Correction to: Prediction for pathological and immunohistochemical characteristics of triple‑negative invasive breast carcinomas: the performance comparison between quantitative and qualitative sonographic feature analysis

Li, Jiawei; Cao, Yucheng; Zhao, Zhijin; Shi, Zhaoting; Duan, Xi; Chang, Cai; Chen, Jian‐gang

doi:10.1007/s00330-021-08345-3

Cited by 2 publications

(2 citation statements)

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“…In contrast with models 1 and 2, which yielded moderate diagnostic performance (AUC = 0.694 and 0.774, respectively), model 3, which further incorporated clinicopathological features, demonstrated a high predictive performance (AUC = 0.853). Other studies 38,39 have consistently found that this combination of US and clinicopathological features is better than either alone.…”

Section: Discussionmentioning

confidence: 80%

A Model Combining Conventional Ultrasound Characteristics, Strain Elastography and Clinicopathological Features to Predict Ki-67 Expression in Small Breast Cancer

Xing,

Miao,

Wang

et al. 2024

Ultrason Imaging

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To establish a predictive model incorporating conventional ultrasound, strain elastography and clinicopathological features for Ki-67 expression in small breast cancer (SBC) which defined as maximum diameter less than2 cm. In this retrospective study, 165 SBC patients from our hospital were allocated to a high Ki-67 group ( n = 104) and a low Ki-67 group ( n = 61). Multivariate regression analysis was performed to identify independent indicators for developing predictive models. The area under the receiver operating characteristic (AUC) curve was also determined to establish the diagnostic performance of different predictive models. The corresponding sensitivities and specificities of different models at the cutoff value were compared. Conventional ultrasound parameters (spiculated margin, absence of posterior shadowing and Adler grade 2–3), strain elastic scores and clinicopathological information (HER2 positive) were significantly correlated with high expression of Ki-67 in SBC (all p < .05). Model 2, which incorporated conventional ultrasound features and strain elastic scores, yielded good diagnostic performance (AUC = 0.774) with better sensitivity than model 1, which only incorporated ultrasound characteristics (78.85%vs. 55.77%, p = .000), with specificities of 77.05% and 62.30% ( p = .035), respectively. Model 3, which incorporated conventional ultrasound, strain elastography and clinicopathological features, yielded better performance (AUC = 0.853) than model 1 (AUC = 0.694) and model 2 (AUC = 0.774), and the specificity was higher than model 1 (86.89% vs. 77.05%, p = .001). The predictive model combining conventional ultrasound, strain elastic scores and clinicopathological features could improve the predictive performance of Ki-67 expression in SBC.

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Section: Discussionmentioning

confidence: 80%

A Model Combining Conventional Ultrasound Characteristics, Strain Elastography and Clinicopathological Features to Predict Ki-67 Expression in Small Breast Cancer

Xing,

Miao,

Wang

et al. 2024

Ultrason Imaging

View full text Add to dashboard Cite

show abstract

“…Cui et a.l ( 41 ) and Li et al. ( 42 ) found that ultrasound radiomics models enabled preoperative classification of ki67 and p53 status. Furthermore, it is noteworthy that recent studies have shown that ultrasound radiomics features are not only a potential imaging biomarker for disease-free survival risk stratification, but also can predict the risk of postoperative recurrence in patients with triple-negative BC ( 43 , 44 ).…”

Section: Ultrasound Radiomics In the Evaluation Of Molecular Subtypementioning

confidence: 99%

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

2022

Front. Oncol.

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Breast cancer is the most common cancer in women worldwide. Providing accurate and efficient diagnosis, risk stratification and timely adjustment of treatment strategies are essential steps in achieving precision medicine before, during and after treatment. Radiomics provides image information that cannot be recognized by the naked eye through deep mining of medical images. Several studies have shown that radiomics, as a second reader of medical images, can assist physicians not only in the detection and diagnosis of breast lesions but also in the assessment of risk stratification and prediction of treatment response. Recently, more and more studies have focused on the application of ultrasound radiomics in breast management. We summarized recent research advances in ultrasound radiomics for the diagnosis of benign and malignant breast lesions, prediction of molecular subtype, assessment of lymph node status, prediction of neoadjuvant chemotherapy response, and prediction of survival. In addition, we discuss the current challenges and future prospects of ultrasound radiomics.

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Correction to: Prediction for pathological and immunohistochemical characteristics of triple‑negative invasive breast carcinomas: the performance comparison between quantitative and qualitative sonographic feature analysis

Cited by 2 publications

References 0 publications

A Model Combining Conventional Ultrasound Characteristics, Strain Elastography and Clinicopathological Features to Predict Ki-67 Expression in Small Breast Cancer

A Model Combining Conventional Ultrasound Characteristics, Strain Elastography and Clinicopathological Features to Predict Ki-67 Expression in Small Breast Cancer

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

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