A Practical Predictive Model Based on Ultrasound Imaging and Clinical Indices for Estimation of Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

Ye, Pingping; Duan, Hongbo; Zhao, Zhenya; Fang, Shibo

doi:10.2147/cmar.s331384

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…The possible explanation for this finding is that larger tumors have a higher chance of revealing heterogeneity of elevation, which may affect the sensitivity of chemotherapy [ 37 ]. Many prediction models involved clinical tumor staging (tumor size) as a predictive factor [ 38 , 39 , 40 ], which indicates it is a reliable factor for predicting pCR.…”

Section: Discussionmentioning

confidence: 99%

Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer

Lan

Chen

et al. 2023

IJERPH

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Purpose: Pathological complete response (pCR), the goal of NAC, is considered a surrogate for favorable outcomes in breast cancer (BC) patients administrated neoadjuvant chemotherapy (NAC). This study aimed to develop and assess a novel nomogram model for predicting the probability of pCR based on the core biopsy. Methods: This was a retrospective study involving 920 BC patients administered NAC between January 2012 and December 2018. The patients were divided into a primary cohort (769 patients from January 2012 to December 2017) and a validation cohort (151 patients from January 2017 to December 2018). After converting continuous variables to categorical variables, variables entering the model were sequentially identified via univariate analysis, a multicollinearity test, and binary logistic regression analysis, and then, a nomogram model was developed. The performance of the model was assessed concerning its discrimination, accuracy, and clinical utility. Results: The optimal predictive threshold for estrogen receptor (ER), Ki67, and p53 were 22.5%, 32.5%, and 37.5%, respectively (all p < 0.001). Five variables were selected to develop the model: clinical T staging (cT), clinical nodal (cN) status, ER status, Ki67 status, and p53 status (all p ≤ 0.001). The nomogram showed good discrimination with the area under the curve (AUC) of 0.804 and 0.774 for the primary and validation cohorts, respectively, and good calibration. Decision curve analysis (DCA) showed that the model had practical clinical value. Conclusions: This study constructed a novel nomogram model based on cT, cN, ER status, Ki67 status, and p53 status, which could be applied to personalize the prediction of pCR in BC patients treated with NAC.

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Section: Discussionmentioning

confidence: 99%

Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer

Lan

Chen

et al. 2023

IJERPH

View full text Add to dashboard Cite

show abstract

“…The possible explanation for this nding is that larger tumors have a higher chance of revealing heterogeneity of elevation, which may affect the sensitivity of chemotherapy [21]. Many prediction models involved clinical tumor staging (tumor size) as a predictive factor [22][23][24], which indicates it is a reliable factor for predicting pCR.…”

Section: Clinical Tumor Stagingmentioning

confidence: 99%

Development and assessment of a novel core biopsy-based prediction model for pathological complete response to neoadjuvant chemotherapy in breast cancer

Lan

Chen

et al. 2022

Preprint

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Purpose Pathological complete response (pCR), the goal of NAC, is considered a surrogate for favorable outcomes in breast cancer (BC) patients administrated neoadjuvant chemotherapy (NAC). This study aimed to develop and assess a novel nomogram model for predicting the probability of pCR based on the core biopsy. Methods It was a retrospective study involving 769 BC patients administrated NAC between January 2012 and December 2017. After converting continuous variables to categorical variables, variables entering the model were sequentially identified by univariate analysis, multicollinearity test, and binary logistic regression analysis, and then a nomogram model was developed. The performance of the model was assessed concerning its discrimination, accuracy, and clinical utility. Internal validation was performed by the Bootstrap method. Results The optimal predictive threshold for estrogen receptor (ER), progesterone receptor (PR), Ki67, and p53 were 22.5% (95%CI: 0.603-0.718), 6.5% (95%CI: 0.578-0.693), 32.5% (95%CI: 0.610-0.731), and 37.5% (95%CI: 0.586-0.716), respectively (all P <0.001). Five variables were selected to develop the model, clinical T staging (cT), clinical nodal status (cN), ER status, Ki67 status, and p53 status (all P ≤0.001). The nomogram showed good discrimination with the area under the curve (AUC) of 0.804 (95% CI: 0.756-0.853; P <0.001) and good calibration. Decision curve analysis (DCA) showed that the model had practical clinical value. Conclusion This study constructed a novel nomogram model based on cT, cN, ER status, Ki67 status, and p53 status, which could be applied to personalize the prediction of pCR in BC patients treated with NAC.

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Construction and validation of a personalized nomogram of ultrasound for pretreatment prediction of breast cancer patients sensitive to neoadjuvant chemotherapy

Zhang

Zha

et al. 2022

BJR

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Objective: To construct a combined radiomics model based on pre-treatment ultrasound for predicting of advanced breast cancers sensitive to neoadjuvant chemotherapy (NAC). Methods: A total of 288 eligible breast cancer patients who underwent NAC before surgery were enrolled in the retrospective study cohort. Radiomics features reflecting the phenotype of the pre-NAC tumors were extracted. With features selected using the least absolute shrinkage and selection operator (LASSO) regression, radiomics signature (Rad-score) was established based on the pre-NAC ultrasound. Then, radiomics nomogram of ultrasound (RU) was established on the basis of the best radiomic signature incorporating independent clinical features. The performance of RU was evaluated in terms of calibration curve, area under the curve (AUC), and decision curve analysis (DCA). Results: Nine features were selected to construct the radiomics signature in the training cohort. Combined with independent clinical characteristics, the performance of RU for identifying Grade 4–5 patients was significantly superior than the clinical model and Rad-score alone (p < 0.05, as per the Delong test), which achieved an AUC of 0.863 (95% CI, 0.814–0.963) in the training group and 0.854 (95% CI, 0.776–0.931) in the validation group. DCA showed that this model satisfactory clinical utility, suggesting its robustness as a response predictor. Conclusion: This study demonstrated that RU has a potential role in predicting drug-sensitive breast cancers. Advances in knowledge: Aiming at early detection of Grade 4–5 breast cancer patients, the radiomics nomogram based on ultrasound has been approved as a promising indicator with high clinical utility. It is the first application of ultrasound-based radiomics nomogram to distinguish drug-sensitive breast cancers.

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A Practical Predictive Model Based on Ultrasound Imaging and Clinical Indices for Estimation of Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

Cited by 3 publications

References 36 publications

Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer

Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer

Development and assessment of a novel core biopsy-based prediction model for pathological complete response to neoadjuvant chemotherapy in breast cancer

Construction and validation of a personalized nomogram of ultrasound for pretreatment prediction of breast cancer patients sensitive to neoadjuvant chemotherapy

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