A preliminary study of deep learning‐based treatment response prediction for soft tissue sarcoma using longitudinal diffusion MRI

2021 ◽  
Author(s):  
Yu Gao ◽  
Vahid Ghodrati ◽  
Anusha Kalbasi ◽  
Jie Fu ◽  
Dan Ruan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Treatment Response ◽  
Diffusion Mri ◽  
Response Prediction ◽  
Longitudinal Diffusion ◽  
Preliminary Study ◽  
Treatment Response Prediction

scholarly journals Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data

Biomedicines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1733
Author(s):  
Thi Mai Nguyen ◽  
Nackhyoung Kim ◽  
Da Hae Kim ◽  
Hoang Long Le ◽  
Md Jalil Piran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Treatment Response ◽  
Human Disease ◽  
Model Performance ◽  
Response Prediction ◽  
Disease Detection ◽  
Sequencing Data ◽  
Subtype Classification ◽  
Treatment Response Prediction ◽  
Classification And Treatment

Deep learning (DL) is a distinct class of machine learning that has achieved first-class performance in many fields of study. For epigenomics, the application of DL to assist physicians and scientists in human disease-relevant prediction tasks has been relatively unexplored until very recently. In this article, we critically review published studies that employed DL models to predict disease detection, subtype classification, and treatment responses, using epigenomic data. A comprehensive search on PubMed, Scopus, Web of Science, Google Scholar, and arXiv.org was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Among 1140 initially identified publications, we included 22 articles in our review. DNA methylation and RNA-sequencing data are most frequently used to train the predictive models. The reviewed models achieved a high accuracy ranged from 88.3% to 100.0% for disease detection tasks, from 69.5% to 97.8% for subtype classification tasks, and from 80.0% to 93.0% for treatment response prediction tasks. We generated a workflow to develop a predictive model that encompasses all steps from first defining human disease-related tasks to finally evaluating model performance. DL holds promise for transforming epigenomic big data into valuable knowledge that will enhance the development of translational epigenomics.

scholarly journals Radiomics, deep learning and early diagnosis in oncology

2021 ◽  
Author(s):  
Peng Wei
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Early Detection ◽  
Treatment Response ◽  
Critical Role ◽  
Response Prediction ◽  
Computational Implementation ◽  
Magnetic Resonance Imaging Mri ◽  
Treatment Response Prediction

Medical imaging, including X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response prediction of cancer. To ease radiologists’ task and help with challenging cases, computer-aided diagnosis has been developing rapidly in the past decade, pioneered by radiomics early on, and more recently, driven by deep learning. In this mini-review, I use breast cancer as an example and review how medical imaging and its quantitative modeling, including radiomics and deep learning, have improved the early detection and treatment response prediction of breast cancer. I also outline what radiomics and deep learning share in common and how they differ in terms of modeling procedure, sample size requirement, and computational implementation. Finally, I discuss the challenges and efforts entailed to integrate deep learning models and software in clinical practice.

Liquid Biopsies in the Management of Bladder Cancer: Next-Generation Biomarkers for Diagnosis, Surveillance, and Treatment-Response Prediction

2017 ◽  
Vol 22 (5-6) ◽  
pp. 389-401 ◽  
Author(s):  
Yu Yang ◽  
Catherine R. Miller ◽  
Antonio Lopez-Beltran ◽  
Rodolfo Montironi ◽  
Monica Cheng ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Treatment Response ◽  
Response Prediction ◽  
Next Generation ◽  
Liquid Biopsies ◽  
Treatment Response Prediction

PO-1579: Deep learning based gross tumor volume definition on planning CTs of soft tissue sarcoma

2020 ◽  
Vol 152 ◽  
pp. S856-S857
Author(s):  
D. Lang ◽  
J.C. Peeken ◽  
M.B. Spraker ◽  
M.J. Nyflot ◽  
S.E. Combs ◽  
...  
Keyword(s):  
Deep Learning ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Gross Tumor Volume ◽  
Tumor Volume ◽  
Tumor Volume Definition
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