Evidence map›Paper›PMID 36776542›Full record

ArticleBreast cancer (Dove Medical Press)2023

Optimized Radiomics Nomogram Based on Automated Breast Ultrasound System: A Potential Tool for Preoperative Prediction of Metastatic Lymph Node Burden in Breast Cancer.

Ning Li, Chao Song, Xian Huang, Hongjiang Zhang, Juan Su, Lichun Yang, Juhua He, Guihua Cui

Abstract read
In one paragraph

Article in Breast cancer (Dove Medical Press), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

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0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ning Li *Department of Ultrasound, Anning First People's Hospital, Kunming City, People's Republic of China.
Chao SongDepartment of Radiology, Anning First People's Hospital, Kunming City, People's Republic of China.ORCID 0000-0003-3010-7629
Xian Huang *Department of Ultrasound, Kunming City Maternal and Child Health Hospital, Kunming City, People's Republic of China.
Hongjiang ZhangDepartment of Ultrasound, Anning First People's Hospital, Kunming City, People's Republic of China.ORCID 0000-0003-4262-907X
Juan SuDepartment of Ultrasound, Yulong People's Hospital, Lijiang City, People's Republic of China.
Lichun YangDepartment of Ultrasound, Yunnan Cancer Hospital, Kunming City, People's Republic of China.
Juhua HeDepartment of Function Examination, Yunnan Provincial Hospital of Traditional Chinese Medicine, Kunming City, People's Republic of China.
Guihua CuiDepartment of Ultrasound, Anning First People's Hospital, Kunming City, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Axillary lymph node dissection (ALND) can be safely avoided in women with T1 or T2 primary invasive breast cancer (BC) and one to two metastatic sentinel lymph nodes (SLNs). However, cancellation of ALND based solely on SLN biopsy (SLNB) may lead to adverse outcomes. Therefore, preoperative assessment of LN tumor burden becomes a new focus for ALN status. Objective: This study aimed to develop and validate a nomogram incorporating the radiomics score (rad-score) based on automated breast ultrasound system (ABUS) and other clinicopathological features for evaluating the ALN status in patients with early-stage BC preoperatively. Methods: Totally 354 and 163 patients constituted the training and validation cohorts. They were divided into ALN low burden (<3 metastatic LNs) and high burden (≥3 metastatic LNs) based on the histopathological diagnosis. The radiomics features of the segmented breast tumor in ABUS images were extracted and selected to generate the rad-score of each patient. These rad-scores, along with the ALN burden predictors identified from the clinicopathologic characteristics, were included in the multivariate analysis to establish a nomogram. It was further evaluated in the training and validation cohorts. Results: High ALN burdens accounted for 11.2% and 10.8% in the training and validation cohorts. The rad-score for each patient was developed based on 7 radiomics features extracted from the ABUS images. The radiomics nomogram was built with the rad-score, tumor size, US-reported LN status, and ABUS retraction phenomenon. It achieved better predictive efficacy than the nomogram without the rad-score and exhibited favorable discrimination, calibration and clinical utility in both cohorts. Conclusion: We developed an ABUS-based radiomics nomogram for the preoperative prediction of ALN burden in BC patients. It would be utilized for the identification of patients with low ALN burden if further validated, which contributed to appropriate axillary treatment and might avoid unnecessary ALND.

Indexed as

automated breast ultrasound systemaxillary lymph nodeinvasive breast cancernomogramradiomicssentinel lymph node biopsytumor burden

Identifiers

PMID36776542
PMCPMC9910101

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