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The role of radiogenomics in the diagnosis of breast cancer: a systematic review

Abstract

Background

One of the most common cancers diagnosed worldwide is breast cancer (BC), which is the leading cause of cancer death among women. The radiogenomics method is more accurate for managing and inhibiting this disease, which takes individual diagnosis on genes, environments, and lifestyles of each person. The present study aims to highlight the current state-of-the-art, the current role and limitations, and future directions of radiogenomics in breast cancer.

Method

This systematic review article was searched from databases such as Embase, PubMed, Web of Science, Google Scholar, Scopus, and Cochrane Library without any date or language limitations of databases. Searches were performed using Boolean OR and AND operators between the main terms and keywords of particular topic of the subject under investigation. All retrospective, prospective, cohort, and pilot studies were included, which were provided with more details about the topic. Articles such as letter to the editor, review, and short communications were excluded because of lack of information, discussions, or use of radiogenomics method on other cancers. For quality assessment of articles, STROBE checklist was used.

Result

For the systematic review, 18 articles were approved after assessing the full text of selected articles. In this review, 3614 patients with BC of selected articles were evaluated, and all radiogenomics were associated with more power in classification, differential diagnosis, and prognosis of BC. Among the various modalities to predict genomic indicators and molecular subtypes, DCE-MRI has the higher performance and finally the highest amount of AUC value (0.956) belonged to PI3K gene.

Conclusion

This review shows that radiogenomics can help with the diagnosis and treatment of breast cancer in patients. It has shown that recognizing and specifying radiogenomic phenotypes in the genomic signatures can be helpful in treatment and diagnosis of disease. The molecular methods used in these articles are limited to miRNAs expression, gene expression, Ki67 proliferation index, next-generation RNA sequencing, whole RNA sequencing, and molecular histopathology that can be completed in future studies by other methods such as exosomal miRNAs, specific proteins expression, DNA repair capacity, and other biomarkers that have prognostic and predictive value for cancer treatment response. Studies with control group and large sample size for evaluation of radiogenomics in diagnosis and treatment recommended.

Introduction

One of the second most common cancers diagnosed worldwide is breast cancer, which is the leading cause of cancer death among women [1]. The Global Cancer Statistics 2020 report revealed 2.3 million new cases recognized and showed that female breast cancer was the most common [2]. Human breast cancer (BC) shows great interest in exploring the multivariate relationships of cancer [3, 4]. At clinical and molecular levels, BC is defined as a heterogeneous illness which it has distinguished into different subgroups concerning the situation of hormone receptors and altered clinical result. Identifying the biological context associated with tumor progression through imaging features provides additional data that can help in pre-treatment and pre-prognosis, as well as it helps us to know more about the biological features of the tumor [5, 6]. Association of the tumor genome and imaging phenotypes is called radiogenomics [7]. Radiogenomics method is more accurate for managing and inhibiting this disease which takes individual diagnosis in genes, environments, and lifestyles of each person [8]. For genetic examinations, radiogenomics can be achieved by developing an imaging surrogate, and they can be costly, time-consuming, and need invasive tissue sampling. To understand the biology of tumors, radiogenomics recognizes the association between imaging phenotypes and genotypes, and combining them into an integrated sample can enhance the forecast of clinical outcomes [9, 10]. The most common and heterogeneous disease in women is BC. Human epidermal growth factor receptor-2 (HER2), basal BC, luminal A, and luminal B tumors were classified into distinct molecular subtypes (MSTs) to explain differences in the biology of BC by genetic research [11, 12]. Each MST has different subsequent metastatic spread and different patterns of primary disease [13, 14]. Radiation therapy (RT) and rates of response to chemotherapy are different for each MST [15]. BC survival has improved dramatically, with the current 10-year survival rate estimated to be over 80%. Issues such as survival and quality of life are very important focuses for cancer research. More than 70% of BC patients undergo RT [16]. The side effects that are caused by RT are a reduction in overall morality and the risk of local recurrence. Radiation has different degrees of toxicity which it can affect the patients. Dosimetry, body habit, and smoking are the clinical factors that depend on the individual radiation sensitivity, and this sensitivity has an important role [17]. To help in treatment decision-making procedures, singular risk forecast model for toxicity of radiation was produced through a combination of patient factor, clinical factors, and predictive biomarkers. Genetic association studies have identified several potential predictors of genetic markers for radiation toxicity [18,19,20]. Although current reference standard is expensive and needs specific equipment as well as technical expertise to classify the MSTs by genetic analysis, it needs alternative tools to classify BCs into distinct MSTs [15]. Early detection of highly malignant BC is important in treatment and diagnosis of BC. Until now, analysis of needle biopsy, immunohistochemistry, or removed surgical specimens (partial tumor tissue) have helped in the discovery of these molecular markers. Due to tumor heterogeneity, this method has particular limitations; in contrast, tumor tissue's general anatomical and functional features can be provided by imaging [21]. Consequently, radiogenomic approaches in BC are still in its early stages and many problems remain in fusion between radiomics features and genomics indexes, to be solved. The present study aims to highlight the current state-of-the-art, the current role and limitations, and future directions of radiogenomics in breast cancer.

