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Artificial Intelligence and Radiomic Analysis in Medicine

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Physics General".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 17619

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Special Issue Editors

Dr. Daniele La Forgia

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IRCCS IstitutoTumori "Giovanni Paolo II", Bari, Italy
Interests: radiology; mammography; senology; RM breast; CESM; BI-ALCL
Special Issues, Collections and Topics in MDPI journals

Dr. Marco Moschetta

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Guest Editor

Department of Emergency and Organ Transplantation, University of Bari Medical School, Piazza Giulio Cesare 11, 70121 Bari, Italy
Interests: breast cancer; mammography; computer-aided diagnosis; tomosynthesis

Prof. Dr. Alberto Tagliafico

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Guest Editor

Department of Health Sciences, University of Genova, Genova, Italy
Interests: radiology; ultrasound; magnetic resonance imaging; radiomics; breast cancer; oncologic imaging; musculoskeletal imaging; peripheral nervous system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, the applications of Artificial Intelligence (AI) are of increasing use in medicine, from diagnostic imaging to oncology, radiotherapy, dosomics, and surgery.

AI is leading to a significant evolution of the systems supporting researchers and the scientific method. The paradigms of AI allow you to create machines capable of reasoning, perceiving reality, learning from examples, identifying models and grouping data and information.

In recent years, the works published in the literature and the investments of companies in these sectors have increased exponentially to indicate a growing interest in these issues.

Many works in the literature refer to machine learning (deep learning) in general and, more specifically, to CAD and neural networks but the applications range from the detection and characterization of tumors to diagnostic-therapeutic pathways, prognostic and predictive markers of diseases.

This Special Issue aims to present artificial intelligence progress and development models for personalized medicine. It will be an opportunity to exchange research results and new techniques developed in this biomedical research field with promising prospects.

Dr. Daniele La Forgia
Prof. Marco Moschetta
Prof. Alberto Tagliafico
Guest Editors

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Keywords

Artificial Intelligence in Medicine
Big Data
Personalized Medicine
Radiomic Analysis
Genomics
Neural Network
CAD
Machine Learning
Deep Learning
Pattern recognition
Robotics
Dosomics
Cancer detection
Predictive cancer risk
Predictive response to therapies
Prognostic markers

Published Papers (6 papers)

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Research

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11 pages, 17396 KiB

Open AccessArticle

Machine Learning Pipeline for the Automated Prediction of MicrovascularInvasion in HepatocellularCarcinomas

by Riccardo Biondi, Matteo Renzulli, Rita Golfieri, Nico Curti, Gianluca Carlini, Claudia Sala, Enrico Giampieri, Daniel Remondini, Giulio Vara, Arrigo Cattabriga, Maria Adriana Cocozza, Luigi Vincenzo Pastore, Nicolò Brandi, Antonino Palmeri, Leonardo Scarpetti, Gaia Tanzarella, Matteo Cescon, Matteo Ravaioli, Gastone Castellani and Francesca Coppola

Appl. Sci. 2023, 13(3), 1371; https://0-doi-org.brum.beds.ac.uk/10.3390/app13031371 - 20 Jan 2023

Cited by 2 | Viewed by 1414

Abstract

Background: Microvascular invasion (MVI) is a necessary step in the metastatic evolution of hepatocellular carcinoma liver tumors. Predicting the onset of MVI in the initial stages of the tumors could improve patient survival and the quality of life. In this study, the possibility of using radiomic features to predict the presence/absence of MVI was evaluated. Methods: Multiphase contrast-enhanced computed tomography (CECT) images were collected from 49 patients, and the radiomic features were extracted from the tumor region and the zone of transition. The most-relevant features were selected; the dataset was balanced, and the presence/absence of MVI was classified. The dataset was split into training and test sets in three ways using cross-validation: the first applied feature selection and dataset balancing outside cross-validation; the second applied dataset balancing outside and feature selection inside; the third applied the entire pipeline inside the cross-validation procedure. Results: The features from the tumor areas on CECT showed both the portal and the arterial phases to be the most predictive. The three pipelines showed receiver operating characteristic area under the curve (ROC AUC) scores of 0.89, 0.84, and 0.61, respectively. Conclusions: The results obtained confirmed the efficiency of multiphase CECT and the ZOT in detecting MVI. The results showed a significant difference in the performance of the three pipelines, highlighting that a non-rigorous pipeline design could lead to model performance and generalization capabilities that are too optimistic. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomic Analysis in Medicine)

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14 pages, 1839 KiB

Open AccessFeature PaperArticle

Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

by Roberta Fusco, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria Luisa Barretta, Teresa Petrosino, Claudio Siani, Raimondo Di Giacomo, Maurizio Di Bonito, Gerardo Botti and Antonella Petrillo

Appl. Sci. 2021, 11(4), 1880; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041880 - 20 Feb 2021

Cited by 5 | Viewed by 1818

Abstract

Purpose: The aim of the study was to estimate the diagnostic accuracy of textural, morphological and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were analyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomic Analysis in Medicine)

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10 pages, 915 KiB

Open AccessArticle

A Machine Learning Tool to Predict the Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Cervical Cancer

by Francesca Arezzo, Daniele La Forgia, Vincenzo Venerito, Marco Moschetta, Alberto Stefano Tagliafico, Claudio Lombardi, Vera Loizzi, Ettore Cicinelli and Gennaro Cormio

Appl. Sci. 2021, 11(2), 823; https://0-doi-org.brum.beds.ac.uk/10.3390/app11020823 - 17 Jan 2021

