The Application of Radiomics in Breast MRI: A Review

Ye, Dongman; Wang, Hao-Tian; Yu, Tao

doi:10.1177/1533033820916191

Cited by 59 publications

(60 citation statements)

References 111 publications

(175 reference statements)

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“…Radiogenomics can potentially evaluate the entire tumor load with the possibility of providing a non-invasive diagnosis and to closely monitor the characteristics of the lesions [75][76][77][78][79]. The combination of quantitative radiomics features with histological, clinical, and genomic data may represent a valid possibility for clinicians to develop patient-centered treatments [72,[80][81][82].…”

Section: Texture Analysis and Prognosis-focus On Breast Cancermentioning

confidence: 99%

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

et al. 2021

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The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

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Section: Texture Analysis and Prognosis-focus On Breast Cancermentioning

confidence: 99%

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

et al. 2021

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“…Therefore, more MRI radiomics studies were reported than ultrasound, mammography, and 18F FDG PET/CT (34). MRI radiomics was shown to provide better discrimination than conventional parameters for the diagnosis of breast cancer (23,24). Mammography radiomics analysis has also been performed in several diagnostic studies (19)(20)(21)(22).…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence (AI) based radiomics study has been widely applied for medical applications. The method allows for high-throughput extraction of quantitative features from radiographic images (18), and it has been shown as a feasible approach for diagnosis of breast cancer using mammography (19)(20)(21)(22) and MRI (23)(24)(25). However, the combined model using different imaging modalities was rarely reported.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

et al. 2021

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ObjectiveTo build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer.Materials and Methods266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed diagnosis were analyzed. Training dataset had 146 malignant and 56 benign, and testing dataset had 48 malignant and 18 benign lesions. Fuzzy-C-means clustering algorithm was used to segment the enhanced lesion on subtraction MRI maps. Two radiologists manually outlined the corresponding lesion on mammography by consensus, with the guidance of MRI maximum intensity projection. Features were extracted using PyRadiomics from three DCE-MRI parametric maps, and from the lesion and a 2-cm bandshell margin on mammography. The support vector machine (SVM) was applied for feature selection and model building, using 5 datasets: DCE-MRI, mammography lesion-ROI, mammography margin-ROI, mammography lesion+margin, and all combined.ResultsIn the training dataset evaluated using 10-fold cross-validation, the diagnostic accuracy of the individual model was 83.2% for DCE-MRI, 75.7% for mammography lesion, 64.4% for mammography margin, and 77.2% for lesion+margin. When all features were combined, the accuracy was improved to 89.6%. By adding mammography features to MRI, the specificity was significantly improved from 69.6% (39/56) to 82.1% (46/56), p<0.01. When the developed models were applied to the independent testing dataset, the accuracy was 78.8% for DCE-MRI and 83.3% for combined MRI+Mammography.ConclusionThe radiomics model built from the combined MRI and mammography has the potential to provide a machine learning-based diagnostic tool and decrease the false positive diagnosis of contrast-enhanced benign lesions on MRI.

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“…Currently, radiomics is an appealing technique in research but it has not been fully applied yet in the clinical setting but multidisciplinary and translational studies are still required, gather the amount of data needed to implement radiomics on a wide scale. As it is difficult to acquire consistent imaging and acquire uniform results that can be applied in clinical practice [ 89 ] (due to imaging acquisition from different machines, varied technical parameters and slice thickness and diverse reconstruction algorithms), some techniques to deal with multicentric data have been proposed such as ComBat [ 90 ]. However, radiomics models based on transparency of methodologies and developed using standardized acquisition techniques and high-quality data can overcome confounders arising from differences among centers’ workflows.…”

Section: What Next?mentioning

confidence: 99%

Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future

et al. 2021

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Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer’s molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.

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The Application of Radiomics in Breast MRI: A Review

Cited by 59 publications

References 111 publications

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography

Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future

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