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Cancers
Special Issues
Application of Advanced Biomedical Imaging in Cancer Treatment
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Application of Advanced Biomedical Imaging in Cancer Treatment

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 16250

Special Issue Editors

Prof. Dr. Edyta Szurowska

E-Mail Website
Guest Editor
2nd Division of Radiology, Medical University of Gdansk, 17 M. Smoluchowskiego Str., 80-214 Gdansk, Poland
Interests: medical imaging; radiology
Special Issues, Collections and Topics in MDPI journals
Dr. Maciej Bobowicz

E-Mail Website
Guest Editor
2nd Division of Radiology, Medical University of Gdansk, 17 M. Smoluchowskiego Str., 80-214 Gdansk, Poland
Interests: medical imaging; radiology

Special Issue Information

Dear Colleagues,

Tackling cancer, one of the major causes of morbidity and mortality, requires action at both the research and treatment levels. Modern advanced biomedical imaging is used to provide patient- and tissue-level information, but it can also provide cell- and molecular-level information.

Biomedical imaging, characterized by modern diagnostic methods, scanners, protocols and contrast media, improves the way we diagnose and monitor cancer. However, it is not possible without multiple technical inventions, supercomputers, machine learning and artificial intelligence support. Such inventions and discoveries are needed to enable patient-tailored treatment in personalized medicine. Imaging is also used for monitoring outcomes, and emerging techniques such as radiomics will enable the prediction of treatment outcomes before treatment begins.

We would like to invite researchers in the broad field of advanced biomedical imaging, including, but not limited to, radiology, digital pathology, oncology, radiotherapy, surgical oncology, machine learning, artificial intelligence and radiomics.

We welcome manuscripts mostly on original articles and reviews (case reports are not considered) of imaging modalities related to the diagnosis, treatment and monitoring of primary and secondary solid tumors. We would like to present the most recent discoveries in advanced biomedical imaging in the most common cancer types (such as lung, breast, colorectal, prostate and liver), but we also encourage manuscripts on rare neoplasms.

The goal of this Special Issue is to highlight new horizons in cancer imaging for diagnosis, treatment and monitoring, with special emphasis on:

  1. Imaging-based methods:
  • Oncological, radiotherapy and surgical therapy planning;
  • Therapy response assessment;
  • Recurrence assessment.
  1. Experimental imaging.
  2. The application of ML, AI and radiomics in cancer imaging.

Prof. Dr. Edyta Szurowska
Dr. Maciej Bobowicz
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 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. 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

  • new horizons in cancer imaging
  • image-guided
  • therapy planning
  • oncology
  • therapy response assessment
  • recurrence assessment
  • experimental imaging
  • breast cancer
  • lung cancer
  • colorectal cancer
  • liver cancer
  • varia (lymphoma, CNS)

Published Papers (7 papers)

