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Precision Medicine in Radiomics and Radiogenomics
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Precision Medicine in Radiomics and Radiogenomics

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 10148

Special Issue Editor

Dr. Serena Monti

E-Mail Website
Guest Editor
Istituto di Biostrutture e Bioimmagini, Consiglio Nazionale delle Ricerche, 80125 Naples, Italy
Interests: image processing; quantitative MRI; medical imaging; toxicity modeling; precision medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the current era of precision medicine, the tailoring of medical treatment to the individual characteristics of each patient is fundamental to direct personalized preventive or therapeutic intervention to those who will benefit, reducing cost and minimizing side effects. Imaging plays an essential role because it allows screening, early diagnosis, response evaluation, and recurrence assessment. A field that shows great promise in this context is radiomics, i.e., the process of extracting mineable, high-dimensional data from routine, standard of care images to provide an “imaging phenotype” for scoring, categorizing, and classifying disease severity, predicting response to therapy and patient outcome. The further correlation of imaging phenotype with gene expressions is known as radiogenomics, and it will serve as the foundation for surveillance of disease manifestation in terms of occurrence, location, extent, severity, and discovery of genetic polymorphisms. This Special Issue of the Journal of Personalized Medicine aims to delineate present and future perspectives of Radiomics and Radiogenomics in the era of precision medicine to better outline the increasingly prominent role of imaging in the management of complex, genetically heterogeneous diseases in oncology and non-oncological conditions.

Dr. Serena Monti
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 Personalized Medicine is an international peer-reviewed open access monthly 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 
  • radiogenomics 
  • imaging phenotype 
  • gene expression 
  • precision medicine 
  • outcome prediction

Published Papers (7 papers)

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Editorial

Jump to: Research, Review

3 pages, 174 KiB  
Editorial
Precision Medicine in Radiomics and Radiogenomics
by Serena Monti
J. Pers. Med. 2022, 12(11), 1806; https://doi.org/10.3390/jpm12111806 - 1 Nov 2022
Cited by 1 | Viewed by 1310
Abstract
Precision medicine is an innovative and emerging approach to treatment that accounts for individual variability in genetic and environmental factors to identify and utilize the specific biomedical profile of a patient’s disease [...] Full article
(This article belongs to the Special Issue Precision Medicine in Radiomics and Radiogenomics)