Cited by 19 | Viewed by 2266

Abstract

Despite several studies having identified factors associated with successful treatment outcomes in locally advanced cervical cancer, there is the lack of accurate predictive modeling for progression-free survival (PFS) in patients who undergo radical hysterectomy after neoadjuvant chemotherapy (NACT). Here we investigated whether machine learning (ML) may have the potential to provide a tool to predict neoadjuvant treatment response as PFS. In this retrospective observational study, we analyzed patients with locally advanced cervical cancer (FIGO stages IB2, IB3, IIA1, IIA2, IIB, and IIIC1) who were followed in a tertiary center from 2010 to 2018. Demographic and clinical characteristics were collected at either treatment baseline or at 24-month follow-up. Furthermore, we recorded data about magnetic resonance imaging (MRI) examinations and post-surgery histopathology. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with 10-fold cross-validation to predict 24-month PFS. Our analysis included n. 92 patients. The attribute core set used to train machine learning algorithms included the presence/absence of fornix infiltration at pre-treatment MRI as well as of either parametrium invasion and lymph nodes involvement at post-surgery histopathology. RFF showed the best performance (accuracy 82.4%, precision 83.4%, recall 96.2%, area under receiver operating characteristic curve (AUROC) 0.82). We developed an accurate ML model to predict 24-month PFS. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomic Analysis in Medicine)

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14 pages, 8253 KiB

Open AccessArticle

Intravoxel Incoherent Motion Model of Diffusion Weighted Imaging and Diffusion Kurtosis Imaging in Differentiating of Local Colorectal Cancer Recurrence from Scar/Fibrosis Tissue by Multivariate Logistic Regression Analysis

by Roberta Fusco, Vincenza Granata, Mario Sansone, Robert Grimm, Paolo Delrio, Daniela Rega, Fabiana Tatangelo, Antonio Avallone, Nicola Raiano, Giuseppe Totaro, Vincenzo Cerciello, Biagio Pecori and Antonella Petrillo

Appl. Sci. 2020, 10(23), 8609; https://0-doi-org.brum.beds.ac.uk/10.3390/app10238609 - 01 Dec 2020

Cited by 1 | Viewed by 2684

Abstract

Purpose: The aim of the study is to evaluate the potential of Intravoxel incoherent motion model of diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in the differentiation of local colorectal cancer recurrence (LCR) from scar/fibrosis tissue in patients that underwent chemo-radiation therapy followed by the total mesorectal excision (TME) for locally advanced rectal cancer (LARC). Methods: Fifty-six patients were retrospectively included for the image analysis. Diffusion and perfusion parameters were extracted by DWI data (apparent diffusion coefficient (ADC), pseudo-diffusion coefficient (Dp), perfusion fraction (fp), and tissue diffusivity (Dt)) and DKI data (mean of diffusion coefficient (MD) and mean of diffusional Kurtosis). Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analyses, and area under ROC curve (AUC) were used in a univariate statistical analysis. Backward stepwise multivariate logistic regression analysis was also performed. Results: LCR was found in 34 patients and treatment related changes such as scar/fibrosis tissue in 22 patients. At univariate analysis, low performance was reached by the mean value of Kurtosis with and AUC of 0.72 and an accuracy of 75%, respectively. Considering a regression model obtained as weighted sum of the ADC, Kurtosis, fp and Dp mean values, reached an AUC of 0.82 with a sensitivity of 72%, a specificity of 93%, and an accuracy of 81%. Conclusions: DWI derived parameters combined with DKI derived metrics in a multivariate model could allow differentiating of local colorectal recurrence from scar/fibrosis tissue after TME of LARC. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomic Analysis in Medicine)

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23 pages, 3367 KiB

Open AccessArticle

Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features

by Gökalp Çinarer, Bülent Gürsel Emiroğlu and Ahmet Haşim Yurttakal

Appl. Sci. 2020, 10(18), 6296; https://0-doi-org.brum.beds.ac.uk/10.3390/app10186296 - 10 Sep 2020

Cited by 29 | Viewed by 6139

Abstract

Gliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I–II-III–IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II, n = 77; Grade III, n = 44) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with MannWhitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomic Analysis in Medicine)

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Review

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12 pages, 268 KiB

Open AccessReview

Radiomics Analysis in Ovarian Cancer: A Narrative Review

by Francesca Arezzo, Vera Loizzi, Daniele La Forgia, Marco Moschetta, Alberto Stefano Tagliafico, Viviana Cataldo, Adam Abdulwakil Kawosha, Vincenzo Venerito, Gerardo Cazzato, Giuseppe Ingravallo, Leonardo Resta, Ettore Cicinelli and Gennaro Cormio

Appl. Sci. 2021, 11(17), 7833; https://0-doi-org.brum.beds.ac.uk/10.3390/app11177833 - 25 Aug 2021

Cited by 13 | Viewed by 2259

Abstract

Ovarian cancer (OC) is the second most common gynecological malignancy, accounting for about 14,000 deaths in 2020 in the US. The recognition of tools for proper screening, early diagnosis, and prognosis of OC is still lagging. The application of methods such as radiomics to medical images such as ultrasound scan (US), computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) in OC may help to realize so-called “precision medicine” by developing new quantification metrics linking qualitative and/or quantitative data imaging to achieve clinical diagnostic endpoints. This narrative review aims to summarize the applications of radiomics as a support in the management of a complex pathology such as ovarian cancer. We give an insight into the current evidence on radiomics applied to different imaging methods. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomic Analysis in Medicine)

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