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Research

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12 pages, 1921 KiB  
Article
Whole-Body Imaging for the Primary Staging of Melanomas—A Single-Center Retrospective Study
by Kristine E. Mayer, Jochen Gaa, Sophia Wasserer, Tilo Biedermann and Oana-Diana Persa
Cancers 2023, 15(21), 5265; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15215265 - 02 Nov 2023
Viewed by 804
Abstract
Background: Melanoma staging at diagnosis predominantly depends on the tumor thickness. Sentinel lymph node biopsy (SLNB) is a common tool for primary staging. However, for tumors of >4 mm with ulceration, 3D whole-body imaging and, in particular, Fluor-18-Deoxyglucose positron emission tomography combined with [...] Read more.
Background: Melanoma staging at diagnosis predominantly depends on the tumor thickness. Sentinel lymph node biopsy (SLNB) is a common tool for primary staging. However, for tumors of >4 mm with ulceration, 3D whole-body imaging and, in particular, Fluor-18-Deoxyglucose positron emission tomography combined with computed tomography (18F-FDG-PET/CT), is recommended beforehand. This study aimed to investigate the real-world data of whole-body imaging for initial melanoma staging and its impact on the subsequent diagnostic and therapeutic procedures. Methods: In this retrospective single-center study, 94 patients receiving 18F-FDG-PET/CT and six patients with whole-body computed tomography (CT) scans were included. The clinical characteristics, imaging results, and histologic parameters of the primary tumors and metastases were analyzed. Results: Besides the patients with primary tumors characterized as pT4b (63%), the patients with pT4a tumors and pT3 tumors close to 4 mm in tumor thickness also received initial whole-body imaging. In 42.6% of the patients undergoing 18F-FDG-PET/CT, the imaging results led to a change in the diagnostic or therapeutic procedure following on from this. In 29% of cases, sentinel lymph node biopsy was no longer necessary. The sensitivity and specificity of 18F-FDG-PET/CT were 66.0% and 93.0%, respectively. Conclusion: Whole-body imaging as a primary diagnostic tool is highly valuable and influences the subsequent diagnostic and therapeutic procedures in a considerable number of patients with a relatively high tumor thickness. It can help avoid the costs and invasiveness of redundant SLNB and simultaneously hasten the staging of patients at the time of diagnosis. Full article
(This article belongs to the Special Issue Application of Advanced Biomedical Imaging in Cancer Treatment)
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12 pages, 2576 KiB  
Article
Radiological Biomarkers in MRI directed Rectal Cancer Radiotherapy Volume Delineation
by Charleen Chan Wah Hak, Svetlana Balyasnikova, Samuel Withey, Diana Tait, Gina Brown and Irene Chong
Cancers 2023, 15(21), 5176; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15215176 - 27 Oct 2023
Viewed by 699
Abstract
Our study evaluated whether an MRI reporting system highlighting areas of contiguous and discontinuous extramural venous invasion (EMVI) can improve the accuracy of gross tumour volume (GTV) delineation. Initially, 27 consecutive patients with locally advanced rectal cancer treated between 2012 and 2014 were [...] Read more.
Our study evaluated whether an MRI reporting system highlighting areas of contiguous and discontinuous extramural venous invasion (EMVI) can improve the accuracy of gross tumour volume (GTV) delineation. Initially, 27 consecutive patients with locally advanced rectal cancer treated between 2012 and 2014 were evaluated. We used an MRI reporting proforma that documented the position of the primary tumour, lymph nodes and EMVI. The new GTVs delineated were compared with historical radiotherapy treatment volumes to identify the frequency of GTV geographical miss. We observed that the delineation of involved nodes and areas of EMVI was more likely to represent sources of uncertainty wherein nodal GTV geographical miss was evident in 5 out of 27 patients (19%). Complete EMVI GTV geographical miss occurred in two patients (7%). We re-evaluated our radiotherapy practice in a further 27 patients after the implementation of a modified MRI reporting system. An improvement was seen; nodal miss was observed in two patients (7%) and partial EMVI miss in one patient (4%), although these areas were encompassed in the planning target volume (PTV). Our study shows that extramural venous invasion and involved nodes need to be highlighted on MRI to improve the accuracy of rectal cancer GTV delineation. Full article
(This article belongs to the Special Issue Application of Advanced Biomedical Imaging in Cancer Treatment)
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29 pages, 9285 KiB  
Article
Attention-Based Deep Learning System for Classification of Breast Lesions—Multimodal, Weakly Supervised Approach
by Maciej Bobowicz, Marlena Rygusik, Jakub Buler, Rafał Buler, Maria Ferlin, Arkadiusz Kwasigroch, Edyta Szurowska and Michał Grochowski
Cancers 2023, 15(10), 2704; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15102704 - 10 May 2023
Cited by 4 | Viewed by 2623
Abstract
Breast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained [...] Read more.
