Cargando…
Machine Learning and Radiomics of Bone Scintigraphy: Their Role in Predicting Recurrence of Localized or Locally Advanced Prostate Cancer
Background: Machine-learning (ML) and radiomics features have been utilized for survival outcome analysis in various cancers. This study aims to investigate the application of ML based on patients’ clinical features and radiomics features derived from bone scintigraphy (BS) and to evaluate recurrenc...
Autores principales: | Wang, Yu-De, Huang, Chi-Ping, Yang, You-Rong, Wu, Hsi-Chin, Hsu, Yu-Ju, Yeh, Yi-Chun, Yeh, Pei-Chun, Wu, Kuo-Chen, Kao, Chia-Hung |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648785/ https://www.ncbi.nlm.nih.gov/pubmed/37958276 http://dx.doi.org/10.3390/diagnostics13213380 |
Ejemplares similares
-
Radiomics of Multiparametric MRI to Predict Biochemical Recurrence of Localized Prostate Cancer After Radiation Therapy
por: Zhong, Qiu-Zi, et al.
Publicado: (2020) -
Applying Machine Learning to Carotid Sonographic Features for Recurrent Stroke in Patients With Acute Stroke
por: Lin, Shih-Yi, et al.
Publicado: (2022) -
Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning
por: Cheng, Da-Chuan, et al.
Publicado: (2021) -
Erectile dysfunction and the risk of prostate cancer
por: Lin, Wei-Yu, et al.
Publicado: (2017) -
MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy
por: Siow, Tiing Yee, et al.
Publicado: (2022)