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Radiomics and Pathomics: Clinical Applications and Next Steps

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 6550

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Special Issue Editor

Prof. Dr. Prateek Prasanna

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Guest Editor

Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
Interests: biomedical informatics; computational imaging; image analysis/machine learning

Special Issue Information

Dear Colleagues,

In the era of precision medicine, characterization of multifactorial diseases, such as cancer, requires information integration across multiple scales. Of late, features extracted from diagnostic radiology and pathology modalities have provided exciting opportunities toward the development of imaging indicators of cancer diagnosis and treatment outcome. Radiomics and pathomics featuring mining techniques have enabled the quantification of the tumor microenvironment across large-scale imaging datasets at an unprecedented level. These features have been further enriched by the incorporation of genomics into model development. The field continues to evolve with the rapid advancement in machine learning and promises to elevate the role of medical imaging by facilitating objective and more standardized cancer characterization.

The purpose of this Special Issue is to present new methods for radiomics and pathomics featuring interrogation and integration, and their subsequent applications in multiscale exploration of cancer pathophysiology. We especially encourage submissions addressing issues in feature generalizability, creation of open-source frameworks, and pathway to the translation of such features from bench to bedside.

Prof. Dr. Prateek Prasanna
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

Radiomics
Pathomics
Diagnostic radiology
Machine learning
Cancer pathophysiology

Published Papers (3 papers)

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Research

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12 pages, 548 KiB

Open AccessArticle

Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern

by Vincenza Granata, Roberta Fusco, Federica De Muzio, Carmen Cutolo, Mauro Mattace Raso, Michela Gabelloni, Antonio Avallone, Alessandro Ottaiano, Fabiana Tatangelo, Maria Chiara Brunese, Vittorio Miele, Francesco Izzo and Antonella Petrillo

Diagnostics 2022, 12(5), 1115; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12051115 - 29 Apr 2022

Cited by 20 | Viewed by 2377

Abstract

To assess Radiomics and Machine Learning Analysis in Liver Colon and Rectal Cancer Metastases (CRLM) Growth Pattern, we evaluated, retrospectively, a training set of 51 patients with 121 liver metastases and an external validation set of 30 patients with a single lesion. All patients were subjected to MRI studies in pre-surgical setting. For each segmented volume of interest (VOI), 851 radiomics features were extracted using PyRadiomics package. Nonparametric test, univariate, linear regression analysis and patter recognition approaches were performed. The best results to discriminate expansive versus infiltrative front of tumor growth with the highest accuracy and AUC at univariate analysis were obtained by the wavelet_LHH_glrlm_ShortRunLowGray Level Emphasis from portal phase of contrast study. With regard to linear regression model, this increased the performance obtained respect to the univariate analysis for each sequence except that for EOB-phase sequence. The best results were obtained by a linear regression model of 15 significant features extracted by the T2-W SPACE sequence. Furthermore, using pattern recognition approaches, the diagnostic performance to discriminate the expansive versus infiltrative front of tumor growth increased again and the best classifier was a weighted KNN trained with the 9 significant metrics extracted from the portal phase of contrast study, with an accuracy of 92% on training set and of 91% on validation set. In the present study, we have demonstrated as Radiomics and Machine Learning Analysis, based on EOB-MRI study, allow to identify several biomarkers that permit to recognise the different Growth Patterns in CRLM. Full article

(This article belongs to the Special Issue Radiomics and Pathomics: Clinical Applications and Next Steps)

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12 pages, 1439 KiB

Open AccessArticle

Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy

by Benedetta Favati, Rita Borgheresi, Marco Giannelli, Carolina Marini, Vanina Vani, Daniela Marfisi, Stefania Linsalata, Monica Moretti, Dionisia Mazzotta and Emanuele Neri

Diagnostics 2022, 12(4), 771; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12040771 - 22 Mar 2022

Cited by 1 | Viewed by 1916

Abstract

Background: A fair amount of microcalcifications sent for biopsy are false positives. The study investigates whether quantitative radiomic features extracted from digital breast tomosynthesis (DBT) can be an additional and useful tool to discriminate between benign and malignant BI-RADS category 4 microcalcification. Methods: This retrospective study included 252 female patients with BI-RADS category 4 microcalcifications. The patients were divided into two groups according to micro-histopathology: 126 patients with benign lesions and 126 patients with certain or possible malignancies. A total of 91 radiomic features were extracted for each patient, and the 12 most representative features were selected by using the agglomerative hierarchical clustering method. The binary classification task of the two groups was carried out by using four different machine-learning algorithms (i.e., linear support vector machine (SVM), radial basis function (RBF) SVM, logistic regression (LR), and random forest (RF)). Accuracy, sensitivity, sensibility, and the area under the curve (AUC) were calculated for each of them. Results: The best performance was achieved using the RF classifier (AUC = 0.59, 95% confidence interval 0.57–0.60; sensitivity = 0.56, 95% CI 0.54–0.58; specificity = 0.61, 95% CI 0.59–0.63; accuracy = 0.58, 95% CI 0.57–0.59). Conclusions: DBT-based radiomic analysis seems to have only limited potential in discriminating benign from malignant microcalcifications. Full article

(This article belongs to the Special Issue Radiomics and Pathomics: Clinical Applications and Next Steps)

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11 pages, 676 KiB

Open AccessSystematic Review

The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment

by Giacomo Aringhieri, Salvatore Claudio Fanni, Maria Febi, Leonardo Colligiani, Dania Cioni and Emanuele Neri

Diagnostics 2022, 12(12), 3002; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12123002 - 01 Dec 2022

Cited by 7 | Viewed by 1500

Abstract

Background: Radiomics of salivary gland imaging can support clinical decisions in different clinical scenarios, such as tumors, radiation-induced xerostomia and sialadenitis. This review aims to evaluate the methodological quality of radiomics studies on salivary gland imaging. Material and Methods: A systematic search was performed, and the methodological quality was evaluated using the radiomics quality score (RQS). Subgroup analyses according to the first author’s professional role (medical or not medical), journal type (radiological journal or other) and the year of publication (2021 or before) were performed. The correlation of RQS with the number of patients was calculated. Results: Twenty-three articles were included (mean RQS 11.34 ± 3.68). Most studies well-documented the imaging protocol (87%), while neither prospective validations nor cost-effectiveness analyses were performed. None of the included studies provided open-source data. A statistically significant difference in RQS according to the year of publication was found (p = 0.009), with papers published in 2021 having slightly higher RQSs than older ones. No differences according to journal type or the first author’s professional role were demonstrated. A moderate relationship between the overall RQS and the number of patients was found. Conclusions: Radiomics application in salivary gland imaging is increasing. Although its current clinical applicability can be affected by the somewhat inadequate quality of the papers, a significant improvement in radiomics methodologies has been demonstrated in the last year. Full article

(This article belongs to the Special Issue Radiomics and Pathomics: Clinical Applications and Next Steps)

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