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Diagnostics
Special Issues
Imaging Diagnosis in Abdomen, 2nd Edition
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Imaging Diagnosis in Abdomen, 2nd Edition

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1127

Special Issue Editors

Dr. Piero Boraschi

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Guest Editor
Department of Diagnostic and Interventional Radiology, and Nuclear Medicine, Pisa University Hospital, Via Paradisa 2, 56124 Pisa, Italy
Interests: abdominal radiology; MR imaging; diffusion and perfusion MR imaging; oncologic imaging; liver; pancreas; biliary tract; recctal cancer; inflammatory bowel disease; liver transplantation
Special Issues, Collections and Topics in MDPI journals
Dr. Francescamaria Donati

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Guest Editor
Department of Diagnostic and Interventional Radiology, and Nuclear Medicine, Pisa University Hospital, Via Paradisa 2, 56124 Pisa, Italy
Interests: abdominal radiology; MR imaging; diffusion and perfusion MR imaging; oncologic imaging; liver; pancreas; biliary tract; recctal cancer; inflammatory bowel disease; liver transplantation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After a successful first edition of the Special Issue “Imaging Diagnosis in Abdomen” (https://www.mdpi.com/journal/diagnostics/special_issues/Abdomen_Imaging), with a total of eleven papers—including eight review articles—dealing with various aspects of imaging in abdominal disease, we are pleased to announce a second edition.

Various imaging modalities play important roles in evaluating abdominal abnormalities, with each technique having specific strengths and weaknesses. As the title suggests, our Special Issue aims to address the most recent advancements and new frontiers in imaging diagnosis in the abdomen. Novel diagnostic approaches are now available for the abdomen, although they are not always routinely used in clinical practice. However, their respective roles in the diagnostic management of abdominal diseases are still debated, even in dedicated multidisciplinary teams.

In conclusion, this Special Issue aims to present a collection of review articles and original contributions on current progress in abdominal imaging with the aim of improving the quality of patient care.

Dr. Piero Boraschi
Dr. Francescamaria Donati
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

  • ultrasound computed tomography
  • MR imaging
  • PET imaging diagnosis
  • oncologic imaging
  • therapeutic response assessment
  • biomarkers
  • multidisciplinary management

Published Papers (1 paper)

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Research

16 pages, 2728 KiB  
Article
Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment
by Vincenza Granata, Roberta Fusco, Maria Chiara Brunese, Gerardo Ferrara, Fabiana Tatangelo, Alessandro Ottaiano, Antonio Avallone, Vittorio Miele, Nicola Normanno, Francesco Izzo and Antonella Petrillo
Diagnostics 2024, 14(2), 152; https://doi.org/10.3390/diagnostics14020152 - 09 Jan 2024
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Abstract
Purpose: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases. Methods: Patients with MRI in a pre-surgical setting were [...] Read more.
Purpose: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases. Methods: Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon–Mann–Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Results: The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%. Conclusions: Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature. Full article
(This article belongs to the Special Issue Imaging Diagnosis in Abdomen, 2nd Edition)
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