دورية أكاديمية

Development and Validation of a Clinic Machine Learning Classifier for the Prediction of Risk Stratifications of Prostate Cancer Bone Metastasis Progression to Castration Resistance

التفاصيل البيبلوغرافية
العنوان: Development and Validation of a Clinic Machine Learning Classifier for the Prediction of Risk Stratifications of Prostate Cancer Bone Metastasis Progression to Castration Resistance
المؤلفون: Li X, Cui P, Zhao X, Liu Z, Qi Y, Liu B
المصدر: International Journal of General Medicine, Vol Volume 17, Pp 2821-2831 (2024)
بيانات النشر: Dove Medical Press, 2024.
سنة النشر: 2024
المجموعة: LCC:Medicine (General)
مصطلحات موضوعية: metastatic prostate cancer, resistance to castration, psa minimum value, lactate dehydrogenase, prediction model, Medicine (General), R5-920
الوصف: Xin Li,1,* Peng Cui,1,* XingXing Zhao,1 Zhao Liu,1 YanXiang Qi,1 Bo Liu2 1Department of Urology, Baotou Cancer Hospital, Baotou, Inner Mongolia, People’s Republic of China; 2Department of Gynaecological Oncology, Baotou Cancer Hospital, Baotou, Inner Mongolia, People’s Republic of China*These authors contributed equally to this workCorrespondence: Bo Liu, Email keyaxinbt@163.comObjective: To explore the predictive factors and predictive model construction for the progression of prostate cancer bone metastasis to castration resistance.Methods: Clinical data of 286 patients diagnosed with prostate cancer with bone metastasis, initially treated with endocrine therapy, and progressing to metastatic castration resistant prostate cancer (mCRPC) were collected. By comparing the differences in various factors between different groups with fast and slow occurrence of castration-resistant prostate cancer (CRPC). Kaplan-Meier survival analysis and COX multivariate risk proportional regression model were used to compare the differences in the time to progression to CRPC in different groups. The COX multivariate risk proportional regression model was used to evaluate the impact of candidate factors on the time to progression to CRPC and establish a predictive model. The accuracy of the model was then tested using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).Results: The median time for 286 mCRPC patients to progress to CRPC was 17 (9.5– 28.0) months. Multivariate analysis showed that the lowest value of PSA (PSA nadir), the time when PSA dropped to its lowest value (timePSA), and the number of BM, and LDH were independent risk factors for rapid progression to CRPC. Based on the four independent risk factors mentioned above, a prediction model was established, with the optimal prediction model being a random forest with area under curve (AUC) of 0.946[95% CI: 0.901– 0.991] and 0.927[95% CI: 0.864– 0.990] in the training and validation cohort, respectively.Conclusion: After endocrine therapy, the PSA nadir, timePSA, the number of BM, and LDH are the main risk factors for rapid progression to mCRPC in patients with prostate cancer bone metastases. Establishing a CRPC prediction model is helpful for early clinical intervention decision-making.Keywords: metastatic prostate cancer, resistance to castration, PSA minimum value, lactate dehydrogenase, prediction model
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1178-7074
العلاقة: https://www.dovepress.com/development-and-validation-of-a-clinic-machine-learning-classifier-for-peer-reviewed-fulltext-article-IJGMTest; https://doaj.org/toc/1178-7074Test
الوصول الحر: https://doaj.org/article/c3bad05bcaf84abd87ffe0c63de173d5Test
رقم الانضمام: edsdoj.3bad05bcaf84abd87ffe0c63de173d5
قاعدة البيانات: Directory of Open Access Journals