Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of-texture features

Frontiers in Oncology

Oncology

Daniel DiCenzo¹  Lakshmanan Sannachi¹  Aryan Safakish²  Gregory J. Czarnota³  Christopher Kolios³  Ana Pejović-Milić⁴ 

[1] Czarnota Lab, Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada;Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto ON, Canada;Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada;Czarnota Lab, Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada;Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto ON, Canada;Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada;Czarnota Lab, Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada;Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto ON, Canada;Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada;Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada;Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada;

关键词: quantitative ultrasound;    radiomics;    texture analysis;    texture-of-texture;    head and neck cancer;    response prediction;    deep texture analysis;   

DOI  :  10.3389/fonc.2023.1258970

 received in 2023-07-14, accepted in 2023-09-05,  发布年份 2023

来源: Frontiers

【 摘 要 】

AimCancer treatments with radiation present a challenging physical toll for patients, which can be justified by the potential reduction in cancerous tissue with treatment. However, there remain patients for whom treatments do not yield desired outcomes. Radiomics involves using biomedical images to determine imaging features which, when used in tandem with retrospective treatment outcomes, can train machine learning (ML) classifiers to create predictive models. In this study we investigated whether pre-treatment imaging features from index lymph node (LN) quantitative ultrasound (QUS) scans parametric maps of head & neck (H&N) cancer patients can provide predictive information about treatment outcomes.Methods72 H&N cancer patients with bulky metastatic LN involvement were recruited for study. Involved bulky neck nodes were scanned with ultrasound prior to the start of treatment for each patient. QUS parametric maps and related radiomics texture-based features were determined and used to train two ML classifiers (support vector machines (SVM) and k-nearest neighbour (k-NN)) for predictive modeling using retrospectively labelled binary treatment outcomes, as determined clinically 3-months after completion of treatment. Additionally, novel higher-order texture-of-texture (TOT) features were incorporated and evaluated in regards to improved predictive model performance.ResultsIt was found that a 7-feature multivariable model of QUS texture features using a support vector machine (SVM) classifier demonstrated 81% sensitivity, 76% specificity, 79% accuracy, 86% precision and an area under the curve (AUC) of 0.82 in separating responding from non-responding patients. All performance metrics improved after implementation of TOT features to 85% sensitivity, 80% specificity, 83% accuracy, 89% precision and AUC of 0.85. Similar trends were found with k-NN classifier.ConclusionBinary H&N cancer treatment outcomes can be predicted with QUS texture features acquired from index LNs. Prediction efficacy improved by implementing TOT features following methodology outlined in this work.

【 授权许可】

Unknown   
Copyright © 2023 Safakish, Sannachi, DiCenzo, Kolios, Pejović-Milić and Czarnota