A Multiparametric MRI and Baseline-Clinical-Feature-Based Dense Multimodal Fusion Artificial Intelligence (MFAI) Model to Predict Castration-Resistant Prostate Cancer Progression DOI Open Access
Dianning He, Haoming Zhuang, Ying Ma

et al.

Cancers, Journal Year: 2025, Volume and Issue: 17(9), P. 1556 - 1556

Published: May 3, 2025

Objectives: The primary objective of this study was to identify whether patients with prostate cancer (PCa) could progress denervation-resistant (CRPC) after 12 months hormone therapy. Methods: A total 96 PCa baseline clinical data who underwent multiparametric magnetic resonance imaging (MRI) between September 2018 and 2022 were included in retrospective study. Patients classified as progressing or not CRPC on the basis their outcome dense multimodal fusion artificial intelligence (Dense-MFAI) model constructed by incorporating a squeeze-and-excitation block spatial pyramid pooling layer into convolutional network (DenseNet), well integrating eXtreme Gradient Boosting machine learning algorithm. accuracy, sensitivity, specificity, positive predictive value, negative receiver operating characteristic curves, area under curve (AUC) confusion matrices used classification performance metrics. Results: Dense-MFAI demonstrated an accuracy 94.2%, AUC 0.945, when predicting progression experimental validation that combining radiomics feature mapping characteristics significantly improved model’s performance, confirming importance data. Conclusions: proposed has ability more accurately predict patient CRPC. This can assist urologists developing most appropriate treatment plan prognostic measures.

Language: Английский

Revolutionizing Prostate Cancer Therapy: Artificial intelligence – based Nanocarriers for Precision Diagnosis and Treatment DOI
Moein Shirzad,

Afsaneh Salahvarzi,

Sobia Razzaq

et al.

Critical Reviews in Oncology/Hematology, Journal Year: 2025, Volume and Issue: unknown, P. 104653 - 104653

Published: Feb. 1, 2025

Language: Английский

Citations

4
Harnessing machine learning to predict prostate cancer survival: a review DOI Creative Commons
Sungun Bang, Y. Ahn, Kyo Chul Koo

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 14

Published: Jan. 10, 2025

The prediction of survival outcomes is a key factor in making decisions for prostate cancer (PCa) treatment. Advances computer-based technologies have increased the role machine learning (ML) methods predicting prognosis. Due to various effective treatments available each non-linear landscape PCa, integration ML can help offer tailored treatment strategies and precision medicine approaches, thus improving patients with PCa. There has been an upsurge studies utilizing predict these using complex datasets, including patient tumor features, radiographic data, population-based databases. This review aims explore evolving associated Specifically, we will focus on applications forecasting biochemical recurrence-free, progression castration-resistance-free, metastasis-free, overall survivals. Additionally, suggest areas need further research future enhance utility more clinically-utilizable PCa prognosis optimization.

Language: Английский

Citations

1
Ultrasound Radiogenomics-based Prediction Models for Gene Mutation Status in Breast Cancer DOI Creative Commons
Yue Zhai,

Dianhuan Tan,

Xiaona Lin

et al.

Advanced ultrasound in diagnosis and therapy, Journal Year: 2025, Volume and Issue: 9(1), P. 10 - 10

Published: Jan. 1, 2025

Language: Английский

Citations

0
A Multiparametric MRI and Baseline-Clinical-Feature-Based Dense Multimodal Fusion Artificial Intelligence (MFAI) Model to Predict Castration-Resistant Prostate Cancer Progression DOI Open Access
Dianning He, Haoming Zhuang, Ying Ma

et al.

Cancers, Journal Year: 2025, Volume and Issue: 17(9), P. 1556 - 1556

Published: May 3, 2025

Objectives: The primary objective of this study was to identify whether patients with prostate cancer (PCa) could progress denervation-resistant (CRPC) after 12 months hormone therapy. Methods: A total 96 PCa baseline clinical data who underwent multiparametric magnetic resonance imaging (MRI) between September 2018 and 2022 were included in retrospective study. Patients classified as progressing or not CRPC on the basis their outcome dense multimodal fusion artificial intelligence (Dense-MFAI) model constructed by incorporating a squeeze-and-excitation block spatial pyramid pooling layer into convolutional network (DenseNet), well integrating eXtreme Gradient Boosting machine learning algorithm. accuracy, sensitivity, specificity, positive predictive value, negative receiver operating characteristic curves, area under curve (AUC) confusion matrices used classification performance metrics. Results: Dense-MFAI demonstrated an accuracy 94.2%, AUC 0.945, when predicting progression experimental validation that combining radiomics feature mapping characteristics significantly improved model’s performance, confirming importance data. Conclusions: proposed has ability more accurately predict patient CRPC. This can assist urologists developing most appropriate treatment plan prognostic measures.

Language: Английский

Citations

0