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Research ArticleARTIFICIAL INTELLIGENCE

Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms

Girish Bathla, Neetu Soni, Ian T. Mark, Yanan Liu, Nicholas B. Larson, Blake A. Kassmeyer, Suyash Mohan, John C. Benson, Saima Rathore and Amit K. Agarwal
American Journal of Neuroradiology July 2024, DOI: https://doi.org/10.3174/ajnr.A8280
Girish Bathla
aFrom the Department of Radiology (G.B., N.S.), University of Iowa Hospitals and Clinics, Iowa City, Iowa
bDepartment of Radiology (G.B., I.T.M., J.C.B.), Mayo Clinic, Rochester, Minnesota
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  • ORCID record for Girish Bathla
Neetu Soni
aFrom the Department of Radiology (G.B., N.S.), University of Iowa Hospitals and Clinics, Iowa City, Iowa
cDepartment of Radiology (N.S., A.K.A.), Mayo Clinic, Jacksonville, Florida
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Ian T. Mark
bDepartment of Radiology (G.B., I.T.M., J.C.B.), Mayo Clinic, Rochester, Minnesota
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Yanan Liu
dAdvanced Pulmonary Physiomic Imaging Laboratory (Y.L.), University of Iowa, Iowa City, Iowa
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Nicholas B. Larson
eDivision of Clinical Trials and Biostatistics (N.B.L., B.A.K.), Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Rochester, Minnesota
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  • ORCID record for Nicholas B. Larson
Blake A. Kassmeyer
eDivision of Clinical Trials and Biostatistics (N.B.L., B.A.K.), Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Rochester, Minnesota
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Suyash Mohan
fDepartment of Radiology (S.M.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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John C. Benson
bDepartment of Radiology (G.B., I.T.M., J.C.B.), Mayo Clinic, Rochester, Minnesota
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  • ORCID record for John C. Benson
Saima Rathore
gAvid Radiopharmaceuticals (S.R.), Philadelphia, Pennsylvania
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Amit K. Agarwal
cDepartment of Radiology (N.S., A.K.A.), Mayo Clinic, Jacksonville, Florida
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  • ORCID record for Amit K. Agarwal
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Article Figures & Data

Figures

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  • FIG 1.
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    FIG 1.

    Schematic depicting the study workflow.

  • FIG 2.
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    FIG 2.

    Violin plots for all 4 feature sets using the external data show the range of mAUC across different pipelines. Feature set 1: CE_ET and F_PTR; 2: CE_ET and T2_PTR; 3: CE_ET, A_ET and F_PTR; 4: CE_ET only. A indicates ADC; F, FLAIR.

  • FIG 3.
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    FIG 3.

    Maximum mAUC heatmaps for the internal and external data for the various models based on the ML algorithm. svmRBF indicates support vector machine-Gaussian kernel; XGB, extreme gradient boosting; RF, random forest; GBRM, generalized boosted regression mode; ENET, multinomial elastic net; MAX, maximum; svmPoly, support vector machine-polynomial kernel.

  • FIG 4.
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    FIG 4.

    Histogram plot showing mean differences between the internal and external data model performance for the different pipelines. The red line depicts the mean difference in model performance.

Tables

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    Table 1:

    Patient demographic details, scanner, and class distributions in the internal and external data sets

    Internal (n = 410)External (n = 83)
    Scanner
     1.5T371 (90.4%)51 (61.4%)
     3T39 (9.5%)32 (38.6%)
    Age (yr)
     Mean (SD)62.2 (12.3)62.6 (12.2)
     Range11.0–90.0026.0–83.0
    Sex
     Female196 (47.8%)40 (48.2%)
     Male214 (52.1%)43 (51.8%)
    Class
     GB171 (41.7%)25 (30.1%)
     IMD188 (45.8%)32 (38.6%)
     PCNSL51 (12.4%)26 (31.3%)
    • View popup
    Table 2:

    Summary of top 3 performing models in the external data set for each feature set

    Feature SetProcessingAlgorithmFeature SelectionmAUCLogLossBrier Score
    CE_ET and F_PTRSD/noneSVM-PICC0.8330.8710.521
    SD/ComBatGBRMLinearComb0.8320.8600.507
    SD/noneSVM-RBFCorr0.8310.8350.519
    CE_ET and T2_PTRNone/ComBatENETNone0.8410.9220.492
    None/ComBatSVM-PLASSO0.8400.8960.505
    None/ComBatSVM-PlinearComb0.8390.9150.509
    CE, ET, A, ET and F, PTRSD/noneSVM-PICC0.8860.7120.414
    SD/noneSVM-PPCA0.8740.6990.398
    None/noneSVM-PICC0.8730.7640.433
    CE_ETSD/noneSVM-PICC0.8590.7890.472
    SD/noneSVM-PNone0.8560.8000.499
    SD/noneSVM-PLASSO0.8560.7860.494
    • Note:—ENET indicates multinomial elastic net; GBRM, generalized boosted regression mode; LASSO, least absolute shrinkage and selection operator; PCA, principal component analysis; SVM-P, support vector machine-polynomial kernel; SVM-RBF, support vector machine-Gaussian kernel; LinearComb, Linear combination filter; A, ADC; F, FLAIR; Corr, Correlation filter.

    • View popup
    Table 3:

    Top performing model for each feature set, along with the 3 other data-preprocessing results using the same modeling strategy (external data)

    Feature SetProcessingAlgorithmFeature SelectionmAUCLogLossBrier ScoreBest Model
    CE_ET and F_PTRaNone/noneSVM-PICC0.8180.9290.548False
    SD/nonebSVM-PICC0.8330.8710.521True
    None/ComBatSVM-PICC0.8081.0290.588False
    SD/ComBatSVM-PICC0.8170.9490.564False
    CE_ET and T2_PTRaNone/noneENETNone0.8080.9040.520False
    SD/noneENETNone0.8170.8670.499False
    None/ComBatbENETNone0.8410.9220.492True
    SD/ ComBatENETNone0.8350.8910.487False
    CE_ET, A_ET and F_PTRaNone/noneSVM-PICC0.8730.7640.433False
    SD/nonebSVM-PICC0.8860.7120.414True
    None/ComBatSVM-PICC0.8360.8720.520False
    SD/ ComBatSVM-PICC0.8730.7490.444False
    CE_ETaNone/noneSVM-PICC0.8190.8810.499False
    SD/NonebSVM-PICC0.8590.7890.472True
    None/ComBatSVM-PICC0.8210.9620.531False
    SD/ ComBatSVM-PICC0.8420.8500.512False
    • Note:—ENET indicates multinomial elastic net; SVM-P, support vector machine-polynomial kernel, LogLoss, ??????; A, ADC; F, FLAIR

    • ↵aRow indicates models using BIP only, but with otherwise the same modeling strategy.

    • ↵bRow indicates the top-performing model for each feature set.

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Girish Bathla, Neetu Soni, Ian T. Mark, Yanan Liu, Nicholas B. Larson, Blake A. Kassmeyer, Suyash Mohan, John C. Benson, Saima Rathore, Amit K. Agarwal
Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms
American Journal of Neuroradiology Jul 2024, DOI: 10.3174/ajnr.A8280

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Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms
Girish Bathla, Neetu Soni, Ian T. Mark, Yanan Liu, Nicholas B. Larson, Blake A. Kassmeyer, Suyash Mohan, John C. Benson, Saima Rathore, Amit K. Agarwal
American Journal of Neuroradiology Jul 2024, DOI: 10.3174/ajnr.A8280
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