Is Multifocal Regression a Risk Factor for Ipsilateral Breast Tumor Recurrence in the Modern Era After Neoadjuvant Chemotherapy and Breast Conservation Therapy?

Ling, Diane C.; Sutera, Philip; Iarrobino, Nick A.; Diego, Emilia; Soran, Atilla; Johnson, R. R.; Bhargava, Rohit; Champ, Colin E.; Beriwal, Sushil

doi:10.1016/j.ijrobp.2019.03.012

Cited by 15 publications

(12 citation statements)

References 22 publications

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“…In clinical practice, however, the recurrence rate is somewhat higher in patients receiving NAC than in those receiving chemotherapy, which can be explained by false negative margins in surgery. Ling et al divided tumor regression patterns into 2 categories [ 6 ]. Patients with pCR and unifocal residual disease after NAC had a higher rate of 4-year IBTR-free survival than those with multifocal disease.…”

Section: Discussionmentioning

confidence: 99%

“…The primary purposes of NAC are to downstage breast cancers and improve the possibility of breast-conserving therapy in most breast cancers [ [2] , [3] , [4] ]. After preoperative chemotherapy, breast tumors shrink to different extents and show various regression patterns, including pathologic complete response (pCR), unifocal shrinkage, multiple residual foci, main residual disease with satellite foci, stable disease (SD) and progressive disease (PD) [ [5] , [6] , [7] , [8] ]. Among these patterns, unifocal residual disease and pCR favor the adoption of breast-conserving surgery (BCS).…”

Section: Introductionmentioning

confidence: 99%

“…Though the higher IBTR did not cause a significant difference in overall survival, it brought an extra operation and might cause mental stress to patients. A previous study from the University of Pittsburgh determined that multifocal regression independently predicts IBTR [ 6 ]. Tumor regression patterns were divided into 2 types: pCR plus unifocal regression and multifocal regression.…”

Section: Introductionmentioning

confidence: 99%

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Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy

Zhuang

Chen

Liu

et al. 2020

Translational Oncology

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Objectives Breast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on radiomic features and clinicopathological factors to predict regression patterns in breast cancer patients. Methods We retrospectively reviewed 144 breast cancer patients who received neoadjuvant chemotherapy and underwent definitive surgery in our center from January 2016 to December 2019. Tumor regression patterns were categorized as type 1 (concentric regression + pCR) and type 2 (multifocal residues + SD + PD) based on pathological results. We extracted 1158 multidimensional features from 2 sequences of MRI images. After feature selection, machine learning was applied to construct a radiomic signature. Clinical characteristics were selected by backward stepwise selection. The combined prediction model was built based on both the radiomic signature and clinical factors. The predictive performance of the combined prediction model was evaluated. Results Two radiomic features were selected for constructing the radiomic signature. Combined with two significant clinical characteristics, the combined prediction model showed excellent prediction performance, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval 0.8343–0.9701) in the primary cohort and 0.826 (95% confidence interval 0.6774–0.9753) in the validation cohort. Conclusions Our study established a unique model combining a radiomic signature and clinicopathological factors to predict tumor regression patterns prior to the initiation of NAC. The early prediction of type 2 regression offers the opportunity to modify preoperative treatments or aids in determining surgical options.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy

Zhuang

Chen

Liu

et al. 2020

Translational Oncology

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“…It enables the downstaging of breast cancers and improves the possibility of breast-conserving therapy in most breast cancers [2,3]. Primary tumors shrink after NAC and show various regression patterns, including pathologic complete response (pCR), concentric shrinkage, multiple residual foci, main residual disease with satellite foci, stable disease (SD) and progressive disease (PD) [4][5][6]. Among these patterns, unifocal residual disease and pCR favor the adoption of breastconserving surgery (BCS).…”

Section: Introductionmentioning

confidence: 99%

“…However, it brought an extra operation and may cause mental burden to patients. Recently, researchers from the University of Pittsburgh showed that multifocal regression is an independent predictive factor for IBTR [6]. A total of 9.5% of patients underwent a second operation due to positive margins in their rst attempted BCS.…”

Section: Introductionmentioning

confidence: 99%

A Nomogram to Predict Breast Tumor Regression Patterns after Neoadjuvant Chemotherapy Based on Baseline Characteristics

Zhuang

Cheng

Zhang

et al. 2020

Preprint

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Background Breast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on clinicopathological factors and laboratory indicators to predict regression patterns in breast cancer patients.Methods We retrospectively reviewed 320 patients with stage II-III breast cancer who received neoadjuvant chemotherapy and had definitive surgery in our center from January 2016 to December 2019. Tumor regression patterns were categorized as type 1 (concentric regression + pCR) and type 2 (multifocal residues + SD + PD) based on mass pathological results. A multivariate logistic regression model was applied to distinguish predictive factors for tumor regression patterns. A nomogram was built based on five predictive factors from the multivariate logistic regression model.Results Molecular subtypes were not significantly associated with tumor regression patterns. Multivariate analysis identified five independent indicators: menopausal status, estrogen receptor status, T stage, N stage and lymphocyte to monocyte ratio. The nomogram yielded an area under the curve (AUC) of 0.70 (95% confidence interval 0.62-0.78) based on these factors. The calibration plot accompanied by the Hosmer–Lemeshow goodness-of-fit (GOF) test (p-value =0.9) showed good consistency between the estimated probabilities and the actual rate of type 1 cases.Conclusion HR+/HER2- breast cancers are more likely to have type 2 regression after neoadjuvant chemotherapy. Five baseline factors, including clinicopathological factors and laboratory indexes, were incorporated to establish a nomogram, which exhibited a satisfactory discriminatory ability for predicting different patterns of tumor regression.

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Use of Pretreatment Multiparametric MRI to Predict Tumor Regression Pattern to Neoadjuvant Chemotherapy in Breast Cancer

Liu¹,

Huang²,

Chen³

et al. 2023

Academic Radiology

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Is Multifocal Regression a Risk Factor for Ipsilateral Breast Tumor Recurrence in the Modern Era After Neoadjuvant Chemotherapy and Breast Conservation Therapy?

Cited by 15 publications

References 22 publications

Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy

Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy

A Nomogram to Predict Breast Tumor Regression Patterns after Neoadjuvant Chemotherapy Based on Baseline Characteristics

Use of Pretreatment Multiparametric MRI to Predict Tumor Regression Pattern to Neoadjuvant Chemotherapy in Breast Cancer

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