Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer

Yu, Feihong; Wang, Jianxiang; Ye, Xinhua; Deng, Jing; Hang, Jing; Yang, Bin

doi:10.1016/j.ejrad.2019.108658

Cited by 89 publications

(85 citation statements)

References 31 publications

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“…In that study, the dominant radiomic feature was first-order kurtosis, which aligned with our results. The conclusion of that study was also consistent with ours; however, its clinical model consisted of age, mass size, and US-reported lymph node status, and the known predictors of tumor location and multiplicity were not included in its analysis [18]. Additionally, more than 40% of patients in the study by Yu et al [18] were found to have axillary lymph node metastasis, which was higher than the percentage observed in our study (30.8%, 153 of 496).…”

Section: Discussionsupporting

confidence: 74%

“…The conclusion of that study was also consistent with ours; however, its clinical model consisted of age, mass size, and US-reported lymph node status, and the known predictors of tumor location and multiplicity were not included in its analysis [18]. Additionally, more than 40% of patients in the study by Yu et al [18] were found to have axillary lymph node metastasis, which was higher than the percentage observed in our study (30.8%, 153 of 496). The incidence of axillary metastases in patients with invasive breast cancer was previously reported to be 30%-40%, but this value has decreased gradually because the size of detected Fig.…”

Section: Discussionsupporting

confidence: 74%

“…Among the few published studies that have used US-based radiomics to predict axillary lymph node metastasis in patients with breast cancer, two have not been verified with a validation cohort, and overfitting remained a problem for those studies [16,17,23]. Recently, Yu et al [18] analyzed 426 patients (300 in a training cohort and 126 in a validation cohort) and found the combined model to have additional value over the clinical model. In that study, the dominant radiomic feature was first-order kurtosis, which aligned with our results.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, magnetic resonance imaging-based radiomics features extracted from primary tumors showed high predictive performance for axillary lymph node metastasis [12][13][14][15]. However, fewer studies have been conducted on US-based radiomics than on magnetic resonance imaging-based radiomics [16][17][18]. In the few studies that have been published, US-based radiomic features have displayed good diagnostic performance, but these results were not validated in a separate cohort [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer

et al. 2021

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The purpose of this study was to evaluate the predictive performance of ultrasonography (US)-based radiomics for axillary lymph node metastasis and to compare it with that of a clinicopathologic model. Methods: A total of 496 patients (mean age, 52.5±10.9 years) who underwent breast cancer surgery between January 2014 and December 2014 were included in this study. Among them, 306 patients who underwent surgery between January 2014 and August 2014 were enrolled as a training cohort, and 190 patients who underwent surgery between September 2014 and December 2014 were enrolled as a validation cohort. To predict axillary lymph node metastasis in breast cancer, we developed a preoperative clinicopathologic model using multivariable logistic regression and constructed a radiomics model using 23 radiomic features selected via least absolute shrinkage and selection operator regression. Results: In the training cohort, the areas under the curve (AUC) were 0.760, 0.812, and 0.858 for the clinicopathologic, radiomics, and combined models, respectively. In the validation cohort, the AUCs were 0.708, 0.831, and 0.810, respectively. The combined model showed significantly better diagnostic performance than the clinicopathologic model. Conclusion: A radiomics model based on the US features of primary breast cancers showed additional value when combined with a clinicopathologic model to predict axillary lymph node metastasis.

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Section: Discussionsupporting

confidence: 74%

Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer

et al. 2021

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“…Compared with traditional statistical methods, which usually consider and evaluate limited assumptions, machine learning methods are superior in generating models to analyze images for prediction by extensively searching models and parameter spaces [32]. Currently, many definitive diagnoses have been made for breast disease by using the computational algorithms of ultrasomics, including identifying benign and malignant breast lesions based on the texture features of ultrasound images [33], predicting axillary lymph node metastasis and associated potential biomarkers in breast cancer [34], and analyzing the biological behavior of infiltrating ductal carcinoma of the breast [3].…”

Section: Discussionmentioning

confidence: 99%

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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Contrast‐Enhanced Ultrasound‐Based Radiomics for the Prediction of Axillary Lymph Nodes Status in Breast Cancer

Lun,

Huang,

Zhao

et al. 2024

Cancer Reports

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BackgroundBreast cancer is the leading cause of cancer‐related deaths in the female population. Axillary lymph nodes (ALN) are a group of the most common metastatic sites of breast cancer. Timely assessment of ALN status is of paramount clinical importance for medical decision making.AimsTo utilize contrast‐enhanced ultrasound (CEUS)‐based radiomics models for noninvasive pretreatment prediction of ALN status.Methods and ResultsClinical data and pretreatment CEUS images of primary breast tumors were retrospectively studied to build radiomics signatures for pretreatment prediction of nodal status between May 2015 and July 2021. The cases were divided into the training cohorts and test cohorts in a 9:1 ratio. The mRMR approach and stepwise forward logistic regression technique were used for feature selection, followed by the multivariate logistic regression technique for building radiomics signatures in the training cohort. The confusion matrix and receiver operating characteristic (ROC) analysis were used for accessing the prediction efficacy of the radiomics models. The radiomics models, which consist of six features, achieved predictive accuracy with the area under the ROC curve (AUC) of 0.713 in the test set for predicting lymph node metastasis.ConclusionThe CEUS‐based radiomics is promising to be developed as a reliable noninvasive tool for predicting ALN status.

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Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer

Cited by 89 publications

References 31 publications

Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer

Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer

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

Contrast‐Enhanced Ultrasound‐Based Radiomics for the Prediction of Axillary Lymph Nodes Status in Breast Cancer

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