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Diagnostics
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Advanced Diagnostic Imaging in Thoracic and Abdominal Oncological Pathology
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Advanced Diagnostic Imaging in Thoracic and Abdominal Oncological Pathology

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 7362

Special Issue Editors

Dr. Paolo Marra

E-Mail Website
Guest Editor
1. Department of Radiology, ASST Papa Giovanni XXIII Hospital, 24127 Bergamo, Italy
2. School of Medicine and Surgery, University of Milano-Bicocca, 24127 Bergamo, Italy
Interests: liver vascular malformations and portal vein thrombosis; vascular and biliary complications of liver transplant; liver cancer; pediatric interventional radiology; contrast-enhanced and color doppler ultrasound; multiparametric MRI; CT-angiography
Special Issues, Collections and Topics in MDPI journals
Dr. Davide Ippolito

E-Mail Website
Guest Editor
1. Department of Diagnostic Radiology, "San Gerardo" Hospital, Via Pergolesi 33, 20900 Monza, MB, Italy
2. School of Medicine, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, MB, Italy
Interests: liver; biliary; prostate; lung; emergency

Special Issue Information

Dear Colleagues,

Imaging plays a crucial role for cancer patients since cancer management, from diagnosis to treatment, primarily relies on radiological findings. Radiological and nuclear medicine assessment provides accurate information about disease staging, which is the key to selecting the most effective therapeutic strategy.

Recently, due to the development and rapid spread of immunotherapy—which relies on immune check points inhibitors, monoclonal antibodies, T-cell transfer, immune system modulators and vaccines—the ability of imaging to provide specific answers to clinical demands has been significantly challenged. Indeed, immunotherapy may produce paradoxical responses which are difficult to define according to conventional response morphological criteria. All these elements render it necessary to update cancer imaging knowledge.

The aim of this Special Issue is to report advancements in cancer imaging interpretation according to the most recent scientific evidence. It will focus on technological developments offering new tools for cancer imaging, such as whole-body MRI protocols, PET/MRI scanner and dual energy/spectral Computed Tomography. Emphasis will also be given to new tools for non-invasive tissue characterization, based on multiparametric and functional imaging and radiomics. Contributions regarding the rapidly growing importance of artificial intelligence (AI) and machine learning would be particularly appreciated. We believe that AI and machine learning may represent a milestone for objective imaging interpretation, which is key in cancer imaging.

Reflecting the aim of the journal, we encourage authors to submit original works even when their findings do not confirm the initial hypothesis in order to also highlight the pitfalls of imaging.

We invite authors from different radiological and nuclear medicine subspecialities to share their knowledge about advanced imaging in thoracic and abdominal cancers, contributing both original research and review articles on diagnosis, staging, follow-up and therapy response assessment.

Dr. Paolo Marra
Dr. Davide Ippolito
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. 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

  • immunotherapy response assessment
  • radiomics for tissue characterization
  • artificial intelligence and machine learning
  • functional and molecular imaging

Published Papers (2 papers)

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25 pages, 2250 KiB  
Review
Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future
by Michaela Cellina, Maurizio Cè, Giovanni Irmici, Velio Ascenti, Natallia Khenkina, Marco Toto-Brocchi, Carlo Martinenghi, Sergio Papa and Gianpaolo Carrafiello
Diagnostics 2022, 12(11), 2644; https://doi.org/10.3390/diagnostics12112644 - 31 Oct 2022
Cited by 28 | Viewed by 5113
Abstract
Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential [...] Read more.
Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario. Full article
(This article belongs to the Special Issue Advanced Diagnostic Imaging in Thoracic and Abdominal Oncological Pathology)
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4 pages, 1418 KiB  
Interesting Images
Sarcoidosis Mimicking Primary Lung Cancer on 99mTc-3PRGD2 Scintigraphy in a PTC Patient
by Ye Yang, Xi Jia, Yuanbo Wang, Yan Liu, Yu Liu and Rui Gao
Diagnostics 2022, 12(6), 1419; https://doi.org/10.3390/diagnostics12061419 - 8 Jun 2022
Viewed by 1630
Abstract
Sarcoidosis is a multi-system disease of unknown etiology that typically occurs in middle-aged adults, often presenting as the formation of granulomas in various organs, including the lungs. Non-typical pulmonary sarcoidosis is rare, and it isnecessary to distinguish its imaging features from lung cancer [...] Read more.
Sarcoidosis is a multi-system disease of unknown etiology that typically occurs in middle-aged adults, often presenting as the formation of granulomas in various organs, including the lungs. Non-typical pulmonary sarcoidosis is rare, and it isnecessary to distinguish its imaging features from lung cancer and tuberculosis. They may appear as an irregular mass with multiple nodules on thoracic computed tomography (CT). In this case, primary lung cancer was suspected in a 57-year-old papillary thyroid carcinoma patient, as the pulmonary lesions were non-radioiodine avid and progressed shortly afterward. The asymmetrical focal uptake that was demonstrated in integrin receptor imaging (99mTc-PEG4-E[PEG4-c(RGDfK)]2 (99mTc-3PRGD2)) warranted flexible-bronchoscope biopsy. Meanwhile, no evidence of malignancy was found, and pathological manifestations led to the subsequent six months of anti-tuberculosis treatment. Combined with the fact that standard anti-tuberculosis showed no improvement, and the patient’s condition was stabilized by corticosteroid treatment alone, a final diagnosis of sarcoidosis was made by an MDT (multidisciplinary consultation). Reported herein is the first case of a hyper vascularization condition within the non-typical asymmetrical sarcoidosis lesions, which should help to establish that the uptake of 3PRDG2 in sarcoidosis can avoid imaging pitfalls. Full article
(This article belongs to the Special Issue Advanced Diagnostic Imaging in Thoracic and Abdominal Oncological Pathology)
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