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Life
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Advances in Imaging Oncology
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Advances in Imaging Oncology

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: closed (26 December 2023) | Viewed by 3068

Special Issue Editor

Dr. Carmine Picone

E-Mail Website
Guest Editor
Istituto Nazionale Tumori di Napoli, IRCCS “G. Pascale”, Via Mariano Semmola, 80131 Naples, Italy
Interests: cancer; lung; imaging oncology

Special Issue Information

Dear Colleagues,

Proper cancer management requires timely and reliable diagnosis to identify the primary tumor and assess its spread. This process, technically called ‘staging’, is of paramount importance for planning the best therapeutic approach to be taken, as staging determines prognosis and consequently therapy.

There are powerful diagnostic modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine techniques such as single photon emission computed tomography (SPECT) and positron emission tomography (PET). Diagnostic radiology techniques such as CT and conventional magnetic resonance imaging identify morphological alterations, while nuclear medicine techniques, particularly PET, and to some extent advanced MRI techniques, have the ability to study the metabolism of injuries. The purpose of this section is to discuss the potential of recent advances in cancer imaging for diagnosis, staging, prognosis, therapy planning, and evaluation of response to treatment.

Recent developments and new technical imaging applications will be analyzed, such as elastography, virtual endoscopy, dual-energy spectral computed tomography, advanced computing techniques (including volumetric rendering techniques and machine learning), and magnetic resonance imaging (MR) spectroscopy. In addition, the clinical value of functional and molecular imaging techniques such as diffusion-weighted MR imaging, dynamic imaging with a contrast material, blood-oxygen-dependent imaging, positron emission tomography using different radiotracers, and MR spectroscopy is examined.

Finally, the future role of tumor-heterogeneity-imaging-based analysis and multiparametric imaging, the development of radiomics and radiogenomics, and future challenges for imaging are discussed.

Dr. Carmine Picone
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. Life 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 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

  • imaging oncology
  • dual-energy spectral CT
  • MR imaging
  • PET/CT imaging
  • future imaging

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Published Papers (1 paper)

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Review

17 pages, 2410 KiB  
Review
Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives
by Pasquale Avella, Micaela Cappuccio, Teresa Cappuccio, Marco Rotondo, Daniela Fumarulo, Germano Guerra, Guido Sciaudone, Antonella Santone, Francesco Cammilleri, Paolo Bianco and Maria Chiara Brunese
Life 2023, 13(10), 2027; https://doi.org/10.3390/life13102027 - 9 Oct 2023
Cited by 6 | Viewed by 2734
Abstract
Background: Artificial Intelligence (AI)-based analysis represents an evolving medical field. In the last few decades, several studies have reported the diagnostic efficiency of AI applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to early detect liver metastases (LM), mainly from colorectal [...] Read more.
Background: Artificial Intelligence (AI)-based analysis represents an evolving medical field. In the last few decades, several studies have reported the diagnostic efficiency of AI applied to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to early detect liver metastases (LM), mainly from colorectal cancer. Despite the increase in information and the development of different procedures in several radiological fields, an accurate method of predicting LM has not yet been found. This review aims to compare the diagnostic efficiency of different AI methods in the literature according to accuracy, sensibility, precision, and recall to identify early LM. Methods: A narrative review of the literature was conducted on PubMed. A total of 336 studies were screened. Results: We selected 17 studies from 2012 to 2022. In total, 14,475 patients were included, and more than 95% were affected by colorectal cancer. The most frequently used imaging tool to early detect LM was found to be CT (58%), while MRI was used in three cases. Four different AI analyses were used: deep learning, radiomics, machine learning, and fuzzy systems in seven (41.18%), five (29.41%), four (23.53%), and one (5.88%) cases, respectively. Four studies achieved an accuracy of more than 90% after MRI and CT scan acquisition, while just two reported a recall rate ≥90% (one method using MRI and CT and one CT). Conclusions: Routinely acquired radiological images could be used for AI-based analysis to early detect LM. Simultaneous use of radiomics and machine learning analysis applied to MRI or CT images should be an effective method considering the better results achieved in the clinical scenario. Full article
(This article belongs to the Special Issue Advances in Imaging Oncology)
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