{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T18:42:13Z","timestamp":1750531333354},"reference-count":26,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Surgical Oncology"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The mutation status of rat sarcoma viral oncogene homolog (RAS) has prognostic significance and serves as a key predictive biomarker for the effectiveness of antiepidermal growth factor receptor (EGFR) therapy. However, there remains a lack of effective models for predicting RAS mutation status in colorectal liver metastases (CRLMs). This study aimed to construct and validate a diagnostic model for predicting RAS mutation status among patients undergoing hepatic resection for CRLMs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A diagnostic multivariate prediction model was developed and validated in patients with CRLMs who had undergone hepatectomy between 2014 and 2020. Patients from Institution A were assigned to the model development group (i.e., Development Cohort), while patients from Institutions B and C were assigned to the external validation groups (i.e., Validation Cohort_1 and Validation Cohort_2). The presence of CRLMs was determined by examination of surgical specimens. RAS mutation status was determined by genetic testing. The final predictors, identified by a group of oncologists and radiologists, included several key clinical, demographic, and radiographic characteristics derived from magnetic resonance images. Multiple imputation was performed to estimate the values of missing non\u2010outcome data. A penalized logistic regression model using the adaptive least absolute shrinkage and selection operator\u00a0penalty was implemented to select appropriate variables for the development of the model. A single nomogram was constructed from the model. The performance of the prediction model, discrimination,\u00a0and calibration were estimated and reported by the area under the receiver operating characteristic curve (AUC) and calibration plots. Internal validation with a bootstrapping procedure and external validation of the nomogram were assessed. Finally, decision curve analyses were used to characterize the clinical outcomes of the Development and Validation Cohorts.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 173 patients were enrolled in this study between January 2014 and May 2020. Of the 173 patients, 117 patients from Institution A were assigned to the Model Development group, while 56 patients (33 from Institution B and 23 from Institution C) were assigned to the Model Validation groups. Forty\u2010six (39.3%) patients harbored RAS mutations in the Development Cohort compared to 14 (42.4%) in Validation Cohort_1 and 8 (34.8%) in Validation Cohort_2. The final model contained the following predictor variables: time of occurrence of CRLMs, location of primary lesion, type of intratumoral necrosis, and early enhancement of liver parenchyma. The diagnostic model based on clinical and MRI data demonstrated satisfactory predictive performance in distinguishing between mutated and wild\u2010type RAS, with AUCs of 0.742 (95% confidence interval [CI]: 0.651\u25000.834), 0.741 (95% CI: 0.649\u25000.836), 0.703 (95% CI: 0.514\u25000.892), and 0.708 (95% CI: 0.452\u25000.964) in the Development Cohort, bootstrapping internal validation, external Validation Cohort_1 and Validation Cohort_2, respectively. The Hosmer\u2010Lemeshow goodness\u2010of\u2010fit values for the Development Cohort, Validation Cohort_1 and Validation Cohort_2 were 2.868 (<jats:italic>p<\/jats:italic>\u2009=\u20090.942), 4.616 (<jats:italic>p<\/jats:italic>\u2009=\u20090.465),\u00a0and 6.297 (<jats:italic>p<\/jats:italic>\u2009=\u20090.391), respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Integrating clinical, demographic, and radiographic modalities with a magnetic resonance imaging\u2010based approach may accurately predict the RAS mutation status of CRLMs, thereby aiding in triage and possibly reducing the time taken to perform diagnostic and life\u2010saving procedures. Our diagnostic multivariate prediction model may serve as a foundation for prognostic stratification and therapeutic decision\u2010making.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jso.27508","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T03:39:50Z","timestamp":1700192390000},"page":"556-567","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Development and external validation of MRI\u2010based RAS mutation status prediction model for liver metastases of colorectal cancer"],"prefix":"10.1002","volume":"129","author":[{"given":"Zhe","family":"Han","sequence":"first","affiliation":[{"name":"Department of Radiology, Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences Hangzhou Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahan","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Radiology, Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences Hangzhou Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Pathology, Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences Hangzhou Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diandian","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Anorectal Surgery Shaoxing Second Hospital Shaoxing Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ningyang","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Radiology Third Affiliated Hospital of Naval Medical University Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yayuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Radiology Third Affiliated Hospital of Naval Medical University Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery Second Affiliated Hospital of Naval Medical University Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongpeng","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Hepatic Surgery Third Affiliated Hospital of Naval Medical University Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"He","sequence":"additional","affiliation":[{"name":"Department of Radiology, Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HaiXing","family":"Ju","sequence":"additional","affiliation":[{"name":"Department of Colorectal Surgery, Zhejiang Cancer Hospital Hangzhou 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