Almir Bitencourt1,2, Peter Gibbs3, Carolina Rossi Saccarelli3, Isaac Daimiel3, Roberto Lo Gullo3, Michael Fox3, Sunitha Thakur3, Katja Pinker3, Elizabeth A Morris3, Monica Morrow4, and Maxime Jochelson3
1Breast Radiology, MSKCC, New York, NY, United States, 2Department of Imaging, A.C. Camargo Cancer Center, Sao Paulo, Brazil, 3MSKCC, New York, NY, United States, 4Breast Surgery, MSKCC, New York, NY, United States
Synopsis
In this study, we used
magnetic resonance (MR)-based clinical and radiomic features to assess tumor heterogeneity
in 311 HER2 overexpressing breast cancer patients receiving neoadjuvant
chemotherapy (NAC), and correlated these findings with tumor heterogeneity and
pathologic response. Tumor heterogeneity was evaluated based on the HER2
expression (IHC vs. FISH)
. Pathologic complete response (pCR) was defined as no residual invasive
carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics
analysis and machine learning with MRI were able to assess tumor heterogeneity
and predict pCR after neoadjuvant chemotherapy in these patients, with a
diagnostic accuracy of 97.4%
and 85.2%, respectively.
Introduction
Overexpression of the HER2 gene and its protein is
observed in 20% to 30% of breast cancer patients and determines prognosis and
treatment. 1 Protein overexpression detected by immunohistochemistry
(IHC) or amplification of the HER2 gene analyzed by fluorescence in situ
hybridization (FISH) are the methods used to detect HER2 status in clinical
practice. 2 However, tumors may contain multiple clones with
distinct HER2 amplification characteristics within the tumor, resulting in HER2
intratumoral heterogeneity. Recently, studies have suggested that intratumoral heterogeneity
based on HER2 expression levels can affect response to neoadjuvant chemotherapy
(NAC). 3-7 The
aim of this study was to use MR-based clinical and radiomic features coupled
with machine learning to assess tumor heterogeneity in HER2 overexpressing
breast cancer patients receiving NAC, and correlate these findings with pathologic
response.Methods
This retrospective single-center study included 311
patients with HER2 overexpressing invasive breast carcinoma who received NAC following
pretreatment MRI. Tumor heterogeneity was evaluated based on the HER2 expression:
HER2 3+ on IHC; or HER2
less than 3+ on IHC with HER2 gene amplification detected by FISH. Pathologic
complete response (pCR) was defined as no residual
invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). A
breast radiologist performed 3D segmentations of the tumor in the first minute
post-contrast sequence using ITK-SNAP software. Enhancement maps were
calculated as the percentage increase in signal from the pre-contrast image to
the first post-contrast image. Radiomics and statistical analysis were
performed using MATLAB and publicly available CERR (Computational Environment
for Radiological Research) software. For analysis, data was reduced to 16 gray
levels and only an interpixel distance of one was considered. CERR analysis
results in 102 texture parameters sub-divided into six categories – 22 first
order statistics, 26 statistics based on gray level cooccurrence matrices, 16
statistics based on run length matrices, 16 statistics based on size zone
matrices, 17 statistics based on neighborhood gray level dependence matrices,
and finally 5 statistics based on neighborhood gray tone difference matrices.
Initially univariate analysis was performed to identify significant parameters.
The 2-tailed Mann Whitney U-test for two independent samples was used to
determine significant differences between groups. Correlation analysis was then
employed to remove redundant parameters from advancing to model development. If
a highly positive (>0.9) or highly negative (<-0.9) correlation was noted
the parameter with the lowest AUC from ROC curve analysis was removed. After ROC
and correlation analysis, radiomics parameters that demonstrated significant
differences between groups were retained and advanced to modelling alongside
clinical MR-based parameters, including lesion type (mass/NME/both),
multifocality, size and nodal status. Robust machine learning models were
developed utilizing coarse decision trees and 5-fold cross-validation.Results
The mean patient mean age was 49.4 years (range: 24-78
years) and mean tumor size by MRI was 4.7cm (range: 0.9-14.8cm). The index
tumor presented as mass in 147 cases (47.3%), non-mass enhancement (NME) in 34
(10.9%) and both mass and NME in 130 (41.8%). Most tumors were multifocal and
had suspicious axillary lymph nodes on MRI (65.9% each). 293 tumors (89.6%)
were classified as HER2 3+ on IHC and 34 (10.4%) had HER2 gene amplification by
FISH. Overall pCR rate was 60.5% (188/311). Statistical analysis resulted in 5
and 8 radiomics parameters advancing for tumor heterogeneity and pathologic
response analysis respectively. The four clinical parameters (size,
multifocality, type, and nodal status) were also advanced to modelling. The
final model to predict HER2 intratumoral expression levels (IHC vs. FISH) utilized
3 MR parameters (2 clinical [lesion type and multifocality] and 1 radiomic
[lze]) for a sensitivity of 99.3% (277/279), specificity of 81.3% (26/32),
positive predictive value of 97.9% (277/283), negative predictive value of 92.9%
(26/28), and diagnostic accuracy of 97.4% (303/311). The final model to predict
pCR included 4 MR parameters (2 clinical [lesion type and size] and 2 radiomic
[variance and zlv]) for a sensitivity of 85.6% (161/188), specificity of 84.6%
(104/123), positive predictive value of 89.4% (161/180), negative predictive
value of 79.4% (104/131), and diagnostic accuracy of 85.2% (265/311). These
results were independent of age and estrogen-receptor status.Discussion
Because HER2 status is usually based on the analysis of a
small sample of the tumor obtained through needle biopsy before NAC, the
development of noninvasive biomarkers to evaluate whole-tumor heterogeneity are
needed. Breast MRI is the most accurate imaging method to predict pathologic
response in HER2-overexpressing breast cancer after NAC. 8 Radiomics
analysis has been shown to enable pathologic response prediction from the
pretreatment breast MRI. 9 Imaging intratumoral heterogeneity with
MRI delivers additional prognostic value beyond current clinical-pathologic and
biologic predictors in breast cancer, and could potentially be used to stratify
patients for individualized therapy. 10Conclusion
The robust
machine learning models, including both clinical and radiomics-based MR
features, can be used to assess intratumor heterogeneity and predict pCR after
NAC in HER2 overexpressing breast cancer patients.Acknowledgements
This work was
partially supported by the NIH/NCI Cancer Center Support Grant (P30 CA008748)
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