Methods

Literature review and search strategy

Articles were searched by two individual researchers. Briefly, the list of collected articles was complemented without any date or language limitations by databases such as Embase, PubMed, Web of Science, Google Scholar, Scopus, and Cochrane Library. The keywords for search were as follows: “radiogenomics,” “estrogen receptor,” “progesterone receptor,” “ER,” “PR,” “HER2,” “ki67,” “molecular subtype,” and “breast cancer.” Searches were performed using Boolean OR and AND operators between the main terms and keywords of particular topic of the subject under investigation. In addition, relevant keywords and Boolean operators were selected to refine the search strategy in each database.

Data extraction and data collection

Independent searches were conducted by two researchers from July 2021 to September 2021. This paper includes all published articles from 2012 to 2020. Studies of patients who have BC and performed radiogenomics analysis on their tumor samples were included. All retrospective, prospective, cohort, pilot studies, and the patients who undergo the radiogenomics method in breast cancer were included. Articles such as letter to the editor, review, short communications, and the patients who undergo the radiogenomics method in other cancers except to breast cancer were excluded. Of 96 full-text articles, 76 were excluded because of irrelevant and without precise quantity information (Fig. 1). From qualified studies, the following data were extracted individually from each study in a standardized way: publication year, authors, study design, method, genomic analysis, as well as outcome (Table 1). Any disagreement was decided by discussion after additional study of the articles.

Fig. 1
figure 1

Flowchart to describe the process of the selected studies

Table 1 Details of the studies included in this review

Assessment of study quality

For methodological validation prior to entry, two independent reviewers evaluated the selected papers. For quality assessment of articles, Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist was used [37]. The STROBE checklist was used to analyze the data. Epidemiological study design that can be conducted as a cross-sectional study, cohort study, case study, is an observational study. When presenting observational studies in the article, the author should know clear information about the work and provide the reader with the suitable information to make a critical assessment of the research. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines are designed to help the author in confirming a high-quality presentation of the observational study. To provide inclusive reporting of descriptive observational articles, 22 questions were used in the STROBE checklist in order to test relation between exposures and health results [38].

