Breast Cancer Radiogenomics: Current Status and Future Directions

doi:10.1016/j.acra.2019.09.012

Academic Radiology

Volume 27, Issue 1, January 2020, Pages 39-46

https://doi.org/10.1016/j.acra.2019.09.012 Get rights and content

Radiogenomics is an area of research that aims to identify associations between imaging phenotypes (“radio-”) and tumor genome (“-genomics”). Breast cancer radiogenomics research in particular has been an especially prolific area of investigation in recent years as evidenced by the wide number and variety of publications and conferences presentations. To date, research has primarily been focused on dynamic contrast enhanced pre-operative breast MRI and breast cancer molecular subtypes, but investigations have extended to all breast imaging modalities as well as multiple additional genetic markers including those that are commercially available. Furthermore, both human and computer-extracted features as well as deep learning techniques have been explored. This review will summarize the specific imaging modalities used in radiogenomics analysis, describe the methods of extracting imaging features, and present the types of genomics, molecular, and related information used for analysis. Finally, the limitations and future directions of breast cancer radiogenomics research will be discussed.

Section snippets

INTRODUCTION

Radiogenomics is the study of the relationship between imaging phenotypes (expressed in “radio-”) and tumor genome (expressed in “-genomics”). Radiogenomics research often fits within the larger field of precision medicine, which according to the National Institute of Health is “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person” (1). Radiogenomics could impact patient and cancer research

IMAGING MODALITIES USED FOR RADIOGENOMIC RESEARCH IN BREAST CANCER

The major imaging modalities utilized for radiogenomics analysis and select primary references are shown in Table 1.

Imaging Features

The imaging features used for analysis can either be human or computer derived. For human features, a reader will review the images and report on the imaging variables of interest. These imaging variables must be well-defined to limit inter- or intra-observer variability and therefore features used in clinical practice such as the BI-RADS descriptors are often used (40). For example, Wu et al. found that HER2-enriched molecular subtype breast cancers were associated with the BI-RADS ultrasound

GENOMICS

The major genomics outcomes studied via radiogenomics and select major reference is shown in Table 2.

LIMITATIONS AND FUTURE DIRECTIONS

The work to date on breast cancer radiogenomics has been promising. In our opinion, convincing evidence has emerged showing that there is a moderate association between imaging characteristics and genomic or related characteristics of breast cancer. However, adoption of this work into clinical practice will require overcoming significant challenges.

First, nearly all the published studies have relied on retrospective datasets. MRI is the primary modality of analysis, but there is notable

CONCLUSION

Breast cancer radiogenomics is a very promising area of investigation that has the potential to capitalize on the rapid growth in data analytics and the deep wellspring of breast cancer genetic knowledge. The steadily increasing rate of radiogenomics publications and presentations has resulted from many different investigators, full spectrum of imaging modalities, and wide variety of analytic techniques. To date, radiogenomics work has primarily been focused on single institutions and

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Cited by (41)

