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Call for papers - Advances in cancer imaging: innovations, challenges, and clinical impacts

Guest Editors

Aldo Carnevale, MD, University of Ferrara, Italy
François Lucia, MD, PhD, University of Brest, France
Luca Urso, MD, University of Ferrara, Italy

Submission Status: Open   |   Submission Deadline: 4 November 2025


BMC Cancer is calling for submissions to our Collection on "Advances in cancer imaging." This Collection invites contributions focused on the latest advancements in cancer imaging, including innovations in MRI, CT, and PET scans, as well as the integration of AI and molecular imaging technologies. We seek research that addresses both the clinical impact and the challenges of implementing these techniques in practice, with the ultimate goal of improving cancer detection, treatment, and patient outcomes.

Meet the Guest Editors

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Aldo Carnevale, MD, University of Ferrara, Italy

Dr Aldo Carnevale is an Associate Professor of Radiology at the University of Ferrara with expertise in diagnostic imaging and clinical radiology. He also serves as the director of the postgraduate school in radiodiagnostics. His research focuses on the diagnosis and prognostication of interstitial lung diseases, oncology imaging with applications of artificial intelligence, the development and refinement of imaging-guided interventional procedures, and the study of metabolic skeletal diseases. Dr Carnevale is deeply engaged in academic and clinical research, striving to advance innovative diagnostic tools and therapeutic strategies to enhance patient care in these specialized areas.

François Lucia, MD, PhD, Brest University, France

Since 2023, Dr Lucia has been an Associate Professor and Hospital Practitioner in the Department of Radiation Oncology at Brest University Hospital. His research focuses on the impact of functional imaging in radiotherapy. He is investigating imaging biomarkers, particularly quantitative (radiomic) parameters to develop non-invasive predictive models of radiotherapy efficacy and toxicity, which involves artificial intelligence techniques, especially neural networks. Additionally, he is exploring new PET/CT radiotracers in lung cancer to limit radiotherapy toxicity and improve quality of life. His ongoing randomized trial evaluates lung perfusion PET/CT to personalize radiotherapy in small-volume lung tumors treated with stereotactic body radiotherapy. He is also developing new methods for defining functional lung volumes, and his future research will extend to advanced-stage lung cancers treated with chemoradiotherapy and immunotherapy. 

Luca Urso, MD, University of Ferrara, Italy

Dr Urso is an Assistant Professor at the University of Ferrara and Nuclear Medicine Physician at the University Hospital of Ferrara. He is author of more than 70 articles, and his main research topic is PET/CT imaging in oncology (i.e. uro-oncology, breast cancer and neuroendocrine tumors), radioligand therapy (in particular in neuroendocrine tumors and prostate cancer), and radiomic and artificial intelligence applied to molecular imaging. Since 2022, he has been an elected member of the scientific board of the oncology study group of the Italian Association of Nuclear Medicine and an elected member of GUOnext, a group of young researchers within GUONE, a multidisciplinary scientific association of uro-oncology. He has also been a member of BMC Cancer's editorial board since August 2023.

About the Collection

Cancer imaging has made huge advancements over recent years, enhancing our understanding of tumor biology and improving clinical outcomes. For example, techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) have evolved, offering greater sensitivity and specificity in tumor detection and characterization. CT has become the defining standard for the detection of most tumors, with helical and multidetector CT enhancing diagnostic accuracy and enabling 3D reconstruction of the urinary tract. MRI provides detailed, non-invasive imaging of soft tissues, facilitating advanced analysis of cardiac wall motion, perfusion, and in utero fetal development. PET enables functional non-invasive tumor imaging and is increasingly used for staging and assessing treatment response in most cancers.

Furthermore, the integration of molecular imaging and emerging technologies has facilitated more precise imaging modalities, allowing clinicians to visualize biological processes at the molecular level. Recent breakthroughs, such as the incorporation of artificial intelligence (AI) in imaging analysis and the development of radiomics, have the ability to transform cancer diagnosis and monitoring. These innovations not only improve tumor detection accuracy but also offer valuable insights into treatment response and prognosis.

Understanding the role of advanced imaging technologies is essential for early detection, personalized medicine, and comprehensive cancer management. This Collection aims to explore these innovations in cancer imaging, highlighting their clinical applications and the challenges that remain in translating these advancements into routine practice. Topics of interest include:

  • Advances in molecular imaging techniques
  • Radiomics and its role in cancer diagnosis
  • Early detection strategies using imaging modalities
  • Artificial intelligence in cancer imaging
  • Personalized medicine approaches in imaging
  • Imaging-guided precision oncology


All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Image credit: © Gorodenkoff / Stock.adobe.com

  1. The Node Reporting and Data System (Node-RADS) offers a reliable framework for lymph node assessment, but its prognostic significance remains unexplored. This study aims to investigate the added prognostic val...

    Authors: Yan Sun, Lu Wen, Wang Xiang, Xiangtong Luo, Lian Chen, Xiaohuang Yang, Yanhui Yang, Yi Zhang, Sanqiang Yu, Hua Xiao and Xiaoping Yu
    Citation: BMC Cancer 2025 25:598
    Content type: Research Published on:

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Advances in cancer imaging: innovations, challenges, and clinical impacts" from the dropdown menu.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.