Breast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) classifier able to train under data scarcity effectively. We used a private dataset with 1174 non-cancer and 794 cancer images labelled at the image level with pathological ground truth confirmation. We used feature extractors (ResNet-18, ResNet-34, ResNet-50 and EfficientNet-B0) pre-trained on ImageNet. The best results were achieved with multimodal-view classification using both CC and MLO images simultaneously, resized by half, with a patch size of 224 px and an overlap of 0.25. It resulted in AUC-ROC = 0.896 ± 0.017, F1-score 81.8 ± 3.2, accuracy 81.6 ± 3.2, precision 82.4 ± 3.3, and recall 81.6 ± 3.2. Evaluation with the Chinese Mammography Database, with 5-fold cross-validation, patient-wise breakdowns, and transfer learning, resulted in AUC-ROC 0.848 ± 0.015, F1-score 78.6 ± 2.0, accuracy 78.4 ± 1.9, precision 78.8 ± 2.0, and recall 78.4 ± 1.9. The CLAM algorithm’s attentional maps indicate the features most relevant to the algorithm in the images. Our approach was more effective than in many other studies, allowing for some explainability and identifying erroneous predictions based on the wrong premises. Full article
(This article belongs to the Special Issue Application of Advanced Biomedical Imaging in Cancer Treatment)
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15 pages, 295 KiB  
Article
Diagnostic Accuracy of Magnetic Resonance Imaging in the Pre-Operative Staging of Cervical Cancer Patients Who Underwent Neoadjuvant Treatment: A Clinical–Surgical–Pathologic Comparison
by Antonino Ditto, Umberto Leone Roberti Maggiore, Giulio Evangelisti, Giorgio Bogani, Valentina Chiappa, Fabio Martinelli and Francesco Raspagliesi
Cancers 2023, 15(7), 2061; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15072061 - 30 Mar 2023
Cited by 3 | Viewed by 1431
Abstract
Magnetic resonance imaging (MRI) has been proven to ensure high diagnostic accuracy in the identification of vaginal, parametrial, and lymph node involvement in patients affected by cervical cancer (CC), thus playing a crucial role in the preoperative staging of the disease. This study [...] Read more.
Magnetic resonance imaging (MRI) has been proven to ensure high diagnostic accuracy in the identification of vaginal, parametrial, and lymph node involvement in patients affected by cervical cancer (CC), thus playing a crucial role in the preoperative staging of the disease. This study aims to compare the accuracy of MRI for the preoperative staging of patients with CC who underwent neoadjuvant treatment (NAT) or direct surgery. Retrospective data analysis of 126 patients with primary CC International Federation of Gynecology and Obstetrics stage IB3-IIB who underwent NAT before radical surgery (NAT group = 94) or received surgical treatment alone (control arm = 32) was prospectively performed. All enrolled patients were clinically assessed with both a pelvic examination and MRI before surgical treatment. Data from the clinical examination were compared with the histopathological findings to assess the accuracy of MRI for staging purposes after NAT or before direct surgery. MRI showed an overall accuracy of 46.1%, proving it to be not superior to pelvic and physical examination. The overall MRI accuracy for the evaluation of parametrial, vaginal, and lymph node status was 65.8%, 79.4%, and 79.4%, respectively. In the NAT group, the accuracy for the detection of parametrial, lymph node, and vaginal involvement was lower than the control group; however, the difference was not significant (p ≥ 0.05). The overall accuracy of MRI for the preoperative staging of CC after NAT is shown to be not unsatisfactory. The limits of MRI staging are especially evident when dealing with pre-treated patients. Full article
(This article belongs to the Special Issue Application of Advanced Biomedical Imaging in Cancer Treatment)
23 pages, 8548 KiB  
Article
CBCT-to-CT Translation Using Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer
by Chitchaya Suwanraksa, Jidapa Bridhikitti, Thiansin Liamsuwan and Sitthichok Chaichulee
Cancers 2023, 15(7), 2017; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15072017 - 28 Mar 2023
Cited by 8 | Viewed by 2417
Abstract
Recently, deep learning with generative adversarial networks (GANs) has been applied in multi-domain image-to-image translation. This study aims to improve the image quality of cone-beam computed tomography (CBCT) by generating synthetic CT (sCT) that maintains the patient’s anatomy as in CBCT, while having [...] Read more.
Recently, deep learning with generative adversarial networks (GANs) has been applied in multi-domain image-to-image translation. This study aims to improve the image quality of cone-beam computed tomography (CBCT) by generating synthetic CT (sCT) that maintains the patient’s anatomy as in CBCT, while having the image quality of CT. As CBCT and CT are acquired at different time points, it is challenging to obtain paired images with aligned anatomy for supervised training. To address this limitation, the study incorporated a registration network (RegNet) into GAN during training. RegNet can dynamically estimate the correct labels, allowing supervised learning with noisy labels. The study developed and evaluated the approach using imaging data from 146 patients with head and neck cancer. The results showed that GAN trained with RegNet performed better than those trained without RegNet. Specifically, in the UNIT model trained with RegNet, the mean absolute error (MAE) was reduced from 40.46 to 37.21, the root mean-square error (RMSE) was reduced from 119.45 to 108.86, the peak signal-to-noise ratio (PSNR) was increased from 28.67 to 29.55, and the structural similarity index (SSIM) was increased from 0.8630 to 0.8791. The sCT generated from the model had fewer artifacts and retained the anatomical information as in CBCT. Full article
(This article belongs to the Special Issue Application of Advanced Biomedical Imaging in Cancer Treatment)
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Review

