Ultrasound Radiomics in Breast Cancer - A Literature Review

Chen, MB Yi; Liu, MB Xia; Wang, Run; Lin, MM Xiaona; Zhong, Jieyu; Chen, MM Yan; Cai, MB Huali; Li, MM Yongbin; Li, Zhicheng; Hu, Ziqiang; Sun, Desheng

doi:10.37015/audt.2021.200052

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Radiomics is a high-throughput quantitative image analysis technique involving the extraction of metrics invisible to the naked eye, and has greatly enhanced the diagnosis and prediction of malignant diseases ( 11 – 14 ). Studies on the use of ultrasound radiomics models for the classification of GPs currently exist ( 15 – 19 ), but some of them are limited in sample size ( 15 , 16 ).…”

Section: Introductionmentioning

confidence: 99%

Preliminary study on diagnosis of gallbladder neoplastic polyps based on contrast-enhanced ultrasound and grey scale ultrasound radiomics

Qin,

Ding,

et al. 2024

Front. Oncol.

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ObjectiveNeoplastic gallbladder polyps (GPs), including adenomas and adenocarcinomas, are considered absolute indications for surgery; however, the distinction of neoplastic from non-neoplastic GPs on imaging is often challenging. This study thereby aimed to develop a CEUS radiomics nomogram, and evaluate the role of a combined grey-scale ultrasound and CEUS model for the prediction and diagnosis of neoplastic GPs.MethodsPatients with GPs of ≥ 1 cm who underwent CEUS between January 2017 and May 2022 were retrospectively enrolled. Grey-scale ultrasound and arterial phase CEUS images of the largest section of the GPs were used for radiomics feature extraction. Features with good reproducibility in terms of intraclass correlation coefficient were selected. Grey-scale ultrasound and CEUS Rad-score models were first constructed using the Mann-Whitney U and LASSO regression test, and were subsequently included in the multivariable logistic regression analysis as independent factors for construction of the combined model.ResultsA total of 229 patients were included in our study. Among them, 118 cholesterol polyps, 68 adenomas, 33 adenocarcinomas, 6 adenomyomatoses, and 4 inflammatory polyps were recorded. A total of 851 features were extracted from each patient. Following screening, 21 and 15 features were retained in the grey-scale and CEUS models, respectively. The combined model demonstrated AUCs of 0.88 (95% CI: 0.83 – 0.93) and 0.84 (95% CI: 0.74 – 0.93) in the training and testing set, respectively. When applied to the whole dataset, the combined model detected 111 of the 128 non-neoplastic GPs, decreasing the resection rate of non-neoplastic GPs to 13.3%.ConclusionOur proposed combined model based on grey-scale ultrasound and CEUS radiomics features carries the potential as a non-invasive, radiation-free, and reproducible tool for the prediction and identification of neoplastic GPs. Our model may not only guide the treatment selection for GPs, but may also reduce the surgical burden of such patients.

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