Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging

Marino, Maria Adele; Leithner, Doris; Sung, Janice S.; Avendaño, Daly; Morris, Elizabeth A.; Pinker, Katja; Jochelson, Maxine S.

doi:10.3390/diagnostics10070492

Cited by 32 publications

(22 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Magnetic resonance imaging (MRI) is the "gold standard" in imaging diagnosis of DLM with a clear diagnostic criterion and a high sensitivity (95%) and specificity (97%) 5 , but it has high costs and long examination times, and is contraindicated in patients with installed pacemakers, magnetic metal implanted, and claustrophobia 6,7 . Besides, MRI is difficult to perform in young children who may not remain motionless during the imaging study.…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

A Reliable, Ultrasound-Based Method for the Diagnosis of Discoid Lateral Meniscus

Yang¹,

Zhang²,

Li³

et al. 2021

Arthroscopy: The Journal of Arthroscopic & Related Surgery

View full text Add to dashboard Cite

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

show abstract

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

A Reliable, Ultrasound-Based Method for the Diagnosis of Discoid Lateral Meniscus

Yang¹,

Zhang²,

Li³

et al. 2021

Arthroscopy: The Journal of Arthroscopic & Related Surgery

View full text Add to dashboard Cite

show abstract

“…An interesting study [ 6 ] investigated and compared the potential of radiomics analysis of CESM and MRI of the breast for the non-invasive assessment of tumor invasiveness, hormone receptor status and tumor grade in patients with primary breast cancer.…”

Section: Discussionmentioning

confidence: 99%

“…In image processing, feature extraction plays an important role. In fact, the quantitative information extracted from images by means of physical and mathematical methods completes those detected by the qualitative survey carried out by the operator [ 6 ]. Image analysis, known as radiomics, is an emerging discipline in cancer studies and is often used in models aimed at characterizing tissues and recognizing lesions of various kinds in biomedical images.…”

Section: Introductionmentioning

confidence: 99%

Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome

et al. 2020

View full text Add to dashboard Cite

Contrast-Enhanced Spectral Mammography (CESM) is a recently introduced mammographic method with characteristics particularly suitable for breast cancer radiomic analysis. This work aims to evaluate radiomic features for predicting histological outcome and two cancer molecular subtypes, namely Human Epidermal growth factor Receptor 2 (HER2)-positive and triple-negative. From 52 patients, 68 lesions were identified and confirmed on histological examination. Radiomic analysis was performed on regions of interest (ROIs) selected from both low-energy (LE) and ReCombined (RC) CESM images. Fourteen statistical features were extracted from each ROI. Expression of estrogen receptor (ER) was significantly correlated with variation coefficient and variation range calculated on both LE and RC images; progesterone receptor (PR) with skewness index calculated on LE images; and Ki67 with variation coefficient, variation range, entropy and relative smoothness indices calculated on RC images. HER2 was significantly associated with relative smoothness calculated on LE images, and grading tumor with variation coefficient, entropy and relative smoothness calculated on RC images. Encouraging results for differentiation between ER+/ER−, PR+/PR−, HER2+/HER2−, Ki67+/Ki67−, High-Grade/Low-Grade and TN/NTN were obtained. Specifically, the highest performances were obtained for discriminating HER2+/HER2− (90.87%), ER+/ER− (83.79%) and Ki67+/Ki67− (84.80%). Our results suggest an interesting role for radiomics in CESM to predict histological outcomes and particular tumors’ molecular subtype.

show abstract

“…The methods of lesion delineation adopted by different studies on CEM images are not completely consistent. Most studies segmented the lesions in both LE and DES images (21,25,37), while others segmented only in DES images (38,39). In addition, some studies delineated the lesions in LE or DES images and mapped the lesion ROIs to the matched DES or LE images (11,12).…”

Section: Discussionmentioning

confidence: 99%

Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study

Wang¹,

Sun²,

Mao³

et al. 2021

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background: Contrast-enhanced mammography (CEM) is a promising breast imaging technique. A limited number of studies have focused on the radiomics analysis of CEM. We intended to explore whether a model constructed with both clinical and radiomics features of CEM can better classify benign and malignant breast lesions. Methods: This retrospective, double-center study included women who underwent CEM between August 2017 and February 2020. The data from Center 1 were used as training set and the data from Center 2 were used as external testing set (training: testing =2:1). Models were constructed with the clinical, radiomics, and clinical + radiomics features of CEM. The clinical features included patient age and clinical image features interpreted by the radiologists. The radiomics features were extracted from high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images of CEM. The Mann-Whitney U test, Pearson correlationand Boruta's approach were used to select the radiomics features. Random Forest (RF) and logistic regression were used to establish the models. For the testing set, the areas under the curve (AUCs) and 95% confidence intervals (CIs) were employed to evaluate the performance of the models. For the training set, the mean AUCs were obtained by performing internal validation for 100 iterations and then compared by the Kruskal-Wallis and Mann-Whitney U tests.Results: A total of 226 women (mean age: 47.4±10.1 years) with 226 pathologically proven breast lesions (101 benign; 125 malignant) were included. For the external testing set, the AUCs were 0.964 (95% CI: 0.918-1.000) for the combined model, 0.947 (95% CI: 0.891-0.997) for the radiomics model, and 0.882 (95% CI: 0.803-0.962) for the clinical model. In the internal validation process, the combined model achieved a mean AUC of 0.934±0.030, which was significantly higher than those of the radiomics (mean AUC =0.921±0.031, adjusted P<0.050) and clinical models (mean AUC =0.907±0.036; adjusted P<0.050). Conclusion:Incorporating both clinical and radiomics features of CEM may achieve better classification results for breast lesions.

show abstract

Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging

Cited by 32 publications

References 37 publications

A Reliable, Ultrasound-Based Method for the Diagnosis of Discoid Lateral Meniscus

A Reliable, Ultrasound-Based Method for the Diagnosis of Discoid Lateral Meniscus

Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome

Incorporating the clinical and radiomics features of contrast-enhanced mammography to classify breast lesions: a retrospective study

Contact Info

Product

Resources

About