Oncological Comorbidities (e. g. Hypertension, Diabetes, Malnutrition)GCOG-CG001NCT07136727
AI-Assisted Comprehensive Management for Cancer Patients With Comorbidities (GCOG-CG001)
A study testing AI-assisted comprehensive management system in people with Oncological Comorbidities (e. g. Hypertension, Diabetes, Malnutrition), run by The First Affiliated Hospital of Xinxiang Medical College at 1 site in China.
Trial summary
Combined with the digital whole process management data pool, a multi-modal data fusion framework is developed, and an AI model is established to realize risk stratification and personalized treatment Recommendation and dynamic prognosis prediction; validation of whole-process management based on multimodal digital… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Oncological Comorbidities (e. g. Hypertension, Diabetes, Malnutrition)
- What is being tested?
- AI-assisted comprehensive management system
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 5,000 participants
- Where
- 1 siteChina
- Study dates
- 15 Aug 2025to 1 May 2031
- Phase
- Interventional
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients with a definite diagnosis of malignancy by histopathology and/or cytology;
- Age ≥18 years;
- There is no gender limit
- Plan to receive antineoplastic therapy within 2 weeks or are receiving standard antineoplastic care (surgery, radiation, chemotherapy, or targeted therapy) ;
- Conscious and able to answer questions and use electronic devices autonomously;
- Patients were able to understand the study and voluntarily sign an informed consent form;
Who cannot join
- Having severe mental or cognitive impairments that prevent them from understanding the content of the study or implementing the programme;
- With severe heart disease, acute respiratory failure, liver kidney failure and other critical illness;
- Women during pregnancy or lactation；
- Have participated in other interventional studies in the past 1 month or are currently participating;
- Patients with ECOG ≥ 3 that do not respond to treatment;
- Patients with an expected survival of < 3 months that do not respond to treatment;
- Cases deemed unsuitable for enrollment by the investigator.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
The First Affiliated Hospital of Xinxiang Medical University
Xinxiang, Henan, 453000
Ping Lu Ping Lu, MD, Doctor of Medicine, +86 13598722864, lupingdoctor@126.com
About this study
Official title: The Impact of Multimodal Digital Fusion AI-Assisted Decision Support System-Based Comprehensive Management on Clinical Outcomes in County-Level Patients With Comorbid Cancer:A Prospective Non-randomized Controlled Interventional Study.
Combined with the digital whole process management data pool, a multi-modal data fusion framework is developed, and an AI model is established to realize risk stratification and personalized treatment Recommendation and dynamic prognosis prediction; validation of whole-process management based on multimodal digital fusion AI-aided decision support system through prospective non-randomized controlled interventional study The effect on survival, complication control and utilization of medical resources in patients with comorbid malignant tumors.
Read the detailed description
The title of this study is"The Impact of Multimodal Digital Fusion AI-Assisted Decision Support System-Based Comprehensive Management on Clinical Outcomes in County-Level Patients with Comorbid Cancer: A prospective non-randomized controlled interventional study", to evaluate the impact of full-course management based on a multimodal digital fusion AI-assisted decision support system on the clinical outcomes of county-level oncologic comorbid patients through a prospective non-randomized controlled interventional study. The study plans to enroll 5,000 patients with pathologically confirmed malignancies and at least one comorbid condition (diabetes, hypertension, etc.) , in the first stage, the epidemiological characteristics of co-morbidity and its impact on prognosis, treatment response and quality of life were analyzed In the second phase, patients with comorbid pulmonary malignancies were selected to compare the clinical effects of the voluntary whole-process management group (including personalized intervention such as nutritional screening and dynamic monitoring) and the conventional treatment group, the third stage integrates multi-center Electronic Medical Records, genomic data, wearable device monitoring and other multi-modal data to construct an AI decision-making system, developing risk stratification, personalized treatment recommendation, and dynamic prognostic prediction models, finally, the differences in core indicators such as survival rate (PFS, OS) , complication control and medical resource efficiency between AI-assisted management and traditional mode were compared. This study realizes the integrated intervention of in-hospital and out-of-hospital through digital whole-process management, which is expected to provide an AI-driven precise decision support paradigm for primary medical institutions and improve the efficiency of comprehensive management of tumor comorbidity.
