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Research ArticleClinical Investigation
Open Access

Clinical Evaluation of Deep Learning for Tumor Delineation on 18F-FDG PET/CT of Head and Neck Cancer

David G. Kovacs, Claes N. Ladefoged, Kim F. Andersen, Jane M. Brittain, Charlotte B. Christensen, Danijela Dejanovic, Naja L. Hansen, Annika Loft, Jørgen H. Petersen, Michala Reichkendler, Flemming L. Andersen and Barbara M. Fischer
Journal of Nuclear Medicine April 2024, 65 (4) 623-629; DOI: https://doi.org/10.2967/jnumed.123.266574
David G. Kovacs
1Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;
2Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
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Claes N. Ladefoged
1Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;
3Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark;
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Kim F. Andersen
1Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;
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Jane M. Brittain
1Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;
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Charlotte B. Christensen
4Department of Clinical Physiology and Nuclear Medicine, Herlev Hospital, University of Copenhagen, Copenhagen, Denmark;
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Danijela Dejanovic
1Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;
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Naja L. Hansen
1Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;
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Annika Loft
1Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;
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Jørgen H. Petersen
5Section of Biostatistics, Institute of Public Health, Faculty of Health Sciences, University of Copenhagen, Denmark; and
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Michala Reichkendler
1Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;
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Flemming L. Andersen
1Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;
2Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
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Barbara M. Fischer
1Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark;
2Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;
6PET Centre, School of Biomedical Engineering and Imaging Science, King’s College London, London, United Kingdom
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  • FIGURE 1.
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    FIGURE 1.

    Summary of study. Causes of exclusion were data incompleteness and failure to pass visual validation. No external scans were excluded to these criteria. Model training used 805 patients (835 scans). Each scan represented unique patient in steps 2, 3, and 4.

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    FIGURE 2.

    Study design comparing expert IOV and AI-to-expert variability. Delineations are exemplified on random patient’s axial 18F-FDG PET/CT intravenous contrast scan slice.

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    FIGURE 3.

    (A) Comparison of 5 implemented methods trained on 196 patient scans based on DSC. Values above boxes are mean followed by 95% CI in parentheses, with P values below. nnU-Net achieved highest DSC and was further analyzed (denoted AI). (B) Paired comparison of AI-to-expert variability and expert IOV on 64 independent internal test scans. (C) Comparison of AI-to-expert variability on 196 internal (same as nnU-Net in A) and 125 external patients. All 3 comparisons used expert-delineated tumor volumes as reference. Values above boxes in B and C are mean difference followed by 95% CI in parentheses, with P values below. Rhombus shape indicates mean value, and central line represents median. Boxes enclose interquartile range. Whiskers extend to most significant measurement no further than 1.5 × interquartile range from hinge. Data beyond whiskers are plotted individually. Notch roughly represents 95% CI around median. (D) DSC, F1 score (F1), and Hausdorff distance (HD) summary statistics in mean ± SD. Hausdorff distance is undefined when expert or AI includes no volume. Hence, numbers marked with *, **, ***, and **** were based on n = 195, 186, 61, and 63, respectively.

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    FIGURE 4.

    Clinical scan delineated by expert (reference) and by AI, along with AI-to-expert agreement (DSC, 0.92). Shown are axial images of 50-y-old man with HNC of rhinopharynx. 18F-FDG PET/CT with intravenous contrast agent showed greatly increased activity corresponding to large tumor process in right rhinopharynx, crossing midline and growing frontally into cavum nasi on right, intracranially on right, medially in fossa media, and along dura laterally. In addition, multiple lymph nodes in neck had greatly increased activity bilaterally. AI correctly avoided including physiologically active areas such as saliva, metal artifact–induced activity, nose tip, brain, and optic nerve. HU = Hounsfield units.

  • FIGURE 5.
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    FIGURE 5.

    Tumor-volume–derived biomarkers based on AI delineation in good agreement with experts. Shown are Bland–Altman plots for agreement between AI and expert PET GTV–derived biomarkers. Shaded regions and dashed lines represent LoA and mean bias, respectively. Limits of agreement for SUVmax were not included because of violation of normality assumption. To improve visualization, single outlier with extreme SUVmax of 151 in B was excluded from plot.

