Big data and machine learning in radiation oncology: state of the art and future prospects

JE Bibault, P Giraud, A Burgun - Cancer letters, 2016 - Elsevier
Precision medicine relies on an increasing amount of heterogeneous data. Advances in
radiation oncology, through the use of CT Scan, dosimetry and imaging performed before …

Regional responses in radiation-induced normal tissue damage

DC Voshart, J Wiedemann, P van Luijk, L Barazzuol - Cancers, 2021 - mdpi.com
Simple Summary Side effects caused by the concomitant irradiation of normal tissue during
radiotherapy for cancer treatment can negatively affect the patient's quality of life and limit …

IMRT QA using machine learning: a multi‐institutional validation

G Valdes, MF Chan, SB Lim… - Journal of applied …, 2017 - Wiley Online Library
Purpose To validate a machine learning approach to Virtual intensity‐modulated radiation
therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using …

Deep nets vs expert designed features in medical physics: an IMRT QA case study

Y Interian, V Rideout, VP Kearney, E Gennatas… - Medical …, 2018 - Wiley Online Library
Purpose The purpose of this study was to compare the performance of Deep Neural
Networks against a technique designed by domain experts in the prediction of gamma …

Method, system and computer-readable media for treatment plan risk analysis

T Mcnutt, J Moore, S Robertson, F Marungo - US Patent 11,495,355, 2022 - Google Patents
(57) ABSTRACT A method, system and computer readable medium of: pro viding feature
data of at least one organ at risk or target volume of said patient from a database of non …

[HTML][HTML] Parotid gland stem cell sparing radiation therapy for patients with head and neck cancer: a double-blind randomized controlled trial

RJHM Steenbakkers, MI van Rijn–Dekker… - International Journal of …, 2022 - Elsevier
Purpose Radiation therapy for head and neck cancer frequently leads to salivary gland
damage and subsequent xerostomia. The radiation response of the parotid glands of rats …

[HTML][HTML] The big data effort in radiation oncology: Data mining or data farming?

CS Mayo, ML Kessler, A Eisbruch, G Weyburne… - Advances in Radiation …, 2016 - Elsevier
Although large volumes of information are entered into our electronic health care records,
radiation oncology information systems and treatment planning systems on a daily basis, the …

[HTML][HTML] Machine learning methods uncover radiomorphologic dose patterns in salivary glands that predict xerostomia in patients with head and neck cancer

W Jiang, P Lakshminarayanan, X Hui, P Han… - Advances in radiation …, 2019 - Elsevier
Purpose Patients with head-and-neck cancer (HNC) may experience xerostomia after
radiation therapy (RT), which leads to compromised quality of life. The purpose of this study …

[HTML][HTML] Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy

Z Cheng, M Nakatsugawa, C Hu, SP Robertson… - Advances in Radiation …, 2018 - Elsevier
Objective We explore whether a knowledge–discovery approach building a Classification
and Regression Tree (CART) prediction model for weight loss (WL) in head and neck cancer …

Spatial descriptions of radiotherapy dose: normal tissue complication models and statistical associations

MA Ebert, S Gulliford, O Acosta… - Physics in Medicine …, 2021 - iopscience.iop.org
For decades, dose-volume information for segmented anatomy has provided the essential
data for correlating radiotherapy dosimetry with treatment-induced complications. Dose …