A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy
2024, Clinical Breast Cancer
The incidence of breast cancer ranks highest among cancers and is exceedingly heterogeneous. Immunohistochemical staining is commonly used clinically to identify the molecular subtype for subsequent treatment and prognosis.
Raman spectroscopy and support vector machine (SVM) learning algorithm were utilized to identify blood samples from breast cancer patients in order to investigate a novel molecular typing approach.
Tumor tissue coarse needle aspiration biopsy samples, and peripheral venous blood samples were gathered from 459 invasive breast cancer patients admitted to the breast department of Sichuan Cancer Hospital between June 2021 and September 2022. Immunohistochemical staining and in situ hybridization were performed on the coarse needle aspiration biopsy tissues to obtain their molecular typing pathological labels, including: 70 cases of Luminal A, 167 cases of Luminal B (HER2-positive), 57 cases of Luminal B (HER2-negative), 84 cases of HER2-positive, and 81 cases of triple-negative. Blood samples were processed to obtained Raman spectra taken for SVM classification models establishment with machine algorithms (using 80% of the sample data as the training set), and then the performance of the SVM classification models was evaluated by the independent validation set (20% of the sample data).
The AUC values of SVM classification models remained above 0.85, demonstrating outstanding model performance and excellent subtype discrimination of breast cancer molecular subtypes.
Raman spectroscopy of serum samples can promptly and precisely detect the molecular subtype of invasive breast cancer, which has the potential for clinical value.
Differentiation Between Phyllodes Tumors and Fibroadenomas of Breast Using Mammography-based Machine Learning Methods: A Preliminary Study
2023, Clinical Breast Cancer
To investigate the diagnostic performance of a mammography-based radiomics model for distinguishing phyllodes tumors (PTs) from fibroadenomas (FAs) of the breast.
A total of 156 patients were retrospectively included (75 with PTs, 81 with FAs) and divided into training and validation groups at a ratio of 7:3. Radiomics features were extracted from craniocaudal and mediolateral oblique images. The least absolute shrinkage and selection operator (LASSO) algorithm and principal component analysis (PCA) were performed to select features. Three machine learning classifiers, including logistic regression (LR), K-nearest neighbor classifier (KNN) and support vector machine (SVM), were implemented in the radiomics model, imaging model and combined model. Receiver operating characteristic curves, area under the curve (AUC), sensitivity and specificity were computed.
Among 1084 features, the LASSO algorithm selected 17 features, and PCA further selected 6 features. Three machine learning classifiers yielded the same AUC of 0.935 in the validation group for the radiomics model. In the imaging model, KNN yielded the highest accuracy rate of 89.4% and AUC of 0.947 in the validation set. For the combined model, the SVM classifier reached the highest AUC of 0.918 with an accuracy rate of 86.2%, sensitivity of 83.9%, and specificity of 89.4% in the training group. In the validation group, LR yielded the highest AUC of 0.973. The combined model had a relatively higher AUC than the radiomics model or imaging model, especially in the validation group.
Mammography-based radiomics features demonstrate good diagnostic performance for discriminating PTs from FAs.
An omics-to-omics joint knowledge association subtensor model for radiogenomics cross-modal modules from genomics and ultrasonic images of breast cancers
2023, Computers in Biology and Medicine
The radiogenomics analysis can provide the connections between genomics and radiomics, which can infer the genomic features of tumors from their radiogenomic associations through the low-cost and non-invasiveness screening ultrasonic images. Although there are a number of pioneer approaches exploring the connections between genomic aberrations and ultrasonic features, these studies mainly focus on the relationship between ultrasonic features and only the most popular cancer genes, confronting two difficulties: missing many-to-many relationships as omics-to-omics view, and confounding group-specific associations with whole sample associations. To overcome the difficulty of omics-to-omics view and the issue of tumor heterogeneity, we propose an omics-to-omics joint knowledge association subtensor model. Specifically, the subtensor factorization framework can successfully discover the joint cross-modal module via an omics-to-omics view, while the sparse weight sample indication strategy can mine sample subgroups from the multi-omic data with tumor heterogeneity. The experimental evaluation result shows the jointness of the discovered modules across omics, their association with tumorigenesis contribution, and their relation for cancer related functions. In summary, our proposed omics-to-omics joint knowledge association subtensor model can serve as an efficient tool for radiogenomic knowledge associations, promoting the cross-modal knowledge graph construction of in explainable artificial intelligence cancer diagnosis.
Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs
2022, Academic Radiology
Citation Excerpt :
Radiomics, however, in a non-invasive and cost-effective way, can quantitatively extract these features directly from clinical medical images capturing the entire tumor heterogeneity and genotypic characteristics which can be used to enhance the accuracy of BC diagnosis, prognosis, and prediction. The potential value and capability of radiomics as alternative means for non-invasive and cost-effective assessment of BC have been shown by the studies in this review and many other reviews (16,24,35,38,62), specifically on its potential role around predicting factors that describe BC characteristics as well as recurrence and survival as mentioned above. Some key values of radiomics include, the possibility of serving as a non-invasive and effective biomarker for preoperative prediction of ALNM (32,74) and tumor invasiveness (31,73), detecting aggressive cancer early such as TNBC (30,76), and predicting therapeutic effect (69) and patients survival outcomes (28) which are essential for guiding therapy decisions including eliminating unnecessary surgery and/or biopsy.
Breast cancer is a highly complex heterogeneous disease. Current validated prognostic factors (e.g., histological grade, lymph node involvement, receptor status, and proliferation index), as well as multigene tests (e.g., Oncotype DX and PAM50) are helpful to describe breast cancer characteristics and predict the chance of recurrence risk and survival. Nevertheless, they are invasive and cannot capture a complete heterogeneity of the entire breast tumor resulting in up to 30% of patients being either over- or under-treated for breast cancer. Furthermore, multigene testings are time consuming and expensive. Radiomics is emerging as a reliable, accurate, non-invasive, and cost-effective approach of using quantitative image features to classify breast cancer characteristics and predict patient outcomes. Several recent radiomics reviews have been conducted in breast cancer, however, specific mammography-based radiomics studies have not been well discussed. This scoping review aims to assess and summarize the current evidence on the potential usefulness of mammography-based (i.e., digital mammography, digital breast tomosynthesis, and contrast-enhanced mammography) radiomics in predicting factors that describe breast cancer characteristics, recurrence, and survival.
PubMed database and eligible text reference were searched using relevant keywords to identify studies published between 2015 and December 19, 2020. Studies collected were screened and assessed based on the inclusion and exclusion criteria.
Eighteen eligible studies were included and organized into three main sections: radiomics predicting breast cancer characteristics, radiomics predicting breast cancer recurrence and survival, and radiomics integrating with clinical data. Majority of publications reported retrospective studies while three studies examined prospective cohorts. Encouraging results were reported, suggesting the potential clinical value of mammography-based radiomics. Further efforts are required to standardize radiomics approaches and catalogue reproducible and relevant mammographic radiomic features. The role of integrating radiomics with other information is discussed.
The potential role of mammography-based radiomics appears promising but more efforts are required to further evaluate its reliability as a routine clinical tool.
Noninterpretive Uses of Artificial Intelligence in Radiology
2021, Academic Radiology
Citation Excerpt :
AI can be applied to these databases in either unsupervised learning approaches (e.g., to identify patterns) or supervised learning approaches (in which outcome data or confirmed pathological diagnoses are used to train a learning model). Radiomics is often used in oncological scenarios, such as for identification of imaging features that predict specific subtypes or grades of tumors [86]. Applying a discovery radiomics approach to chest CT resulted in better prediction of pathologically proven lung cancer than current state-of-the-art approaches [87].
We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI models to recognize anatomy and detect pathology on medical images, sometimes at the level of expert physicians. However, AI can also be used to solve a wide range of noninterpretive problems that are relevant to radiologists and their patients. This review summarizes some of the newer noninterpretive uses of AI in radiology.
The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics
2024, Cancers

