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Research ArticleHead & Neck
Open Access

Clinical Validation and Extension of an Automated, Deep Learning–Based Algorithm for Quantitative Sinus CT Analysis

C.J. Massey, L. Ramos, D.M. Beswick, V.R. Ramakrishnan and S.M. Humphries
American Journal of Neuroradiology September 2022, 43 (9) 1318-1324; DOI: https://doi.org/10.3174/ajnr.A7616
C.J. Massey
aFrom the Department of Otolaryngology-Head and Neck Surgery (C.J.M., L.R., V.R.R.), University of Colorado School of Medicine, Aurora, Colorado
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  • ORCID record for C.J. Massey
L. Ramos
aFrom the Department of Otolaryngology-Head and Neck Surgery (C.J.M., L.R., V.R.R.), University of Colorado School of Medicine, Aurora, Colorado
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D.M. Beswick
bDepartment of Otolaryngology–Head and Neck Surgery (D.M.B.), University of California–Los Angeles School of Medicine, Los Angeles, California
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V.R. Ramakrishnan
aFrom the Department of Otolaryngology-Head and Neck Surgery (C.J.M., L.R., V.R.R.), University of Colorado School of Medicine, Aurora, Colorado
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S.M. Humphries
cQuantitative Imaging Laboratory (S.M.H.), Department of Radiology, National Jewish Health, Denver, Colorado
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Article Figures & Data

Figures

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  • FIG 1.
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    FIG 1.

    3D reconstructed sinus CT scan with bony overlay (gray) and segmentation of the paranasal sinuses (colored) produced by the algorithm.

  • FIG 2.
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    FIG 2.

    Illustration demonstrating application of osteitis segmentation rind. Left, Coronal CT with total GOSS score of 32. Right, Segmentation rind overlaid in red. Forty-eight percent of the rind is occupied by CT voxels corresponding to bone.

  • FIG 3.
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    FIG 3.

    Flow diagram demonstrating the cohort-selection process.

  • FIG 4.
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    FIG 4.

    Scatterplot demonstrating algorithm-generated %SO versus LMS. The gray shaded area represents a 95% confidence interval for the regression line (ρ = 0.85, P < .001).

  • FIG 5.
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    FIG 5.

    Algorithm-derived %OST versus total GOSS as stratified by surgical status. Overall ρ = 0.48 (P < .05). For surgically naïve patients, ρ = 0.29 (P = .08), and for postoperative subjects, ρ = 0.59 (P < .001).

Tables

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    Table 1:

    Demographics of the study population

    Cohort Featuresn = 88 (%, or range)
    Female (%)52 (59)
    Avg. age (range) (yr)48.5 (22–78)
    White (%)70 (80)
    Polyps (%)59 (67)
    Asthma (%)53 (60)
    AERD (%)8 (9)
    Never smoker (%)55 (62)
    Prior surgery (%)48 (55)
    Avg. absolute serum eosinophils × 109/L0.31 (0–2.6)
    Avg. tissue eosinophils per HPF80 (0–413)
    Avg. SNOT-2249.6 (15–95)
    Avg. LKS6.8 (1–12)
    Avg. LMS13.5 (2–24)
    Avg. GOSS2.7 (0–35)
    Avg. sinus cavity volume (mL)60.8 (24.8–109.0)
    Avg. %SO60.1 (20.7–99.6)
    Avg. mHU10.5 (−139.2 to +57.4)
    Avg. %OST41.4 (32.1–52.2)
    • Note:—AERD indicates aspirin-exacerbated respiratory disease; HPF, high-power field; Avg., average.

    • View popup
    Table 2:

    CT scanner metrics and acquisition protocols used for the included scans

    Model (No.)Reconstruction Kernel (No.)Tube Potential (No.)Tube Current (No.)Pitch (No.)Axial Section Thickness (No.)Axial Section Spacing (No.)
    Siemens (n = 77)
     Sensation 64 (42)H31s (2)100 kVp (2)Modulated (22)0.7 (43)1.0 mm (77)0.6 mm (2)
     Definition (4)H60f (2)120 kVp (75)91 mA (41)0.8 (19)0.9 mm (63)
     Definition AS (3)H60s (1)100 mA (8)0.9 (14)1.0 mm (12)
     Definition AS+ (4)H70h (54)108 mA (6)1.0 (1)
     Definition Flash (18)J40s\\2 (1)
     Biograph 40 (6)J70h\\2 (17)
    GE Healthcare (n = 5)
     LightSpeed Pro 16 (1)Bone (5)120 kVp (5)110 mA (1)0.50–0.75 (2)0.625 mm (3)0.625 mm (3)
     LightSpeed VCT (1)150 mA (1)0.90–1.0 (3)1.25 mm (2)1.0 mm (1)
     Optima CT540 (2)160 mA (1)1.25 mm (1)
     Optima CT660 (1)>200 mA (2)
    Philips Healthcare (n = 4)
     Brilliance 64 (4)YC (3)120 kVp (4)85 mA (1)1.0 (4)1.0 mm (4)0.8 mm (1)
    YD (1)119 mA (2)0.9 mm (3)
    170 mA (1)
    Toshiba/Canon (n = 2)
     Aquilion ONE (1)FC30 (2)120 kVp (2)150 mA (1)1.0 (2)1.0 mm (2)0.8 mm (1)
     Aquilion Prime (1)200 mA (1)1.0 mm (1)
    • View popup
    Table 3:

    Correlation (ρ) values for CT-derived metrics and clinical parameters

    Clinical Parameter(%SO)LMSmHU of Opacified Regions
    LKS0.58a0.58a0.48a
    SNOT-220.05−0.020.08
    Absolute serum eosinophils0.26b0.34b0.14
    Tissue eosinophil count0.31b0.33b0.22b
    • ↵a P < .001.

    • ↵b P < .05.

    • View popup
    Table 4:

    Correlation (ρ) values for percentage sinus opacification stratified by sinus cavity and clinical parameters

    Clinical ParameterLeft MaxillaryRight MaxillaryAnterior EthmoidPosterior EthmoidFrontalSphenoid
    LKS0.43a0.34b0.64a0.56a0.53a0.35b
    SNOT-220.100.060.06−0.030.10−0.10
    Absolute serum eosinophils0.030.150.23b0.26b0.210.18
    Tissue eosinophil count0.130.210.37a0.39a0.23b0.11
    • ↵a P < .001.

    • ↵b P < .05.

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American Journal of Neuroradiology: 43 (9)
American Journal of Neuroradiology
Vol. 43, Issue 9
1 Sep 2022
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Cite this article
C.J. Massey, L. Ramos, D.M. Beswick, V.R. Ramakrishnan, S.M. Humphries
Clinical Validation and Extension of an Automated, Deep Learning–Based Algorithm for Quantitative Sinus CT Analysis
American Journal of Neuroradiology Sep 2022, 43 (9) 1318-1324; DOI: 10.3174/ajnr.A7616

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Automated Deep Learning for Sinus CT Analysis
C.J. Massey, L. Ramos, D.M. Beswick, V.R. Ramakrishnan, S.M. Humphries
American Journal of Neuroradiology Sep 2022, 43 (9) 1318-1324; DOI: 10.3174/ajnr.A7616
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