Research

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22 pages, 4892 KiB  
Article
Stability of Multi-Parametric Prostate MRI Radiomic Features to Variations in Segmentation
by Sithin Thulasi Seetha, Enrico Garanzini, Chiara Tenconi, Cristina Marenghi, Barbara Avuzzi, Mario Catanzaro, Silvia Stagni, Sergio Villa, Barbara Noris Chiorda, Fabio Badenchini, Elena Bertocchi, Sebastian Sanduleanu, Emanuele Pignoli, Giuseppe Procopio, Riccardo Valdagni, Tiziana Rancati, Nicola Nicolai and Antonella Messina
J. Pers. Med. 2023, 13(7), 1172; https://doi.org/10.3390/jpm13071172 - 22 Jul 2023
Cited by 2 | Viewed by 1306
Abstract
Stability analysis remains a fundamental step in developing a successful imaging biomarker to personalize oncological strategies. This study proposes an in silico contour generation method for simulating segmentation variations to identify stable radiomic features. Ground-truth annotation provided for the whole prostate gland on [...] Read more.
Stability analysis remains a fundamental step in developing a successful imaging biomarker to personalize oncological strategies. This study proposes an in silico contour generation method for simulating segmentation variations to identify stable radiomic features. Ground-truth annotation provided for the whole prostate gland on the multi-parametric MRI sequences (T2w, ADC, and SUB-DCE) were perturbed to mimic segmentation differences observed among human annotators. In total, we generated 15 synthetic contours for a given image-segmentation pair. One thousand two hundred twenty-four unfiltered/filtered radiomic features were extracted applying Pyradiomics, followed by stability assessment using ICC(1,1). Stable features identified in the internal population were then compared with an external population to discover and report robust features. Finally, we also investigated the impact of a wide range of filtering strategies on the stability of features. The percentage of unfiltered (filtered) features that remained robust subjected to segmentation variations were T2w—36% (81%), ADC—36% (94%), and SUB—43% (93%). Our findings suggest that segmentation variations can significantly impact radiomic feature stability but can be mitigated by including pre-filtering strategies as part of the feature extraction pipeline. Full article
(This article belongs to the Special Issue Precision Medicine in Radiomics and Radiogenomics)
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13 pages, 907 KiB  
Article
Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study
by Lucija Kovačević, Andrija Štajduhar, Karlo Stemberger, Lea Korša, Zlatko Marušić and Maja Prutki
J. Pers. Med. 2023, 13(7), 1150; https://doi.org/10.3390/jpm13071150 - 18 Jul 2023
Cited by 1 | Viewed by 1187
Abstract
This study aimed to explore the potential of multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for classifying breast cancer surrogate subtypes. This retrospective study analyzed 360 breast cancers from 319 patients who underwent pretreatment DCE-MRI between January 2015 and January 2019. The [...] Read more.
This study aimed to explore the potential of multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for classifying breast cancer surrogate subtypes. This retrospective study analyzed 360 breast cancers from 319 patients who underwent pretreatment DCE-MRI between January 2015 and January 2019. The cohort consisted of 33 triple-negative, 26 human epidermal growth factor receptor 2 (HER2)-positive, 109 luminal A-like, 144 luminal B-like HER2-negative, and 48 luminal B-like HER2-positive lesions. A total of 1781 radiomic features were extracted from manually segmented breast cancers in each DCE-MRI sequence. The model was internally validated and selected using ten times repeated five-fold cross-validation on the primary cohort, with further evaluation using a validation cohort. The most successful models were logistic regression models applied to the third post-contrast subtraction images. These models exhibited the highest area under the curve (AUC) for discriminating between luminal A like vs. others (AUC: 0.78), luminal B-like HER2 negative vs. others (AUC: 0.57), luminal B-like HER2 positive vs. others (AUC: 0.60), HER2 positive vs. others (AUC: 0.81), and triple negative vs. others (AUC: 0.83). In conclusion, the radiomic features extracted from multi-phase DCE-MRI are promising for discriminating between breast cancer subtypes. The best-performing models relied on tissue changes observed during the mid-stage of the imaging process. Full article
(This article belongs to the Special Issue Precision Medicine in Radiomics and Radiogenomics)
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16 pages, 2711 KiB  
Article
A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions
by Alfonso Maria Ponsiglione, Francesca Angelone, Francesco Amato and Mario Sansone
J. Pers. Med. 2023, 13(7), 1104; https://doi.org/10.3390/jpm13071104 - 7 Jul 2023
Cited by 2 | Viewed by 1178
Abstract
Despite mammography (MG) being among the most widespread techniques in breast cancer screening, tumour detection and classification remain challenging tasks due to the high morphological variability of the lesions. The extraction of radiomics features has proved to be a promising approach in MG. [...] Read more.
Despite mammography (MG) being among the most widespread techniques in breast cancer screening, tumour detection and classification remain challenging tasks due to the high morphological variability of the lesions. The extraction of radiomics features has proved to be a promising approach in MG. However, radiomics features can suffer from dependency on factors such as acquisition protocol, segmentation accuracy, feature extraction and engineering methods, which prevent the implementation of robust and clinically reliable radiomics workflow in MG. In this study, the variability and robustness of radiomics features is investigated as a function of lesion segmentation in MG images from a public database. A statistical analysis is carried out to assess feature variability and a radiomics robustness score is introduced based on the significance of the statistical tests performed. The obtained results indicate that variability is observable not only as a function of the abnormality type (calcification and masses), but also among feature categories (first-order and second-order), image view (craniocaudal and medial lateral oblique), and the type of lesions (benign and malignant). Furthermore, through the proposed approach, it is possible to identify those radiomics characteristics with a higher discriminative power between benign and malignant lesions and a lower dependency on segmentation, thus suggesting the most appropriate choice of robust features to be used as inputs to automated classification algorithms. Full article
(This article belongs to the Special Issue Precision Medicine in Radiomics and Radiogenomics)
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13 pages, 1565 KiB  
Article
Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction
by Marianna Inglese, Matteo Ferrante, Tommaso Boccato, Allegra Conti, Chiara A. Pistolese, Oreste C. Buonomo, Rolando M. D’Angelillo and Nicola Toschi
J. Pers. Med. 2023, 13(6), 1004; https://doi.org/10.3390/jpm13061004 - 15 Jun 2023
Viewed by 1211
Abstract
Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as [...] Read more.
Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of dynamic 18F-Fluorothymidine (FLT) PET scans from BC patients, extending conventional static radiomics methods to the time domain—termed as ‘Dynomics’. Radiomic features were extracted from both static and dynamic PET images on lesion and reference tissue masks. The extracted features were used to train an XGBoost model for classifying tumor versus reference tissue and complete versus partial responders to neoadjuvant chemotherapy. The results underscored the superiority of dynamic and static radiomics over standard PET imaging, achieving accuracy of 94% in tumor tissue classification. Notably, in predicting BC prognosis, dynomics delivered the highest performance, achieving accuracy of 86%, thereby outperforming both static radiomics and standard PET data. This study illustrates the enhanced clinical utility of dynomics in yielding more precise and reliable information for BC diagnosis and prognosis, paving the way for improved treatment strategies. Full article
(This article belongs to the Special Issue Precision Medicine in Radiomics and Radiogenomics)
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15 pages, 3585 KiB  
Article
Neck Lymph Node Recurrence in HNC Patients Might Be Predicted before Radiotherapy Using Radiomics Extracted from CT Images and XGBoost Algorithm
by Yi-Lun Tsai, Shang-Wen Chen, Chia-Hung Kao and Da-Chuan Cheng
J. Pers. Med. 2022, 12(9), 1377; https://doi.org/10.3390/jpm12091377 - 25 Aug 2022
Cited by 2 | Viewed by 1939
Abstract
The five-year overall survival rate of patients without neck lymph node recurrence is over 50% higher than those with lymph node metastasis. This study aims to investigate the prognostic impact of computed tomogram (CT)-based radiomics on the outcome of metastatic neck lymph nodes [...] Read more.
The five-year overall survival rate of patients without neck lymph node recurrence is over 50% higher than those with lymph node metastasis. This study aims to investigate the prognostic impact of computed tomogram (CT)-based radiomics on the outcome of metastatic neck lymph nodes in patients with head and neck cancer (HNC) receiving definitive radiotherapy or chemoradiotherapy for organ preservation. The pretreatment 18F-FDG PET/CT of 79 HNC patients was retrospectively analyzed with radiomics extractors. The imbalanced data was processed using two techniques: over-sampling and under-sampling, after which the prediction model was established with a machine learning model using the XGBoost algorithm. The imbalanced dataset strategies slightly decreased the specificity but greatly improved the sensitivity. To have a higher chance of predicting neck cancer recurrence, however, clinical data combined with CT-based radiomics provides the best prediction effect. The original dataset performed was as follows: accuracy = 0.76 ± 0.07, sensitivity = 0.44 ± 0.22, specificity = 0.88 ± 0.06. After we used the over-sampling technique, the accuracy, sensitivity, and specificity values were 0.80 ± 0.05, 0.67 ± 0.11, and 0.84 ± 0.05, respectively. Furthermore, after using the under-sampling technique, the accuracy, sensitivity, and specificity values were 0.71 ± 0.09, 0.73 ± 0.13, and 0.70 ± 0.13, respectively. The outcome of metastatic neck lymph nodes in patients with HNC receiving radiotherapy for organ preservation can be predicted based on the results of machine learning. This way, patients can be treated alternatively. A further external validation study is required to verify our findings. Full article
(This article belongs to the Special Issue Precision Medicine in Radiomics and Radiogenomics)
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Review

