Clinical trial · Interventional
AI-Assisted Comprehensive Management for Cancer Patients With Comorbidities (GCOG-CG001)
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.
NCT07136727CI-TRIAL-00093999GCOG-CG001not yet recruitingN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Oncological Comorbidities (e. g. Hypertension, Diabetes, Malnutrition) | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-assisted comprehensive management system | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- EXPERIMENTAL
- label
- AI management unit
- description
- 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.
- interventionNames
- Other: AI-assisted comprehensive management system
- type
- NO_INTERVENTION
- label
- Standard Clinical Management
- description
- 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.
Primary outcomes (2)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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.
References
Publications (11)
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- BACKGROUNDChao 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
- BACKGROUNDSogaard 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
- BACKGROUNDJorgensen 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
- BACKGROUNDSarfati 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
- BACKGROUNDWedding 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.