Evaluating the sensitivity of deep learning to inter-reader variations in lesion delineations on bi-parametric MRI in identifying clinically significant prostate cancer

Roge, Ansh; Hiremath, Amogh; Sobota, Michael; Tirumani, Sree Harsha; Bittencourt, Leonardo Kayat; Ream, Justin; Ward, Ryan D.; Olaniyan, Halimat; Verma, Sadhna; Purysko, Andrei S.; Madabhushi, Anant; Shiradkar, Rakesh

doi:10.1117/12.2613245

Cited by 1 publication

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…305,306,309 To better predict the disease progression, machine learning algorithms have been designed and applied to assist clinicians in analyzing patient information (some recent published papers are listed in Table 3). 190,193,230,[310][311][312][313][314][315][316][317][318][319][320][321] Recent disease risk prediction methods have mostly focused on head-and-neck cancer, 311,316 breast cancer, 190,193 rectal cancer, 312,313,315,319,321 brain cancer, 314,320 and liver disease, 317 For example, after stratifying glioblastoma patients were treated with chemoradiation therapy, 306 machine learning models were applied to patient OS time prediction. 305,306,309 Besides using mpMRI images, clinical profiles such as patient personal information, treatment factors, and genotypic-related features were combined for model training to improve the prediction accuracy.…”

Section: Patient Risk and Overall Survival Time Predictionmentioning

confidence: 99%

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Liu

et al. 2022

Medical Physics

View full text Add to dashboard Cite

Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availabilities of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting.However,the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI.

show abstract

Section: Patient Risk and Overall Survival Time Predictionmentioning

confidence: 99%

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Liu

et al. 2022

Medical Physics

View full text Add to dashboard Cite

show abstract

Evaluating the sensitivity of deep learning to inter-reader variations in lesion delineations on bi-parametric MRI in identifying clinically significant prostate cancer

Cited by 1 publication

References 20 publications

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Artificial intelligence in multiparametric magnetic resonance imaging: A review

Contact Info

Product

Resources

About