2021
DOI: 10.3389/fonc.2021.718531
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Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram

Abstract: Background and AimsPrediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer is critical for surgical planning and evaluation of NAC efficacy. The purpose of this project was to assess the efficiency of a novel nomogram based on ultrasound and clinicopathological features for predicting pCR after NAC.MethodsThis retrospective study included 282 patients with advanced breast cancer treated with NAC from two centers. Patients received breast ultrasound before NAC a… Show more

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Cited by 10 publications
(7 citation statements)
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“…Primarily, pathologic images and clinicopathologic parameters alone provide only limited information in predicting pCR. If multimodal data are added, such as ultrasound ( Cui et al, 2021 ), CT ( Moghadas-Dastjerdi et al, 2021 ), MRI ( Huang et al, 2023 ; Shi et al, 2023 ), PET-CT ( Yang et al, 2022 ) examination data, or even genetic testing data, the predictive efficacy of the model can be further improved and the uncertainty of model application can be reduced. Secondly, the number of enrolled cases and the number of clinicopathologic features also affect the uncertainty of model application.…”
Section: Discussionmentioning
confidence: 99%
“…Primarily, pathologic images and clinicopathologic parameters alone provide only limited information in predicting pCR. If multimodal data are added, such as ultrasound ( Cui et al, 2021 ), CT ( Moghadas-Dastjerdi et al, 2021 ), MRI ( Huang et al, 2023 ; Shi et al, 2023 ), PET-CT ( Yang et al, 2022 ) examination data, or even genetic testing data, the predictive efficacy of the model can be further improved and the uncertainty of model application can be reduced. Secondly, the number of enrolled cases and the number of clinicopathologic features also affect the uncertainty of model application.…”
Section: Discussionmentioning
confidence: 99%
“…Te emerging deep learning representation of ultrasound image features, based on pre-NAC and post-NAC ultrasound images, uses deep learning radiomics to establish a pCR prediction model, which can provide an efective diagnostic reference for clinical routine pCR identifcation [29,30]. Studies have shown that during the whole process of preoperative NAC treatment, ultrasound can dynamically observe the changes in the tumor and evaluate the efectiveness of NAC, so that the treatment plan can be changed in time when it is inefective [31].…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, this study aims to predict the pCR of HER2-positive breast cancer, so as to assist the clinical accurate evaluation of its curative effect and prognosis. Cui et al [ 42 ] predicted pCR by analyzing the US characteristics of 282 patients with advanced breast cancer who underwent NACT. The results showed that the change of mass size, posterior acoustic mode, and elasticity score were independent predictors of pCR.…”
Section: Discussionmentioning
confidence: 99%
“…Among them, the posterior acoustic mode was consistent with the conclusion of this study. In addition, Cui et al [ 42 ] constructed a US prediction model based on nomogram on this basis, with an AUC of 0.79, and achieved good prediction performance. However, this study included all breast cancers for analysis, while this study only targeted at HER2-positive breast cancer, and achieved better results in terms of prediction effect.…”
Section: Discussionmentioning
confidence: 99%