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Cancers
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Artificial Intelligence and Deep Learning in Radiology Oncology
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Artificial Intelligence and Deep Learning in Radiology Oncology

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 5353

Special Issue Editors

Dr. Eyal Klang

E-Mail Website
Guest Editor
Department of Diagnostic Imaging, Chaim Sheba Medical Center, Sackler School of Medicine, Tel-Aviv University, Tel Aviv-Yafo, Israel
Interests: radiology; deep learning; artificial intelligence; natural language processing; large language models
Special Issues, Collections and Topics in MDPI journals
Dr. Vera Sorin

E-Mail Website
Guest Editor
Department of Diagnostic Imaging, Chaim Sheba Medical Center, Sackler School of Medicine, Tel-Aviv University, Tel Aviv-Yafo, Israel
Interests: radiology; breast cancer; deep learning; artificial intelligence; natural language processing; large language models

Special Issue Information

Dear Colleagues,

Deep learning is revolutionizing oncology and cancer imaging. This technology is reshaping our capabilities for cancer detection, treatment planning, and prognostic prediction, with the potential to significantly enhance patient outcomes. Rapid advancements in the field may transform our future approach to cancer care and research.

However, the integration of deep learning into clinical practice comes with challenges. Notable issues such as the algorithms’ transparency, data privacy, and ethical considerations are obstacles that require careful attention. Additionally, a pressing concern is the assurance of the performance, reliability, and robustness of models in diverse, real-world settings.

In this Special Issue, our aim is to explore the role of deep learning in oncology and cancer imaging. We invite contributions that assess deep learning methodologies in oncology, consider their limitations, and examine potential impacts of these advancements. Through these discussions, we hope to deepen our understanding of the transformative potential of artificial intelligence in improving cancer care.

Dr. Eyal Klang
Dr. Vera Sorin
Guest Editors

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

  • cancer
  • radiology
  • deep learning
  • artificial intelligence
  • natural language processing
  • large language models

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Published Papers (3 papers)

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Research

17 pages, 3262 KiB  
Article
Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction
by Georg Gohla, Till-Karsten Hauser, Paula Bombach, Daniel Feucht, Arne Estler, Antje Bornemann, Leonie Zerweck, Eliane Weinbrenner, Ulrike Ernemann and Christer Ruff
Cancers 2024, 16(10), 1827; https://doi.org/10.3390/cancers16101827 - 10 May 2024
Viewed by 1499
Abstract
A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was [...] Read more.
A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical–pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Radiology Oncology)
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15 pages, 3377 KiB  
Article
Empowering Vision Transformer by Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation
by Hyeonjeong Cho, Jae Sung Lee, Jin Sung Kim, Woong Sub Koom and Hojin Kim
Cancers 2023, 15(23), 5507; https://doi.org/10.3390/cancers15235507 - 21 Nov 2023
Cited by 2 | Viewed by 1527
Abstract
U-Net, based on a deep convolutional network (CNN), has been clinically used to auto-segment normal organs, while still being limited to the planning target volume (PTV) segmentation. This work aims to address the problems in two aspects: 1) apply one of the newest [...] Read more.
U-Net, based on a deep convolutional network (CNN), has been clinically used to auto-segment normal organs, while still being limited to the planning target volume (PTV) segmentation. This work aims to address the problems in two aspects: 1) apply one of the newest network architectures such as vision transformers other than the CNN-based networks, and 2) find an appropriate combination of network hyper-parameters with reference to recently proposed nnU-Net (“no-new-Net”). VT U-Net was adopted for auto-segmenting the whole pelvis prostate PTV as it consisted of fully transformer architecture. The upgraded version (v.2) applied the nnU-Net-like hyper-parameter optimizations, which did not fully cover the transformer-oriented hyper-parameters. Thus, we tried to find a suitable combination of two key hyper-parameters (patch size and embedded dimension) for 140 CT scans throughout 4-fold cross validation. The VT U-Net v.2 with hyper-parameter tuning yielded the highest dice similarity coefficient (DSC) of 82.5 and the lowest 95% Haussdorff distance (HD95) of 3.5 on average among the seven recently proposed deep learning networks. Importantly, the nnU-Net with hyper-parameter optimization achieved competitive performance, although this was based on the convolution layers. The network hyper-parameter tuning was demonstrated to be necessary even for the newly developed architecture of vision transformers. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Radiology Oncology)
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17 pages, 2841 KiB  
Article
Automatic Detection of Brain Metastases in T1-Weighted Construct-Enhanced MRI Using Deep Learning Model
by Zichun Zhou, Qingtao Qiu, Huiling Liu, Xuanchu Ge, Tengxiang Li, Ligang Xing, Runtao Yang and Yong Yin
Cancers 2023, 15(18), 4443; https://doi.org/10.3390/cancers15184443 - 6 Sep 2023
Cited by 4 | Viewed by 1878
Abstract
As a complication of malignant tumors, brain metastasis (BM) seriously threatens patients’ survival and quality of life. Accurate detection of BM before determining radiation therapy plans is a paramount task. Due to the small size and heterogeneous number of BMs, their manual diagnosis [...] Read more.
As a complication of malignant tumors, brain metastasis (BM) seriously threatens patients’ survival and quality of life. Accurate detection of BM before determining radiation therapy plans is a paramount task. Due to the small size and heterogeneous number of BMs, their manual diagnosis faces enormous challenges. Thus, MRI-based artificial intelligence-assisted BM diagnosis is significant. Most of the existing deep learning (DL) methods for automatic BM detection try to ensure a good trade-off between precision and recall. However, due to the objective factors of the models, higher recall is often accompanied by higher number of false positive results. In real clinical auxiliary diagnosis, radiation oncologists are required to spend much effort to review these false positive results. In order to reduce false positive results while retaining high accuracy, a modified YOLOv5 algorithm is proposed in this paper. First, in order to focus on the important channels of the feature map, we add a convolutional block attention model to the neck structure. Furthermore, an additional prediction head is introduced for detecting small-size BMs. Finally, to distinguish between cerebral vessels and small-size BMs, a Swin transformer block is embedded into the smallest prediction head. With the introduction of the F2-score index to determine the most appropriate confidence threshold, the proposed method achieves a precision of 0.612 and recall of 0.904. Compared with existing methods, our proposed method shows superior performance with fewer false positive results. It is anticipated that the proposed method could reduce the workload of radiation oncologists in real clinical auxiliary diagnosis. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Radiology Oncology)
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