- Being tested
- AI-assisted comprehensive management system
- Keywords
- Malignant neoplasm, Comorbidity, Artificial intelligence
- Medical subject headings
- Diabetes Mellitus, Malnutrition, Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted comprehensive management systemOther
Precision Risk Stratification and personalized treatment recommendation through AI models may improve the suitability of treatment regimens and thus reduce the incidence of antineoplastic therapy-related adverse effects (e.g. , reduction of chemotherapy toxicity through nutritional intervention) , and improve the efficacy of chemotherapy, and prolonged progression-free survival (PFS) and overall survival (OS)
| Study group | Role | What they receive |
|---|---|---|
| AI management unit | Experimental | For patients with comorbid pulmonary malignancies who have been included, the registration process is guided by the management platform. Researchers will use digital management throughout The platform carries out screening assessment and Comprehensive Evaluation of nutrition, exercise, psychology and symptoms of the subjects, and the system will be combined with the patient's disease and treatment Information, intelligent management of the whole project. The clinician can review the protocol in the light of the patient's disease status and give the full management instructions Case to patient side. Receives: Other: AI-assisted comprehensive management system |
| Standard Clinical Management | No intervention | Patients who are not willing to accept the whole program will only be followed up, and will receive standard clinical management without AI-assisted digital platform support. Patients will receive conventional treatment. In the data analysis phase, subjects were stratified to explore the feasibility and effectiveness of digital whole-course management in patients with oncological comorbidities. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Progression-free survival (PFS)
Progression-free survival (PFS) : the time from randomization (or study enrollment) to the observation of disease progression or the occurrence of death from any cause. This period was assessed every 6-8 weeks using RECIST 1.1 criteria.
Measured: 24 months
Overall survival (OS）
Overall survival (OS） : the time from study enrollment to death from any cause from any cause, every 3 months during treatment, and every 3 months after the end of treatment. The patients were followed up at 6 months and the cause of death was recorded.
Measured: 24 months
Secondary outcomes (4)
Comorbidity control rate.
Comorbidity control rate: the proportion of comorbidities achieving guideline-recommended control targets during the study period; stratified criteria should be established based on specific comorbidity types.
Measured: 24 months
Quality of life(QLQ-C30).
Quality of life: changes in scores at baseline, on-treatment, and follow-up were assessed using the European Organisation for Research and Treatment of Cancer QLQ-C30 scale, between-group differences
Measured: 24 months
Medical resource consumption index.
Medical resource consumption index: Comparing DRG-adjusted medical resource consumption indices between two groups.
Measured: 24 months
Adherence to AI system interventions.
Adherence to AI Interventions:
- In-Hospital Rate - Percentage of inpatients completing AI-recommended actions (e.g., nutritional screening, real-time monitoring).
- Out-of-Hospital Completion Rate: Percentage of discharged/outpatients adhering to AI-guided care (e.g., telehealth, wearable data tracking).
Enables precise evaluation of AI-driven care across clinical settings.
Measured: 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Aug 2025
- Study start15 Aug 2025 (estimated)
- Primary completion1 May 2027 (estimated)
- Study completion1 May 2031 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Oncological Comorbidities (e. g. Hypertension, Diabetes, Malnutrition)Studies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The First Affiliated Hospital of Xinxiang Medical College
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Wei Shen Wei Shen, MD, Doctor of Medicine, Study chair, First Affiliated Hospital of Xinjiang Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07136727
- Sponsor's protocol ID
- GCOG-CG001
- Acronym
- GCOG-CG001
- Status last verified
- Aug 2025
- First submitted
- 9 Aug 2025
- Last updated
- 22 Aug 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Data security and privacy protection measures.
Publications
Stairmand J, Signal L, Sarfati D, Jackson C, Batten L, Holdaway M, Cunningham C. Consideration of comorbidity in treatment decision making in multidisciplinary cancer team meetings: a systematic review. Ann Oncol. 2015 Jul;26(7):1325-32. doi: 10.1093/annonc/mdv025. Epub 2015 Jan 20.
PubMed 25605751Ding R, Zhu D, He P, Ma Y, Chen Z, Shi X. Comorbidity in lung cancer patients and its association with medical service cost and treatment choice in China. BMC Cancer. 2020 Mar 24;20(1):250. doi: 10.1186/s12885-020-06759-8.
