Deep Learning-Based Prediction of HER2 Status from Whole-Brain MRI in Breast Cancer Brain Metastases
Authors: Sara Early, Chris Kang, Vibujithan Vigneshwaran, Kimberly Amador, Roger Y. Tsang, Nils D. Forkert
Organizations: Department of Radiology, University of Calgary, Calgary, AB, Canada · Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada · Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada · Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada · Annie Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada · Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada · Arthur J.E. Child Comprehensive Cancer Centre, Calgary, AB, Canada · Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
Purpose: Human epidermal growth factor receptor 2 (HER2) status is a key biomarker relevant for prognostication and treatment decision making in metastatic breast cancer. Thus, the aim of this work was to explore whether HER2 status can be predicted directly from whole-brain post-contrast T1-weighted (T1w) MRI scans in breast cancer brain metastases (BCBM) using deep learning, and whether providing explicit tumour segmentation information improves prediction. Methods: Using the publicly available BCBM-Radiogenomics dataset, we analysed 135 patients, including 70 HER2+ and 65 HER2- patients. Three approaches were implemented and evaluated: a conventional radiomics model, a simple fully convolutional network (SFCN) using T1w MRI scans alone, and an SFCN using T1w MRI scans with an expert-derived tumour mask as an additional input channel. Results: The radiomics model achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.602 (95% confidence interval [CI]: 0.528--0.677) while the SFCN achieved a ROC-AUC of 0.667 (95% CI, 0.63--0.71) using T1w MRI scans alone, which increased to 0.692 (95% CI, 0.58--0.81) when an explicit tumour mask was additionally provided. Conclusion: However, the modest performance across all approaches demonstrates the difficulty of predicting HER2 status from structural MRI scans alone. The difference between the segmentation-free and mask-guided SFCN models suggests that much of the predictive signal captured by the deep-learning model may be learned directly from whole-brain T1w MRI without requiring explicit tumour delineation. These findings support further investigation of segmentation-free deep-learning approaches as a potential non-invasive strategy to support HER2 status determination in BCBM.
Figures & tables
Figure 1: Three approaches were evaluated for HER2 status classification: (a) a conventional radiomics model, (b) a simple fully convolutional network (SFCN) using T1w MRI scans alone, and (c) an SFCN using T1w MRI scans with an expert-delineated tumour mask as an additional input channel
Cohort variable
Overall ( n=135 )
HER2+ ( n=70 )
HER2- ( n=65 )
Age, years (mean ± SD)
56.9 ± 10.4
56.6 ± 10.4
57.2 ± 10.5
Number of metastases (mean ± SD; median)
3.3 ± 2.9; 2
3.1 ± 2.9; 2
3.4 ± 2.8; 2
Total lesion volume, cm 3 (mean ± SD; median)
6.7 ± 8.3; 3.6
5.8 ± 8.9; 3.1
7.6 ± 7.5; 5.3
1.5T scans, n
126
63
63
3T scans, n
9
7
2
Table 1: MRI acquisition characteristics of the study cohort stratified by HER2 status.
Feature representation
No. of features
Definition
Tumour burden
4
Number of lesions, total lesion volume, largest lesion volume, and log-transformed total lesion volume.
Largest-lesion radiomics
107
Complete radiomic feature vector from the largest lesion for each patient.
Mean-pooled radiomics
107
Mean of each radiomic feature across all lesions for each patient.
Max-pooled radiomics
107
Maximum value of each radiomic feature across all lesions for each patient.
Mean- and max-pooled radiomics
214
Concatenation of the mean- and max-pooled radiomic feature vectors.
Largest-lesion radiomics + age
108
Largest-lesion radiomic feature vector combined with patient age.
Table 2: Patient-level feature representations evaluated for HER2 classification.
Feature representation
No. of features
ROC-AUC
95% CI
Tumour burden
4
0.602
0.528–0.677
Largest-lesion radiomics
107
0.553
0.460–0.646
Mean+max radiomics
214
0.552
0.451–0.653
Mean-pooled radiomics
107
0.541
0.422–0.659
Max-pooled radiomics
107
0.536
0.431–0.642
Largest-lesion + clinical
108
0.525
0.305–0.745
Table 3: Patient-level HER2 classification performance of the seven feature representations evaluated using nested cross-validation. ROC-AUC, area under the receiver operating characteristic curve; CI, confidence interval.
Model
Input
Segmentation required
ROC-AUC
95% CI
Radiomics (Logistic Regression)
4 radiomic features
Yes
0.602
0.528–0.677
SFCN
T1w MRI
No
0.667
0.63–0.71
SFCN
T1w MRI + tumour mask
Yes
0.692
0.58–0.81
Table 4: Comparison of radiomics and SFCN approaches for patient-level HER2 classification performance.
Figure 2: Receiver operating characteristic (ROC) curves for HER2 status prediction. Curves are shown for the tumour-burden radiomics model, the T1w-only SFCN, and the T1w + tumour mask SFCN. The corresponding mean-of-folds AUCs were 0.602, 0.667, and 0.692, respectively
Computer Science Department, University of Oviedo, Asturias, Spain · Biomedical Engineering Center (BME), University of Oviedo, Asturias, Spain · Electrical Engineering Department, University of Oviedo, Asturias, Spain +7