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    TABLE 1.

    Summary of Patient Demographics and Key Clinical Characteristics in Each Dataset

    CharacteristicTraining and validationMethod comparisonInternal clinical evaluationExternal clinical evaluation
    Number of patients80519664125
    Age (y)62.6 ± 10.562.8 ± 9.365.6 ± 10.064.2 ± 8.6
    Weight (kg)75.0 ± 18.176.3 ± 18.771.8 ± 16.974.8 ± 18.6
    Dose (MBq)300.4 ± 72.1306.8 ± 72.9283.1 ± 67.6289.1 ± 61.5
    Injection-to-scan time (h)1.1 ± 21.1 ± 31.1 ± 11.1 ± 1
    Sex584 (73%) men141 (72%) men42 (66%) men90 (72%) men
    Oropharynx301/805 (37%)78/196 (40%)23/64 (40%)76/125 (61%)
    Larynx123/805 (15%)30/196 (15%)9/64 (15%)7/125 (6%)
    Cavum oris87/805 (11%)22/196 (11%)7/64 (11%)8/125 (6%)
    Hypopharynx84/805 (10%)21/196 (11%)5/64 (8%)22/125 (18%)
    Rhinopharynx50/805 (6%)12/196 (6%)6/64 (9%)5/125 (4%)
    Vestibulum nasi or sinus paranasalis33/805 (4%)6/196 (3%)3/64 (2%)1/125 (1%)
    Unknown primary with lymph nodes18/805 (2%)3/196 (2%)0/64 (0%)6/125 (5%)
    Salivary gland tumor10/805 (1%)0/196 (0%)0/64 (0%)0/125 (0%)
    Unspecified99/805 (12%)24/196 (12%)12/64 (19%)0/125 (0%)
    • Qualitative data are number and percentage; continuous data are mean and SD (total n = 1,190). There was no difference in age between men and women in any of 4 datasets (all P > 0.05). Mean age was higher in internal clinical evaluation test set than in training data (P = 0.01). At same time, there was no evidence of age differences from training data in 2 other test sets (P = 0.74 for method comparison test set and P = 0.10 for external test set).

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    TABLE 2.

    Summary of Datasets for Model Training, Validation, and Independent Testing

    SetStartEndSourcenPurpose
    Training by cross-validation
     Training folds (80%)2014 January2019 JuneInternal668Train model
     Validation fold (20%)2014 January2019 JuneInternal167Validate model
    Test
     12014 January2019 JuneInternal196Compare models performance
     22019 July2019 DecemberInternal64Compare models with experts’ IOV
     32018 January2019 DecemberExternal125Compare internal with external model performance

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Journal of Nuclear Medicine: 65 (4)
Journal of Nuclear Medicine
Vol. 65, Issue 4
April 1, 2024
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Clinical Evaluation of Deep Learning for Tumor Delineation on 18F-FDG PET/CT of Head and Neck Cancer
David G. Kovacs, Claes N. Ladefoged, Kim F. Andersen, Jane M. Brittain, Charlotte B. Christensen, Danijela Dejanovic, Naja L. Hansen, Annika Loft, Jørgen H. Petersen, Michala Reichkendler, Flemming L. Andersen, Barbara M. Fischer
Journal of Nuclear Medicine Apr 2024, 65 (4) 623-629; DOI: 10.2967/jnumed.123.266574

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Clinical Evaluation of Deep Learning for Tumor Delineation on 18F-FDG PET/CT of Head and Neck Cancer
David G. Kovacs, Claes N. Ladefoged, Kim F. Andersen, Jane M. Brittain, Charlotte B. Christensen, Danijela Dejanovic, Naja L. Hansen, Annika Loft, Jørgen H. Petersen, Michala Reichkendler, Flemming L. Andersen, Barbara M. Fischer
Journal of Nuclear Medicine Apr 2024, 65 (4) 623-629; DOI: 10.2967/jnumed.123.266574
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Keywords

  • 18F-FDG PET/CT
  • head and neck cancer
  • tumor volume delineation
  • imaging biomarkers
  • deep learning
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