Overview of radiomics in breast cancer diagnosis and prognostication

Tagliafico, Alberto; Piana, Michele; Schenone, Daniela; Lai, Rita; Massone, Anna Maria; Houssami, Nehmat

doi:10.1016/j.breast.2019.10.018

Cited by 213 publications

(168 citation statements)

References 53 publications

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“…The present findings indicate that the texture features extracted from intratumoral subregions have potential value for distinguishing the HER2 2+ status of breast cancer. Intratumoral heterogeneity is an indicator of differences in gene expression, angiogenesis, metabolism, and other biological characteristics (45)(46)(47). Previous studies explored heterogeneity based on DCE-MRI texture features from the whole lesion area (25,48).…”

Section: Discussionmentioning

confidence: 99%

Texture Analysis of Breast DCE-MRI Based on Intratumoral Subregions for Predicting HER2 2+ Status

Yin

2020

Front. Oncol.

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Background: Breast tumor heterogeneity is related to risk factors that lead to aggressive tumor growth; however, such heterogeneity has not been thoroughly investigated.Purpose: To evaluate the performance of texture features extracted from heterogeneity subregions on subtraction MRI images for identifying human epidermal growth factor receptor 2 (HER2) 2+ status of breast cancers.Materials and Methods: Seventy-six patients with HER2 2+ breast cancer who underwent dynamic contrast-enhanced magnetic resonance imaging were enrolled, including 42 HER2 positive and 34 negative cases confirmed by fluorescence in situ hybridization. The lesion area was delineated semi-automatically on the subtraction MRI images at the second, fourth, and sixth phases (P-1, P-2, and P-3). A regionalization method was used to segment the lesion area into three subregions (rapid, medium, and slow) according to peak arrival time of the contrast agent. We extracted 488 texture features from the whole lesion area and three subregions independently. Wrapper, least absolute shrinkage and selection operator (LASSO), and stepwise methods were used to identify the optimal feature subsets. Univariate analysis was performed as well as support vector machine (SVM) with a leave-one-out-based cross-validation method. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the classifiers.Results: In univariate analysis, the variance from medium subregion at P-2 was the best-performing feature for distinguishing HER2 2+ status (AUC = 0.836); for the whole lesion region, the variance at P-2 achieved the best performance (AUC = 0.798). There was no significant difference between the two methods (P = 0.271). In the machine learning with SVM, the best performance (AUC = 0.929) was achieved with LASSO from rapid subregion at P-2; for the whole region, the highest AUC value was 0.847 obtained at P-2 with LASSO. The difference was significant between the two methods (P = 0.021). Conclusion:The texture analysis of heterogeneity subregions based on intratumoral regionalization method showed potential value for recognizing HER2 2+ status in breast cancer.

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Section: Discussionmentioning

confidence: 99%

Texture Analysis of Breast DCE-MRI Based on Intratumoral Subregions for Predicting HER2 2+ Status

Yin

2020

Front. Oncol.

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“…Meanwhile, the subjective evaluation of clinical findings made breast DCE‐MRI interpretation a challenging task for radiologists, which resulted in a low specificity (30% to 70%) reported in previous studies 6‐8 . As a result, computer‐aided diagnosis of breast DCE‐MRI has been a hot subject of research 9‐18 …”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE‐MRI in classifying benign from malignant lesions

