Radiomics Analysis on Ultrasound for Prediction of Biologic Behavior in Breast Invasive Ductal Carcinoma

Guo, Yi; Hu, Yuchong; Qiao, Mengyun; Wang, Yuanyuan; Yu, Jinhua; Li, Jiawei; Chang, Cai

doi:10.1016/j.clbc.2017.08.002

Cited by 117 publications

(99 citation statements)

References 31 publications

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“…Radiomic features based on imaging reflect the microscopic structure and biological behavior of the tumor, which has a direct relation to intra-tumoral heterogeneity (18,40). Intra-tumoral heterogeneity may be associated with early microvascular invasion or a worse prognosis (41,42).…”

Section: Discussionmentioning

confidence: 99%

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images

Liu

Xia

et al. 2020

Front. Oncol.

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Objectives: To establish a radiomic algorithm based on grayscale ultrasound images and to make preoperative predictions of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods: In this retrospective study, 322 cases of histopathologically confirmed HCC lesions were included. The classifications based on preoperative grayscale ultrasound images were performed in two stages: (1) classifier #1, MVI-negative and MVI-positive cases; (2) classifier #2, MVI-positive cases were further classified as M1 or M2 cases. The gross-tumoral region (GTR) and peri-tumoral region (PTR) signatures were combined to generate gross-and peri-tumoral region (GPTR) radiomic signatures. The optimal radiomic signatures were further incorporated with vital clinical information. Multivariable logistic regression was used to build radiomic models. Results: Finally, 1,595 radiomic features were extracted from each HCC lesion. At the classifier #1 stage, the radiomic signatures based on features of GTR, PTR, and GPTR showed area under the curve (AUC) values of 0.708 (95% CI, 0.603-0.812), 0.710 (95% CI, 0.609-0.811), and 0.726 (95% CI, 0.625-0.827), respectively. Upon incorporation of vital clinical information, the AUC of the GPTR radiomic algorithm was 0.744 (95% CI, 0.646-0.841). At the classifier #2 stage, the AUC of the GTR radiomic signature was 0.806 (95% CI, 0.667-0.944). Conclusions: Our radiomic algorithm based on grayscale ultrasound images has potential value to facilitate preoperative prediction of MVI in HCC patients. The GTR radiomic signature may be helpful for further discriminating between M1 and M2 levels among MVI-positive patients.

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Section: Discussionmentioning

confidence: 99%

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images

Liu

Xia

et al. 2020

Front. Oncol.

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“…However, the primary limitation of ultrasound as a means of radiogenomics analysis is that image acquisition is highly user-dependent which hampers retrospective datasets. US is consequently the least studied modality for radiogenomics (25)(26)(27)(28)(29). To improve image reproducibility, images from automatic breast ultrasound images can be analyzed such as in the work by Zheng et al (27).…”

Section: Usmentioning

confidence: 99%

Breast Cancer Radiogenomics: Current Status and Future Directions

Grimm

Mazurowski

2020

Academic Radiology

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“…Nowadays, breast cancer is screened by medical imaging techniques and diagnosed by immunohistochemistry (IHC) in clinical practices [3]. Medical imaging techniques, such as magnetic resonance imaging (MRI), mammography, and ultrasound, are commonly used in breast disorders because they are noninvasive and provide information about the overall anatomy of the breast tumor.…”

Section: (Iii) Full Text Introductionmentioning

confidence: 99%

“…IHC is extensively applied to estimate biomarkers and differentiate molecular subtypes [3]. Specifically, Ki67, is present during all active phases of the cell cycle (G(1), S, G(2), and mitosis) and is recognized as a biomarker closely connected to cell proliferation [7].…”

Section: (Iii) Full Text Introductionmentioning

confidence: 99%

“…Recent studies have shown that the hereditary and molecular features of a tumor can be captured inside medical images with the aid of computer-aided diagnosis (CAD) [3]. CAD can help analyze the tumor's overall features, excavate and extract hundreds or even thousands of quantitative features from medical images that are indistinguishable with the naked eye, and unveil the predictive and prognostic relevance between images and medical outcomes [3,21]. Numerous studies have explored the association between breast ultrasound images and some biological features [3,[22][23][24].…”

Section: (Iii) Full Text Introductionmentioning

confidence: 99%

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Image-Based Ultrasomics Classifiers: A Potential Method to Noninvasively Predict the Proliferation-Related Biomarker Ki67

Yuan¹,

Xu²,

Fang³

et al. 2020

Preprint

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Objectives: The proliferation-related biomarker Ki67 is excellent in predicting the prognosis of breast cancer. 3D ultrasound imaging can provide information about coronal section images that help diagnose breast cancer. Our study aims to develop an ultrasomics method that extracts information related to Ki67 by analyzing the maximum transverse/sagittal/coronal section images of breast cancer mass. Methods: Our study retrospectively collected data on patients who had finished 3D ultrasound examinations and were pathologically diagnosed with breast cancer. Images met the criteria were segmented, and then regions of interest (ROIs) were outlined for extracting ultrasomics features, such as statistical, morphological, texture, filter, and wavelet features.Results: The least absolute shrinkage and selection operator (LASSO) regression model selected 16 features that were closely related to the Ki67. The classification results of sensitivity, specificity, accuracy, and area under curve (AUC) of the transverse-sectional images were 0.6451, 0.8064, 0.7258, and 0.8065 (95% CI, 0.6915-0.9214), respectively; for sagittal-sectional images were 0.5806, 0.7741, 0.6774, and 0.6660 (95% CI, 0.5283-0.8037) respectively; for coronal-sectional images were 0.5806, 0.6774, 0.6290, and 0.7159 (95% CI, 0.5847-0.8471) respectively, and for a combination of three-section images were 0.7667, 0.7500, 0.7580, and 0.8510 (95% CI, 0.7537 -0.9483).Conclusions: The model classifier based on the transverse section images performed better than that based on the sagittal/coronal section images. The model classifier based on a combination of three-section images had a better outcome than that used only single section images. Image-based ultrasomics classifiers can noninvasively predict the Ki67 of breast cancer.

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Radiomics Analysis on Ultrasound for Prediction of Biologic Behavior in Breast Invasive Ductal Carcinoma

Cited by 117 publications

References 31 publications

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images

Breast Cancer Radiogenomics: Current Status and Future Directions

Image-Based Ultrasomics Classifiers: A Potential Method to Noninvasively Predict the Proliferation-Related Biomarker Ki67

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