Breast Cancer
Conditions
Brief summary
The goal of this observational study is to develop and evaluate a large language model (LLM)-based decision support system for exercise prescription in breast cancer patients, aiming to provide personalized decision-making support for postoperative breast cancer rehabilitation. The main questions it aims to answer are: How accurate, personalized, and safe are the exercise prescriptions generated by the fine-tuned LLM? How does the model's performance compare with other mainstream or non-fine-tuned models across different stages and subtypes of breast cancer? Participants are postoperative breast cancer rehabilitation patients treated at Sun Yat-sen Memorial Hospital of Sun Yat-sen University. They will have demographic, tumor, treatment, and physical fitness data collected; receive personalized exercise prescriptions automatically generated by the LLM-based system; and provide subjective evaluations on the feasibility and executability of the prescriptions.
Interventions
None listed
Sponsors
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
* Adult patients aged 18-75 years with early-stage breast cancer who have undergone surgical treatment, such as mastectomy or breast-conserving surgery. * ECOG performance status of 0-1, with adequate physical condition to participate in rehabilitation assessment and exercise prescription activities. * Availability of essential clinical data, including demographic characteristics, tumor stage and subtype, treatment history, and baseline physical fitness assessment. * Able to communicate effectively, maintain stable follow-up contact, and voluntarily participate in evaluation and feedback on exercise prescriptions.
Exclusion criteria
* Presence of severe postoperative complications or comorbidities (e.g., uncontrolled cardiac or pulmonary disease) that may interfere with participation in rehabilitation or pose a safety risk. * Significant physical or mobility impairments preventing the performance of prescribed exercises. * Severe psychiatric illness or cognitive dysfunction that hinders cooperation with assessments or follow-up. * Incomplete or missing key clinical data, making evaluation or follow-up impossible. * Any other condition deemed inappropriate for participation by the investigators.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Average 5-point Likert scores across five expert-defined dimensions-individualization, comprehensiveness, scientific rationality, safety, and executability-are used to compare the performance of fine-tuned models with that of mid-level physicians. | From enrollment to completion of prescription evaluation at 1 week |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Evaluation Form for Consistency Between Model Diagnostic Logic and Medical Consensus | From enrollment to completion of prescription evaluation at 1 week | Measurement Method/Unit: A panel of expert reviewers (at least 3 senior physicians) conducts a blinded assessment of the model's diagnostic reasoning pathways in test cases using a dedicated evaluation form. The outcome is expressed as the mean score (points). Rating Scale: 5-point Likert scale (1=Highly Unsound, 5=Highly Sound) Interpretation of Scores: A higher score indicates better consistency of the model's diagnostic logic with established medical consensus. |
Countries
China
Outcome results
None listed