Search

Your search keyword '"Morrow, Monica"' showing total 2,602 results
Start Over Author "Morrow, Monica"
2,602 results on '"Morrow, Monica"'

28. Tumor-Nipple Distance of ≥ 1 cm Predicts Negative Nipple Pathology After Neoadjuvant Chemotherapy.

Author

Moo, Tracy-Ann, Saccarelli, Carolina, Sutton, Elizabeth, Sevilimedu, Varadan, Pawloski, Kate, DAlfonso, Timothy, Hughes, Mary, Gluskin, Jill, Bitencourt, Almir, Tadros, Audree, Morrow, Monica, Gemignani, Mary, Sacchini, Virgilio, and Morris, Elizabeth

Subjects
Breast Neoplasms, Female, Humans, Magnetic Resonance Imaging, Mastectomy, Neoadjuvant Therapy, Nipples, Retrospective Studies
Abstract

BACKGROUND: As neoadjuvant chemotherapy (NAC) for breast cancer has become more widely used, so has nipple-sparing mastectomy. A common criterion for eligibility is a 1 cm tumor-to-nipple distance (TND), but its suitability after NAC is unclear. In this study, we examined factors predictive of negative nipple pathologic status (NS-) in women undergoing total mastectomy after NAC. METHODS: Women with invasive breast cancer treated with NAC and total mastectomy from August 2014 to April 2018 at our institution were retrospectively identified. Following review of pre- and post-NAC magnetic resonance imaging (MRI) and mammograms, the association of clinicopathologic and imaging variables with NS- was examined and the accuracy of 1 cm TND on imaging for predicting NS- was determined. RESULTS: Among 175 women undergoing 179 mastectomies, 74% of tumors were cT1-T2 and 67% were cN+ on pre-NAC staging; 10% (18/179) had invasive or in situ carcinoma in the nipple on final pathology. On multivariable analysis, after adjusting for age, grade, and tumor stage, three factors, namely number of positive nodes, pre-NAC nipple-areolar complex retraction, and decreasing TND, were significant predictors of nipple involvement (p

Published
2021

38. MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.

Author

Bitencourt, Almir, Gibbs, Peter, Rossi Saccarelli, Carolina, Daimiel, Isaac, Lo Gullo, Roberto, Fox, Michael, Thakur, Sunitha, Pinker, Katja, Morrow, Monica, Jochelson, Maxine, and Morris, Elizabeth

Subjects
Breast invasive ductal carcinoma, ErbB-2 receptor, HER2, Machine learning, Magnetic resonance imaging, Neoadjuvant therapy, Adult, Aged, Biomarkers, Breast Neoplasms, Female, Gene Expression, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Machine Learning, Magnetic Resonance Imaging, Middle Aged, Neoadjuvant Therapy, ROC Curve, Receptor, ErbB-2, Young Adult
Abstract

BACKGROUND: To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC). METHODS: This retrospective study included 311 patients. pCR was defined as no residual invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics/statistical analysis was performed using MATLAB and CERR software. After ROC and correlation analysis, selected radiomics parameters were advanced to machine learning modelling alongside clinical MRI-based parameters (lesion type, multifocality, size, nodal status). For predicting pCR, the data was split into a training and test set (80:20). FINDINGS: The overall pCR rate was 60.5% (188/311). The final model to predict HER2 heterogeneity utilised three MRI parameters (two clinical, one radiomic) for a sensitivity of 99.3% (277/279), specificity of 81.3% (26/32), and diagnostic accuracy of 97.4% (303/311). The final model to predict pCR included six MRI parameters (two clinical, four radiomic) for a sensitivity of 86.5% (32/37), specificity of 80.0% (20/25), and diagnostic accuracy of 83.9% (52/62) (test set); these results were independent of age and ER status, and outperformed the best model developed using clinical parameters only (p=0.029, comparison of proportion Chi-squared test). INTERPRETATION: The machine learning models, including both clinical and radiomics MRI features, can be used to assess HER2 expression level and can predict pCR after NAC in HER2 overexpressing breast cancer patients. FUNDING: NIH/NCI (P30CA008748), Susan G. Komen Foundation, Breast Cancer Research Foundation, Spanish Foundation Alfonso Martin Escudero, European School of Radiology.

Published
2020

Catalog

Books, media, physical & digital resources
See catalog results