View all citing articles on Scopus

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Special ReviewBreast Cancer Radiogenomics: Current Status and Future Directions

Section snippets

INTRODUCTION

IMAGING MODALITIES USED FOR RADIOGENOMIC RESEARCH IN BREAST CANCER

Imaging Features

GENOMICS

LIMITATIONS AND FUTURE DIRECTIONS

CONCLUSION

Eur J Radiol

Clin Imaging

Eur J Radiol

Eur J Radiol

Clin Nucl Med

Acad Radiol

Comput Methods Programs Biomed

Cancer Treat Rev

Ann Oncol

Ann Oncol

Expert Syst Appl

Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape

AJR Am J Roentgenol

Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging

Radiology

Computational approach to radiogenomics of breast cancer: luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms

J Magn Reson Imaging

A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features

Br J Cancer

Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer

Breast Cancer

MRI phenotype of breast cancer: kinetic assessment for molecular subtypes

J Magn Reson Imaging

Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles

Radiology

MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays

Radiology

Correlations between diffusion-weighted imaging and breast cancer biomarkers

Eur Radiol

Histogram analysis of apparent diffusion coefficient at 3.0t: correlation with prognostic factors and subtypes of invasive ductal carcinoma

J Magn Reson Imaging

Correlations between apparent diffusion coefficient values of invasive ductal carcinoma and pathologic factors on diffusion-weighted MRI at 3.0 Tesla

J Magn Reson Imaging

Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging

Eur Radiol

Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors

Eur Radiol

Multiparametric MRI of the breast: a review

J Magn Reson Imaging

BI-RADS 3-5 microcalcifications can preoperatively predict breast cancer HER2 and luminal a molecular subtype

Oncotarget

Microcalcification-associated breast cancer: HER2-enriched molecular subtype is associated with mammographic features

Br J Radiol

Qualitative radiogenomics: association between Oncotype DX test recurrence score and BI-RADS mammographic and breast MR imaging features

Radiology

The association of mammographic density and molecular breast cancer subtype

Cancer Epidemiol Biomarkers Prev

Is there a correlation between breast cancer molecular subtype using receptors as surrogates and mammographic appearance?

Ann Surg Oncol

Screening mammography and digital breast tomosynthesis: utilization updates

AJR Am J Roentgenol

Imaging features of breast cancers on digital breast tomosynthesis according to molecular subtype: association with breast cancer detection

Br J Radiol

Radiomics analysis on ultrasound for prediction of biologic behavior in breast invasive ductal carcinoma

Clin Breast Cancer

The role of ultrasonographic findings to predict molecular subtype, histologic grade, and hormone receptor status of breast cancer

Diagn Interv Radiol

Identifying ultrasound and clinical features of breast cancer molecular subtypes by ensemble decision

Sci Rep

[(18)F]FDG PET/CT features for the molecular characterization of primary breast tumors

Eur J Nucl Med Mol Imaging

Prediction of biological characteristics of breast cancer using dual-phase FDG PET/CT

Eur J Nucl Med Mol Imaging

Quantitative assessment of metabolic tumor burden in molecular subtypes of primary breast cancer with FDG PET/CT

Diagn Interv Radiol

(18)F-Fluorodeoxyglucose positron emission tomography/computed tomography features in locally advanced breast cancer and their correlation with molecular subtypes

Indian J Nucl Med

Special Review
Breast Cancer Radiogenomics: Current Status and Future Directions