Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
3.0
Journals
JPM
Special Issues
Artificial Intelligence (AI) in Radiation Oncology
share announcement

Artificial Intelligence (AI) in Radiation Oncology

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (5 May 2023) | Viewed by 8037

Special Issue Editor

Dr. Francesco Cuccia

E-Mail Website
Guest Editor
Radiation Oncology Unit - ARNAS Civico Hospital, 90100 Palermo, Italy
Interests: radiation oncology; radiotherapy physics; stereotactic radiosurgery; intensity-modulated radiotherapy; adaptive radiotherapy; lung cancer; prostate cancer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technological advancements are leading the way in the field of radiation oncology, with the recent introduction of new hybrid MR-guided linear accelerators and the global widespread of daily adaptive radiotherapy. Alongside a deeper knowledge of cancer biological and genomic signatures, there is a growing amount of data available from imaging exams with radionics that should allow clinicians to gain new information in terms of outcome prediction. In this scenario, the role of artificial intelligence is gaining attractiveness in the scientific community as a helpful tool for both refining accuracy in treatment delivery and improving knowledge about predictive factors for clinical outcomes. 

The aim of this Special Issue is to cover novel innovative findings and concepts within the field of artificial intelligence applied to all aspects of radiation oncology.

Dr. Francesco Cuccia
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • radiation oncology
  • adaptive radiotherapy

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review, Other

2 pages, 184 KiB  
Editorial
What We Talk about When We Talk about Artificial Intelligence in Radiation Oncology
by Francesco Cuccia, Giuseppe Carruba and Guseppe Ferrera
J. Pers. Med. 2022, 12(11), 1834; https://doi.org/10.3390/jpm12111834 - 3 Nov 2022
Cited by 1 | Viewed by 1168
Abstract
The constant evolution of technology has dramatically changed the history of radiation oncology, allowing clinicians to deliver increasingly accurate and precise treatments, moving from 2D radiotherapy to 3D conformal radiotherapy, leading to intensity-modulated image-guided (IMRT-IGRT) and stereotactic body radiotherapy treatments [...] Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Radiation Oncology)

Research

Jump to: Editorial, Review, Other

12 pages, 1912 KiB  
Article
Predicting Local Failure after Partial Prostate Re-Irradiation Using a Dosiomic-Based Machine Learning Model
by Giovanni Pirrone, Fabio Matrone, Paola Chiovati, Stefania Manente, Annalisa Drigo, Alessandra Donofrio, Cristina Cappelletto, Eugenio Borsatti, Andrea Dassie, Roberto Bortolus and Michele Avanzo
J. Pers. Med. 2022, 12(9), 1491; https://doi.org/10.3390/jpm12091491 - 13 Sep 2022
Cited by 8 | Viewed by 1931
Abstract
The aim of this study is to predict local failure after partial prostate re-irradiation for the treatment of isolated locally recurrent prostate cancer by using a machine learning classifier based on radiomic features from pre-treatment computed tomography (CT), positron-emission tomography (PET) and biological [...] Read more.
The aim of this study is to predict local failure after partial prostate re-irradiation for the treatment of isolated locally recurrent prostate cancer by using a machine learning classifier based on radiomic features from pre-treatment computed tomography (CT), positron-emission tomography (PET) and biological effective dose distribution (BED) of the radiotherapy plan. The analysis was conducted on a monocentric dataset of 43 patients with evidence of isolated intraprostatic recurrence of prostate cancer after primary external beam radiotherapy. All patients received partial prostate re-irradiation delivered by volumetric modulated arc therapy. The gross tumor volume (GTV) of each patient was manually contoured from planning CT, choline-PET and dose maps. An ensemble machine learning pipeline including unbalanced data correction and feature selection was trained using the radiomic and dosiomic features as input for predicting occurrence of local failure. The model performance was assessed using sensitivity, specificity, accuracy and area under receiver operating characteristic curves of the score function in 10-fold cross validation repeated 100 times. Local failure was observed in 13 patients (30%), with a median time to recurrence of 36.7 months (range = 6.1–102.4 months). A four variables ensemble machine learning model resulted in accuracy of 0.62 and AUC 0.65. According to our results, a dosiomic machine learning classifier can predict local failure after partial prostate re-irradiation. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Radiation Oncology)
►▼ Show Figures

