2020
DOI: 10.1016/j.ebiom.2020.103018
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Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer

Abstract: Background Completion axillary lymph node dissection is overtreatment for patients with sentinel lymph node (SLN) metastasis in whom the metastatic risk of residual non-SLN (NSLN) is low. However, the National Comprehensive Cancer Network panel posits that none of the previous studies has successfully identified such subset patients. Here, we develop a multicentre deep learning radiomics of ultrasonography model (DLRU) to predict the risk of SLN and NSLN metastasis. Methods … Show more

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Cited by 71 publications
(56 citation statements)
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“…Additionally, many studies include ALNs on standard imaging without suspicion for biopsy for data augmentation, but this approach conflates the number of benign findings and does not mimic the clinical scenario of decision to biopsy suspicious nodes. Guo et al 19 developed a deep learning radiomics model of ultrasound images to identify the metastatic risk in sentinel and non-sentinel lymph nodes in primary breast cancer. Their proposed deep learning radiomics of ultrasonography model using both images of the primary tumor and ALN assigned 51% of the clinically over-treated patients to a low-risk group which could theoretically avoid LN biopsy.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Additionally, many studies include ALNs on standard imaging without suspicion for biopsy for data augmentation, but this approach conflates the number of benign findings and does not mimic the clinical scenario of decision to biopsy suspicious nodes. Guo et al 19 developed a deep learning radiomics model of ultrasound images to identify the metastatic risk in sentinel and non-sentinel lymph nodes in primary breast cancer. Their proposed deep learning radiomics of ultrasonography model using both images of the primary tumor and ALN assigned 51% of the clinically over-treated patients to a low-risk group which could theoretically avoid LN biopsy.…”
Section: Discussionmentioning
confidence: 99%
“…15 Studies focused particularly on breast cancer metastasis prediction showed promising results for use as a second reader and assist in decision making. [16][17][18][19][20][21][22][23] However, this work to date has lacked standardization and relies on supervised machine learning models that require both high computational costs and extremely large datasets for algorithms training.…”
Section: Introductionmentioning
confidence: 99%
“… 80 , 81 With the application of deep learning to ultrasound radiomics datasets, known as deep learning radiomics ultrasonography (DLRU), axillary lymph node status may be accurately predicted with AUC of >0.90. 82 , 83 Although radiomics is still in its infancy, it has great potential to pave the way for the personalization of surgical management of the axilla and “virtual biopsies.”…”
Section: New Applications and Technologiesmentioning
confidence: 99%
“…However, reliable methods for identifying SLN positive patients with low risk of NSLN metastasis have remained elusive. Given the recent progress of deep learning in a wide range of diagnostic tasks and the availability of electronic health records (EHRs) [4] , [5] , [6] , deep artificial neural networks may be well-suited for detecting lymph node metastases in breast cancer patients [7] .…”
mentioning
confidence: 99%
“…In this issue of EBioMedicine, Guo et al. designed deep learning models based on the DenseNet architecture to determine patients’ SLN and NSLN metastasis status using axillary ultrasonography (AUS) images [7] . Prediction of SLN metastasis using a combined deep learning model and AUS report achieved an area under the receiver operating characteristic curve (AUC) of 0.848 (95% CI: 0.811–0.886) with a sensitivity of 0.984 (95% CI: 0.966–1) in the test set.…”
mentioning
confidence: 99%