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Cancers
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Using AI and Imaging Biomarkers for Insights into the Tumor...
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Using AI and Imaging Biomarkers for Insights into the Tumor Microenvironment of Breast Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Tumor Microenvironment".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 10420

Special Issue Editor

Prof. Dr. Katja Pinker-Domenig

E-Mail Website1 Website2
Guest Editor
1. Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, 300 E 66th St , New York, NY 10065, USA 2. Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
Interests: Breast cancer; Imaging; MRI; Radiomics/ Radiogenomics; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

BC is the most common cancer in women and, despite advances in early disease detection and treatment, remains the second-leading cause of female cancer deaths, posing a major societal, medical, and health-economic burden. Molecular profiling has proven that BC is a disease with a remarkable heterogeneity and that various cancer cell populations co-exist in a given primary tumor that differ significantly in their genetic, phenotypic, and behavioral characteristics. The current lack of understanding of breast cancer heterogeneity contributes to treatment failures and patients’ deaths. BC heterogeneity is not solely driven by the combined effect of genomic instability within the tumor, but also by differential selective pressures from the tumor microenvironment (TME). Treatment management decisions are currently based on tumor information from invasive tissue sampling, but current invasive tools cannot provide a comprehensive assessment of BC heterogeneity of tumor in its entirety. One of the most promising areas of health innovation is the application of AI in biomedical imaging. Medical imaging has always been an integral part in breast cancer care, ranging from diagnosis and staging to therapy monitoring and post-therapeutic follow-up. With the possibility to enhance medical imaging with AI, there is a unique opportunity to develop imaging biomarkers that significantly broaden the understanding of BC heterogeneity and the TME challenge, with the ultimate goal to revolutionize risk stratification and treatment of BC.

This Special Issue of Cancers with a focus on “Using AI and Imaging Biomarkers for Insights into the Tumor Microenvironment of Breast Cancer” invites submission of the recent advances, current possibilities, and emerging techniques in imaging of the TME of breast cancers and enhancements with AI.

Prof. Dr. Katja Pinker-Domenig
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

  • Breast cancer
  • Tumor microenvironment
  • Radiomics/-genomics
  • Artificial intelligence
  • Imaging biomarker
  • Mammography
  • Ultrasound
  • Contrast-enhanced mammography
  • Digital breast tomosynthesis
  • MRI
  • PET/MRI

Published Papers (4 papers)

