Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.3
2.8
Journals
Current Oncology
Special Issues
Imaging-Based Early Diagnosis of Cancers Using Artificial Intelligence
share announcement

Imaging-Based Early Diagnosis of Cancers Using Artificial Intelligence

A special issue of Current Oncology (ISSN 1718-7729).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 7937

Special Issue Editors

Prof. Dr. Timm Denecke

E-Mail Website
Guest Editor
Department for Diagnostic and Interventional Radiology, University Hospital Leipzig, Liebigstraße 20, 04103 Leipzig, Germany
Interests: radiology; CT; MRI; PET; nuclear medicine; neuroendocrine tumors; liver; pancreas; gastrointestinal; tumor ablation
Special Issues, Collections and Topics in MDPI journals
Dr. Anselm Schulz

E-Mail Website
Co-Guest Editor
Department of Radiology and Nuclear Medicine, Oslo University Hospital Ullevål, 0027 Oslo, Norway
Interests: abdominal imaging; CT technology; liver imaging; pancreatic cancer: early detection and screening; non-vascular intervention; body composition; CT radiostereometric analysis (CT-RSA)

Special Issue Information

Dear Colleagues,

Early diagnosis plays a crucial role in the effective treatment of cancer, as it significantly improves patient outcomes. Artificial intelligence (AI) methods have emerged as promising tools in the field of medicine, particularly in the early detection of cancer. By leveraging advanced algorithms and machine learning techniques, AI can analyze vast amounts of medical data to identify patterns and markers indicative of cancer. AI-based diagnostic systems can analyze various types of medical imaging, such as mammograms, CT scans, and MRIs, with exceptional accuracy. These systems can detect subtle abnormalities and have the potential to assist radiologists in making more confident and timely diagnoses. Moreover, AI can integrate multiple data sources, including genetic profiles and patient histories, to provide a comprehensive assessment of cancer risk. The use of AI methods in early cancer diagnosis offers several advantages. It can facilitate the identification of cancer at its earliest stages when treatment options are more effective and less invasive. Additionally, AI systems promise to help to reduce diagnostic errors and improve the efficiency of healthcare processes, leading to better patient outcomes and reduced healthcare costs. However, there are challenges to overcome in implementing AI-based cancer diagnosis. Ensuring the privacy and security of patient data, addressing ethical concerns, and integrating AI seamlessly into existing healthcare systems are important considerations.

This Special Issue invites authors to present their findings, comments, and challenging experiences with AI regarding imaging-based early diagnosis as well as imaging biomarkers serving as risk factors and prognostic co-factors in different types of human cancers.

Prof. Dr. Timm Denecke
Dr. Anselm Schulz
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. Current Oncology 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 2200 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

  • screening
  • prevention
  • detection
  • staging
  • body composition
  • computer-aided diagnosis
  • PET
  • CT
  • MRI
  • mammography
  • artificial intelligence
  • machine learning
  • deep learning
  • diagnostic radiology
  • cancers

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 (3 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:

Research

Jump to: Review

16 pages, 4575 KiB  
Article
Deep-Learning-Based Reconstruction of Single-Breath-Hold 3 mm HASTE Improves Abdominal Image Quality and Reduces Acquisition Time: A Quantitative Analysis
by Felix Kubicka, Qinxuan Tan, Tom Meyer, Dominik Nickel, Elisabeth Weiland, Moritz Wagner and Stephan Rodrigo Marticorena Garcia
Curr. Oncol. 2025, 32(1), 30; https://doi.org/10.3390/curroncol32010030 - 3 Jan 2025
Viewed by 523
Abstract
Purpose: Breath-hold T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) magnetic resonance imaging (MRI) of the upper abdomen with a slice thickness below 5 mm suffers from high image noise and blurring. The purpose of this prospective study was to improve image quality [...] Read more.
Purpose: Breath-hold T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) magnetic resonance imaging (MRI) of the upper abdomen with a slice thickness below 5 mm suffers from high image noise and blurring. The purpose of this prospective study was to improve image quality and accelerate imaging acquisition by using single-breath-hold T2-weighted HASTE with deep learning (DL) reconstruction (DL-HASTE) with a 3 mm slice thickness. Method: MRI of the upper abdomen with DL-HASTE was performed in 35 participants (5 healthy volunteers and 30 patients) at 3 Tesla. In a subgroup of five healthy participants, signal-to-noise ratio (SNR) analysis was used after DL reconstruction to identify the smallest possible layer thickness (1, 2, 3, 4, 5 mm). DL-HASTE was acquired with a 3 mm slice thickness (DL-HASTE-3 mm) in 30 patients and compared with 5 mm DL-HASTE (DL-HASTE-5 mm) and with standard HASTE (standard-HASTE-5 mm). Image quality and motion artifacts were assessed quantitatively using Laplacian variance and semi-quantitatively by two radiologists using five-point Likert scales. Results: In the five healthy participants, DL-HASTE-3 mm was identified as the optimal slice (SNR 23.227 ± 3.901). Both DL-HASTE-3 mm and DL-HASTE-5 mm were assigned significantly higher overall image quality scores than standard-HASTE-5 mm (Laplacian variance, both p < 0.001; Likert scale, p < 0.001). Compared with DL-HASTE-5 mm (1.10 × 10−5 ± 6.93 × 10−6), DL-HASTE-3 mm (1.56 × 10−5 ± 8.69 × 10−6) provided a significantly higher SNR Laplacian variance (p < 0.001) and sharpness sub-scores for the intestinal tract, adrenal glands, and small anatomic structures (bile ducts, pancreatic ducts, and vessels; p < 0.05). Lesion detectability was rated excellent for both DL-HASTE-3 mm and DL-HASTE-5 mm (both: 5 [IQR4–5]) and was assigned higher scores than standard-HASTE-5 mm (4 [IQR4–5]; p < 0.001). DL-HASTE reduced the acquisition time by 63–69% compared with standard-HASTE-5 mm (p < 0.001). Conclusions: DL-HASTE is a robust abdominal MRI technique that improves image quality while at the same time reducing acquisition time compared with the routine clinical HASTE sequence. Using ultra-thin DL-HASTE-3 mm results in an even greater improvement with a similar SNR. Full article
(This article belongs to the Special Issue Imaging-Based Early Diagnosis of Cancers Using Artificial Intelligence)
►▼ Show Figures

