The purpose of this study is to construct and validate a multimodal survival prediction model by integrating 3D radiomics features derived from preoperative magnetic resonance imaging (MRI) with systemic clinical baseline indicators for patients with colorectal liver metastases (CRLM) after surgery. By combining micro-level radiomics signatures reflecting tumor micro-heterogeneity with macro-level clinical parameters (such as liver function and tumor biomarkers), the study aims to accurately evaluate individual post-operative prognostic risks. This quantitative tool will provide reliable decision support for clinicians to customize post-operative follow-up and personalized adjuvant treatment strategies.
This study is based on a strictly screened multicenter cohort of patients with colorectal liver metastases who underwent surgical intervention. First, high-throughput quantitative features are automatically extracted from the 3D regions of interest (ROIs) segmented based on pre-operative magnetic resonance imaging (MRI). Advanced machine learning dimensionality reduction algorithms are subsequently applied to eliminate redundant variables and select core imaging signatures that deeply reflect tumor micro-heterogeneity, microvascular proliferation, and invasive status. On this basis, these micro-level radiomics features are fused with macro-level clinical parameters, including liver function indexes and tumor load markers. Multivariable survival analysis models are performed to identify independent prognostic factors, which are further used to develop a visual and intuitive predictive nomogram tool. Finally, time-dependent evaluation metrics and an external validation cohort will be utilized to systematically test the dynamic predictive performance and cross-platform generalizability of the model. This multimodal dual-track data paradigm aims to achieve a non-invasive and efficient "digital optical biopsy" approach for prognostic evaluation.
Study Type
OBSERVATIONAL
Enrollment
205
Observational and non-invasive assessment based on preoperative MRI-derived 3D modeling and three-dimensional lesion reconstruction, combined with clinical parameters, to build a survival prediction model. No experimental intervention is assigned.
The First Affiliated Hospital of University of Science and Technology of China
Hefei, Anhui, China
RECRUITINGRecurrence-Free Survival (RFS)
Recurrence-free survival (RFS) is defined as the time from the date of surgical resection to the date of first tumor recurrence (local, regional, or distant) or death from any cause, whichever occurs first.
Time frame: Up to 5 years post-surgery
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