Figure 1

Review

Jump to: Editorial, Research, Other

15 pages, 893 KiB  
Review
Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review
by Ciro Franzese, Damiano Dei, Nicola Lambri, Maria Ausilia Teriaca, Marco Badalamenti, Leonardo Crespi, Stefano Tomatis, Daniele Loiacono, Pietro Mancosu and Marta Scorsetti
J. Pers. Med. 2023, 13(6), 946; https://doi.org/10.3390/jpm13060946 - 2 Jun 2023
Cited by 6 | Viewed by 2293
Abstract
Background: Head and neck cancer (HNC) is characterized by complex-shaped tumors and numerous organs at risk (OARs), inducing challenging radiotherapy (RT) planning, optimization, and delivery. In this review, we provided a thorough description of the applications of artificial intelligence (AI) tools in the [...] Read more.
Background: Head and neck cancer (HNC) is characterized by complex-shaped tumors and numerous organs at risk (OARs), inducing challenging radiotherapy (RT) planning, optimization, and delivery. In this review, we provided a thorough description of the applications of artificial intelligence (AI) tools in the HNC RT process. Methods: The PubMed database was queried, and a total of 168 articles (2016–2022) were screened by a group of experts in radiation oncology. The group selected 62 articles, which were subdivided into three categories, representing the whole RT workflow: (i) target and OAR contouring, (ii) planning, and (iii) delivery. Results: The majority of the selected studies focused on the OARs segmentation process. Overall, the performance of AI models was evaluated using standard metrics, while limited research was found on how the introduction of AI could impact clinical outcomes. Additionally, papers usually lacked information about the confidence level associated with the predictions made by the AI models. Conclusions: AI represents a promising tool to automate the RT workflow for the complex field of HNC treatment. To ensure that the development of AI technologies in RT is effectively aligned with clinical needs, we suggest conducting future studies within interdisciplinary groups, including clinicians and computer scientists. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Radiation Oncology)
►▼ Show Figures

Figure 1

Other

7 pages, 682 KiB  
Brief Report
Machine Learning Based Prediction of Gamma Passing Rate for VMAT Radiotherapy Plans
by Bartłomiej Sadowski, Karolina Milewska and Józef Ginter
J. Pers. Med. 2022, 12(12), 2071; https://doi.org/10.3390/jpm12122071 - 15 Dec 2022
Cited by 1 | Viewed by 1686
Abstract
The use of machine learning algorithms (ML) in radiotherapy is becoming increasingly popular. More and more groups are trying to apply ML in predicting the so-called gamma passing rate (GPR). Our team has developed a customized approach of using ML algorithms to predict [...] Read more.
The use of machine learning algorithms (ML) in radiotherapy is becoming increasingly popular. More and more groups are trying to apply ML in predicting the so-called gamma passing rate (GPR). Our team has developed a customized approach of using ML algorithms to predict global GPR for electronic portal imaging device (EPID) verification for dose different 2% and distance to agreement 2 mm criteria for VMAT dynamic plans. Plans will pass if the GPR is greater than 98%. The algorithm was learned and tested on anonymized clinical data from 13 months which resulted in more than 3000 treatment plans. The obtained results of GPR prediction are very interesting. Average specificity of the algorithm based on an ensemble of 50 decision tree regressors is 91.6% for our criteria. As a result, we can reduce the verification process by 50%. The novel approach described by our team can offer a new insight into the application of ML and neural networks in GPR prediction and dosimetry. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Radiation Oncology)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop