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

NCT07136727 | Not Yet Recruiting

• 1 other identifier: GCOG-CG001
• Study Type: interventional
• Target: 5,000
• Locations: 1 country
• Sites: 1

Brief 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 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.

Conditions

Oncological Comorbidities (e. g. Hypertension, Diabetes, Malnutrition)

Keywords

Malignant neoplasm; Comorbidity; Artificial intelligence

Outcome Measures

Primary Outcomes (2)

• 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.

  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.

  24 months

Secondary Outcomes (4)

• Comorbidity control rate.

  24 months

• Quality of life(QLQ-C30).

  24 months

• Medical resource consumption index.

  24 months

• Adherence to AI system interventions.

  24 months

Study Arms (2)

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.

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.

Interventions

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)

AI management unit

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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;

You may not qualify if:

• 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.

Sponsors & Collaborators

• The First Affiliated Hospital of Xinxiang Medical College: lead

Study Sites (1)

The First Affiliated Hospital of Xinxiang Medical University

Xinxiang, Henan, 453000, China

Location

Related Publications (11)

• 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.

  PMID: 25605751 BACKGROUND

• Ding 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.

  PMID: 32209058 BACKGROUND

• Chao 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.

  PMID: 24915871 BACKGROUND

• Sogaard 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.

  PMID: 24227920 BACKGROUND

• Jorgensen 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.

  PMID: 22353805 BACKGROUND

• Sarfati 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.

  PMID: 26891458 BACKGROUND

• Wedding 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.

  PMID: 17207632 BACKGROUND

• Abravan 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.

  PMID: 39232185 BACKGROUND

• Vrinzen 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.

  PMID: 36779863 BACKGROUND

• Siembida 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.

  PMID: 33447956 BACKGROUND

• Williams 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.

  PMID: 26725537 BACKGROUND

MeSH Terms

Conditions

Diabetes Mellitus; Malnutrition; Neoplasms

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Nutrition Disorders

Study Officials

• Wei Shen Wei Shen, MD, Doctor of Medicine

  First Affiliated Hospital of Xinjiang Medical University

  STUDY CHAIR

Central Study Contacts

Wei Shen Wei Shen, MD, Doctor of Medicine

CONTACT

+86 15638800873; swccvsw@126.com

Ping Lu Ping Lu, MD, Doctor of Medicine

CONTACT

+86 13598722864; lupingdoctor@126.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 9, 2025
• First Posted: August 22, 2025
• Study Start: August 15, 2025
• Primary Completion (Estimated): May 1, 2027
• Study Completion (Estimated): May 1, 2031
• Last Updated: August 22, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)