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Gregory J. Czarnota

3 papers indexed

crossrefTomography2025-03-13Cited by 4

Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods

Amir Moslemi, Laurentius Oscar Osapoetra, Archya Dasgupta, Schontal Halstead, David Alberico, Maureen Trudeau, et al.

Rationale: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure the delivery of effective care.…

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crossrefRadiation2024-06-14Cited by 1

Deep Texture Analysis Enhanced MRI Radiomics for Predicting Head and Neck Cancer Treatment Outcomes with Machine Learning Classifiers

Aryan Safakish, Amir Moslemi, Daniel Moore-Palhares, Lakshmanan Sannachi, Ian Poon, Irene Karam, et al.

Background: Head and neck cancer treatment does not yield desired outcomes for all patients. This investigation aimed to explore the feasibility of predicting treatment outcomes from routine pre-treatment magnetic resonance images (MRIs). Radiomics features were “mined” and used…

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crossrefRadiation2024-02-28Cited by 8

Deep Texture Analysis—Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach

Aryan Safakish, Lakshmanan Sannachi, Amir Moslemi, Ana Pejović-Milić, Gregory J. Czarnota

(1) Background: Some cancer patients do not experience tumour shrinkage but are still at risk of experiencing unwanted treatment side effects. Radiomics refers to mining biomedical images to quantify textural characterization. When radiomics features are labelled with treatment r…

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