{"title":"基于mri的放射组学图预测新发少转移前列腺癌患者预后的发展和验证。","authors":"Wen-Qi Liu, Yu-Ting Xue, Xu-Yun Huang, Bin Lin, Xiao-Dong Li, Zhi-Bin Ke, Dong-Ning Chen, Jia-Yin Chen, Yong Wei, Qing-Shui Zheng, Xue-Yi Xue, Ning Xu","doi":"10.1002/cam4.70481","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>We aimed to develop and validate a nomogram based on MRI radiomics to predict overall survival (OS) for patients with de novo oligometastatic prostate cancer (PCa).</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>A total of 165 patients with de novo oligometastatic PCa were included in the study (training cohort, <i>n</i> = 115; validating cohort, <i>n</i> = 50). Among them, MRI scans were conducted and T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences were collected for radiomics features along with their clinicopathological features. Radiological features were extracted from T2WI and ADC sequences for prostate tumors. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation were used to select the optimal features on each sequence. Then, a weighted radiomics score (Rad-score) was generated and independent risk factors were obtained from univariate and multivariate Cox regressions to build the nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA).</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Eastern Cooperative Oncology Group (ECOG) score, absolute neutrophil count (ANC) and Rad-score were included in the nomogram as independent risk factors for OS in de novo oligometastatic PCa patients. We found that the areas under the curves (AUCs) in the training cohort were 0.734, 0.851, and 0.773 for predicting OS at 1, 2, and 3 years, respectively. In the validating cohort, the AUCs were 0.703, 0.799, and 0.833 for predicting OS at 1, 2, and 3 years, respectively. Furthermore, the clinical relevance of the predictive nomogram was confirmed through the analysis of DCA and calibration curve analysis.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>The MRI-based nomogram incorporating Rad-score and clinical data was developed to guide the OS assessment of oligometastatic PCa. This helps in understanding the prognosis and improves the shared decision-making process.</p>\n </section>\n </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"13 24","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cam4.70481","citationCount":"0","resultStr":"{\"title\":\"Development and Validation of an MRI-Based Radiomics Nomogram to Predict the Prognosis of De Novo Oligometastatic Prostate Cancer Patients\",\"authors\":\"Wen-Qi Liu, Yu-Ting Xue, Xu-Yun Huang, Bin Lin, Xiao-Dong Li, Zhi-Bin Ke, Dong-Ning Chen, Jia-Yin Chen, Yong Wei, Qing-Shui Zheng, Xue-Yi Xue, Ning Xu\",\"doi\":\"10.1002/cam4.70481\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>We aimed to develop and validate a nomogram based on MRI radiomics to predict overall survival (OS) for patients with de novo oligometastatic prostate cancer (PCa).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>A total of 165 patients with de novo oligometastatic PCa were included in the study (training cohort, <i>n</i> = 115; validating cohort, <i>n</i> = 50). Among them, MRI scans were conducted and T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences were collected for radiomics features along with their clinicopathological features. Radiological features were extracted from T2WI and ADC sequences for prostate tumors. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation were used to select the optimal features on each sequence. Then, a weighted radiomics score (Rad-score) was generated and independent risk factors were obtained from univariate and multivariate Cox regressions to build the nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Eastern Cooperative Oncology Group (ECOG) score, absolute neutrophil count (ANC) and Rad-score were included in the nomogram as independent risk factors for OS in de novo oligometastatic PCa patients. We found that the areas under the curves (AUCs) in the training cohort were 0.734, 0.851, and 0.773 for predicting OS at 1, 2, and 3 years, respectively. In the validating cohort, the AUCs were 0.703, 0.799, and 0.833 for predicting OS at 1, 2, and 3 years, respectively. Furthermore, the clinical relevance of the predictive nomogram was confirmed through the analysis of DCA and calibration curve analysis.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>The MRI-based nomogram incorporating Rad-score and clinical data was developed to guide the OS assessment of oligometastatic PCa. 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Development and Validation of an MRI-Based Radiomics Nomogram to Predict the Prognosis of De Novo Oligometastatic Prostate Cancer Patients
Objective
We aimed to develop and validate a nomogram based on MRI radiomics to predict overall survival (OS) for patients with de novo oligometastatic prostate cancer (PCa).
Methods
A total of 165 patients with de novo oligometastatic PCa were included in the study (training cohort, n = 115; validating cohort, n = 50). Among them, MRI scans were conducted and T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences were collected for radiomics features along with their clinicopathological features. Radiological features were extracted from T2WI and ADC sequences for prostate tumors. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation were used to select the optimal features on each sequence. Then, a weighted radiomics score (Rad-score) was generated and independent risk factors were obtained from univariate and multivariate Cox regressions to build the nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA).
Results
Eastern Cooperative Oncology Group (ECOG) score, absolute neutrophil count (ANC) and Rad-score were included in the nomogram as independent risk factors for OS in de novo oligometastatic PCa patients. We found that the areas under the curves (AUCs) in the training cohort were 0.734, 0.851, and 0.773 for predicting OS at 1, 2, and 3 years, respectively. In the validating cohort, the AUCs were 0.703, 0.799, and 0.833 for predicting OS at 1, 2, and 3 years, respectively. Furthermore, the clinical relevance of the predictive nomogram was confirmed through the analysis of DCA and calibration curve analysis.
Conclusion
The MRI-based nomogram incorporating Rad-score and clinical data was developed to guide the OS assessment of oligometastatic PCa. This helps in understanding the prognosis and improves the shared decision-making process.
期刊介绍：
Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas:
Clinical Cancer Research
Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations
Cancer Biology:
Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery.
Cancer Prevention:
Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach.
Bioinformatics:
Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers.
Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.
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