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Clinical Advances in Oncology Imaging
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Clinical Advances in Oncology Imaging

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nuclear Medicine & Radiology".

Deadline for manuscript submissions: 8 July 2024 | Viewed by 3479

Special Issue Editor

Dr. Claudia Lucia Piccolo

E-Mail Website
Guest Editor
Department of Radiology, Fondazione Policlinico Campus Bio-Medico University, 00128 Rome, Italy
Interests: oncologic imaging; female pelvic imaging; endometriosis; breast imaging; magnetic resonance imaging; computed tomography; contrast-enhanced mammography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radiology is crucial in cancer imaging, addressing information in diagnosis staging, response assessment, and prognosis. Compared to histopathological analysis, it has the unique advantage of being a non-invasive tool which can assess the whole tumor unbiased by sampling errors and is routinely acquired at multiple time points in oncological practice.

For more than a decade, we have witnessed the emergence of new tools and techniques in oncologic imaging, such as dual energy CT, contrast-enhanced mammography, and artificial intelligence. The use of dual-energy spectral, weighted average, color-coded maps, and virtual unenhanced images provides increased visual detection and easy lesion delineation. Lesion detectability and sensitivity are significantly improved by means of DECT. In breast imaging, contrast-enhanced mammography has been demonstrated to be useful in indications such as abnormal screenings, symptomatic patients, preoperative staging of breast cancer, evaluation of response to neoadjuvant chemotherapy, screening of women with dense breasts, and screening of women at an increased risk of developing breast cancer. Artificial intelligence can reveal previously undetected radiographic patterns that are difficult to ascertain via the human sensory system. AI may shift the clinical workflow of radiological detection, management decisions, and subsequent observation to a paradigm that is yet to be envisioned.

This Special Issue aims to provide new information on emerging techniques in oncologic imaging, focusing on imaging acquisition, cancer screening, treatment planning, and response monitoring. It will also cover studies on artificial intelligence and its emerging paradigms and opportunities.

Dr. Claudia Lucia Piccolo
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. Journal of Clinical Medicine 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
  • dual energy technique
  • artificial intelligence
  • magnetic resonance imaging
  • preoperative local staging
  • response assessment

Published Papers (4 papers)

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Research

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10 pages, 1130 KiB  
Article
Contrast-Enhanced Mammography-Guided Biopsy: Preliminary Results of a Single-Center Retrospective Experience
by Matteo Sammarra, Claudia Lucia Piccolo, Marina Sarli, Rita Stefanucci, Manuela Tommasiello, Paolo Orsaria, Vittorio Altomare and Bruno Beomonte Zobel
J. Clin. Med. 2024, 13(4), 933; https://doi.org/10.3390/jcm13040933 - 06 Feb 2024
Viewed by 674
Abstract
Background: CEM-guided breast biopsy is an advanced diagnostic procedure that takes advantage of the ability of CEM to enhance suspicious breast lesions. The aim pf this paper is to describe a single-center retrospective experience on CEM-guided breast biopsy in terms of procedural features [...] Read more.
Background: CEM-guided breast biopsy is an advanced diagnostic procedure that takes advantage of the ability of CEM to enhance suspicious breast lesions. The aim pf this paper is to describe a single-center retrospective experience on CEM-guided breast biopsy in terms of procedural features and histological outcomes. Methods: 69 patients underwent the procedure. Patient age, breast density, presentation, dimensions, and lesion target enhancement were recorded. All the biopsy procedures were performed using a 7- or 10-gauge (G) vacuum-assisted biopsy needle. The procedural approach (horizontal or vertical) and the decubitus of the patient (lateral or in a sitting position) were noted. Results: A total of 69 patients underwent a CEM-guided biopsy. Suspicious lesions presented as mass enhancement in 35% of cases and non-mass enhancement in 65% of cases. The median size of the target lesions was 20 mm. The median procedural time for each biopsy was 10 ± 4 min. The patients were placed in a lateral decubitus position in 52% of cases and seated in 48% of cases. The most common approach was horizontal (57%). The mean AGD was 14.8 mGy. At histology, cancer detection rate was 28% (20/71). Conclusions: CEM-guided biopsy was feasible, with high procedure success rates and high tolerance by the patients. Full article
(This article belongs to the Special Issue Clinical Advances in Oncology Imaging)
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12 pages, 13460 KiB  
Article
Usefulness of Three-Dimensional Iodine Mapping Quantified by Dual-Energy CT for Differentiating Thymic Epithelial Tumors
by Shuhei Doi, Masahiro Yanagawa, Takahiro Matsui, Akinori Hata, Noriko Kikuchi, Yuriko Yoshida, Kazuki Yamagata, Keisuke Ninomiya, Shoji Kido and Noriyuki Tomiyama
J. Clin. Med. 2023, 12(17), 5610; https://doi.org/10.3390/jcm12175610 - 28 Aug 2023
Viewed by 688
Abstract
Background: Dual-energy CT has been reported to be useful for differentiating thymic epithelial tumors. The purpose is to evaluate thymic epithelial tumors by using three-dimensional (3D) iodine density histogram texture analysis on dual-energy CT and to investigate the association of extracellular volume [...] Read more.
Background: Dual-energy CT has been reported to be useful for differentiating thymic epithelial tumors. The purpose is to evaluate thymic epithelial tumors by using three-dimensional (3D) iodine density histogram texture analysis on dual-energy CT and to investigate the association of extracellular volume fraction (ECV) with the fibrosis of thymic carcinoma. Methods: 42 patients with low-risk thymoma (n = 20), high-risk thymoma (n = 16), and thymic carcinoma (n = 6) were scanned by dual-energy CT. 3D iodine density histogram texture analysis was performed for each nodule on iodine density mapping: Seven texture features (max, min, median, average, standard deviation [SD], skewness, and kurtosis) were obtained. The iodine effect (average on DECT180s—average on unenhanced DECT) and ECV on DECT180s were measured. Tissue fibrosis was subjectively rated by one pathologist on a three-point grade. These quantitative data obtained by examining associations with thymic carcinoma and high-risk thymoma were analyzed with univariate and multivariate logistic regression models (LRMs). The area under the curve (AUC) was calculated by the receiver operating characteristic curves. p values < 0.05 were significant. Results: The multivariate LRM showed that ECV > 21.47% in DECT180s could predict thymic carcinoma (odds ratio [OR], 11.4; 95% confidence interval [CI], 1.18–109; p = 0.035). Diagnostic performance was as follows: Sensitivity, 83.3%; specificity, 69.4%; AUC, 0.76. In high-risk thymoma vs. low-risk thymoma, the multivariate LRM showed that the iodine effect ≤1.31 mg/cc could predict high-risk thymoma (OR, 7; 95% CI, 1.02–39.1; p = 0.027). Diagnostic performance was as follows: Sensitivity, 87.5%; specificity, 50%; AUC, 0.69. Tissue fibrosis significantly correlated with thymic carcinoma (p = 0.026). Conclusions: ECV on DECT180s related to fibrosis may predict thymic carcinoma from thymic epithelial tumors, and the iodine effect on DECT180s may predict high-risk thymoma from thymoma. Full article
(This article belongs to the Special Issue Clinical Advances in Oncology Imaging)
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Review

