Literature DB >> 33754824

Factors Affecting Pathologic Complete Response Following Neoadjuvant Chemotherapy in Breast Cancer: Development and Validation of a Predictive Nomogram.

Soo-Yeon Kim1, Nariya Cho1, Yunhee Choi1, Su Hyun Lee1, Su Min Ha1, Eun Sil Kim1, Jung Min Chang1, Woo Kyung Moon1.   

Abstract

Background There is an increasing need to develop a more accurate prediction model for pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer. Purpose To develop a nomogram based on MRI and clinical-pathologic variables to predict pCR. Materials and Methods In this single-center retrospective study, consecutive women with stage II-III breast cancer who underwent NAC followed by surgery between January 2011 and December 2017 were considered for inclusion. The women were divided into a development cohort between January 2011 and September 2015 and a validation cohort between October 2015 and December 2017. Clinical-pathologic data were collected, and mammograms and MRI scans obtained before and after NAC were analyzed. Logistic regression analyses were performed to identify independent variables associated with pCR in the development cohort from which the nomogram was created. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration slope. Results A total of 359 women (mean age, 49 years ± 10 [standard deviation]) were in the development cohort and 351 (49 years ± 10) in the validation cohort. Hormone receptor negativity (odds ratio [OR], 3.1; 95% CI: 1.4, 7.1; P = .006), high Ki-67 index (OR, 1.05; 95% CI: 1.03, 1.07; P < .001), and post-NAC MRI variables, including small tumor size (OR, 0.6; 95% CI: 0.4, 0.9; P = .03), low lesion-to-background parenchymal signal enhancement ratio (OR, 0.2; 95% CI: 0.1, 0.6; P = .004), and absence of enhancement in the tumor bed (OR, 3.8; 95% CI: 1.4, 10.5; P = .009) were independently associated with pCR. The nomogram incorporating these variables showed good discrimination (AUC, 0.90; 95% CI: 0.86, 0.94) and calibration abilities (calibration slope, 0.91; 95% CI: 0.69, 1.13) in the independent validation cohort. Conclusion A nomogram incorporating hormone receptor status, Ki-67 index, and MRI variables showed good discrimination and calibration abilities in predicting pathologic complete response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco and Ponsiglione in this issue.

Entities:  

Year:  2021        PMID: 33754824     DOI: 10.1148/radiol.2021203871

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  6 in total

1.  Development and Validation of Nomograms to Predict Cancer-Specific Survival and Overall Survival in Elderly Patients With Prostate Cancer: A Population-Based Study.

Authors:  Zhaoxia Zhang; Chenghao Zhanghuang; Jinkui Wang; Xiaomao Tian; Xin Wu; Maoxian Li; Tao Mi; Jiayan Liu; Liming Jin; Mujie Li; Dawei He
Journal:  Front Oncol       Date:  2022-06-23       Impact factor: 5.738

2.  Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer.

Authors:  Yanbo Li; Yongzi Chen; Rui Zhao; Yu Ji; Junnan Li; Ying Zhang; Hong Lu
Journal:  Eur Radiol       Date:  2021-11-12       Impact factor: 7.034

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

Authors:  Pingping Ye; Hongbo Duan; Zhenya Zhao; Shibo Fang
Journal:  Cancer Manag Res       Date:  2021-10-09       Impact factor: 3.989

4.  A Novel Combined Nomogram Model for Predicting the Pathological Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Carcinoma of No Specific Type: Real-World Study.

Authors:  Xuelin Zhu; Jing Shen; Huanlei Zhang; Xiulin Wang; Huihui Zhang; Jing Yu; Qing Zhang; Dongdong Song; Liping Guo; Dianlong Zhang; Ruiping Zhu; Jianlin Wu
Journal:  Front Oncol       Date:  2022-06-06       Impact factor: 5.738

5.  Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma.

Authors:  Qing-Cong Kong; Wen-Jie Tang; Si-Yi Chen; Wen-Ke Hu; Yue Hu; Yun-Shi Liang; Qiong-Qiong Zhang; Zi-Xuan Cheng; Di Huang; Jing Yang; Yuan Guo
Journal:  Front Oncol       Date:  2022-09-20       Impact factor: 5.738

6.  Construction of a risk prediction model for Alzheimer's disease in the elderly population.

Authors:  Lingling Wang; Ping Li; Ming Hou; Xiumin Zhang; Xiaolin Cao; Hongyan Li
Journal:  BMC Neurol       Date:  2021-07-07       Impact factor: 2.474

  6 in total

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