Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI

Cui, Xiaoyu; Wang, Nian; Zhao, Yue; Chen, Shuo; Li, Songbai; Xu, Mingyu; Chai, Ruimei

doi:10.1038/s41598-019-38502-0

Cited by 72 publications

(56 citation statements)

References 20 publications

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“…Previous studies have also associated several US features of primary tumors with axillary lymph node metastasis [10,11]. 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].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…Recently, several articles have outlined the potential clinical applicability of radiomics in the field of breast cancer for different purposes, e.g. diagnosis 10 , 11 , tumor response prediction 12 – 14 , prediction of molecular tumor subtype 15 , 16 , and prediction of axillary lymph node metastases 17 , 18 .…”

Section: Introductionmentioning

confidence: 99%

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

Granzier

Verbakel

Ibrahim

et al. 2020

Sci Rep

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Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. While MRI-based radiomics is still at an early stage, it showed some promising results in studies focusing on breast cancer patients in improving diagnoses and therapy response assessment. Nevertheless, the use of radiomics raises a number of issues regarding feature quantification and robustness. Therefore, our study aim was to determine the robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI. A total of 129 histologically confirmed breast tumors were segmented manually in three dimensions on the first post-contrast T1-weighted MR exam by four observers: a dedicated breast radiologist, a resident, a Ph.D. candidate, and a medical student. Robust features were assessed using the intraclass correlation coefficient (ICC > 0.9). The inter-observer variability was evaluated by the volumetric Dice Similarity Coefficient (DSC). The mean DSC for all tumors was 0.81 (range 0.19-0.96), indicating a good spatial overlap of the segmentations based on observers of varying expertise. In total, 41.6% (552/1328) and 32.8% (273/833) of all RadiomiX and Pyradiomics features, respectively, were identified as robust and were independent of inter-observer manual segmentation variability. Radiomics is a technique that is used to extract large amounts of quantitative information from routine medical images that decode information about a region of interest (ROI). The majority of radiomics articles published concerns its application in the oncological field 1-4. Here, radiomics bears the advantage of non-invasively quantifying the underlying phenotype of the entire tumor for multiple lesions simultaneously, in contrast to tissue biopsy, which samples only a small part of a single (often heterogeneous) tumor 2,5. The ability to characterize the tumor and to establish links to the underlying biology 6 and ultimately clinical outcomes, allows a more patienttailored treatment 7 , enabling 'precision medicine' 8,9. Recently, several articles have outlined the potential clinical applicability of radiomics in the field of breast cancer for different purposes, e.g. diagnosis 10,11 , tumor response prediction 12-14 , prediction of molecular tumor subtype 15,16 , and prediction of axillary lymph node metastases 17,18. Although these results are promising, issues regarding features robustness as well as the comparability of results, including inter-observer segmentation variability, need to be addressed 19-24. In order to extract clinically useful information from medical images and to use features as clinical biomarkers, it is important that extracted

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“…Unlike previous studies just focusing on the tumor region for predicting ALNM, [16][17][18] in this study, ALNtumor radiomic signature that combined multi-sequence key radiomic features of ALN and tumor regions showed high predictive ability and could be applied to predict the ALN status precisely and noninvasively. The multiomic radiomic signature integrated radiomic features above and ALNM-associated clinicopathologic characteristics, molecular subtype demonstrated better performance for predicting ALNM.…”

Section: Discussionmentioning

confidence: 78%

Machine learning magnetic resonance imaging radiomics predicts axillary lymph node metastasis in invasive breast cancer

Yu¹,

He²,

Ouyang³

et al. 2020

Preprint

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In current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer is based on the invasive procedure and many patients will suffer from operative associated complications. Hence, a novel signature incorporated tumor and lymph node magnetic resonance imaging (MRI) radiomics, clinical and pathological characteristics, and molecular subtypes based on the machine learning approach was established to accurately identify ALN metastasis in early-stage invasive breast cancer patients. Although the misjudgment of ALN status by clinicians according to preoperative MRI are common during clinical practice and even the senior radiologists make mistakes sometimes, this multiomic radiomic signature showed the superiority over clinicians and could precisely discriminate ALN metastasis among different molecular subtype patients. Furthermore, the association between MRI radiomic features and tumor-microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites were found, which revealed the potential biological underpinning of MRI radiomics.

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Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI

Cited by 72 publications

References 20 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

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

Machine learning magnetic resonance imaging radiomics predicts axillary lymph node metastasis in invasive breast cancer

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