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Molecular Basis of Radiomics in Oncology
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Molecular Basis of Radiomics in Oncology

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 8505

Special Issue Editor

Dr. Luca Nicosia

E-Mail Website
Guest Editor
Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
Interests: breast imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is now widely acknowledged that imaging data contain numerous and useful information concerning a tumor’s biology, behavior and pathophysiology. In particular, radiomics could be described as a procedure based on the computerized extraction of features from radiological images, which aims to discover hidden features of a certain region.

The challenge today's research is confronted with regards defining the information that radiomics can provide so that it could be used in clinical practice. In that respect, personalized and precision medicine is one of the main protagonists of scientific development nowadays, especially considering the advances in molecular imaging and genetic knowledge and the introduction of artificial intelligence technology into radiological practice.

The oncological research field is one of the most important areas in which radiomics seems to offer the most promising results: original research able to understand the heterogeneity of cancer phenotypes and the use of radiomics in targeted molecular imaging studies will be strongly encouraged in this Special Issue. As a result, the management of oncological patients could be profoundly renewed by the development of research on radiomics, genetics and molecular studies. Radiogenomics, as it might be called, will, therefore, certainly be one of the most encouraging and interesting fields of study in the next few years.

In this Special Issue, we encourage the submission of radiomics-based research, including biomolecular experiments in the field of oncology. Multidisciplinary approaches that aim to combine both biomolecular experience and clinical outcomes will be strongly valued: radiologists, oncologists, physicists and biomolecular researchers should be vividly encouraged to collaborate in order to develop knowledge in radiomics, as it does represent a promising research field with extraordinary potential.

Dr. Luca Nicosia
Guest Editor

Manuscript Submission Information

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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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • breast imaging
  • oncology
  • molecular imaging
  • personalized medicine
  • radiomics
  • radiogenomics
  • artificial intelligence

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Published Papers (3 papers)

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Research

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11 pages, 1749 KiB  
Article
Radiomic Features Applied to Contrast Enhancement Spectral Mammography: Possibility to Predict Breast Cancer Molecular Subtypes in a Non-Invasive Manner
by Luca Nicosia, Anna Carla Bozzini, Daniela Ballerini, Simone Palma, Filippo Pesapane, Sara Raimondi, Aurora Gaeta, Federica Bellerba, Daniela Origgi, Paolo De Marco, Giuseppe Castiglione Minischetti, Claudia Sangalli, Lorenza Meneghetti, Giuseppe Curigliano and Enrico Cassano
Int. J. Mol. Sci. 2022, 23(23), 15322; https://doi.org/10.3390/ijms232315322 - 5 Dec 2022
Cited by 7 | Viewed by 2205
Abstract
We aimed to investigate the association between the radiomic features of contrast-enhanced spectral mammography (CESM) images and a specific receptor pattern of breast neoplasms. In this single-center retrospective study, we selected patients with neoplastic breast lesions who underwent CESM before a biopsy and [...] Read more.
We aimed to investigate the association between the radiomic features of contrast-enhanced spectral mammography (CESM) images and a specific receptor pattern of breast neoplasms. In this single-center retrospective study, we selected patients with neoplastic breast lesions who underwent CESM before a biopsy and surgical assessment between January 2013 and February 2022. Radiomic analysis was performed on regions of interest selected from recombined CESM images. The association between the features and each evaluated endpoint (ER, PR, Ki-67, HER2+, triple negative, G2–G3 expressions) was investigated through univariate logistic regression. Among the significant and highly correlated radiomic features, we selected only the one most associated with the endpoint. From a group of 321 patients, we enrolled 205 malignant breast lesions. The median age at the exam was 50 years (interquartile range (IQR) 45–58). NGLDM_Contrast was the only feature that was positively associated with both ER and PR expression (p-values = 0.01). NGLDM_Coarseness was negatively associated with Ki-67 expression (p-value = 0.02). Five features SHAPE Volume(mL), SHAPE_Volume(vx), GLRLM_RLNU, NGLDM_Busyness and GLZLM_GLNU were all positively and significantly associated with HER2+; however, all of them were highly correlated. Radiomic features of CESM images could be helpful to predict particular molecular subtypes before a biopsy. Full article
(This article belongs to the Special Issue Molecular Basis of Radiomics in Oncology)
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Review

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11 pages, 500 KiB  
Review
Current Role of Delta Radiomics in Head and Neck Oncology
by David C. Marcu, Cristian Grava and Loredana G. Marcu
Int. J. Mol. Sci. 2023, 24(3), 2214; https://doi.org/10.3390/ijms24032214 - 22 Jan 2023
Cited by 7 | Viewed by 2227
Abstract
The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or the extraction of data from various imaging modalities, is [...] Read more.
The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or the extraction of data from various imaging modalities, is a tool often used to evaluate specific features related to the tumour or normal tissue that are not identifiable by the naked eye and which can add value to existing clinical data. Furthermore, the assessment of feature variations from one time point to another based on subsequent images, known as delta radiomics, was shown to have even higher value for treatment-outcome prediction or patient stratification into risk categories. The information gathered from delta radiomics can, further, be used for decision making regarding treatment adaptation or other interventions found to be beneficial to the patient. The aim of this work is to collate the existing studies on delta radiomics in head and neck cancer and evaluate its role in tumour response and normal-tissue toxicity predictions alike. Moreover, this work also highlights the role of holomics, which brings under the same umbrella clinical and radiomic features, for a more complex patient characterization and treatment optimisation. Full article
(This article belongs to the Special Issue Molecular Basis of Radiomics in Oncology)
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23 pages, 586 KiB  
Review
PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review
by Luca Urso, Luigi Manco, Angelo Castello, Laura Evangelista, Gabriele Guidi, Massimo Castellani, Luigia Florimonte, Corrado Cittanti, Alessandro Turra and Stefano Panareo
Int. J. Mol. Sci. 2022, 23(21), 13409; https://doi.org/10.3390/ijms232113409 - 2 Nov 2022
Cited by 29 | Viewed by 3604
Abstract
Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still [...] Read more.
Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence. Full article
(This article belongs to the Special Issue Molecular Basis of Radiomics in Oncology)
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