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This multicenter, retrospective study aims to develop and validate a multimodal deep learning model for predicting the risk of metachronous liver metastasis in patients with stage I-III colorectal cancer following curative resection. The model will integrate preoperative contrast-enhanced CT imaging, digitized histopathological whole-slide images, and standard clinical-pathological data.
The primary objective is to assess the model's discriminatory performance, measured by the area under the receiver operating characteristic curve (AUC), and to compare its predictive accuracy against traditional prognostic factors such as TNM staging and serum carcinoembryonic antigen levels. This research utilizes existing archival data; no direct patient contact or intervention is involved. The ultimate goal is to provide a robust, data-driven tool for improved risk stratification, which could potentially guide personalized surveillance strategies and adjuvant therapy decisions in the future.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Colorectal Cancer Resection Cohort | A retrospective cohort of adult patients (aged 18-75) with stage I-III primary colorectal adenocarcinoma who underwent curative (R0) resection. This cohort is defined for the purpose of developing and validating a multimodal deep learning model to predict the risk of metachronous liver metastasis. All data, including preoperative contrast-enhanced CT scans, postoperative digitized pathology slides, and clinical records, were collected retrospectively from routine clinical practice. No interventions were administered as part of this study. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Multimodal Deep Learning Model Analysis | Other | This is a non-interventional study. The primary study procedure is the application of a multimodal deep learning model to retrospectively analyze existing clinical data (contrast-enhanced CT images, digitized pathology slides, and structured clinical variables) for the purpose of predicting the risk of metachronous liver metastasis. No therapeutic or diagnostic interventions are administered to participants as part of this research protocol. |
| Measure | Description | Time Frame |
|---|---|---|
| Area Under the Receiver Operating Characteristic Curve (AUC) | The discriminatory performance of the multimodal deep learning model for predicting the 3-year risk of metachronous liver metastasis. The model integrates preoperative contrast-enhanced CT images, digitized whole-slide pathology images, and clinical data. The AUC will be calculated on the held-out independent test set. The assessment is based on data collected from the date of curative surgery (baseline) to the date of first imaging-confirmed liver metastasis or last follow-up. | up to 3 years |
| Measure | Description | Time Frame |
|---|---|---|
| Liver Metastasis-Free Survival (LMFS) by Risk Group | The difference in liver metastasis-free survival between the high-risk and low-risk groups as stratified by the multimodal model. LMFS is defined as the time from surgery to the first radiological diagnosis of liver metastasis. From the date of surgery until the date of first documented liver metastasis or last follow-up. | up to 3 years |
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Inclusion Criteria:
Exclusion Criteria:
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Adult patients (aged 18-75) with stage I-III primary colorectal cancer who underwent curative resection at participating medical centers between 2015 and 2025, and for whom complete preoperative imaging, postoperative pathological data, and follow-up records are available for retrospective analysis.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yang wu, M.D. | Contact | 13636076910 | 255001907@qq.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Tongji Hospital | Recruiting | Wuhan | Hubei | China |
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