scholarly journals Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

2018 ◽  
Vol 8 ◽  
Author(s):  
Hesham Elhalawani ◽  
Timothy A. Lin ◽  
Stefania Volpe ◽  
Abdallah S. R. Mohamed ◽  
Aubrey L. White ◽  
...  
Keyword(s):  
Machine Learning ◽  
Head And Neck ◽  
Open Source ◽  
Radiation Oncology ◽  
Machine Learning Applications ◽  
Head And Neck Radiation

scholarly journals Machine Learning for Head and Neck Cancer: A Safe Bet?—A Clinically Oriented Systematic Review for the Radiation Oncologist

2021 ◽  
Vol 11 ◽  
Author(s):  
Stefania Volpe ◽  
Matteo Pepa ◽  
Mattia Zaffaroni ◽  
Federica Bellerba ◽  
Riccardo Santamaria ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Treatment Planning ◽  
Neck Cancer ◽  
Radiation Oncology ◽  
Imaging Modality ◽  
Oncological Outcome ◽  
Image Features

Background and PurposeMachine learning (ML) is emerging as a feasible approach to optimize patients’ care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT).Materials and MethodsElectronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1.ResultsForty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation).Discussion and ConclusionThe range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making.

scholarly journals Machine learning applications in radiation oncology

2021 ◽  
Vol 19 ◽  
pp. 13-24
Author(s):  
Matthew Field ◽  
Nicholas Hardcastle ◽  
Michael Jameson ◽  
Noel Aherne ◽  
Lois Holloway
Keyword(s):  
Machine Learning ◽  
Radiation Oncology ◽  
Machine Learning Applications

Body Weight Status, Clinical Factors, and Short-term Outcomes Among Head and Neck Radiation Oncology Patients

2018 ◽  
Vol 33 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Whitney C. Wallace ◽  
Steven J. Feigenberg ◽  
Tiffani N. Tyer ◽  
Janet F. Pope ◽  
Dawn M. Erickson ◽  
...  
Keyword(s):  
Body Weight ◽  
Head And Neck ◽  
Radiation Oncology ◽  
Weight Status ◽  
Clinical Factors ◽  
Short Term ◽  
Oncology Patients ◽  
Body Weight Status ◽  
Head And Neck Radiation

A Pragmatic Contouring Guideline for Salivary Gland Structures in Head and Neck Radiation Oncology

2013 ◽  
Vol 36 (1) ◽  
pp. 70-76 ◽  
Author(s):  
Frank Hoebers ◽  
Eugene Yu ◽  
Avi Eisbruch ◽  
Wade Thorstad ◽  
Brian O’Sullivan ◽  
...  
Keyword(s):  
Salivary Gland ◽  
Head And Neck ◽  
Radiation Oncology ◽  
Head And Neck Radiation

scholarly journals ReactionCode: a new versatile format for searching, analysis, classification, transform, and encoding/decoding of reactions

2020 ◽  
Author(s):  
Victorien Delannée ◽  
Marc Nicklaus
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Exchange ◽  
Similarity Searching ◽  
Classification Analysis ◽  
The Past ◽  
Machine Learning Applications ◽  
Database Organization ◽  
Machine Readable ◽  
Condensed Graph Of Reaction

In the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts have been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we developed a new open-source format which allows to encode and decode a reaction into multi-layers machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language.

scholarly journals ReactionCode: Format for Reaction Searching, Analysis, Classification, Transform, and Encoding/Decoding

2020 ◽  
Author(s):  
Victorien Delannée ◽  
Marc Nicklaus
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Exchange ◽  
Similarity Searching ◽  
Classification Analysis ◽  
The Past ◽  
Machine Learning Applications ◽  
Database Organization ◽  
Machine Readable ◽  
Condensed Graph Of Reaction

In the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts have been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we developed a new open-source format which allows to encode and decode a reaction into multi-layers machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language.

scholarly journals ReactionCode: format for reaction searching, analysis, classification, transform, and encoding/decoding

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Victorien Delannée ◽  
Marc C. Nicklaus
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Exchange ◽  
Similarity Searching ◽  
Classification Analysis ◽  
The Past ◽  
Machine Learning Applications ◽  
Database Organization ◽  
Machine Readable ◽  
Condensed Graph Of Reaction

AbstractIn the past two decades a lot of different formats for molecules and reactions have been created. These formats were mostly developed for the purposes of identifiers, representation, classification, analysis and data exchange. A lot of efforts have been made on molecule formats but only few for reactions where the endeavors have been made mostly by companies leading to proprietary formats. Here, we present ReactionCode: a new open-source format that allows one to encode and decode a reaction into multi-layer machine readable code, which aggregates reactants and products into a condensed graph of reaction (CGR). This format is flexible and can be used in a context of reaction similarity searching and classification. It is also designed for database organization, machine learning applications and as a new transform reaction language.

Machine Learning Applications for Head and Neck Imaging

2020 ◽  
Vol 30 (4) ◽  
pp. 517-529
Author(s):  
Farhad Maleki ◽  
William Trung Le ◽  
Thiparom Sananmuang ◽  
Samuel Kadoury ◽  
Reza Forghani
Keyword(s):  
Machine Learning ◽  
Head And Neck ◽  
Head And Neck Imaging ◽  
Machine Learning Applications
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