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This multi-modal deep radiomics model, using PET/CT, clinical and histopathological data, was able to identify patients with bevacizumab-sensitive unresectable colorectal cancer liver metastases, providing a favorable approach for precise patient treatment.
Accurately predicting tumor response to targeted therapies is essential for guiding personalized conversion therapy in patients with unresectable colorectal cancer liver metastases (CRLM). Currently, tumor response evaluation criteria are based on assessments made after at least 2-months treatment. Consequently, there is a compelling need to develop baseline tools that can be used to guide therapy selection. Herein, the investigators proposed a deep radiomics-based fusion model which demonstrates high accuracy in predicting the efficacy of bevacizumab in CRLM patients. Further, the investigators observed a significant and positive association between the predicted-responders and longer progression-free survival as well as longer overall survival in CRLM patients treated with bevacizumab. Moreover, the model exhibits high negative prediction value, indicating its potential to accurately identify individuals who are unresponsive to bevacizumab. Thus, our model provides a valuable baseline method for specifically identifying bevacizumab-sensitive CRLM patients, which is offering a clinically convenient approach to guide precise patient treatment.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Training Cohort | This cohort was derived from Arm A (treated with FOLFOX + bevacizumab) of the BECOME studyand was used for model construction. |
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| Negative Validation Cohort | The cohort was derived from Arm B (treated with FOLFOX) of the BECOME study , which demonstrated that the model specifically predicted the efficacy of bevacizumab. |
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| Internal Validation Cohort | The cohort was derived from an independent Zhongshan Hospital cohort with the same treatment team and imaging instrumentation as the BECOME study, differing only in patient period, and was used for internal validation of the model. |
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| External Validation Cohort | The cohort was obtained from the Zhongshan Hospital - Xiamenand the First Affiliated Hospital of Wenzhou Medical University for external validation of the model. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Deep radiomics-based fusion model | Diagnostic Test | This multi-modal deep radiomics model, using PET/CT, clinical and histopathological data, was able to identify patients with bevacizumab-sensitive CRLM, providing a favorable approach for precise patient treatment. |
| Measure | Description | Time Frame |
|---|---|---|
| ORR | Objective response rate of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX | 2013.10.1-2023.1.1 |
| PFS | Progression-free survival of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX | 2013.10.1-2023.1.1 |
| Measure | Description | Time Frame |
|---|---|---|
| OS | Overall survival of patients with colorectal cancer liver metastases who treated with FOLFOX+bevacizumab/FOLFOX | 2013.10.1-2023.1.1 |
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Inclusion Criteria:
Exclusion Criteria:
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In this multicenter cohort study, the investigators collected 307 patients with colorectal cancer liver metastases. The training cohort and negative validation cohort were derived from the BECOME study (NCT01972490), for whom baseline PET/CT images were available. The internal validation cohort was derived from consecutive metastastic colorectal cancer patients of the multi-disciplinary team (MDT) at Zhongshan Hospital (ZSH), share the same MDT, surgical team, and PET/CT imaging equipment with training cohort, from 01 January 2018 to 31 December 2018. The external validation cohort came from the MDT of Zhongshan Hospital - Xiamen and the First Hospital of Wenzhou Medical University, from 01 January 2020 to 31 December 2020
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| Name | Affiliation | Role |
|---|---|---|
| Jianmin Xu, MD | Fudan University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of General Surgery, Zhongshan Hospital, Fudan University | Shanghai | China |
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