Figure 1

21 pages, 3781 KiB  
Article
Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models
by Ghanashyam Sahoo, Ajit Kumar Nayak, Pradyumna Kumar Tripathy, Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Chandrakanta Mahanty and Saurav Mallik
Curr. Oncol. 2024, 31(11), 6577-6597; https://doi.org/10.3390/curroncol31110486 - 24 Oct 2024
Cited by 1 | Viewed by 1750
Abstract
Relapse and metastasis occur in 30–40% of breast cancer patients, even after targeted treatments like trastuzumab for HER2-positive breast cancer. Accurate individual prognosis is essential for determining appropriate adjuvant treatment and early intervention. This study aims to enhance relapse and metastasis prediction using [...] Read more.
Relapse and metastasis occur in 30–40% of breast cancer patients, even after targeted treatments like trastuzumab for HER2-positive breast cancer. Accurate individual prognosis is essential for determining appropriate adjuvant treatment and early intervention. This study aims to enhance relapse and metastasis prediction using an innovative framework with machine learning (ML) and ensemble learning (EL) techniques. The developed framework is analyzed using The Cancer Genome Atlas (TCGA) data, which has 123 HER2-positive breast cancer patients. Our two-stage experimental approach first applied six basic ML models (support vector machine, logistic regression, decision tree, random forest, adaptive boosting, and extreme gradient boosting) and then ensembled these models using weighted averaging, soft voting, and hard voting techniques. The weighted averaging ensemble approach achieved enhanced performances of 88.46% accuracy, 89.74% precision, 94.59% sensitivity, 73.33% specificity, 92.11% F-Value, 71.07% Mathew’s correlation coefficient, and an AUC of 0.903. This framework enables the accurate prediction of relapse and metastasis in HER2-positive breast cancer patients using H&E images and clinical data, thereby assisting in better treatment decision-making. Full article
(This article belongs to the Special Issue Imaging-Based Early Diagnosis of Cancers Using Artificial Intelligence)
►▼ Show Figures

Figure 1

Review

Jump to: Research

36 pages, 6144 KiB  
Review
Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions
by Tuan D. Pham, Muy-Teck Teh, Domniki Chatzopoulou, Simon Holmes and Paul Coulthard
Curr. Oncol. 2024, 31(9), 5255-5290; https://doi.org/10.3390/curroncol31090389 - 6 Sep 2024
Cited by 2 | Viewed by 4685
Abstract
Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. [...] Read more.
Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes. Full article
(This article belongs to the Special Issue Imaging-Based Early Diagnosis of Cancers Using Artificial Intelligence)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Diagnostic Performance of Combined Conventional CT Imaging Features and Radiomics Signature in Differentiating Grade 1 Tumors from Higher Grade Pancreatic Neuroendocrine Neoplasms
Author: Kawamoto
Highlights: • Conventional imaging features provided higher sensitivity, while radiomics demonstrated greater specificity in identifying G1 PanNETs. • Combining conventional imaging, radiomics, and clinical data yielded the best performance, with 94% specificity, 69% sensitivity, and 79% accuracy in identifying G1 PanNETs. • This model may aid in distinguishing G1 PanNETs from higher-grade tumors and guide patient management.

Back to TopTop