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29 pages, 19457 KiB  
Review
Prostate Cancer and Its Mimics—A Pictorial Review
by Anna Żurowska, Rafał Pęksa, Michał Bieńkowski, Katarzyna Skrobisz, Marek Sowa, Marcin Matuszewski, Wojciech Biernat and Edyta Szurowska
Cancers 2023, 15(14), 3682; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15143682 - 19 Jul 2023
Cited by 2 | Viewed by 4492
Abstract
Background: Multiparametric prostate MRI (mpMRI) is gaining wider recommendations for diagnosing and following up on prostate cancer. However, despite the high accuracy of mpMRI, false positive and false negative results are reported. Some of these may be related to normal anatomic structures, benign [...] Read more.
Background: Multiparametric prostate MRI (mpMRI) is gaining wider recommendations for diagnosing and following up on prostate cancer. However, despite the high accuracy of mpMRI, false positive and false negative results are reported. Some of these may be related to normal anatomic structures, benign lesions that may mimic cancer, or poor-quality images that hamper interpretation. The aim of this review is to discuss common potential pitfalls in the interpretation of mpMRI. Methods: mpMRI of the prostates was performed on 3T MRI scanners (Philips Achieva or Siemens Magnetom Vida) according to European Society of Urogenital Radiology (ESUR) guidelines and technical requirements. Results: This pictorial review discusses normal anatomical structures such as the anterior fibromuscular stroma, periprostatic venous plexus, central zone, and benign conditions such as benign prostate hyperplasia (BPH), post-biopsy hemorrhage, prostatitis, and abscess that may imitate prostate cancer, as well as the appearance of prostate cancer occurring in these locations. Furthermore, suggestions on how to avoid these pitfalls are provided, and the impact of image quality is also discussed. Conclusions: In an era of accelerating prostate mpMRI and high demand for high-quality interpretation of the scans, radiologists should be aware of these potential pitfalls to improve their diagnostic accuracy. Full article
(This article belongs to the Special Issue Application of Advanced Biomedical Imaging in Cancer Treatment)
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24 pages, 384 KiB  
Review
See Lung Cancer with an AI
by Joanna Bidzińska and Edyta Szurowska
Cancers 2023, 15(4), 1321; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers15041321 - 19 Feb 2023
Cited by 4 | Viewed by 3006
Abstract
A lot has happened in the field of lung cancer screening in recent months. The ongoing discussion and documentation published by the scientific community and policymakers are of great importance to the entire European community and perhaps beyond. Lung cancer is the main [...] Read more.
A lot has happened in the field of lung cancer screening in recent months. The ongoing discussion and documentation published by the scientific community and policymakers are of great importance to the entire European community and perhaps beyond. Lung cancer is the main worldwide killer. Low-dose computed tomography-based screening, together with smoking cessation, is the only tool to fight lung cancer, as it has already been proven in the United States of America but also European randomized controlled trials. Screening requires a lot of well-organized specialized work, but it can be supported by artificial intelligence (AI). Here we discuss whether and how to use AI for patients, radiologists, pulmonologists, thoracic surgeons, and all hospital staff supporting screening process benefits. Full article
(This article belongs to the Special Issue Application of Advanced Biomedical Imaging in Cancer Treatment)
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