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Diagnostics
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Advances in the Diagnosis of Cancer/Tumors
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Advances in the Diagnosis of Cancer/Tumors

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 260

Special Issue Editor

Dr. Damiano Caruso

E-Mail Website
Guest Editor
Radiology Unit, Department of Surgical and Medical Sciences and Translational Medicine, Sant’Andrea University Hospital, University of Rome Sapienza, Via di Grottarossa 1035, 00189 Rome, Italy
Interests: imaging; oncology; CT; MRI; artificial intelligence; radiomics; response to therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer imaging stands at the forefront of modern oncology, revolutionizing how we detect, diagnose, and monitor cancerous growths within the human body. Utilizing advanced technologies such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and various molecular imaging techniques, researchers and clinicians can visualize tumors with unprecedented precision and detail. These imaging modalities not only aid in early detection but also play a crucial role in treatment planning and monitoring therapeutic responses.

In the realm of scientific collaboration for paper submissions, cancer imaging presents a rich field for interdisciplinary cooperation. Collaborative efforts could involve radiologists, oncologists, physicists, engineers, computer scientists, and bioinformaticians, among others. Together, these experts can contribute diverse perspectives and skills to addressing complex challenges in cancer imaging, such as image interpretation algorithms, image-guided interventions, novel contrast agents, and multimodal imaging integration. 

Through scientific paper submissions, these collaborations serve as vehicles for disseminating groundbreaking discoveries, fostering innovation, and shaping the future landscape of cancer imaging and oncology.

Dr. Damiano Caruso
Guest Editor

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. Diagnostics 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 2600 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

  • oncologic imaging
  • cancer imaging
  • MRI
  • CT
  • X-ray
  • machine learning
  • radiodiagnostics
  • theranostics

Published Papers (1 paper)

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Research

11 pages, 2471 KiB  
Article
Improvement of Breast Cancer Detection Using Dual-Layer Spectral CT
by Felix Christian Hasse, Athanasios Giannakis, Eckhard Wehrse, Wolfram Stiller, Markus Wallwiener, Hans-Ulrich Kauczor, Tim F. Weber, Jörg Heil and Theresa Mokry
Diagnostics 2024, 14(14), 1560; https://doi.org/10.3390/diagnostics14141560 (registering DOI) - 19 Jul 2024
Abstract
This study aimed to investigate the diagnostic performance of breast mass detection on monoenergetic image data at 40 keV (MonoE40) and on iodine maps (IM) compared with conventional image data (CI). In this prospective single-center case-control study, 50 breast cancer patients were examined [...] Read more.
This study aimed to investigate the diagnostic performance of breast mass detection on monoenergetic image data at 40 keV (MonoE40) and on iodine maps (IM) compared with conventional image data (CI). In this prospective single-center case-control study, 50 breast cancer patients were examined using contrast-enhanced dual-layer spectral CT. For qualitative and quantitative comparison of MonoE40 and IM with CI image data, four blinded, independent readers assessed 300 randomized single slices (two slices for each imaging type per case) with or without cancerous lesions for the presence of a breast mass. Detection sensitivity and specificity were calculated and readers rated their subjective diagnostic certainty. For statistical analysis of sensitivity and specificity, a paired t-test and ANOVA were used (significance level p = 0.05). A total of 50 female patients (median age 51 years, range 28–83 years) participated. IM had the highest overall scores in sensitivity and specificity for breast cancer detection, with 0.97 ± 0.06 and 0.95 ± 0.07, respectively, compared with 0.90 ± 0.04 and 0.92 ± 0.06 in CI. MonoE40 yielded a sensitivity of 0.96 ± 0.02 and specificity of 0.94 ± 0.08. All differences in sensitivity and specificity between MonoE or IM and CI were statistically significant (p < 0.001). The superiority of IM sensitivity and specificity was most pronounced in patients with dense breasts. Spectral CT improved the detection of breast cancer with higher sensitivity and specificity compared to conventional image data in our study. Full article
(This article belongs to the Special Issue Advances in the Diagnosis of Cancer/Tumors)
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