Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging – A systematic review and meta-analysis

Davey, Matthew G.; Davey, Martin S.; Boland, Michael R.; Ryan, Éanna J; Lowery, Aoife; Kerin, Michael J.

doi:10.1016/j.ejrad.2021.109996

Cited by 33 publications

(20 citation statements)

References 61 publications

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“…The linear discriminant analysis method was also able to differentiate Luminal B with 75% AUC by using only one principial component via the quantile method. This was in line with the recent literature, which states that radiomic assessment of breast imaging can provide an option in determining breast cancer molecular subtypes [ 14 ].…”

Section: Discussionsupporting

confidence: 89%

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A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study

Castaldo¹,

Garbino²,

Cavaliere³

et al. 2022

Diagnostics

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Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman’s correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis.

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Section: Discussionsupporting

confidence: 89%

“…In this study, we focused our attention on the luminal A and luminal B subtypes of breast cancer. Several studies have also explored the use of radiomics to investigate Luminal A and B breast cancer patients [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study

Castaldo¹,

Garbino²,

Cavaliere³

et al. 2022

Diagnostics

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“…DCE-MRI features were found to be associated with the deregulation or genetic alterations of some important pathways such as the mTOR pathway and oncogenic signaling pathways ( 22 , 23 ). Some works also attempted to establish prediction models for clinical biomarkers (such as ER and PR) as well as immunohistochemistry (IHC) subtypes of BC based on quantitative imaging features with a machine learning or deep learning approach ( 25 – 33 ). A study combined the MRI features from both peritumoral and intratumoral regions to predict the HER2-enriched molecular subtype and achieve an area under the curve (AUC) of 0.89 ( 31 ).…”

Section: Introductionmentioning

confidence: 99%

“…A study combined the MRI features from both peritumoral and intratumoral regions to predict the HER2-enriched molecular subtype and achieve an area under the curve (AUC) of 0.89 ( 31 ). In a recent large meta-analysis, the IHC subtypes of BC were predicted non-invasively by the radiomics analysis based on MRI features ( 33 ). In addition, uncovering the ability of imaging features to assess the treatment response and predict clinical outcomes in BC is a valuable research aspect.…”

Section: Introductionmentioning

confidence: 99%

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

et al. 2022

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BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.

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“…A newly published meta-analysis has shown that there are currently more than 40 studies concerning the radiomics assessment of molecular subtypes of breast cancer, most of which are based on mammography, ultrasound, and MRI ( 21 ). Huang et al.…”

Section: Discussionmentioning

confidence: 99%

Potential of the Non-Contrast-Enhanced Chest CT Radiomics to Distinguish Molecular Subtypes of Breast Cancer: A Retrospective Study

Wang

et al. 2022

Front. Oncol.

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ObjectivesThe molecular subtype plays an important role in breast cancer, which is the main reference to guide treatment and is closely related to prognosis. The objective of this study was to explore the potential of the non-contrast-enhanced chest CT-based radiomics to predict breast cancer molecular subtypes non-invasively.MethodsA total of 300 breast cancer patients (153 luminal types and 147 non-luminal types) who underwent routine chest CT examination were included in the study, of which 220 cases belonged to the training set and 80 cases to the time-independent test set. Identification of the molecular subtypes is based on immunohistochemical staining of postoperative tissue samples. The region of interest (ROI) of breast masses was delineated on the continuous slices of CT images. Forty-two models to predict the luminal type of breast cancer were established by the combination of six feature screening methods and seven machine learning classifiers; 5-fold cross-validation (cv) was used for internal validation. Finally, the optimal model was selected for external validation on the independent test set. In addition, we also took advantage of SHapley Additive exPlanations (SHAP) values to make explanations of the machine learning model.ResultsDuring internal validation, the area under the curve (AUC) values for different models ranged from 0.599 to 0.842, and the accuracy ranged from 0.540 to 0.775. Eventually, the LASSO_SVM combination was selected as the final model, which included 9 radiomics features. The AUC, accuracy, sensitivity, and specificity of the model to distinguish luminal from the non-luminal type were 0.842 [95% CI: 0.728−0.957], 0.773, 0.818, and 0.773 in the training set and 0.757 [95% CI: 0.640–0.866], 0.713, 0.767, and 0.676 in the test set.ConclusionThe radiomics based on chest CT may provide a new idea for the identification of breast cancer molecular subtypes.

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Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging – A systematic review and meta-analysis

Cited by 33 publications

References 61 publications

A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study

A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

Potential of the Non-Contrast-Enhanced Chest CT Radiomics to Distinguish Molecular Subtypes of Breast Cancer: A Retrospective Study

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