The goal of this observational study is to develop a predictive model for overall survival in patients under the age of 50 who have undergone surgery for early-onset colorectal cancer (EOCRC). The main question it aims to answer is:
Can machine learning models accurately predict the long-term survival of EOCRC patients after surgical treatment?
Participants who have already undergone surgery for EOCRC as part of their regular medical care will have their clinical data analyzed, with survival outcomes tracked through follow-up assessments. An online survival calculator will also be developed to aid clinicians and patients in predicting personalized survival outcomes.
Primary EOCRC confirmed by pathological histological examination (ICD-10 codes: C18.0, C18.2-18.9, C19.9, C20.9)
Radical surgery performed (Specific Surgery Codes 30-70, including partial/subtotal colectomy, hemicolectomy, right/left colectomy, and total colectomy, as well as partial or total removal of other organs and regional lymph nodes)
Stage I-III disease according to the 7th AJCC-TNM system
Patients with multiple primary cancers or other malignancies
Survival time of less than 1 month, or absence of postoperative follow-up information
Incomplete critical clinical feature information
Consecutive patients with early-onset, Stage I-III colorectal cancer who underwent radical resection at Putuo Hospital and Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, both affiliated with Shanghai University of Traditional Chinese Medicine, were retrospectively collected between January 2014 and June 2024.
Cohort
Prospective
Not listed
Assessment of Clinical Characteristics and Survival Status of Subjects Using the CRF Scale
Age, Gender, Race, Primary Site, Tumor Diameter, Differentiation Degree, Histology, TNM Stage, CEA, Surgery Type, Number of Resected Lymph Nodes, Tumor Deposits, Neural Invasion, Radiation Sequence, Chemotherapy Sequence, Systemic Therapy Sequence, Marital Status, Household Income, Survival Status, Postoperative Survival Time.
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