Development and validation of a machine learning-based risk prediction model for moderate-to-severe cancer-related fatigue in patients with gastrointestinal cancer undergoing chemotherapy

Status

Active, not recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2600118625

Enrollment

Unknown

Registered

2026-02-09

Start date

2026-02-10

Completion date

Unknown

Last updated

2026-02-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Gastrointestinal malignancies (gastric cancer, colon cancer, and rectal cancer)

Interventions

Model Development Set:None

Internal validation set:None

External validation set:None

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.Patients with primary gastric, colon, or rectal cancer confirmed by pathological examination and undergoing chemotherapy; 2.Age = 18 years; 3.No reading or writing barriers, capable of completing questionnaires independently; 4.Willing to participate and signed informed consent.

Exclusion criteria

Exclusion criteria: 1.Combined with other primary cancers or severe chronic diseases; 2.Participating in other interventional studies for cancer-related fatigue; 3.Suffering from neuropsychiatric diseases or taking psychotropic drugs, steroids, or non-steroidal anti-inflammatory drugs; 4.Previously diagnosed with chronic fatigue syndrome or fibromyalgia; 5.Diagnosed with moderate-to-severe CRF before the first chemotherapy.

Design outcomes

Primary

Measure	Time frame
Moderate-to-severe cancer-related fatigue (CRF) incidence;Evaluation indicators of predictive models (accuracy rate, precision rate, recall rate, F1 value, AUC value, etc.);	—

Countries

China

Contacts

Public ContactLi Jiuqun

PEKING UNIVERSITY SHENZHEN HOSPITAL

lijiuqun@sina.com+86 755 83923333

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 19, 2026