Result

Research finding

Out of 1118 articles, 962 were individually assessed after being identified through database searching. Duplicate articles were removed, and 163 articles were reviewed. Then, 76 articles were excluded, including irrelevant studies and articles with inadequate information that did not match inclusion criteria. For the systematic review, 18 articles were approved after assessing the full text of selected articles. The evaluation process is shown in Fig. 1. In this review, 3614 patients with BC of selected articles were evaluated, and all radiogenomics were associated with more power in classification, differential diagnosis, and prognosis of BC. Also, the studies showed the relationship between imaging features, molecular subgroups, and tumor molecular biomarkers. In this study, it was observed that radiomic features of magnetic resonance imaging (MRI), dynamic contrast-enhanced MRI, diffusion-weighted MRI, contrast-enhanced ultrasonography (CEUS), and computed tomography (CT) can differentiate the essential molecular features of BC patients. These molecular biomarkers include mRNAs, microRNAs, lncRNAs, ER, PR, HER2, Myc, p53, RTK/RAS, PI3K and Ki-67 expression, human epidermis, and status of growth factor signaling pathways. Imaging phenotypes have related to basic genes, expression patterns, and mutation as the radiogenomics' goals. In radiogenomics of breast cancer, it mostly concentrates on primarily contrast-enhanced MR imaging and the association of its features with molecular subtypes, individual genomic signatures, or recurrence scores. The limited radiogenomic goal is to create imaging biomarkers using phenotypic and genotypic criteria that can predict risk and outcomes and thus better classify patients for accurate therapeutic care. The CT and MRI imaging features were texture, morphologies, and dynamic features. US features included size, orientation, shape, echo pattern, margin, and calcifications. Also, vascular features were assessed by using contrast agent-enhanced US and microvascular US: vessel morphological features, vascular index, penetrating vessels, distribution, margin, internal homogeneity, enhancement degree, and perfusion defect (Fig. 2). Mammography features were mass with or without calcifications, breast composition or margin, the density of mass, the morphology of calcifications, mass density, mass margin, mass shape, and asymmetry or architectural distortion (Fig. 2). The accuracy of random forest model was higher by approximately 13% on average than accuracy of logistic regression model. In order to compare between the predictive power of different radiomics methods, AUC values have been extracted. As shown in Fig. 3, DCE-MRI modality has the higher performance to predict genomic biomarkers (mean = 0.91%), including cell cycle check points, expression of genes such as Mye, PI3K, RTK/RAS, P53, and finally ER + /ER−, PR + /PR−, HER2 + /HER2, and triple-negative indicators. Among the indicators, the highest amount of AUC value (0.956) belonged PI3K gene.

Fig. 2
figure 2

Typical workflow of radiogenomics studies in breast cancer patients. A Image features extraction and selection. B Genomic identification using radiogenomics study. C Prognostic and diagnostic approaches of radiogenomics

Fig. 3
figure 3

Summary of area under the curve (AUC) values of various modalities to predict genomic indicators and molecular subtypes of breast cancer patients (11, 13–17). *Represent P value < 0.05 significant difference with other modalities

Discussion

This study showed that recognizing and specifying radiogenomic phenotypes in the genomic signatures can be helpful. In several studies, clinical phenotypes such as triple-negative status (TN), estrogen receptor (ER), human epidermal growth factor 2 (HER2), and progesterone normalization methods' ability have been evaluated. [28, 30,31,32, 39]. They showed that radiomic features differentiate the essential molecular features of BC as well as may provide potential biomarkers for the development of precision medicine [29].

Associating radiomics with genomics is a developing area of research usually discussed as “radiogenomics” or more specifically “imaging-genomics.” This developing field addresses novel high-throughput methods of relating information-rich radiographic images with genomic data as well as other clinically related information. Radiogenomics has the potential to influence therapeutic and diagnostic approaches by creating more personalized real-time measurements in response to therapy and prognostic signatures, without having to rely on biopsy to represent cancer lesions within a patient [40].

