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Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm

delete2018-07-05
delete28
PRE
AI
R
Richard Ha *
P
Peter Chang
J
Jenika Karcich
S
Simukayi Mutasa
E
Eduardo Pascual Van Sant
E
Eileen P. Connolly
C
Christine Chin
B
Bret Taback
M
Michael Z. Liu
S
Sachin Jambawalikar
DOI:10.1245/s10434-018-6613-4delete
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摘要

摘要

En 中文
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. 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. 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). It is feasible to use CNN architecture to predict post NAC axillary pCR. Larger data set will likely improve our prediction model.
Keyword:
PATHOLOGICAL COMPLETE RESPONSE
SURGICAL ADJUVANT BREAST
LYMPH-NODE SURGERY
CHEMOTHERAPY
CANCER
THERAPY
DISSECTION
RECURRENCE
B-18
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期刊

Annals of Surgical Oncology 封面图
Annals of Surgical Oncology
IF:
3.5
论文数:
2.3W
被引数:
3.4W

机构

C
Columbia University
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7.1W
论文数: 6.4W
被引数: 263
U
university of california san francisco
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5.3W
论文数: 4.0W
被引数: 67
University of California System 封面图
University of California System
学者数:
37.7W
论文数: 33.8W
被引数: 6.6K
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