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13 pages, 5650 KiB  
Review
Fat Matters: Exploring Cancer Risk through the Lens of Computed Tomography and Visceral Adiposity
by Federico Greco, Claudia Lucia Piccolo, Valerio D’Andrea, Arnaldo Scardapane, Bruno Beomonte Zobel and Carlo Augusto Mallio
J. Clin. Med. 2024, 13(2), 453; https://doi.org/10.3390/jcm13020453 - 14 Jan 2024
Viewed by 930
Abstract
Obesity is an established risk factor for cancer. However, conventional measures like body mass index lack precision in assessing specific tissue quantities, particularly of the two primary abdominal fat compartments, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Computed tomography (CT) stands [...] Read more.
Obesity is an established risk factor for cancer. However, conventional measures like body mass index lack precision in assessing specific tissue quantities, particularly of the two primary abdominal fat compartments, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Computed tomography (CT) stands as the gold standard for precisely quantifying diverse tissue types. VAT, distinguished by heightened hormonal and metabolic activity, plays a pivotal role in obesity-related tumor development. Excessive VAT is linked to aberrant secretion of adipokines, proinflammatory cytokines, and growth factors, fostering the carcinogenesis of obesity-related tumors. Accurate quantification of abdominal fat compartments is crucial for understanding VAT as an oncological risk factor. The purpose of the present research is to elucidate the role of CT, performed for staging purposes, in assessing VAT (quantity and distribution) as a critical factor in the oncogenesis of obesity-related tumors. In the field of precision medicine, this work takes on considerable importance, as quantifying VAT in oncological patients becomes fundamental in understanding the influence of VAT on cancer development–the potential “phenotypic expression” of excessive VAT accumulation. Previous studies analyzed in this research showed that VAT is a risk factor for clear cell renal cell carcinoma, non-clear cell renal cell carcinoma, prostate cancer, and hepatocarcinoma recurrence. Further studies will need to quantify VAT in other oncological diseases with specific mutations or gene expressions, in order to investigate the relationship of VAT with tumor genomics. Full article
(This article belongs to the Special Issue Clinical Advances in Oncology Imaging)
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Other

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19 pages, 504 KiB  
Systematic Review
Can Machine Learning Models Detect and Predict Lymph Node Involvement in Prostate Cancer? A Comprehensive Systematic Review
by Eliodoro Faiella, Federica Vaccarino, Raffaele Ragone, Giulia D’Amone, Vincenzo Cirimele, Claudia Lucia Piccolo, Daniele Vertulli, Rosario Francesco Grasso, Bruno Beomonte Zobel and Domiziana Santucci
J. Clin. Med. 2023, 12(22), 7032; https://doi.org/10.3390/jcm12227032 - 10 Nov 2023
Viewed by 942
Abstract
(1) Background: Recently, Artificial Intelligence (AI)-based models have been investigated for lymph node involvement (LNI) detection and prediction in Prostate cancer (PCa) patients, in order to reduce surgical risks and improve patient outcomes. This review aims to gather and analyze the few studies [...] Read more.
(1) Background: Recently, Artificial Intelligence (AI)-based models have been investigated for lymph node involvement (LNI) detection and prediction in Prostate cancer (PCa) patients, in order to reduce surgical risks and improve patient outcomes. This review aims to gather and analyze the few studies available in the literature to examine their initial findings. (2) Methods: Two reviewers conducted independently a search of MEDLINE databases, identifying articles exploring AI’s role in PCa LNI. Sixteen studies were selected, and their methodological quality was appraised using the Radiomics Quality Score. (3) Results: AI models in Magnetic Resonance Imaging (MRI)-based studies exhibited comparable LNI prediction accuracy to standard nomograms. Computed Tomography (CT)-based and Positron Emission Tomography (PET)-CT models demonstrated high diagnostic and prognostic results. (4) Conclusions: AI models showed promising results in LN metastasis prediction and detection in PCa patients. Limitations of the reviewed studies encompass retrospective design, non-standardization, manual segmentation, and limited studies and participants. Further research is crucial to enhance AI tools’ effectiveness in this area. Full article
(This article belongs to the Special Issue Clinical Advances in Oncology Imaging)
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