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Annals of Surgical Oncology

, Volume 25, Issue 10, pp 3037–3043 | Cite as

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

  • Richard Ha
  • Peter Chang
  • Jenika Karcich
  • Simukayi Mutasa
  • Eduardo Pascual Van Sant
  • Eileen Connolly
  • Christine Chin
  • Bret Taback
  • Michael Z. Liu
  • Sachin Jambawalikar
Breast Oncology
  • 213 Downloads

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.

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Copyright information

© Society of Surgical Oncology 2018

Authors and Affiliations

  • Richard Ha
    • 1
  • Peter Chang
    • 2
  • Jenika Karcich
    • 1
  • Simukayi Mutasa
    • 1
  • Eduardo Pascual Van Sant
    • 1
  • Eileen Connolly
    • 3
  • Christine Chin
    • 3
  • Bret Taback
    • 4
  • Michael Z. Liu
    • 5
  • Sachin Jambawalikar
    • 5
  1. 1.Department of RadiologyColumbia University Medical CenterNew YorkUSA
  2. 2.Department of RadiologyUC San Francisco Medical CenterSan FranciscoUSA
  3. 3.Division of Radiation OncologyColumbia University Medical CenterNew YorkUSA
  4. 4.Department of SurgeryColumbia University Medical CenterNew YorkUSA
  5. 5.Department of Medical PhysicsColumbia University Medical CenterNew YorkUSA

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