Skip to main content
OpenTrials
Not Yet Recruiting

NCT Number: NCT07136727

AI-Assisted Comprehensive Management for Cancer Patients With Comorbidities (GCOG-CG001)

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.

Not Yet Recruiting

Trial opening soon.

Get Notified

Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

The First Affiliated Hospital of Xinxiang Medical University

Xinxiang, Henan, 453000, China

Location contact

Ping Lu Ping Lu, MD, Doctor of Medicine

CONTACT

[email protected]

+86 13598722864

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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;

Exclusion criteria

  • 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.

Treatment and study plan

AI-assisted comprehensive management system

Other

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)

Primary outcomes

  1. 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.

  2. 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.

Secondary outcomes

  1. Comorbidity control rate.

    Time frame: 24 months

    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.

  2. Quality of life(QLQ-C30).

    Time frame: 24 months

    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

  3. Medical resource consumption index.

    Time frame: 24 months

    Medical resource consumption index: Comparing DRG-adjusted medical resource consumption indices between two groups.

  4. Adherence to AI system interventions.

    Time frame: 24 months

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Ping Lu Ping Lu, MD, Doctor of Medicine

CONTACT

[email protected]

+86 13598722864

Wei Shen Wei Shen, MD, Doctor of Medicine

CONTACT

[email protected]

+86 15638800873

Sponsors and collaborators

Lead sponsor

The First Affiliated Hospital of Xinxiang Medical College

Other

Registry information

Official study 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.

Acronym: GCOG-CG001

Important dates

Study start
2025
Primary completion
2027
Study completion
2031
First posted
Aug 22, 2025
Registry last updated
Aug 22, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.