Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm.
Publication/Presentation Date
10-1-2018
Abstract
OBJECTIVES: In the postneoadjuvant chemotherapy (NAC) setting, conventional radiographic complete response (rCR) is a poor predictor of pathologic complete response (pCR) of the axilla. We developed a convolutional neural network (CNN) algorithm to better predict post-NAC axillary response using a breast MRI dataset.
METHODS: An institutional review board-approved retrospective study from January 2009 to June 2016 identified 127 breast cancer patients who: (1) underwent breast MRI before the initiation of NAC; (2) successfully completed Adriamycin/Taxane-based NAC; and (3) underwent surgery, including sentinel lymph node evaluation/axillary lymph node dissection with final surgical pathology data. Patients were classified into pathologic complete response (pCR) of the axilla group and non-pCR group based on surgical pathology. Breast MRI performed before NAC was used. Tumor was identified on first T1 postcontrast images underwent 3D segmentation. A total of 2811 volumetric slices of 127 tumors were evaluated. CNN consisted of 10 convolutional layers, 4 max-pooling layers. Dropout, augmentation and L2 regularization were implemented to prevent overfitting of data.
RESULTS: On final surgical pathology, 38.6% (49/127) of the patients achieved pCR of the axilla (group 1), and 61.4% (78/127) of the patients did not with residual metastasis detected (group 2). For predicting axillary pCR, our CNN algorithm achieved an overall accuracy of 83% (95% confidence interval [CI] ± 5) with sensitivity of 93% (95% CI ± 6) and specificity of 77% (95% CI ± 4). Area under the ROC curve (0.93, 95% CI ± 0.04).
CONCLUSIONS: It is feasible to use CNN architecture to predict post NAC axillary pCR. Larger data set will likely improve our prediction model.
Volume
25
Issue
10
First Page
3037
Last Page
3043
ISSN
1534-4681
Published In/Presented At
Ha, R., Chang, P., Karcich, J., Mutasa, S., Van Sant, E. P., Connolly, E., Chin, C., Taback, B., Liu, M. Z., & Jambawalikar, S. (2018). Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm. Annals of surgical oncology, 25(10), 3037–3043. https://doi.org/10.1245/s10434-018-6613-4
Disciplines
Diagnosis | Medicine and Health Sciences | Other Analytical, Diagnostic and Therapeutic Techniques and Equipment | Radiology
PubMedID
29978368
Department(s)
Department of Radiology and Diagnostic Medical Imaging
Document Type
Article