PubMed 32209058Chao C, Page JH, Yang SJ, Rodriguez R, Huynh J, Chia VM. History of chronic comorbidity and risk of chemotherapy-induced febrile neutropenia in cancer patients not receiving G-CSF prophylaxis. Ann Oncol. 2014 Sep;25(9):1821-1829. doi: 10.1093/annonc/mdu203. Epub 2014 Jun 10.
PubMed 24915871Sogaard M, Thomsen RW, Bossen KS, Sorensen HT, Norgaard M. The impact of comorbidity on cancer survival: a review. Clin Epidemiol. 2013 Nov 1;5(Suppl 1):3-29. doi: 10.2147/CLEP.S47150.
PubMed 24227920Jorgensen TL, Hallas J, Friis S, Herrstedt J. Comorbidity in elderly cancer patients in relation to overall and cancer-specific mortality. Br J Cancer. 2012 Mar 27;106(7):1353-60. doi: 10.1038/bjc.2012.46. Epub 2012 Feb 21.
PubMed 22353805Sarfati D, Koczwara B, Jackson C. The impact of comorbidity on cancer and its treatment. CA Cancer J Clin. 2016 Jul;66(4):337-50. doi: 10.3322/caac.21342. Epub 2016 Feb 17.
PubMed 26891458Wedding U, Roehrig B, Klippstein A, Steiner P, Schaeffer T, Pientka L, Hoffken K. Comorbidity in patients with cancer: prevalence and severity measured by cumulative illness rating scale. Crit Rev Oncol Hematol. 2007 Mar;61(3):269-76. doi: 10.1016/j.critrevonc.2006.11.001. Epub 2007 Jan 4.
PubMed 17207632Abravan A, Faivre-Finn C, Gomes F, van Herk M, Price G. Comorbidity in patients with cancer treated at The Christie. Br J Cancer. 2024 Nov;131(8):1279-1289. doi: 10.1038/s41416-024-02838-w. Epub 2024 Sep 4.
PubMed 39232185Vrinzen CEJ, Delfgou L, Stadhouders N, Hermens RPMG, Merkx MAW, Bloemendal HJ, Jeurissen PPT. A Systematic Review and Multilevel Regression Analysis Reveals the Comorbidity Prevalence in Cancer. Cancer Res. 2023 Apr 4;83(7):1147-1157. doi: 10.1158/0008-5472.CAN-22-1336.
PubMed 36779863Siembida EJ, Smith AW, Potosky AL, Graves KD, Jensen RE. Examination of individual and multiple comorbid conditions and health-related quality of life in older cancer survivors. Qual Life Res. 2021 Apr;30(4):1119-1129. doi: 10.1007/s11136-020-02713-0. Epub 2021 Jan 14.
PubMed 33447956Williams GR, Mackenzie A, Magnuson A, Olin R, Chapman A, Mohile S, Allore H, Somerfield MR, Targia V, Extermann M, Cohen HJ, Hurria A, Holmes H. Comorbidity in older adults with cancer. J Geriatr Oncol. 2016 Jul;7(4):249-57. doi: 10.1016/j.jgo.2015.12.002. Epub 2015 Dec 22.
PubMed 26725537
Study at a glance
From the registry record, last updated by the sponsor on 22 Aug 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Impact of Multimodal Digital Fusion AI-Assisted Decision Support System-Based Comprehensive Management on Clinical Outcomes in County-Level Patients With Comorbid Cancer:A Prospective Non-randomized Controlled Interventional Study.
- ClinicalTrials.gov ID
- NCT07136727
- Acronym / sponsor ID
- GCOG-CG001 / GCOG-CG001
- Keywords
- Malignant neoplasm, Comorbidity, Artificial intelligence
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Xinxiang Medical CollegeUniversity, hospital or other
- Study start
- 15 Aug 2025 (estimated)
- Primary completion
- 1 May 2027 (estimated)
- Study completion
- 1 May 2031 (estimated)
- First posted
- 22 Aug 2025 (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.
Combined with the digital whole process management data pool, a multi-modal data fusion framework is developed, and an AI model is established to realize risk stratification and personalized treatment Recommendation and dynamic prognosis prediction; validation of whole-process management based on multimodal digital fusion AI-aided decision support system through prospective non-randomized controlled interventional study The effect on survival, complication control and utilization of medical resources in patients with comorbid malignant tumors.
