Journal Article
. 2020 Feb;180(2).
doi: 10.1007/s10549-020-05533-5.

Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging

Vishwa S Parekh 1 Michael A Jacobs 2 
Affiliations
  • PMID: 32020435
  •     33 References
  •     3 citations

Abstract

Background And Purpose: Multiparametric radiological imaging is vital for detection, characterization, and diagnosis of many different diseases. Radiomics provide quantitative metrics from radiological imaging that may infer potential biological meaning of the underlying tissue. However, current methods are limited to regions of interest extracted from a single imaging parameter or modality, which limits the amount of information available within the data. This limitation can directly affect the integration and applicable scope of radiomics into different clinical settings, since single image radiomics are not capable of capturing the true underlying tissue characteristics in the multiparametric radiological imaging space. To that end, we developed a multiparametric imaging radiomic (mpRad) framework for extraction of first and second order radiomic features from multiparametric radiological datasets.

Methods: We developed five different radiomic techniques that extract different aspects of the inter-voxel and inter-parametric relationships within the high-dimensional multiparametric magnetic resonance imaging breast datasets. Our patient cohort consisted of 138 breast patients, where, 97 patients had malignant lesions and 41 patients had benign lesions. Sensitivity, specificity, receiver operating characteristic (ROC) and areas under the curve (AUC) analysis were performed to assess diagnostic performance of the mpRad parameters. Statistical significance was set at p < 0.05.

Results: The mpRad features successfully classified malignant from benign breast lesions with excellent sensitivity and specificity of 82.5% and 80.5%, respectively, with Area Under the receiver operating characteristic Curve (AUC) of 0.87 (0.81-0.93). mpRad provided a 9-28% increase in AUC metrics over single radiomic parameters.

Conclusions: We have introduced the mpRad framework that extends radiomic analysis from single images to multiparametric datasets for better characterization of the underlying tissue biology.

Keywords: ADC; Breast cancer; Diffusion; Entropy; Gray-level co-occurrence matrix (GLCM); Informatics; Machine learning; Magnetic resonance imaging; Multiparametric imaging; Radiomics; Texture.

Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.
Zhi-Cheng Li, Hongmin Bai, +6 authors, Hairong Zheng.
Eur Radiol, 2018 Mar 23; 28(9). PMID: 29564594
Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer.
Chintan Parmar, Ralph T H Leijenaar, +7 authors, Hugo J W L Aerts.
Sci Rep, 2015 Aug 08; 5. PMID: 26251068    Free PMC article.
Highly Cited.
Dynamic contrast-enhanced MRI of the breast: quantitative method for kinetic curve type assessment.
Riham H El Khouli, Katarzyna J Macura, +4 authors, David A Bluemke.
AJR Am J Roentgenol, 2009 Sep 23; 193(4). PMID: 19770298    Free PMC article.
Magnetic resonance imaging of the breast prior to biopsy.
David A Bluemke, Constantine A Gatsonis, +14 authors, Mitchell D Schnall.
JAMA, 2004 Dec 09; 292(22). PMID: 15585733
Highly Cited.
Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL.
Nola M Hylton, Jeffrey D Blume, +11 authors, ACRIN 6657 Trial Team and I-SPY 1 TRIAL Investigators.
Radiology, 2012 May 25; 263(3). PMID: 22623692    Free PMC article.
Highly Cited.
Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer.
Tianwen Xie, Zhe Wang, +5 authors, He Wang.
Front Oncol, 2019 Jul 02; 9. PMID: 31259153    Free PMC article.
Applications and limitations of radiomics.
Stephen S F Yip, Hugo J W L Aerts.
Phys Med Biol, 2016 Jun 09; 61(13). PMID: 27269645    Free PMC article.
Highly Cited. Review.
Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics.
Luke Peng, Vishwa Parekh, +19 authors, Lawrence Kleinberg.
Int J Radiat Oncol Biol Phys, 2018 Oct 26; 102(4). PMID: 30353872    Free PMC article.
Monitoring of neoadjuvant chemotherapy using multiparametric, ²³Na sodium MR, and multimodality (PET/CT/MRI) imaging in locally advanced breast cancer.
Michael A Jacobs, Ronald Ouwerkerk, +6 authors, Vered Stearns.
Breast Cancer Res Treat, 2011 Apr 02; 128(1). PMID: 21455671    Free PMC article.
Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis.
Rouzbeh Mashayekhi, Vishwa S Parekh, +3 authors, Atif Zaheer.
Eur J Radiol, 2019 Dec 18; 123. PMID: 31846864
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
Thibaud P Coroller, Patrick Grossmann, +7 authors, Hugo J W L Aerts.
Radiother Oncol, 2015 Mar 10; 114(3). PMID: 25746350    Free PMC article.
Highly Cited.
Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study.
Zhenyu Liu, Zhuolin Li, +13 authors, Jie Tian.
Clin Cancer Res, 2019 Mar 08; 25(12). PMID: 30842125
Highly Cited.
Textural analysis of contrast-enhanced MR images of the breast.
Peter Gibbs, Lindsay W Turnbull.
Magn Reson Med, 2003 Jun 20; 50(1). PMID: 12815683
Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI.
Vishwa S Parekh, Michael A Jacobs.
NPJ Breast Cancer, 2017 Nov 21; 3. PMID: 29152563    Free PMC article.
MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas.
Panagiotis Korfiatis, Timothy L Kline, +5 authors, Bradley J Erickson.
Med Phys, 2016 Jun 10; 43(6). PMID: 27277032    Free PMC article.
Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer.
Florent Tixier, Catherine Cheze Le Rest, +5 authors, Dimitris Visvikis.
J Nucl Med, 2011 Feb 16; 52(3). PMID: 21321270    Free PMC article.
Highly Cited.
Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network.
David C Newitt, Dariya Malyarenko, +26 authors, Nola Hylton.
J Med Imaging (Bellingham), 2017 Oct 13; 5(1). PMID: 29021993    Free PMC article.
A global geometric framework for nonlinear dimensionality reduction.
J B Tenenbaum, V de Silva, J C Langford.
Science, 2000 Dec 23; 290(5500). PMID: 11125149
Highly Cited.
Texture analysis and classification with tree-structured wavelet transform.
T Chang, C J Kuo.
IEEE Trans Image Process, 1993 Jan 01; 2(4). PMID: 18296228
Contrast-enhanced MRI for breast cancer screening.
Ritse M Mann, Christiane K Kuhl, Linda Moy.
J Magn Reson Imaging, 2019 Jan 20; 50(2). PMID: 30659696    Free PMC article.
Review.
Radiomics: a new application from established techniques.
Vishwa Parekh, Michael A Jacobs.
Expert Rev Precis Med Drug Dev, 2017 Jan 04; 1(2). PMID: 28042608    Free PMC article.
Highly Cited.
Radiomics: the process and the challenges.
Virendra Kumar, Yuhua Gu, +13 authors, Robert J Gillies.
Magn Reson Imaging, 2012 Aug 18; 30(9). PMID: 22898692    Free PMC article.
Highly Cited. Review.
Diffusion-weighted imaging improves the diagnostic accuracy of conventional 3.0-T breast MR imaging.
Riham H Ei Khouli, Michael A Jacobs, +4 authors, David A Bluemke.
Radiology, 2010 Jun 25; 256(1). PMID: 20574085    Free PMC article.
Highly Cited.
Reproducibility of radiomics for deciphering tumor phenotype with imaging.
Binsheng Zhao, Yongqiang Tan, +4 authors, Lawrence H Schwartz.
Sci Rep, 2016 Mar 25; 6. PMID: 27009765    Free PMC article.
Highly Cited.
Characterization of breast cancer types by texture analysis of magnetic resonance images.
Kirsi Holli, Anna-Leena Lääperi, +6 authors, Hannu Eskola.
Acad Radiol, 2009 Dec 01; 17(2). PMID: 19945302
Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study.
P Tiwari, P Prasanna, +9 authors, A Madabhushi.
AJNR Am J Neuroradiol, 2016 Oct 21; 37(12). PMID: 27633806    Free PMC article.
Tumor radiomic heterogeneity: Multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy.
Stephen R Bowen, William T C Yuh, +11 authors, Nina A Mayr.
J Magn Reson Imaging, 2017 Oct 19; 47(5). PMID: 29044908    Free PMC article.
Repeatability of Multiparametric Prostate MRI Radiomics Features.
Michael Schwier, Joost van Griethuysen, +7 authors, Andriy Fedorov.
Sci Rep, 2019 Jul 03; 9(1). PMID: 31263116    Free PMC article.
Highly Cited.
Deep learning and radiomics in precision medicine.
Vishwa S Parekh, Michael A Jacobs.
Expert Rev Precis Med Drug Dev, 2019 May 14; 4(2). PMID: 31080889    Free PMC article.
Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space.
Sean D McGarry, John D Bukowy, +14 authors, Peter S LaViolette.
Tomography, 2019 Mar 12; 5(1). PMID: 30854450    Free PMC article.
Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer.
Ahmad Chaddad, Michael J Kucharczyk, Tamim Niazi.
Cancers (Basel), 2018 Aug 01; 10(8). PMID: 30060575    Free PMC article.
Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives.
Ji Eun Park, Seo Young Park, Hwa Jung Kim, Ho Sung Kim.
Korean J Radiol, 2019 Jul 05; 20(7). PMID: 31270976    Free PMC article.
Highly Cited. Review.
Radiomics: the bridge between medical imaging and personalized medicine.
Philippe Lambin, Ralph T H Leijenaar, +17 authors, Sean Walsh.
Nat Rev Clin Oncol, 2017 Oct 05; 14(12). PMID: 28975929
Highly Cited. Review.
Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay.
Michael A Jacobs, Christopher B Umbricht, +5 authors, Antonio C Wolff.
Cancers (Basel), 2020 Oct 01; 12(10). PMID: 32992569    Free PMC article.
Toward radiomics for assessment of response to systemic therapies in lung cancer.
Shawn Sun, Florent L Besson, +2 authors, Laurent Dercle.
Oncotarget, 2021 Jan 22; 11(51). PMID: 33473253    Free PMC article.
Application of radiomics and machine learning in head and neck cancers.
Zhouying Peng, Yumin Wang, +4 authors, Weihong Jiang.
Int J Biol Sci, 2021 Feb 23; 17(2). PMID: 33613106    Free PMC article.
Review.