Transitional Cell Carcinoma Research 
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This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.Predicting Positive Repeat Prostate Biopsy Outcomes: Comparison of Machine Learning Approaches to Identify Key Parameters and Optimal Algorithms
CONCLUSIONS: We developed an SVC-based machine learning algorithm for predicting positive repeat prostate biopsy results. Our analysis revealed that initial and latest prostate volumes, initial and latest PSA levels, latest fPSA/PSA ratio and age are significant factors for this model.PMID:37867334 | DOI:10.56434/j.arch.esp.urol.20237607.61 (Source: Archivos Espanoles de Urologia)
Source: Archivos Espanoles de Urologia - October 23, 2023 Category: Urology & Nephrology Authors: Xinru Zhang Chao Feng Xiao Bai Xufeng Peng Qian Guo Lei Chen Jingdong Xue Source Type: research
Evaluating the Role of Morphological Parameters in the Prostate Transition Zone in PHI-Based Predictive Models for Detecting Gray Zone Prostate Cancer
CONCLUSION: Our data confirmed the value of prostate TZ morphological parameters and suggested a significant advantage for the TZ-adjusted PHI predictive model (PHI-TZD) compared with PHI and PHID in the early detection of gray zone csPCa under specific conditions.PMID:37869472 | PMC:PMC10588416 | DOI:10.1177/11795549231201122 (Source: Clinical Prostate Cancer)
Source: Clinical Prostate Cancer - October 23, 2023 Category: Cancer & Oncology Authors: Yu-Hang Qian Yun-Tian Shi Xu-Jun Sheng Hai-Hong Liao Hao-Jie Chen Bo-Wen Shi Yong-Jiang Yu Source Type: research
Predicting Positive Repeat Prostate Biopsy Outcomes: Comparison of Machine Learning Approaches to Identify Key Parameters and Optimal Algorithms
CONCLUSIONS: We developed an SVC-based machine learning algorithm for predicting positive repeat prostate biopsy results. Our analysis revealed that initial and latest prostate volumes, initial and latest PSA levels, latest fPSA/PSA ratio and age are significant factors for this model.PMID:37867334 | DOI:10.56434/j.arch.esp.urol.20237607.61 (Source: Archivos Espanoles de Urologia)
Source: Archivos Espanoles de Urologia - October 23, 2023 Category: Urology & Nephrology Authors: Xinru Zhang Chao Feng Xiao Bai Xufeng Peng Qian Guo Lei Chen Jingdong Xue Source Type: research
Evaluating the Role of Morphological Parameters in the Prostate Transition Zone in PHI-Based Predictive Models for Detecting Gray Zone Prostate Cancer
CONCLUSION: Our data confirmed the value of prostate TZ morphological parameters and suggested a significant advantage for the TZ-adjusted PHI predictive model (PHI-TZD) compared with PHI and PHID in the early detection of gray zone csPCa under specific conditions.PMID:37869472 | PMC:PMC10588416 | DOI:10.1177/11795549231201122 (Source: Clinical Prostate Cancer)
Source: Clinical Prostate Cancer - October 23, 2023 Category: Cancer & Oncology Authors: Yu-Hang Qian Yun-Tian Shi Xu-Jun Sheng Hai-Hong Liao Hao-Jie Chen Bo-Wen Shi Yong-Jiang Yu Source Type: research
Predicting Positive Repeat Prostate Biopsy Outcomes: Comparison of Machine Learning Approaches to Identify Key Parameters and Optimal Algorithms
CONCLUSIONS: We developed an SVC-based machine learning algorithm for predicting positive repeat prostate biopsy results. Our analysis revealed that initial and latest prostate volumes, initial and latest PSA levels, latest fPSA/PSA ratio and age are significant factors for this model.PMID:37867334 | DOI:10.56434/j.arch.esp.urol.20237607.61 (Source: Archivos Espanoles de Urologia)
Source: Archivos Espanoles de Urologia - October 23, 2023 Category: Urology & Nephrology Authors: Xinru Zhang Chao Feng Xiao Bai Xufeng Peng Qian Guo Lei Chen Jingdong Xue Source Type: research
