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Advances in Medical Imaging for Cancer Detection and Diagnosis

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: closed (1 May 2026) | Viewed by 10877

Special Issue Editors


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Guest Editor
Department of Radiology, University of Iowa, Iowa City, IA, USA
Interests: visual search; image perception; artificial intelligence

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Guest Editor
Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA
Interests: imaging; machine learning

Special Issue Information

Dear Colleagues,

Cancer accounts for nearly 10 million annual deaths worldwide, making it a leading cause for mortality. Moreover, the estimated global economic costs of cancers from 2020 to 2050 is $25.2 trillion international dollars1. This is equivalent to an annual tax of 0.55% on the global domestic product1. Medical imaging is a key component of early cancer detection and diagnosis, being it in the form of radiological images, in the form of histopathological or cytopathological images, etc. Screening programs that are based on imaging worldwide have shown the importance of radiology in early-stage cancer detection. Furthermore, digitization in pathology has allowed for collaborations across continents and for the development of computer aid in that domain.

In this Special Issue, we are seeking to determine new imaging techniques, as well as applications of Artificial Intelligence (AI) and Machine Learning (ML), for cancer detection and diagnosis. Applications are sought for (but are not limited to) the following areas:

  • Photon-counting CT applications and developments;
  • Radiomics;
  • AI and ML applications;
  • Collaborative CAD/AI;
  • Computational Pathology;
  • Multimodality imaging of cancer, especially in vivo;
  • Emerging cancer imaging modalities.

Reference
1. Chen S et al. JAMA Oncology 2023; 9(4):465-472.

Dr. Claudia R. Mello-Thoms
Dr. Mark Anastasio
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 250 words) can be sent to the Editorial Office for assessment.

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

  • photon-counting CT applications and developments
  • radiomics
  • AI and ML applications
  • collaborative CAD/AI
  • computational pathology
  • multimodality imaging of cancer, especially in vivo
  • emerging cancer imaging modalities
  • cancer imaging
  • machine learning
  • artificial intelligence
  • medical imaging

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Published Papers (3 papers)

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Research

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12 pages, 1918 KB  
Article
18F-FDG PET/CT for Risk Stratification and Prognosis of Patients with Hypermetabolic Gastrointestinal Stromal Tumors
by Li Zhang, Yu Liu, Chunxia Qin, Huanyu Chen, Yujun Wu, Jinbo Gui, Jingwen Wang, Yong He, Xiaoli Lan and Wei Cao
Cancers 2026, 18(5), 717; https://doi.org/10.3390/cancers18050717 - 24 Feb 2026
Viewed by 687
Abstract
Objectives: We aimed to evaluate the value of various PET parameters derived from 18F- FDG PET/CT for risk stratification and prognosis of hypermetabolic gastrointestinal stromal tumors (GISTs). Methods: This study included 43 patients who underwent 18F-FDG PET/CT imaging with hypermetabolic (SUVmax [...] Read more.
Objectives: We aimed to evaluate the value of various PET parameters derived from 18F- FDG PET/CT for risk stratification and prognosis of hypermetabolic gastrointestinal stromal tumors (GISTs). Methods: This study included 43 patients who underwent 18F-FDG PET/CT imaging with hypermetabolic (SUVmax > 2.5) GIST and underwent surgical treatment. Clinicopathological characteristics, risk stratification, PET parameters including standard uptake values (SUVs), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity index (HI), and follow-up data were reviewed. The relationship between PET parameters and risk stratification based on the modified National Institutes of Health (NIH) criteria was analyzed. PET parameters were assessed to predict relapse-free survival (RFS) and overall survival (OS), based on Cox regression analysis and Kaplan–Meier analysis. Results: The median follow-up duration was 50 months. During follow-up, 11 patients (25.58%) experienced recurrence and 8 (18.60%) died. In risk stratification, the high-risk group exhibited more frequent extragastric location, larger tumor size, higher mitotic count, and elevated PET parameters except SUVmax. MTV (≤32.68 vs. >32.68, 95% CI: 1.358–72.048, p = 0.024) emerged as an independent PET parameter of risk stratification. In univariate analysis, tumor location (gastric vs. extragastric), SUVmax (≤10.25 vs. >10.25), and HI (≤2.44 vs. >2.44) were significant prognostic factors for RFS. Tumor location and SUVmax were significant to OS on univariate analysis. However, in multivariate analysis, only SUVmax (95% CI: 1.549–46.071, p = 0.014) was an independent prognostic factor for both RFS and OS. Conclusions: 18F-FDG PET/CT demonstrates predictive value for hypermetabolic GIST patients. MTV derived from 18F-FDG PET/CT improves the ability of predicting risk stratification. SUVmax is an effective predictor of both RFS and OS. Full article
(This article belongs to the Special Issue Advances in Medical Imaging for Cancer Detection and Diagnosis)
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Review