Detailed description
The title of this study is"The Impact of Multimodal Digital Fusion AI-Assisted Decision Support System-Based Comprehensive Management on Clinical Outcomes in County-Level Patients with Comorbid Cancer: A prospective non-randomized controlled interventional study", to evaluate the impact of full-course management based on a multimodal digital fusion AI-assisted decision support system on the clinical outcomes of county-level oncologic comorbid patients through a prospective non-randomized controlled interventional study. The study plans to enroll 5,000 patients with pathologically confirmed malignancies and at least one comorbid condition (diabetes, hypertension, etc.) , in the first stage, the epidemiological characteristics of co-morbidity and its impact on prognosis, treatment response and quality of life were analyzed In the second phase, patients with comorbid pulmonary malignancies were selected to compare the clinical effects of the voluntary whole-process management group (including personalized intervention such as nutritional screening and dynamic monitoring) and the conventional treatment group, the third stage integrates multi-center Electronic Medical Records, genomic data, wearable device monitoring and other multi-modal data to construct an AI decision-making system, developing risk stratification, personalized treatment recommendation, and dynamic prognostic prediction models, finally, the differences in core indicators such as survival rate (PFS, OS) , complication control and medical resource efficiency between AI-assisted management and traditional mode were compared. This study realizes the integrated intervention of in-hospital and out-of-hospital through digital whole-process management, which is expected to provide an AI-driven precise decision support paradigm for primary medical institutions and improve the efficiency of comprehensive management of tumor comorbidity.
Study design
- Primary purpose
- Treatment
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI management unit | Experimental | Other: AI-assisted comprehensive management systemFor patients with comorbid pulmonary malignancies who have been included, the registration process is guided by the management platform. Researchers will use digital management throughout The platform carries out screening assessment and Comprehensive Evaluation of nutrition, exercise, psychology and symptoms of the subjects, and the system will be combined with the patient's disease and treatment Information, intelligent management of the whole project. The clinician can review the protocol in the light of the patient's disease status and give the full management instructions Case to patient side. |
| Standard Clinical Management | No intervention | –Patients who are not willing to accept the whole program will only be followed up, and will receive standard clinical management without AI-assisted digital platform support. Patients will receive conventional treatment. In the data analysis phase, subjects were stratified to explore the feasibility and effectiveness of digital whole-course management in patients with oncological comorbidities. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- Patients with a definite diagnosis of malignancy by histopathology and/or cytology;
- Age ≥18 years;
- There is no gender limit
- Plan to receive antineoplastic therapy within 2 weeks or are receiving standard antineoplastic care (surgery, radiation, chemotherapy, or targeted therapy) ;
- Conscious and able to answer questions and use electronic devices autonomously;
Key exclusion criteria (7 listed)
- Having severe mental or cognitive impairments that prevent them from understanding the content of the study or implementing the programme;
- With severe heart disease, acute respiratory failure, liver kidney failure and other critical illness;
- Women during pregnancy or lactation；
- Have participated in other interventional studies in the past 1 month or are currently participating;
- Patients with ECOG ≥ 3 that do not respond to treatment;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-assisted comprehensive management systemPrecision Risk Stratification and personalized treatment recommendation through AI models may improve the suitability of treatment regimens and thus reduce the incidence of antineoplastic therapy-related adverse effects (e.g. , reduction of chemotherapy toxicity through nutritional intervention) , and improve the efficacy of chemotherapy, and prolonged progression-free survival (PFS) and overall survival (OS) | Other | AI management unit |
Primary outcome
Progression-free survival (PFS)
Time frame: 24 months
Progression-free survival (PFS) : the time from randomization (or study enrollment) to the observation of disease progression or the occurrence of death from any cause. This period was assessed every 6-8 weeks using RECIST 1.1 criteria.
Overall survival (OS）
Time frame: 24 months
Overall survival (OS） : the time from study enrollment to death from any cause from any cause, every 3 months during treatment, and every 3 months after the end of treatment. The patients were followed up at 6 months and the cause of death was recorded.
4 secondary outcomes
Comorbidity control rate.
Time frame: 24 months
Quality of life(QLQ-C30).
Time frame: 24 months
Medical resource consumption index.
Time frame: 24 months
Adherence to AI system interventions.
Time frame: 24 months
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 22 Aug 2025.
- Background: PubMed 25605751Stairmand J, Signal L, Sarfati D, Jackson C, Batten L, Holdaway M, Cunningham C. Consideration of comorbidity in treatment decision making in multidisciplinary cancer team meetings: a systematic review. Ann Oncol. 2015 Jul;26(7):1325-32. doi: 10.1093/annonc/mdv025. Epub 2015 Jan 20.