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Research

21 pages, 3352 KiB  
Article
Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification
by Zuzanna Anna Magnuska, Benjamin Theek, Milita Darguzyte, Moritz Palmowski, Elmar Stickeler, Volkmar Schulz and Fabian Kießling
Cancers 2022, 14(2), 277; https://doi.org/10.3390/cancers14020277 - 06 Jan 2022
Cited by 7 | Viewed by 2323
Abstract
Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support [...] Read more.
Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems. Full article
(This article belongs to the Special Issue Using AI and Imaging Biomarkers for Insights into the Tumor Microenvironment of Breast Cancer)
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22 pages, 4655 KiB  
Article
Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy
by Katarzyna Sylwia Dobruch-Sobczak, Hanna Piotrzkowska-Wróblewska, Piotr Karwat, Ziemowit Klimonda, Ewa Markiewicz-Grodzicka and Jerzy Litniewski
Cancers 2021, 13(14), 3546; https://doi.org/10.3390/cancers13143546 - 15 Jul 2021
Cited by 1 | Viewed by 1605
Abstract
The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant [...] Read more.
The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was the standard of reference. Alteration in B-mode ultrasound (tumor echogenicity and volume) and the Kullback-Leibler divergence (kld), as a quantitative measure of amplitude difference, were used. Correlations of these parameters with RMC were assessed and Receiver Operating Characteristic curve (ROC) analysis was performed. Thirty-nine patients (mean age 57 y.) with 50 tumors were included. There was a significant correlation between RMC and changes in quantitative parameters (KLD) after the second, third and fourth course of NAC, and alteration in echogenicity after the third and fourth course. Multivariate analysis of the echogenicity and KLD after the third NAC course revealed a sensitivity of 91%, specificity of 92%, PPV = 77%, NPV = 97%, accuracy = 91%, and AUC of 0.92 for non-responding tumors (RMC ≥ 70%). In conclusion, monitoring the echogenicity and KLD parameters made it possible to accurately predict the treatment response from the second course of NAC. Full article
(This article belongs to the Special Issue Using AI and Imaging Biomarkers for Insights into the Tumor Microenvironment of Breast Cancer)
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17 pages, 2479 KiB  
Article
Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study
by Isaac Daimiel Naranjo, Alexis Reymbaut, Patrik Brynolfsson, Roberto Lo Gullo, Karin Bryskhe, Daniel Topgaard, Dilip D. Giri, Jeffrey S. Reiner, Sunitha B. Thakur and Katja Pinker-Domenig
Cancers 2021, 13(7), 1606; https://doi.org/10.3390/cancers13071606 - 31 Mar 2021
Cited by 17 | Viewed by 3285
Abstract
Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 [...] Read more.
Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of “size” (1.43 ± 0.54 × 10−3 mm2/s) and higher mean values of “shape” (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of “size” (2.33 ± 0.22 × 10−3 mm2/s) and lower mean values of “shape” (0.27 ± 0.11) corresponding to bin3 (p < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands. Full article
(This article belongs to the Special Issue Using AI and Imaging Biomarkers for Insights into the Tumor Microenvironment of Breast Cancer)
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14 pages, 2547 KiB  
Article
Non-Invasive Assessment of Hypoxia and Neovascularization with MRI for Identification of Aggressive Breast Cancer
by Barbara Bennani-Baiti, Katja Pinker, Max Zimmermann, Thomas H. Helbich, Pascal A. Baltzer, Paola Clauser, Panagiotis Kapetas, Zsuzsanna Bago-Horvath and Andreas Stadlbauer
Cancers 2020, 12(8), 2024; https://doi.org/10.3390/cancers12082024 - 24 Jul 2020
Cited by 8 | Viewed by 2390
Abstract
The aim of this study was to investigate the potential of magnetic resonance imaging (MRI) for a non-invasive synergistic assessment of tumor microenvironment (TME) hypoxia and induced neovascularization for the identification of aggressive breast cancer. Fifty-three female patients with breast cancer underwent multiparametric [...] Read more.
The aim of this study was to investigate the potential of magnetic resonance imaging (MRI) for a non-invasive synergistic assessment of tumor microenvironment (TME) hypoxia and induced neovascularization for the identification of aggressive breast cancer. Fifty-three female patients with breast cancer underwent multiparametric breast MRI including quantitative blood-oxygen-level-dependent (qBOLD) imaging for hypoxia and vascular architecture mapping for neovascularization. Quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO2), mitochondrial oxygen tension (mitoPO2), microvessel radius (VSI), microvessel density (MVD), and microvessel type indicator (MTI) were calculated. Histopathology was the standard of reference. Histopathological markers (vascular endothelial growth factor receptor 1 (FLT1), podoplanin, hypoxia-inducible factor 1-alpha (HIF-1alpha), carbonic anhydrase 9 (CA IX), vascular endothelial growth factor C (VEGF-C)) were used to confirm imaging biomarker findings. Univariate and multivariate regression analyses were performed to differentiate less aggressive luminal from aggressive non-luminal (HER2-positive, triple negative) malignancies and assess the interplay between hypoxia and neoangiogenesis markers. Aggressive non-luminal cancers (n = 40) presented with significantly higher MRO2 (i.e., oxygen consumption), lower mitoPO2 values (i.e., hypoxia), lower MTI, and higher MVD than less aggressive cancers (n = 13). Data suggest that a model derived from OEF, mitoPO2, and MVD can predict tumor proliferation rate. This novel MRI approach, which can be easily implemented in routine breast MRI exams, aids in the non-invasive identification of aggressive breast cancer. Full article
(This article belongs to the Special Issue Using AI and Imaging Biomarkers for Insights into the Tumor Microenvironment of Breast Cancer)
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