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Cancers
Special Issues
The Roles of Deep Learning in Cancer Radiotherapy
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The Roles of Deep Learning in Cancer Radiotherapy

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2456

Special Issue Editor

Dr. Zhen Tian

E-Mail Website
Guest Editor
Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA
Interests: treatment planning; low dose imaging; Monte Carlo simulation; deep learning applications in radiotherapy

Special Issue Information

Dear Colleagues,

The incorporation of deep learning into cancer radiotherapy signifies a groundbreaking evolution in oncology. By harnessing advanced algorithms and extensive datasets, deep learning models can significantly enhance the precision and efficiency of radiation treatments, including tumor detection, treatment target and critical organs delineation, treatment planning, quality assurance, and patient-outcome prediction. This Special Issue aims to delve into diverse applications of deep learning in radiotherapy, showcasing how these technologies can revolutionize cancer treatment. We welcome original research articles, reviews, and clinical studies that present innovative deep learning applications in radiotherapy, such as tumor detection, tumor and organ segmentation, treatment planning, dose prediction, and adaptive radiotherapy. We are particularly interested in contributions that illustrate the impact of deep learning on clinical workflows and patient outcomes. Potential topics include, but are not limited to, the following:

  • Deep learning algorithms for tumor detection;
  • Deep learning algorithms for tumor and organ segmentation;
  • Artificial Intelligence driven automatic treatment planning;
  • Predictive modeling of radiotherapy outcomes;
  • Adaptive radiotherapy guided by deep learning;
  • Integration of radiomics and deep learning in oncology;
  • Deep learning algorithms for quality assurance procedures;
  • Clinical validation of AI-driven radiotherapy tools;
  • Challenges and solutions in implementing deep learning in clinical practice.

We eagerly anticipate your contributions that showcase the innovative applications of deep learning in advancing cancer radiotherapy.

Dr. Zhen Tian
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 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. Cancers is an international peer-reviewed open access semimonthly 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 2900 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

  • radiotherapy
  • deep learning
  • artificial Intelligence
  • tumor detection
  • image segmentation
  • treatment planning
  • outcome prediction
  • radiomics

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

Published Papers (2 papers)

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Research

13 pages, 2078 KiB  
Article
The Role of MRI Radiomics Using T2-Weighted Images and the Apparent Diffusion Coefficient Map for Discriminating Between Warthin’s Tumors and Malignant Parotid Gland Tumors
by Delia Doris Donci, Carolina Solomon, Mihaela Băciuț, Cristian Dinu, Sebastian Stoia, Georgeta Mihaela Rusu, Csaba Csutak, Lavinia Manuela Lenghel and Anca Ciurea
Cancers 2025, 17(4), 620; https://doi.org/10.3390/cancers17040620 - 12 Feb 2025
Viewed by 663
Abstract
Background/Objectives: Differentiating between benign and malignant parotid gland tumors (PGT) is essential for establishing the treatment strategy, which is greatly influenced by the tumor’s histology. The objective of this study was to evaluate the role of MRI-based radiomics in the differentiation between Warthin’s [...] Read more.
Background/Objectives: Differentiating between benign and malignant parotid gland tumors (PGT) is essential for establishing the treatment strategy, which is greatly influenced by the tumor’s histology. The objective of this study was to evaluate the role of MRI-based radiomics in the differentiation between Warthin’s tumors (WT) and malignant tumors (MT), two entities that proved to present overlapping imaging features on conventional and functional MRI sequences. Methods: In this retrospective study, a total of 106 PGT (66 WT, 40 MT) with confirmed histology were eligible for radiomic analysis, which were randomly split into a training group (79 PGT; 49 WT; 30 MT) and a testing group (27 PGT; 17 WT, 10 MT). The radiomic features were extracted from 3D segmentations of PGT performed on the following sequences: PROPELLER T2-weighted images and the ADC map, using a dedicated software. First- and second-order features were derived for each lesion, using original and filtered images. Results: After employing several feature reduction techniques, including LASSO regression, three final radiomic parameters were identified to be the most significant in distinguishing between the two studied groups, with fair AUC values that ranged between 0.703 and 0.767. All three radiomic features were used to construct a Radiomic Score that presented the highest diagnostic performance in distinguishing between WT and MT, achieving an AUC of 0.785 in the training set, and 0.741 in the testing set. Conclusions: MRI-based radiomic features have the potential to serve as promising novel imaging biomarkers for discriminating between Warthin’s tumors and malignant tumors in the parotid gland. Nevertheless, it is still to prove how radiomic features can consistently achieve higher diagnostic performance, and if they can outperform alternative imaging methods, ideally in larger, multicentric studies. Full article
(This article belongs to the Special Issue The Roles of Deep Learning in Cancer Radiotherapy)
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16 pages, 2017 KiB  
Article
Automated Organ Segmentation for Radiation Therapy: A Comparative Analysis of AI-Based Tools Versus Manual Contouring in Korean Cancer Patients
by Seo Hee Choi, Jong Won Park, Yeona Cho, Gowoon Yang and Hong In Yoon
Cancers 2024, 16(21), 3670; https://doi.org/10.3390/cancers16213670 - 30 Oct 2024
Viewed by 1439
Abstract
Background: Accurate delineation of tumors and organs at risk (OARs) is crucial for intensity-modulated radiation therapy. This study aimed to evaluate the performance of OncoStudio, an AI-based auto-segmentation tool developed for Korean patients, compared with Protégé AI, a globally developed tool that uses [...] Read more.
Background: Accurate delineation of tumors and organs at risk (OARs) is crucial for intensity-modulated radiation therapy. This study aimed to evaluate the performance of OncoStudio, an AI-based auto-segmentation tool developed for Korean patients, compared with Protégé AI, a globally developed tool that uses data from Korean cancer patients. Methods: A retrospective analysis of 1200 Korean cancer patients treated with radiotherapy was conducted. Auto-contours generated via OncoStudio and Protégé AI were compared with manual contours across the head and neck and thoracic, abdominal, and pelvic organs. Accuracy was assessed using the Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff distance (HD). Feedback was obtained from 10 participants, including radiation oncologists, residents, and radiation therapists, via an online survey with a Turing test component. Results: OncoStudio outperformed Protégé AI in 85% of the evaluated OARs (p < 0.001). For head and neck organs, OncoStudio achieved a similar DSC (0.70 vs. 0.70, p = 0.637) but significantly lower MSD and 95% HD values (p < 0.001). In thoracic organs, OncoStudio performed excellently in 90% of cases, with a significantly greater DSC (male: 0.87 vs. 0.82, p < 0.001; female: 0.95 vs. 0.87, p < 0.001). OncoStudio also demonstrated superior accuracy in abdominal (DSC 0.88 vs. 0.81, p < 0.001) and pelvic organs (male: DSC 0.95 vs. 0.85, p < 0.001; female: DSC 0.82 vs. 0.73, p < 0.001). Clinicians favored OncoStudio in 70% of cases, with 90% endorsing its clinical suitability for Korean patients. Conclusions: OncoStudio, which is tailored for Korean patients, demonstrated superior segmentation accuracy across multiple anatomical regions, suggesting its suitability for radiotherapy planning in this population. Full article
(This article belongs to the Special Issue The Roles of Deep Learning in Cancer Radiotherapy)
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