techniques.
Predictive Accuracy of the PROTEGER Machine Learning Model
Discrimination performance of the machine learning predictive model in recommending oncogeriatric treatment decisions (standard treatment, dose-adjusted treatment, or supportive care/no treatment) based on Comprehensive Geriatric Assessment (CGA) data, measured by the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), with scores ranging from 0.5 (no discrimination/chance) to 1.0 (perfect discrimination).
Time frame: Up to 6 months post-enrollment.
Incidence of High-Grade Chemotherapy-Related Adverse Events
Percentage of participants experiencing Grade 3 or higher toxicities/adverse reactions evaluated using the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0.
Time frame: At 3 and 6 months post-enrollment.
General Quality of Life Score (EORTC QLQ-C30)
Global health status and quality of life assessed using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). Scores range from 0 to 100, where higher scores represent a better overall quality of life and higher functioning.
Time frame: Baseline, 3 months, and 6 months post-enrollment.
Elderly-Specific Quality of Life Score (EORTC QLQ-ELD14)
Elderly-specific quality of life issues assessed using the EORTC QLQ-ELD14 module. Scores range from 0 to 100. For symptom scales, higher scores represent worse outcomes (higher level of symptoms/problems); for functional scales, higher scores represent better outcomes.
Time frame: Baseline, 3 months, and 6 months post-enrollment.
Incidence of Hospitalizations
Number of patients requiring unplanned hospital admissions during the treatment course
Time frame: At 3 and 6 months post-enrollment.
Treatment Discontinuation and Dose Reduction Rates
Percentage of patients undergoing chemotherapy dose reductions (planned vs. received dose) or early treatment discontinuation.
Time frame: At 3 and 6 months post-enrollment.
Patient Survival and Disease Progression
Overall survival status and disease progression rate assessed according to RECIST 1.0 criteria.
Time frame: At 3 and 6 months post-enrollment.
Start is estimated for Sep 2026; primary completion for Jan 2027, 4 months later.
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This study is not yet recruiting, as verified in Aug 2026. You cannot join it, but the record below documents what was studied.
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Latin American Cooperative Oncology Group