Radiomics

The idea of radiomics was first discussed by the Dutch researcher Lambin in 2012. This idea presented tumor heterogeneity [41]. Compared to traditional proteomics and genomic methods, radiomics can be a noninvasive method for evaluating tumors and their microenvironment and predicting genetic heterogeneity of the tumor. The use of extracted semiautomatic imaging features has several benefits. The human eye cannot easily recognize the analyzed features. Repeatable and devoid of intra-observable variability, the analysis can be done by computer algorithms when the lesion is annotated by the radiologist. As the radiologist has to study and report, this analysis can be done in the context, so it only requires an extra burden and time for the radiologist to sketch a box circa the lesion. Considering the situation of future, the workflow of radiologist is minimally affected, but the radiologist can provide added value by adding information from the molecular subgroup to his/her dictation report [15]. The main limitation of radiomics is the lack of reproducibility due to the inconsistent radiomic methods and the lack of optimization of the acquisition parameters. Nevertheless, the field of machine vision for image identification has been given potential by the recent advancement of deep learning. In this context, Anderson et al. recently showed that the performance of the computer-aided diagnosis was significantly better than the convolutional neural network feature extraction for contrast-enhanced magnetic resonance imaging (MRI) in the BC diagnosis [42].

From radiomics to radiogenomics

Tumors' biological details are essential in choosing suitable therapy plan and achieving effective therapy results. As the result of cancer, promising genes were identified from the development of genetic research in BC. Also, development of confirmed genomic signatures allows the classification of BCs to differentiate molecular subgroups, predicting the cancer recurrence risk, and predicting response to the treatment. In general, the profiles and pathways of gene expression modification induced by ionizing radiation are cell-related. The information shows that HR condition can be related to the certain genes and pathway's signatures. Genomic biomarkers and gene signatures of specific tumor subtypes, certain subtypes of tumor gene signatures, and genomic biomarkers depending on molecular characteristics and HR condition can simplify RT biomarkers using personal biomarkers itself or with association of selected treatments. Therefore, to enhance the forecast of clinical outcomes, the synergistic power and integrated radiomics to radiogenomics models are needed. Finally, detection of significant features of the tumor tissue by noninvasive molecular, anatomical, and functional methods can provide potential biomarkers for the development of personalized medicine. The typical workflow of radiogenomic studies that was discussed in this article is shown in Fig. 2.

Current status of radiogenomics in precision RT

Current radiogenomics research has been conducted toward understanding tumor biology, heuristic analysis of individual genes, and the development of imaging substitutes for genetic analysis with the aim of developing term clinical care tools [9]. With significant advances in genetics and genomics over the last 30 years, it has been hypothesized that genomic alterations may affect radiation-related adverse events [43]. Regardless of the technological developments produced in past few years that make RT highly targeted to the selected tumor, for each tumor, the RT programs are yet to be considered as the equal total number of dose given biological alterations to different types of tumor. Nevertheless, significant changes in the response to radiation were caused by the diversity of molecular illustration of certain cancer subtypes. Hence, to select a suitable therapies plan, tumors' biological details are essential, consequently achieving effective RT results. Broad scientific confirmation showed that RT is an important treatment for different types of cancer containing BC for different cancer types either alone or by combination with other therapies. For patients who are diagnosed with BC, they have two choices: mastectomy or partial protection (partial care after surgery) followed by RT. These choices will be affected by numerous factors such as medical, psychology, and sociology factors. The important factor of RT is the side effects of high-dose RT which has toxicities radiation. It may result in poor cosmesis or pain that can affect the BC patients' quality of life. Its effect can lead to negative psychological consequences. Therefore, an experiment that can predict the likelihood of radiation damage is helpful because it helps patients with BC and their physicians to find the most appropriate individualized treatment. This information finally allows physicians to prescribe initialed overall doses or certain subtypes of tumor treatment; therefore, it increases the radio-sensitivity [21]. BC is highly heterogeneous, and image performance varies in size, shape, brightness, and lesion values [44]. Studies have shown that personal medicine depends on the combination and contemplating patient characteristics such as tumor phenotypes and genotypic profiles [22]. Therefore, early monitoring and anticipation of the patient's response to treatment are particularly important, especially due to the toxic or expensive drugs in response's heterogeneity as well as the loss of chances for adequate early replacement treatment. Although many drugs have special procedures, they are not targeted for genetic lesions. Therefore, it will face difficulty in selecting patients based on basic genetic features [45]. This has led to the creation of a radiogenomic field that aims to identify genetic factors that form a wide spectrum of responses observed in radiation-treated patients. Although genomic characteristics are likely to influence tumor radiation response, the focus of radiogenomics research is to identify biomarkers that influence sensitivity to normal tissue and radiation-induced tissue. The general purpose of radiogenomics in radiotherapy is to develop assays to predict that patients will likely exhibit RT complications.

