Solid Tumor CancerPROTEGERNCT07805642
Oncogeriatric Screening and Evaluation Program
An observational study of Solid Tumor Cancer, run by Latin American Cooperative Oncology Group at 2 sites in Brazil.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 65 years and older
- Conditions the study is for
- Solid Tumor Cancer
- How many people
- 412 participants
- Where
- 2 sitesBrazil
- Study dates
- Sep 2026to Jul 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 65 years and older
- All sexes
- No healthy volunteers
- Older adult
Study population: 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.
Who may be able to join
- Age 65 years or older.
- Diagnosis of solid tumor cancer.
- Must have been evaluated and followed up by a local oncology team.
- Signed Informed Consent Form (ICF) applied in accordance with the local ethics committee.
- Must have undergone a Comprehensive Geriatric Assessment (CGA).
Who cannot join
- Patients who are unable or unwilling to consent to providing information will be excluded.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Brazil2 sites
BR192 HCOR - Hospital do Coração
São Paulo, São Paulo, 04004-060
Luciola Pontes Leite de Barros
HIAE - Hospital Israelita Albert Einstein
São Paulo, São Paulo, 05653-000
Ludmila de Oliveira Muniz Koch
About this study
Official title: PROgrama de Tamizaje y Evaluación oncoGERiátrica
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.
Read the detailed description
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.
- Conditions
- Solid Tumor Cancer
- Keywords
- Oncogeriatrics
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Retrospective Training Cohort (Chile) | 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. | |
| Prospective Validation Cohort (Chile, Brasil, Peru) | 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: Up to 6 months post-enrollment.
Secondary outcomes (6)
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.
Measured: 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.
Measured: 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.
Measured: Baseline, 3 months, and 6 months post-enrollment.
Incidence of Hospitalizations
Number of patients requiring unplanned hospital admissions during the treatment course
Measured: 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.
Measured: 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.
Measured: At 3 and 6 months post-enrollment.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Sep 2026
- Study startSep 2026 (estimated)
- Primary completionJan 2027 (estimated)
- Study completionJul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Solid Tumor CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 412 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Latin American Cooperative Oncology Group
- Sponsor type
- University, hospital or other
- Collaborators
- ONCOGER SpA
- Responsible party
- Sponsor
- Investigators
- Luciola Pontes Leite de Barros, Principal investigator, Hospital do Coração (HCOR)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07805642
- Sponsor's protocol ID
- LACOG 0325
- Acronym
- PROTEGER
- Status last verified
- Aug 2026
- First submitted
- 24 Aug 2026
- Last updated
- 4 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 4 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- PROgrama de Tamizaje y Evaluación oncoGERiátrica
- ClinicalTrials.gov ID
- NCT07805642
- Acronym / sponsor ID
- PROTEGER / LACOG 0325
- Conditions
- Solid Tumor Cancer
- Keywords
- Oncogeriatrics
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 412 participants (estimated)
- Lead sponsor
- Latin American Cooperative Oncology GroupUniversity, hospital or other
- Collaborators
- ONCOGER SpA
- Study start
- Sep 2026 (estimated)
- Primary completion
- Jan 2027 (estimated)
- Study completion
- Jul 2027 (estimated)
- First posted
- 4 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Retrospective Training Cohort (Chile) | – | –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. |
| Prospective Validation Cohort (Chile, Brasil, Peru) | – | –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. |
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Older adult
- Study population
- 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.
Key inclusion criteria (5 listed)
- Age 65 years or older.
- Diagnosis of solid tumor cancer.
- Must have been evaluated and followed up by a local oncology team.
- Signed Informed Consent Form (ICF) applied in accordance with the local ethics committee.
- Must have undergone a Comprehensive Geriatric Assessment (CGA).
Key exclusion criteria (1 listed)
- Patients who are unable or unwilling to consent to providing information will be excluded.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Predictive Accuracy of the PROTEGER Machine Learning Model
Time frame: Up to 6 months post-enrollment.
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).
6 secondary outcomes
Incidence of High-Grade Chemotherapy-Related Adverse Events
Time frame: At 3 and 6 months post-enrollment.
General Quality of Life Score (EORTC QLQ-C30)
Time frame: Baseline, 3 months, and 6 months post-enrollment.
Elderly-Specific Quality of Life Score (EORTC QLQ-ELD14)
Time frame: Baseline, 3 months, and 6 months post-enrollment.
Incidence of Hospitalizations
Time frame: At 3 and 6 months post-enrollment.
Treatment Discontinuation and Dose Reduction Rates
Time frame: At 3 and 6 months post-enrollment.
Patient Survival and Disease Progression
Time frame: At 3 and 6 months post-enrollment.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Sep 2026.
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Solid Tumors
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Luciola Pontes Leite de Barros principal investigator
Hospital do Coração (HCOR)
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Solid Tumor Cancer, run by Latin American Cooperative Oncology Group at 2 sites in Brazil.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 4 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Brazil. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Project Manager (+55 51 3384 5334, lacog0325@lacog.group), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive Accuracy of the PROTEGER Machine Learning Model (time frame: Up to 6 months post-enrollment.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Sep 2026.