[HTML][HTML] [18F] FDG-PET/CT radiomics and artificial intelligence in lung cancer: technical aspects and potential clinical applications

R Manafi-Farid, E Askari, I Shiri, C Pirich… - Seminars in nuclear …, 2022 - Elsevier
Lung cancer is the second most common cancer and the leading cause of cancer-related
death worldwide. Molecular imaging using [18 F] fluorodeoxyglucose Positron Emission …

[HTML][HTML] Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data …

A Barragán-Montero, A Bibal… - Physics in Medicine …, 2022 - iopscience.iop.org
The interest in machine learning (ML) has grown tremendously in recent years, partly due to
the performance leap that occurred with new techniques of deep learning, convolutional …

Machine learning and deep neural networks in thoracic and cardiovascular imaging

TA Retson, AH Besser, S Sall, D Golden… - Journal of thoracic …, 2019 - journals.lww.com
Advances in technology have always had the potential and opportunity to shape the practice
of medicine, and in no medical specialty has technology been more rapidly embraced and …

CT radiomics, radiologists, and clinical information in predicting outcome of patients with COVID-19 pneumonia

F Homayounieh, S Ebrahimian, R Babaei… - Radiology …, 2020 - pubs.rsna.org
Purpose To compare prediction of disease outcome, severity, and patient triage in
coronavirus disease 2019 (COVID-19) pneumonia with whole lung radiomics, radiologists' …

[HTML][HTML] Precision radiotherapy for non-small cell lung cancer

WC Yang, FM Hsu, PC Yang - Journal of Biomedical Science, 2020 - Springer
Precision medicine is becoming the standard of care in anti-cancer treatment. The
personalized precision management of cancer patients highly relies on the improvement of …

A systematic review and meta-analysis of the prognostic value of radiomics based models in non-small cell lung cancer treated with curative radiotherapy

G Kothari, J Korte, EJ Lehrer, NG Zaorsky… - Radiotherapy and …, 2021 - Elsevier
Background and purpose Radiomics allows extraction of quantifiable features from imaging.
This study performs a systematic review and meta-analysis of the performance of radiomics …

[HTML][HTML] Recent applications of artificial intelligence in radiotherapy: where we are and beyond

M Santoro, S Strolin, G Paolani, G Della Gala… - Applied Sciences, 2022 - mdpi.com
Featured Application Computational models based on artificial intelligence (AI) variants
have been developed and applied successfully in many areas, both inside and outside of …

[HTML][HTML] Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological …

R Hamamoto, T Koyama, N Kouno, T Yasuda… - … hematology & oncology, 2022 - Springer
Abstract Since US President Barack Obama announced the Precision Medicine Initiative in
his New Year's State of the Union address in 2015, the establishment of a precision …

[HTML][HTML] A systematic review of PET textural analysis and radiomics in cancer

M Piñeiro-Fiel, A Moscoso, V Pubul, Á Ruibal… - Diagnostics, 2021 - mdpi.com
Background: Although many works have supported the utility of PET radiomics, several
authors have raised concerns over the robustness and replicability of the results. This study …

Radiomics for predicting lung cancer outcomes following radiotherapy: a systematic review

GM Walls, SOS Osman, KH Brown, KT Butterworth… - Clinical oncology, 2022 - Elsevier
Lung cancer's radiomic phenotype may potentially inform clinical decision-making with
respect to radical radiotherapy. At present there are no validated biomarkers available for …