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31 pages, 2057 KB  
Review
Clinical AI in Radiology: Foundations, Trends, Applications, and Emerging Directions
by Iryna Hartsock, Nikolas Koutsoubis, Sabeen Ahmed, Nathan Parker, Matthew B. Schabath, Cyrillo Araujo, Aliya Qayyum, Cesar Lam, Robert A. Gatenby and Ghulam Rasool
Cancers 2026, 18(6), 942; https://doi.org/10.3390/cancers18060942 - 13 Mar 2026
Viewed by 2917
Abstract
Artificial intelligence (AI) is at the vanguard of transforming radiology in several ways, including augmenting diagnoses, improving workflows, and increasing operational efficiency. Several integration challenges, including concerns over privacy, clinical usability, and workflow compatibility, still remain. This review discusses the foundations and current [...] Read more.
Artificial intelligence (AI) is at the vanguard of transforming radiology in several ways, including augmenting diagnoses, improving workflows, and increasing operational efficiency. Several integration challenges, including concerns over privacy, clinical usability, and workflow compatibility, still remain. This review discusses the foundations and current trends of clinical AI in radiology to provide essential context for ongoing developments. To illustrate translational potential, we describe representative applications, including: (1) local deployment of large language models (LLMs) for restructuring and streamlining radiology reports, improving clarity and consistency without relying on external resources; (2) multimodal AI frameworks combining CT images, clinical data, laboratory biomarkers, and LLM-extracted features from clinical notes for early detection of cachexia in pancreatic cancer; (3) privacy-preserving federated learning (FL) infrastructure enabling collaborative AI model development across institutions without sharing raw patient data; and (4) an uncertainty-aware de-identification pipeline for removing Protected Health Information (PHI) from radiology images and clinical reports to support secure data analysis and sharing. We further discuss emerging opportunities for tumor board decision support, clinical trial matching, radiology report quality assurance, and the development of an imaging complexity index. Collectively, these applications highlight the importance of local deployment, multimodal reasoning, privacy preservation, and human-in-the-loop oversight in translating AI models from research to oncology radiology practice. Full article
(This article belongs to the Special Issue Advances in Medical Imaging for Cancer Detection and Diagnosis)
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21 pages, 978 KB  
Review
Evolving and Novel Applications of Artificial Intelligence in Cancer Imaging
by Mustaqueem Pallumeera, Jonathan C. Giang, Ramanpreet Singh, Nooruddin S. Pracha and Mina S. Makary
Cancers 2025, 17(9), 1510; https://doi.org/10.3390/cancers17091510 - 30 Apr 2025
Cited by 18 | Viewed by 6289
Abstract
Artificial intelligence (AI) is revolutionizing cancer imaging, enhancing screening, diagnosis, and treatment options for clinicians. AI-driven applications, particularly deep learning and machine learning, excel in risk assessment, tumor detection, classification, and predictive treatment prognosis. Machine learning algorithms, especially deep learning frameworks, improve lesion [...] Read more.
Artificial intelligence (AI) is revolutionizing cancer imaging, enhancing screening, diagnosis, and treatment options for clinicians. AI-driven applications, particularly deep learning and machine learning, excel in risk assessment, tumor detection, classification, and predictive treatment prognosis. Machine learning algorithms, especially deep learning frameworks, improve lesion characterization and automated segmentation, leading to enhanced radiomic feature extraction and delineation. Radiomics, which quantifies imaging features, offers personalized treatment response predictions across various imaging modalities. AI models also facilitate technological improvements in non-diagnostic tasks, such as image optimization and automated medical reporting. Despite advancements, challenges persist in integrating AI into healthcare, tracking accurate data, and ensuring patient privacy. Validation through clinician input and multi-institutional studies is essential for patient safety and model generalizability. This requires support from radiologists worldwide and consideration of complex regulatory processes. Future directions include elaborating on existing optimizations, integrating advanced AI techniques, improving patient-centric medicine, and expanding healthcare accessibility. AI can enhance cancer imaging, optimizing precision medicine and improving patient outcomes. Ongoing multidisciplinary collaboration between radiologists, oncologists, software developers, and regulatory bodies is crucial for AI’s growing role in clinical oncology. This review aims to provide an overview of the applications of AI in oncologic imaging while also discussing their limitations. Full article
(This article belongs to the Special Issue Advances in Medical Imaging for Cancer Detection and Diagnosis)
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