Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications

Lei, Chuqian; wei, Wei; Liu, Zhenyu; Xiong, Qinmei; Yang, Ciqiu; Yang, Mei; Zhang, Liulu; Zhu, Teng; Zhuang, Xiaosheng; Liu, Chunling; Liu, Zaiyi; Tian, Jie; Wang, Kun

doi:10.1016/j.ejrad.2019.108711

Cited by 37 publications

(30 citation statements)

References 33 publications

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“…In traditional methods, the diagnosis of these diseases cannot rely on DM alone and must combine US or MRI. Previous studies (20)(21)(22)25) showed that radiomics provided massive features extracted from images to quantify tumors and allowed the possibilities for uncovering the differences that the human eye cannot recognize.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomics can convert medical images into highdimensional, mineable data via high-throughput extraction of quantitative features and allows for quantitative evaluation of tumor heterogeneity (19). DM-based radiomics has been studied for predicting the classification of microcalcification (20) and molecular subtypes of breast cancer (21,22). Applications of DM image texture analysis in the differential diagnosis of benign and malignant breast tumors have also been reported (23).…”

Section: Introductionmentioning

confidence: 99%

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Application of digital mammography-based radiomics in the differentiation of benign and malignant round-like breast tumors and the prediction of molecular subtypes

Wang¹,

Yang²,

Xie³

et al. 2020

Gland Surg

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Background: This study aimed to investigate the diagnostic performance of radiomic features based on digital mammography (DM) in the differential diagnosis of benign and malignant round-like (round and oval) solid tumors with circumscribed or obscured margins but without suspicious malignant or benign macrocalcifications and to investigate whether quantitative radiomic features can distinguish triple-negative breast cancer (TNBC) from non-TNBC (NTNBC).Methods: This retrospective study included 112 patients with round-like tumors who underwent DM within 20 days preoperatively. Breast masses were segmented manually on the DM images, then radiomic features were extracted. The predictive models were used to distinguish between benign and malignant tumors and to predict TNBC in invasive ductal carcinoma. The receiver operating characteristic curves (ROCs) for these models were obtained for initial DM characteristics, radiomic features to predict malignant tumors and TNBC. The decision curve was obtained to evaluate the clinical usefulness of the model for the prediction of benign or malignant tumors. Results:The study cohort included 79 patients with pathologically confirmed malignant masses and 33 patients with benign (training cohort: n=79; testing cohort: n=33). A total of 396 features were extracted from the DM images for each patient. The radiomics model for the prediction of malignant tumors achieved an area under the receiver operating characteristic curve (AUC) of 0.88 [95% confidence interval (CI), 0.76-1.00] in the testing cohort; the radiomics model for the prediction of TNBC achieved an AUC of 0.84 (95% CI, 0.73-0.96). In contrast, DM characteristics alone poorly predicted malignant tumors, with the density achieving an AUC 0.69 (95% CI, 0.59-0.79); there was no significant difference in DM characteristics between TNBC and NTNBC (P>0.05, all). The decision curve showed the good clinical usefulness of the model for the prediction of malignant tumors.Conclusions: This study showed that DM-based radiomics can accurately discriminate between benign and malignant round-like tumors with circumscribed or obscured margins but without suspicious malignant or benign macrocalcifications. Additionally, it can be used to predict TNBC in invasive ductal carcinoma.DM-based radiomics can aid radiologists in mammogram reading, clinical diagnosis and decision-making.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of digital mammography-based radiomics in the differentiation of benign and malignant round-like breast tumors and the prediction of molecular subtypes

Wang¹,

Yang²,

Xie³

et al. 2020

Gland Surg

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“…Radiomics is a research branch in the field of medical imaging (10). Based on the rapid development of machine learning and image processing techniques, radiomic analyses have been successfully applied in the field of oncology (11)(12)(13)(14)(15)(16), including glioma (17). Magnetic resonance imaging (MRI) is a routinely used diagnostic tool for glioma management.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study

et al. 2020

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Purpose: The majority of patients with low-grade gliomas (LGGs) experience tumor-related epilepsy during the disease course. Our study aimed to build a radiomic prediction model for LGG-related epilepsy type based on magnetic resonance imaging (MRI) data.Methods: A total of 205 cases with LGG-related epilepsy were enrolled in the retrospective study and divided into training and validation cohorts (1:1) according to their surgery time. Seven hundred thirty-four radiomic features were extracted from T2-weighted imaging, including six location features. Pearson correlation coefficient, univariate area under curve (AUC) analysis, and least absolute shrinkage and selection operator regression were adopted to select the most relevant features for the epilepsy type to build a radiomic signature. Furthermore, a novel radiomic nomogram was developed for clinical application using the radiomic signature and clinical variables from all patients.Results: Four MRI-based features were selected from the 734 radiomic features, including one location feature. Good discriminative performances were achieved in both training (AUC = 0.859, 95% CI = 0.787-0.932) and validation cohorts (AUC = 0.839, 95% CI = 0.761-0.917) for the type of epilepsy. The accuracies were 80.4 and 80.6%, respectively. The radiomic nomogram also allowed for a high degree of discrimination. All models presented favorable calibration curves and decision curve analyses. Conclusion:Our results suggested that the MRI-based radiomic analysis may predict the type of LGG-related epilepsy to enable individualized therapy for patients with LGG-related epilepsy.

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“…Moreover, the most realistic use in the diagnostic setting is the application of radiomics and AI as decision support tools in the assessment for instance in challenging cases, such as the assessment of microcalcifications and BI-RADS IV lesions as demonstrated in ▶ Fig. 2 [68,69].…”

Section: Lesion Characterizationmentioning

confidence: 99%

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

Dietzel

Clauser

Kapetas

et al. 2021

Rofo

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Background Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology “imaging biomarker”, “radiomics”, and “artificial intelligence” are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. Methods and Results This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. Conclusion Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. Key Points: Citation Format

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Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications

Cited by 37 publications

References 33 publications

Application of digital mammography-based radiomics in the differentiation of benign and malignant round-like breast tumors and the prediction of molecular subtypes

Application of digital mammography-based radiomics in the differentiation of benign and malignant round-like breast tumors and the prediction of molecular subtypes

Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

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