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Cancers
Special Issues
MRI Biomarkers of Cancer
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MRI Biomarkers of Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (5 January 2024) | Viewed by 2512

Special Issue Editor

Prof. Dr. Alexey Surov

E-Mail Website
Guest Editor
Department of Radiology and Nuclear Medicine, University of Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany
Interests: radiology

Special Issue Information

Dear Colleagues, 

Magnetic resonance imaging (MRI) plays an essential role in the diagnosis and local staging of numerous tumors. Moreover, MRI also can predict histopathological features and the behavior of different tumors. So far, diffusion-weighted imaging (DWI) has been shown to be able to reflect relevant histopathological features such as tumor cellularity and/or proliferation activity. DWI can also predict the response to  cytoreductive treatments. Similarly, contrast-enhanced MR images also correlate with different histopathological features. Furthermore, modern imaging analysis techniques such as histogram analysis and radiomics provide additional parameters that also play a predictive role in oncology. However, the reported data are based on small number of patients/tumors.

The purpose of the present Special Issue is to provide evidence-based data about the predictive role of MRI in oncology based on large samples.

Prof. Dr. Alexey Surov
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. Cancers 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 2900 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

  • magnetic resonance imaging
  • cancer
  • histopathology
  • treatment response
  • survival

Published Papers (1 paper)

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Research

10 pages, 1246 KiB  
Article
MRI Radiomics and Machine Learning for the Prediction of Oncotype Dx Recurrence Score in Invasive Breast Cancer
by Valeria Romeo, Renato Cuocolo, Luca Sanduzzi, Vincenzo Carpentiero, Martina Caruso, Beatrice Lama, Dimitri Garifalos, Arnaldo Stanzione, Simone Maurea and Arturo Brunetti
Cancers 2023, 15(6), 1840; https://doi.org/10.3390/cancers15061840 - 18 Mar 2023
Cited by 6 | Viewed by 2081
Abstract
Aim: To non-invasively predict Oncotype DX recurrence scores (ODXRS) in patients with ER+ HER2- invasive breast cancer (IBC) using dynamic contrast-enhanced (DCE) MRI-derived radiomics features extracted from primary tumor lesions and a ML algorithm. Materials and Methods: Pre-operative DCE-MRI of patients [...] Read more.
Aim: To non-invasively predict Oncotype DX recurrence scores (ODXRS) in patients with ER+ HER2- invasive breast cancer (IBC) using dynamic contrast-enhanced (DCE) MRI-derived radiomics features extracted from primary tumor lesions and a ML algorithm. Materials and Methods: Pre-operative DCE-MRI of patients with IBC, no history of neoadjuvant therapy prior to MRI, and for which the ODXRS was available, were retrospectively selected from a public dataset. ODXRS was obtained on histological tumor samples and considered as positive if greater than 16 and 26 in patients aged under and over 50 years, respectively. Tumor lesions were manually annotated by three independent operators on DCE-MRI images through 3D ROIs positioning. Radiomic features were therefore extracted and selected using multistep feature selection process. A logistic regression ML classifier was then employed for the prediction of ODXRS. Results: 248 patients were included, of which 87 with positive ODXRS. 166 (66%) patients were grouped in the training set, while 82 (33%) in the test set. A total of 1288 features was extracted. Of these, 1244 were excluded as 771, 82 and 391 were excluded as not stable (n = 771), not variant (n = 82), and highly intercorrelated (n = 391), respectively. After the use of recursive feature elimination with logistic regression estimator and polynomial transformation, 92 features were finally selected. In the training set, the logistic regression classifier obtained an overall mean accuracy of 60%. In the test set, the accuracy of the ML classifier was 63%, with a sensitivity of 80%, specificity of 43%, and AUC of 66%. Conclusions: Radiomics and ML applied to pre-operative DCE-MRI in patients with IBC showed promises for the non-invasive prediction of ODXRS, aiding in selecting patients who will benefit from NAC. Full article
(This article belongs to the Special Issue MRI Biomarkers of Cancer)
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