- Background: PubMed 32209058Ding R, Zhu D, He P, Ma Y, Chen Z, Shi X. Comorbidity in lung cancer patients and its association with medical service cost and treatment choice in China. BMC Cancer. 2020 Mar 24;20(1):250. doi: 10.1186/s12885-020-06759-8.
- Background: PubMed 24915871Chao C, Page JH, Yang SJ, Rodriguez R, Huynh J, Chia VM. History of chronic comorbidity and risk of chemotherapy-induced febrile neutropenia in cancer patients not receiving G-CSF prophylaxis. Ann Oncol. 2014 Sep;25(9):1821-1829. doi: 10.1093/annonc/mdu203. Epub 2014 Jun 10.
- Background: PubMed 24227920Sogaard M, Thomsen RW, Bossen KS, Sorensen HT, Norgaard M. The impact of comorbidity on cancer survival: a review. Clin Epidemiol. 2013 Nov 1;5(Suppl 1):3-29. doi: 10.2147/CLEP.S47150.
- Background: PubMed 22353805Jorgensen TL, Hallas J, Friis S, Herrstedt J. Comorbidity in elderly cancer patients in relation to overall and cancer-specific mortality. Br J Cancer. 2012 Mar 27;106(7):1353-60. doi: 10.1038/bjc.2012.46. Epub 2012 Feb 21.
- Background: PubMed 26891458Sarfati D, Koczwara B, Jackson C. The impact of comorbidity on cancer and its treatment. CA Cancer J Clin. 2016 Jul;66(4):337-50. doi: 10.3322/caac.21342. Epub 2016 Feb 17.
- Background: PubMed 17207632Wedding U, Roehrig B, Klippstein A, Steiner P, Schaeffer T, Pientka L, Hoffken K. Comorbidity in patients with cancer: prevalence and severity measured by cumulative illness rating scale. Crit Rev Oncol Hematol. 2007 Mar;61(3):269-76. doi: 10.1016/j.critrevonc.2006.11.001. Epub 2007 Jan 4.
- Background: PubMed 39232185Abravan A, Faivre-Finn C, Gomes F, van Herk M, Price G. Comorbidity in patients with cancer treated at The Christie. Br J Cancer. 2024 Nov;131(8):1279-1289. doi: 10.1038/s41416-024-02838-w. Epub 2024 Sep 4.
- Background: PubMed 36779863Vrinzen CEJ, Delfgou L, Stadhouders N, Hermens RPMG, Merkx MAW, Bloemendal HJ, Jeurissen PPT. A Systematic Review and Multilevel Regression Analysis Reveals the Comorbidity Prevalence in Cancer. Cancer Res. 2023 Apr 4;83(7):1147-1157. doi: 10.1158/0008-5472.CAN-22-1336.
- Background: PubMed 33447956Siembida EJ, Smith AW, Potosky AL, Graves KD, Jensen RE. Examination of individual and multiple comorbid conditions and health-related quality of life in older cancer survivors. Qual Life Res. 2021 Apr;30(4):1119-1129. doi: 10.1007/s11136-020-02713-0. Epub 2021 Jan 14.
- Background: PubMed 26725537Williams GR, Mackenzie A, Magnuson A, Olin R, Chapman A, Mohile S, Allore H, Somerfield MR, Targia V, Extermann M, Cohen HJ, Hurria A, Holmes H. Comorbidity in older adults with cancer. J Geriatr Oncol. 2016 Jul;7(4):249-57. doi: 10.1016/j.jgo.2015.12.002. Epub 2015 Dec 22.
- Individual participant dataSharing planned: No. Data security and privacy protection measures.
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.
Wei Shen Wei Shen, MD, Doctor of Medicine study chair
First Affiliated Hospital of Xinjiang Medical University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-assisted comprehensive management system in people with Oncological Comorbidities (e. g. Hypertension, Diabetes, Malnutrition), run by The First Affiliated Hospital of Xinxiang Medical College at 1 site in China.
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 6 inclusion and 7 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 22 Aug 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. 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 Wei Shen Wei Shen, MD, Doctor of Medicine (+86 15638800873, swccvsw@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Progression-free survival (PFS) (time frame: 24 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07136727, which also lists 11 linked publications.
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 22 Aug 2025.