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Research ArticlePediatric Neuroimaging

Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study

J.L. Quon, W. Bala, L.C. Chen, J. Wright, L.H. Kim, M. Han, K. Shpanskaya, E.H. Lee, E. Tong, M. Iv, J. Seekins, M.P. Lungren, K.R.M. Braun, T.Y. Poussaint, S. Laughlin, M.D. Taylor, R.M. Lober, H. Vogel, P.G. Fisher, G.A. Grant, V. Ramaswamy, N.A. Vitanza, C.Y. Ho, M.S.B. Edwards, S.H. Cheshier and K.W. Yeom
American Journal of Neuroradiology August 2020, DOI: https://doi.org/10.3174/ajnr.A6704
J.L. Quon
aFrom the Departments of Neurosurgery (J.L.Q., G.A.G., M.S.B.E.)
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W. Bala
eDepartment of Radiology (W.B., J.S., M.P.L., K.W.Y.)
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L.C. Chen
gDepartment of Urology (L.C.C.)
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J. Wright
iDepartment of Radiology (J.W.), Seattle Children’s Hospital, University of Washington School of Medicine, Seattle, Washington
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L.H. Kim
hStanford University School of Medicine (L.H.K., M.H., K.S.), Stanford, California
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M. Han
hStanford University School of Medicine (L.H.K., M.H., K.S.), Stanford, California
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K. Shpanskaya
hStanford University School of Medicine (L.H.K., M.H., K.S.), Stanford, California
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E.H. Lee
bElectrical Engineering (E.H.L.)
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E. Tong
cRadiology (E.T., M.I.)
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M. Iv
cRadiology (E.T., M.I.)
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J. Seekins
eDepartment of Radiology (W.B., J.S., M.P.L., K.W.Y.)
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M.P. Lungren
eDepartment of Radiology (W.B., J.S., M.P.L., K.W.Y.)
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K.R.M. Braun
jDepartments of Clinical Radiology & Imaging Sciences (K.R.M.B., C.Y.H.), Riley Children’s Hospital, Indiana University, Indianapolis, Indiana
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T.Y. Poussaint
kDepartments of Radiology (T.Y.P.), Boston Children’s Hospital, Boston, Massachusetts
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S. Laughlin
lDepartments of diagnostic Imaging (S.L.)
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M.D. Taylor
mNeurosurgery (M.D.T.)
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R.M. Lober
oDepartment of Neurosurgery (R.M.L.), Dayton Children’s Hospital, Wright State University Boonshoft School of Medicine, Dayton, Ohio
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H. Vogel
dPathology (H.V.), Stanford University, Stanford, California
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P.G. Fisher
fDivision of Child Neurology (P.G.F.), Lucile Packard Children’s Hospital, Stanford University, Palo Alto, California
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G.A. Grant
aFrom the Departments of Neurosurgery (J.L.Q., G.A.G., M.S.B.E.)
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V. Ramaswamy
nHaematology/Oncology (V.R.), The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
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N.A. Vitanza
pDivision of Pediatric Hematology/Oncology (N.A.V.), Department of Pediatrics, University of Washington, Seattle Children’s Hospital, Seattle Washington
qFred Hutchinson Cancer Research Center (N.A.V.), Seattle, Washington
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C.Y. Ho
jDepartments of Clinical Radiology & Imaging Sciences (K.R.M.B., C.Y.H.), Riley Children’s Hospital, Indiana University, Indianapolis, Indiana
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M.S.B. Edwards
aFrom the Departments of Neurosurgery (J.L.Q., G.A.G., M.S.B.E.)
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S.H. Cheshier
rDepartments of Neurosurgery (S.H.C.), University of Utah School of Medicine, Salt Lake City, Utah.
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K.W. Yeom
eDepartment of Radiology (W.B., J.S., M.P.L., K.W.Y.)
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  • FIG 1.
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    FIG 1.

    Comparison of model-with-radiologist performance. A, ROC curve for scan-level tumor detection. Model, individual radiologist, and average radiologist performance are indicated with crosshairs. B, Model and average radiologist performance for tumor subtype classification results. Error bars represent standard error among radiologists.

  • FIG 2.
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    FIG 2.

    CAMs depicting the areas of the input slice that the model preferentially emphasizes when predicting tumor subtype on individual scan slices. The upper row of each subpanel shows the T2 slice with tumor areas manually denoted (upper left) and CAM overlay of the most confident prediction of the model (upper right). The lower row of each panel shows less confident predictions. Examples of correct predictions of PA (A) and MB (B) and incorrect predictions of PA (C) and MB (D) are shown.

  • FIG 3.
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    FIG 3.

    Learned feature vectors were reduced to 2D and visualized using principal component analysis (PCA) (A) and t-SNE (B). DMG has the most distinctive feature space, followed by PA and MB. EP has the least distinctive feature space and overlaps with MB.

Tables

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    Table 1:

    Complete dataset of 803 patients from 5 institutions with 4 tumor typesa

    Institution 1Institution 2Institution 3Institution 4Institution 5
    MB90117203041
    DMG85004521
    EP424141228
    PA129004526
    • ↵a One hundred eighty-six patients with no T2 sequences or only postintervention imaging were excluded.

    • View popup
    Table 2:

    A total of 739 scans were distributed into a training set, a validation set, and a held-out test set

    TrainingValidationTestTotal
    MB2423455331
    DMG881024122
    EP831315111
    PA1142041175
    Total52777135739
    • View popup
    Table 3:

    Comparison of tumor detection and classification results between the deep learning model and radiologistsa

    Tumor DetectionTumor Classification
    SensitivitySpecificityPAccuracyF1 ScoreP
    Model0.961.00–0.920.80–
    Radiologist average0.990.98–0.870.75–
    Radiologist A1.001.00.060.950.89.09
    Radiologist B0.990.971.000.890.79.24
    Radiologist C0.990.981.000.790.61<.01
    Radiologist D0.980.98.730.840.70<.01
    • Note:— –indicates n/a.

    • ↵a P value calculated using the McNemar test comparing the model with individual radiologists.

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J.L. Quon, W. Bala, L.C. Chen, J. Wright, L.H. Kim, M. Han, K. Shpanskaya, E.H. Lee, E. Tong, M. Iv, J. Seekins, M.P. Lungren, K.R.M. Braun, T.Y. Poussaint, S. Laughlin, M.D. Taylor, R.M. Lober, H. Vogel, P.G. Fisher, G.A. Grant, V. Ramaswamy, N.A. Vitanza, C.Y. Ho, M.S.B. Edwards, S.H. Cheshier, K.W. Yeom
Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study
American Journal of Neuroradiology Aug 2020, DOI: 10.3174/ajnr.A6704

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Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study
J.L. Quon, W. Bala, L.C. Chen, J. Wright, L.H. Kim, M. Han, K. Shpanskaya, E.H. Lee, E. Tong, M. Iv, J. Seekins, M.P. Lungren, K.R.M. Braun, T.Y. Poussaint, S. Laughlin, M.D. Taylor, R.M. Lober, H. Vogel, P.G. Fisher, G.A. Grant, V. Ramaswamy, N.A. Vitanza, C.Y. Ho, M.S.B. Edwards, S.H. Cheshier, K.W. Yeom
American Journal of Neuroradiology Aug 2020, DOI: 10.3174/ajnr.A6704
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