Jiang

Yin

2020

Journal of Surgical Oncology

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Background and Objectives: To investigate the performance of texture analysis based on enhancement kinetic parametric maps derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in discriminating benign from malignant tumors.Methods: A total of 192 cases confirmed by histopathology were retrospectively selected from our Picture Archiving and Communication System, including 93 benign and 99 malignant tumors. Lesion areas were delineated semi-automatically, and six kinetic parametric maps reflecting the perfusion information were generated, namely the maximum slope of increase (MSI), slope of signal intensity (SI slope ), initial percentage of peak enhancement (E initial ), percentage of peak enhancement (E peak ), early signal enhancement ratio (ESER), and second enhancement percentage (SEP) maps. A total of 286 texture features were extracted from those quantitative parametric maps. The Student t test or Mann-Whitney U test was used to select features that were statistically significantly different between the benign and malignant groups. A support vector machine was employed with a leave-one-out crossvalidation method to establish the classification model. Classification performance was evaluated according to the receiver operating characteristic (ROC) theory. Results:The areas under ROC curves (AUCs) indicating the diagnostic performance were 0.925 for MSI, 0.854 for SI slope , 0.756 for E initial , 0.923 for E peak , 0.871 for ESER and 0.881 for SEP. Significant differences in AUCs were found between E initial vs MSI, E initial vs E peak and E initial vs SEP (P < .05). There were no significant differences in other pairwise comparisons.Conclusion: Texture analysis of the kinetic parametric maps derived from breast DCE-MRI can contribute to the discrimination between malignant and benign lesions. It can be considered as a supplementary tool for breast diagnosis. K E Y W O R D S breast cancer, kinetics, machine learning, magnetic resonance imaging, receiver operating characteristic

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“…The introduction of radiomics has shown promise of developing noninvasive biomarkers in risk strati cation and response monitoring. Several radiomic studies have been adopted in breast cancer patients using different imaging modalities 12,13 . Radiomic analysis of MRI scans has been correlated with the established genetic tools in prognostication of patients with breast cancer 31 .…”

Section: Discussionmentioning

confidence: 99%

“…More popularly known as "radiomics," advanced imaging analysis has generated promise in disease strati cation and predicting clinical outcomes. Several imaging modalities like mammography (MMG), ultrasonography (USG), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) have proved utility in the eld of radiomics for breast cancer 12,13 . Quantitative ultrasound (QUS) is similar to conventional USG with the advantage of capturing and analysis of raw radiofrequency (RF) data, which can better characterize tissue microstructure 14,15 .…”

Section: Introductionmentioning

confidence: 99%

Radiomics in Predicting Recurrence for Patients with Locally Advanced Breast Cancer using Quantitative Ultrasound

Dasgupta

Bhardwaj

DiCenzo

et al. 2020

Preprint

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Background The purpose of the study was to investigate the role of pre-treatment quantitative ultrasound (QUS)-radiomics in predicting recurrence for patients with locally advanced breast cancer (LABC). Methods A prospective study was conducted with patients with LABC (n=83). Primary tumours were scanned using a clinical ultrasound device before starting treatment. Ninety-five imaging features were extracted-spectral features, texture, and texture-derivatives. Patients were determined to have recurrence or no recurrence based on clinical outcomes. Machine learning classifiers with k-nearest neighbour (KNN) and support vector machine (SVM) were evaluated for model development using a maximum of 3 features and leave-one-out cross-validation.Results With a median follow up of 69 months (range 7-118 months), 28 patients had disease recurrence (local or distant). The best classification results were obtained using an SVM classifier with a sensitivity, specificity, accuracy and area under curve of 71%, 87%, 82%, and 0.76, respectively. Using the SVM model for the predicted non-recurrence and recurrence groups, the estimated 5-year recurrence-free survival was 83% and 54% (p=0.003), and the predicted 5-year overall survival was 85% and 74% (p=0.083), respectively.Conclusion A QUS-radiomics model using higher-order texture derivatives can predict patients with LABC at higher risk of disease recurrence before starting treatment.

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Overview of radiomics in breast cancer diagnosis and prognostication

Cited by 213 publications

References 53 publications

Texture Analysis of Breast DCE-MRI Based on Intratumoral Subregions for Predicting HER2 2+ Status

Texture Analysis of Breast DCE-MRI Based on Intratumoral Subregions for Predicting HER2 2+ Status

Performance evaluation of texture analysis based on kinetic parametric maps from breast DCE‐MRI in classifying benign from malignant lesions

Radiomics in Predicting Recurrence for Patients with Locally Advanced Breast Cancer using Quantitative Ultrasound

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