Limitations and future directions

All reviewed radiogenomic research indicated potential interest in introducing the new candidate biomarkers. In BC, these markers have established possible value for diagnostic/prognostic. However, insufficient number of patients is the most important limitation of studies. Because of inadequate data and overestimated detection accuracy, exterior confirmation cannot be performed. It should take into consideration that even the classification of radiomics is useful for few results, but large samples should be confirmed before clinical use. Finally, it seems that future research is needed to evaluate the worthiness, radiomics biomarkers reproducibility, and confirmation of prospective cohort by using large sample sizes [46]. Anatomic location of the MRI methods like complete pathology is correlated with multiple tumors by biopsy as the more precise approach. Also, standardizing imaging features is a challengeable. Capturing images from a scanner and protocol type has increased the reliability of the radiomics dataset. Researchers admit which protocols of image acquisition are diverse through associations; also, their results are broadened with greater validity. Larger sample sizes and rigorous studies are already required for computer diagnostics [21]. Due to the high prevalence and importance of diagnosis and treatment of breast cancer, health promotion and increased survival of cancer patients are essential. This study shows that radiogenomic protocol can help to recognize the tumors, classification, differential diagnosis, maps before surgery of BC, relationship between imaging features and tumor molecular biomarkers, and forecast the recurrence and benefit of chemotherapy. Because of the heterogeneity of the studies and information in Table 1 using different methods of imaging and genetic assay, meta-analysis was not able to implement on this information.

Conclusion

The general purpose of diagnostic radiogenomics is to discover new imaging features that reflect genomic alterations associated with tumor phenotypes, lymph node status, HR status, HER2, Ki67, and MSTs. Different imaging modalities and molecular biomarkers have been used for this purpose. In imaging modalities, the features of MRI images are mainly used. However, recently published articles show that the features extracted from the US and CT can be applied to the classification of different BC subtypes. The molecular methods used in these articles are limited to miRNAs expression, gene expression, Ki67 proliferation index, next-generation RNA sequencing, whole RNA sequencing, and molecular histopathology that can be completed in future studies by other methods such as exosomal miRNAs, specific proteins expression, DNA repair capacity, and other biomarkers that have prognostic and predictive value for cancer treatment response. The role of radiogenomics in BC and potential applications in radiotherapy were reviewed in this study. This review study shows that radiogenomics can improve diagnosis and treatment of breast cancer in patients. All radiogenomic studies were associated with more power in classification, differential diagnosis, and prognosis of BC. It has shown that recognizing and specifying radiogenomic phenotypes in the genomic signatures can be helpful. Studies with control group and large sample size for evaluation of radiogenomics in diagnosis and treatment recommended.

Availability of data and materials

Not applicable.

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LD and HA conceived the study and participated in design and conduct of study including article collection, statistical analysis, and manuscript preparation. MT-BT and NR contributed to manuscript review and preparation. The author(s) read and approved the final manuscript.

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Correspondence to Hosein Azimian.

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I, Hosein Azimian, hereby declare that I participated in the study and in the development of the manuscript titled “The role of radiogenomics in the diagnosis of breast cancer: a systematic review.” I have read the final version and give my consent for the article to be published in Egyptian Journal of Medical Human Genetics.

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Darvish, L., Bahreyni-Toossi, MT., Roozbeh, N. et al. The role of radiogenomics in the diagnosis of breast cancer: a systematic review. Egypt J Med Hum Genet 23, 99 (2022). https://doi.org/10.1186/s43042-022-00310-z

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  • DOI: https://doi.org/10.1186/s43042-022-00310-z

Keywords

  • Radiogenomics
  • Radiomics
  • Breast cancer
  • Molecular subtype
  • Radiation therapy