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10 pages, 249 KiB  
Review
Radiomics and Radiogenomics in Preclinical Imaging on Murine Models: A Narrative Review
by Serena Monti, Maria Elena Truppa, Sandra Albanese and Marcello Mancini
J. Pers. Med. 2023, 13(8), 1204; https://doi.org/10.3390/jpm13081204 - 29 Jul 2023
Viewed by 1241
Abstract
Over the past decade, medical imaging technologies have become increasingly significant in both clinical and preclinical research, leading to a better understanding of disease processes and the development of new diagnostic and theranostic methods. Radiomic and radiogenomic approaches have furthered this progress by [...] Read more.
Over the past decade, medical imaging technologies have become increasingly significant in both clinical and preclinical research, leading to a better understanding of disease processes and the development of new diagnostic and theranostic methods. Radiomic and radiogenomic approaches have furthered this progress by exploring the relationship between imaging characteristics, genomic information, and outcomes that qualitative interpretations may have overlooked, offering valuable insights for personalized medicine. Preclinical research allows for a controlled environment where various aspects of a pathology can be replicated in animal models, providing radiomic and radiogenomic approaches with the unique opportunity to investigate the causal connection between imaging and molecular factors. The aim of this review is to present the current state of the art in the application of radiomics and radiogenomics on murine models. This review will provide a brief description of relevant articles found in the literature with a discussion on the implications and potential translational relevance of these findings. Full article
(This article belongs to the Special Issue Precision Medicine in Radiomics and Radiogenomics)
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