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Radiomics and Machine Learning Models for Oncological Clinical Applications

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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 (20 May 2023) | Viewed by 14783

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Special Issue Editor

Dr. Francesco Verde

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Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
Interests: genitourinary imaging, hepatobiliary imaging, radiomics, machine learning; US; CT; MRI

Special Issue Information

Dear Colleagues,

In this new era of technological medical advances, machine learning (ML) has emerged as a subset of artificial intelligence focused on algorithms that can make predictions or decision tasks without prior explicit programmed rules. ML algorithms use iterative statistics learning methods from “training” data to progressively improve the model performance over time, enabling the recognition patterns in large datasets and classification of instances.

The application of ML models coupled to radiomics analysis has been embraced in oncological imaging to assess predictive image-based phenotypes for precision medicine.

Radiomics is a multistep process that converts medical images into mineable data through mathematical extraction of quantitative parameters that reflect image tumor heterogeneity, thus empowering precision diagnosis and staging in cancer imaging. Indeed, such a vast amount of data can be more easily handled by ML algorithms than traditional statistical methods.

The development of ML radiomics-based models represents an excellent opportunity to extract additional value and information from medical imaging, thus improving clinical and radiological workup for oncological patients.

This Special Issue aims to present novel applications of ML and radiomics in diagnostic oncological imaging, covering insights from optimising the clinical-radiological workflow (patient screening, image acquisition) to the more specific image-based tasks (cancer detection, characterisation, and treatment monitoring).

Dr. Francesco Verde
Guest Editor

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Keywords

oncological imaging
radiomics
texture analysis
ultrasonography
computed tomography
magnetic resonance imaging
genitourinary cancer
breast cancer
hepatobiliary cancer
gastrointestinal cancer

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Published Papers (5 papers)

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17 pages, 6367 KiB

Open AccessArticle

Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma Using a Clinical-Radiomics Nomogram Based on B-Mode and Contrast-Enhanced Ultrasound

by Liqing Jiang, Shiyan Guo, Yongfeng Zhao, Zhe Cheng, Xinyu Zhong and Ping Zhou

Diagnostics 2023, 13(10), 1734; https://doi.org/10.3390/diagnostics13101734 - 13 May 2023

Cited by 5 | Viewed by 1868

Abstract

Papillary thyroid carcinoma (PTC) is the most common pathological type of thyroid cancer. PTC patients with extrathyroidal extension (ETE) are associated with poor prognoses. The preoperative accurate prediction of ETE is crucial for helping the surgeon decide on the surgical plan. This study aimed to establish a novel clinical-radiomics nomogram based on B-mode ultrasound (BMUS) and contrast-enhanced ultrasound (CEUS) for the prediction of ETE in PTC. A total of 216 patients with PTC between January 2018 and June 2020 were collected and divided into the training set (n = 152) and the validation set (n = 64). The least absolute shrinkage and selection operator (LASSO) algorithm was applied for radiomics feature selection. Univariate analysis was performed to find clinical risk factors for predicting ETE. The BMUS Radscore, CEUS Radscore, clinical model, and clinical-radiomics model were established using multivariate backward stepwise logistic regression (LR) based on BMUS radiomics features, CEUS radiomics features, clinical risk factors, and the combination of those features, respectively. The diagnostic efficacy of the models was assessed using receiver operating characteristic (ROC) curves and the DeLong test. The model with the best performance was then selected to develop a nomogram. The results show that the clinical-radiomics model, which is constructed by age, CEUS-reported ETE, BMUS Radscore, and CEUS Radscore, showed the best diagnostic efficiency in both the training set (AUC = 0.843) and validation set (AUC = 0.792). Moreover, a clinical-radiomics nomogram was established for easier clinical practices. The Hosmer–Lemeshow test and the calibration curves demonstrated satisfactory calibration. The decision curve analysis (DCA) showed that the clinical-radiomics nomogram had substantial clinical benefits. The clinical-radiomics nomogram constructed from the dual-modal ultrasound can be exploited as a promising tool for the pre-operative prediction of ETE in PTC. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning Models for Oncological Clinical Applications)

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16 pages, 984 KiB

Open AccessArticle

Radiomics in Head and Neck Cancer Outcome Predictions

by Maria Gonçalves, Christina Gsaxner, André Ferreira, Jianning Li, Behrus Puladi, Jens Kleesiek, Jan Egger and Victor Alves

Diagnostics 2022, 12(11), 2733; https://doi.org/10.3390/diagnostics12112733 - 8 Nov 2022

Cited by 8 | Viewed by 2718

Abstract

Head and neck cancer has great regional anatomical complexity, as it can develop in different structures, exhibiting diverse tumour manifestations and high intratumoural heterogeneity, which is highly related to resistance to treatment, progression, the appearance of metastases, and tumour recurrences. Radiomics has the potential to address these obstacles by extracting quantitative, measurable, and extractable features from the region of interest in medical images. Medical imaging is a common source of information in clinical practice, presenting a potential alternative to biopsy, as it allows the extraction of a large number of features that, although not visible to the naked eye, may be relevant for tumour characterisation. Taking advantage of machine learning techniques, the set of features extracted when associated with biological parameters can be used for diagnosis, prognosis, and predictive accuracy valuable for clinical decision-making. Therefore, the main goal of this contribution was to determine to what extent the features extracted from Computed Tomography (CT) are related to cancer prognosis, namely Locoregional Recurrences (LRs), the development of Distant Metastases (DMs), and Overall Survival (OS). Through the set of tumour characteristics, predictive models were developed using machine learning techniques. The tumour was described by radiomic features, extracted from images, and by the clinical data of the patient. The performance of the models demonstrated that the most successful algorithm was XGBoost, and the inclusion of the patients’ clinical data was an asset for cancer prognosis. Under these conditions, models were created that can reliably predict the LR, DM, and OS status, with the area under the ROC curve (AUC) values equal to 0.74, 0.84, and 0.91, respectively. In summary, the promising results obtained show the potential of radiomics, once the considered cancer prognosis can, in fact, be expressed through CT scans. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning Models for Oncological Clinical Applications)

