Ultrasound radiomics in personalized breast management: Current status and future prospects

Gu, Jionghui

doi:10.3389/fonc.2022.963612

Cited by 18 publications

(11 citation statements)

References 77 publications

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“…Radiogenomics is an emerging research field that aims to correlate imaging features with the underlying genes or mutated genes [ 102 , 103 ]. Though most of these studies have been based on CT or MRI radiomics tools, ultrasomics has also been used especially for breast cancer, not only for the diagnosis of the lesions, but also for the prediction of the molecular subtype, with a reported accuracy of 95% [ 104 , 105 , 106 , 107 ].…”

Section: Resultsmentioning

confidence: 99%

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

et al. 2023

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Background: This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma. Methods: The PubMed database was searched for papers published in the English language no earlier than October 2022. Results: We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

et al. 2023

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“…There is a growing literature that has reported to predict molecular profiling in BC, but mostly based on MRI and X-ray analysis ( 21 , 22 ). However, there has been a limited number of studies conducted thus far that utilize ultrasound imaging as the primary modality for investigation ( 23 ).…”

Section: Discussionmentioning

confidence: 99%

Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer

Xu,

You,

Liu

et al. 2023

Front. Oncol.

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BackgroundBreast cancer (BC) is the most common cancer in women and is highly heterogeneous. BC can be classified into four molecular subtypes based on the status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and proliferation marker protein Ki-67. However, they can only be obtained by biopsy or surgery, which is invasive. Radiomics can noninvasively predict molecular expression via extracting the image features. Nevertheless, there is a scarcity of data available regarding the prediction of molecular biomarker expression using ultrasound (US) images in BC.ObjectivesTo investigate the prediction performance of US radiomics for the assessment of molecular profiling in BC.MethodsA total of 342 patients with BC who underwent preoperative US examination between January 2013 and December 2021 were retrospectively included. They were confirmed by pathology and molecular subtype analysis of ER, PR, HER2 and Ki-67. The radiomics features were extracted and four molecular models were constructed through support vector machine (SVM). Pearson correlation coefficient heatmaps are employed to analyze the relationship between selected features and their predictive power on molecular expression. The receiver operating characteristic curve was used for the prediction performance of US radiomics in the assessment of molecular profiling.Results359 lesions with 129 ER- and 230 ER+, 163 PR- and 196 PR+, 265 HER2- and 94 HER2+, 114 Ki-67- and 245 Ki-67+ expression were included. 1314 features were extracted from each ultrasound image. And there was a significant difference of some specific radiomics features between the molecule positive and negative groups. Multiple features demonstrated significant association with molecular biomarkers. The area under curves (AUCs) were 0.917, 0.835, 0.771, and 0.896 in the training set, while 0.868, 0.811, 0.722, and 0.706 in the validation set to predict ER, PR, HER2, and Ki-67 expression respectively.ConclusionUltrasound-based radiomics provides a promising method for predicting molecular biomarker expression of ER, PR, HER2, and Ki-67 in BC.

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“…The features could be used to identify tumor information beyond the recognition of naked eye, hence carrying the potential for reflecting biological behaviors and characteristics at the genetic or molecular level [ 15 , 16 ]. For breast cancer, several radiomics models based on US images have been constructed and proved useful in distinguishing benign and malignant tumors, identifying lymph node metastasis, classifying molecular subtypes, evaluating Ki-67 levels, and predicting neoadjuvant treatment response, etc [ [17] , [18] , [19] , [20] ]. These indicated that US-based radiomics analysis carries a great potential for identifying BRCA1/2 mutation in breast cancer patients.…”

Section: Introductionmentioning

confidence: 99%

A nomogram model combining ultrasound-based radiomics features and clinicopathological factors to identify germline BRCA1/2 mutation in invasive breast cancer patients

Guo,

Yu,

Huang

et al. 2024

Heliyon

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Ultrasound radiomics in personalized breast management: Current status and future prospects

Cited by 18 publications

References 77 publications

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer

A nomogram model combining ultrasound-based radiomics features and clinicopathological factors to identify germline BRCA1/2 mutation in invasive breast cancer patients

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