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11 pages, 2102 KiB

Open AccessArticle

Multiregional Radiomic Signatures Based on Functional Parametric Maps from DCE-MRI for Preoperative Identification of Estrogen Receptor and Progesterone Receptor Status in Breast Cancer

by Shiling Zhong, Fan Wang, Zhiying Wang, Minghui Zhou, Chunli Li and Jiandong Yin

Diagnostics 2022, 12(10), 2558; https://doi.org/10.3390/diagnostics12102558 - 21 Oct 2022

Cited by 8 | Viewed by 1996

Abstract

Radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been used for breast estrogen receptor (ER) and progesterone receptor (PR) status evaluation. However, the radiomic features of peritumoral regions were not thoroughly analyzed. This study aimed to establish and validate the multiregional radiomic signatures (RSs) for the preoperative identification of the ER and PR status in breast cancer. A total of 443 patients with breast cancer were divided into training (n = 356) and validation (n = 87) sets. Radiomic features were extracted from intra- and peritumoral regions on six functional parametric maps from DCE-MRI. A two-sample t-test, least absolute shrinkage and selection operator regression, and stepwise were used for feature selections. Three RSs for predicting the ER and PR status were constructed using a logistic regression model based on selected intratumoral, peritumoral, and combined intra- and peritumoral radiomic features. The area under the receiver operator characteristic curve (AUC) was used to assess the discriminative performance of three RSs. The AUCs of intra- and peritumoral RSs for identifying the ER status were 0.828/0.791 and 0.755/0.733 in the training and validation sets, respectively. For predicting the PR status, intra- and peritumoral RSs resulted in AUCs of 0.816/0.749 and 0.806/0.708 in the training and validation sets, respectively. Multiregional RSs achieved the best AUCs among three RSs for evaluating the ER (0.851 and 0.833) and PR (0.848 and 0.763) status. In conclusion, multiregional RSs based on functional parametric maps from DCE-MRI showed promising results for preoperatively evaluating the ER and PR status in breast cancer patients. Further studies using a larger cohort from multiple centers are necessary to confirm the reliability of the established models before clinical application. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning Models for Oncological Clinical Applications)

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11 pages, 2201 KiB

Open AccessArticle

An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data

by Christos Kokkotis, Georgios Giarmatzis, Erasmia Giannakou, Serafeim Moustakidis, Themistoklis Tsatalas, Dimitrios Tsiptsios, Konstantinos Vadikolias and Nikolaos Aggelousis

Diagnostics 2022, 12(10), 2392; https://doi.org/10.3390/diagnostics12102392 - 1 Oct 2022

Cited by 39 | Viewed by 4888

Abstract

Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with premature death and huge economic consequences. Hence, there is an urgency to model the effect of several risk factors on stroke occurrence, and artificial intelligence (AI) seems to be the appropriate tool. In the present study, we aimed to (i) develop reliable machine learning (ML) prediction models for stroke disease; (ii) cope with a typical severe class imbalance problem, which is posed due to the stroke patients’ class being significantly smaller than the healthy class; and (iii) interpret the model output for understanding the decision-making mechanism. The effectiveness of the proposed ML approach was investigated in a comparative analysis with six well-known classifiers with respect to metrics that are related to both generalization capability and prediction accuracy. The best overall false-negative rate was achieved by the Multi-Layer Perceptron (MLP) classifier (18.60%). Shapley Additive Explanations (SHAP) were employed to investigate the impact of the risk factors on the prediction output. The proposed AI method could lead to the creation of advanced and effective risk stratification strategies for each stroke patient, which would allow for timely diagnosis and the right treatments. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning Models for Oncological Clinical Applications)

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15 pages, 1340 KiB

Open AccessSystematic Review

Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study

by Arnaldo Stanzione, Roberta Galatola, Renato Cuocolo, Valeria Romeo, Francesco Verde, Pier Paolo Mainenti, Arturo Brunetti and Simone Maurea

Diagnostics 2022, 12(3), 578; https://doi.org/10.3390/diagnostics12030578 - 24 Feb 2022

Cited by 15 | Viewed by 2389

Abstract

In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.g., segmentation and feature extraction strategy). The methodological quality of each study was evaluated using the radiomics quality score (RQS). After duplicate removal and selection criteria application, 25 full-text articles were included and evaluated. All were retrospective studies, mostly based on CT images (17/25, 68%), with manual (19/25, 76%) and two-dimensional segmentation (13/25, 52%) being preferred. Machine learning was paired to radiomics in about half of the studies (12/25, 48%). The median total and percentage RQS scores were 2 (interquartile range, IQR = −5–8) and 6% (IQR = 0–22%), respectively. The highest and lowest scores registered were 12/36 (33%) and −5/36 (0%). The most critical issues were the absence of proper feature selection, the lack of appropriate model validation and poor data openness. The methodological quality of radiomics studies on adrenal cross-sectional imaging is heterogeneous and lower than desirable. Efforts toward building higher quality evidence are essential to facilitate the future translation into clinical practice. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning Models for Oncological Clinical Applications)

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