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Detection and localization of hyperfunctioning parathyroid glands on [18F]fluorocholine PET/ CT using deep learning – model performance and comparison to human experts

Leon Jarabek

Leon Jarabek

Department of Radiology, General Hospital Novo MestoNovo Mesto, Slovenia
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Jarabek, Leon
, 
Jan Jamsek

Jan Jamsek

Department for Nuclear Medicine, University Medical CentreLjubljana, Slovenia
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Jamsek, Jan
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Anka Cuderman

Anka Cuderman

Department for Nuclear Medicine, University Medical CentreLjubljana, Slovenia
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Cuderman, Anka
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Sebastijan Rep

Sebastijan Rep

  • Department for Nuclear Medicine, University Medical CentreLjubljana, Slovenia
  • Faculty of Health Sciences, University of LjubljanaLjubljana, Slovenia
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Rep, Sebastijan
, 
Marko Hocevar

Marko Hocevar

  • Department of Surgical Oncology, Institute of OncologyLjubljana, Slovenia
  • Faculty of Medicine, University of LjubljanaLjubljana, Slovenia
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Hocevar, Marko
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Tomaz Kocjan

Tomaz Kocjan

  • Faculty of Medicine, University of LjubljanaLjubljana, Slovenia
  • Department for Endocrinology, Diabetes and Metabolic Diseases, University Medical CentreLjubljana, Slovenia
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Kocjan, Tomaz
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Mojca Jensterle

Mojca Jensterle

  • Faculty of Medicine, University of LjubljanaLjubljana, Slovenia
  • Department for Endocrinology, Diabetes and Metabolic Diseases, University Medical CentreLjubljana, Slovenia
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Jensterle, Mojca
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Ziga Spiclin

Ziga Spiclin

Faculty of Electrical Engineering, University of LjubljanaLjubljana, Slovenia
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Spiclin, Ziga
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Ziga Macek Lezaic

Ziga Macek Lezaic

Rožna dolinaLjubljana, Slovenia
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Macek Lezaic, Ziga
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Filip Cvetko

Filip Cvetko

Faculty of Medicine, University of LjubljanaLjubljana, Slovenia
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Cvetko, Filip
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Luka Lezaic

Luka Lezaic

  • Department for Nuclear Medicine, University Medical CentreLjubljana, Slovenia
  • Faculty of Medicine, University of LjubljanaLjubljana, Slovenia
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Lezaic, Luka
  
13 déc. 2022
Detection and localization of hyperfunctioning parathyroid glands on [18F]fluorocholine PET/ CT using deep learning – model performance and comparison to human experts's Cover Image
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Catégorie d'article: Research Article
Publié en ligne: 13 déc. 2022
Pages: 440 - 452
Reçu: 21 avr. 2022
Accepté: 22 août 2022
DOI: https://doi.org/10.2478/raon-2022-0037
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© 2022 Leon Jarabek, Jan Jamsek, Anka Cuderman, Sebastijan Rep, Marko Hocevar, Tomaz Kocjan, Mojca Jensterle, Ziga Spiclin, Ziga Macek Lezaic, Filip Cvetko, Luka Lezaic, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

mPETResnet10 architecture. First, PET-CT images are fed into UNet with a single channel output and tanh+1 activation function. This output is the PET mask. This mask is elementwise multiplied with PET image to produce a masked PET image. Masked PET is concatenated with the original CT and the masked PET-CT is fed into the ResNet10 classifier. Gray boxes represent deep-learning models, coloured boxes represent data, and circles represent operations of tanh+1, multiplication (mul) by element and concatenation (concat).
mPETResnet10 architecture. First, PET-CT images are fed into UNet with a single channel output and tanh+1 activation function. This output is the PET mask. This mask is elementwise multiplied with PET image to produce a masked PET image. Masked PET is concatenated with the original CT and the masked PET-CT is fed into the ResNet10 classifier. Gray boxes represent deep-learning models, coloured boxes represent data, and circles represent operations of tanh+1, multiplication (mul) by element and concatenation (concat).

Figure 2

Example of novel masked-PET Resnet10 model (mRN10) masking of PET signal in a subject with parathyroid adenoma in the region of lower right parathyroid gland (black arrow in row c). Each row represents a different slice through the preprocessed [18F]fluorocholine PET/CT (FCH-PET) images ((A) – mandibular region, (B) – upper neck region (C) – lower neck region containing parathyroid adenoma). The first column shows a pre-processed PET/CT image (64 × 64 × 32 matrix), where colours toward the “warm” (red) part of the spectrum indicate higher PET signal and colours toward the “cool” (blue) part of the spectrum indicate lower PET signal. The second column shows the mask, where regions coloured toward the red part of the spectrum have higher weights (non-masked) and regions toward the yellow part of the spectrum have lower weights (masked). The third column represents the final masked PET/CT images computed by multiplying the mask with the original PET/ CT. The image was correctly classified as containing the adenoma in the lower right region.
Example of novel masked-PET Resnet10 model (mRN10) masking of PET signal in a subject with parathyroid adenoma in the region of lower right parathyroid gland (black arrow in row c). Each row represents a different slice through the preprocessed [18F]fluorocholine PET/CT (FCH-PET) images ((A) – mandibular region, (B) – upper neck region (C) – lower neck region containing parathyroid adenoma). The first column shows a pre-processed PET/CT image (64 × 64 × 32 matrix), where colours toward the “warm” (red) part of the spectrum indicate higher PET signal and colours toward the “cool” (blue) part of the spectrum indicate lower PET signal. The second column shows the mask, where regions coloured toward the red part of the spectrum have higher weights (non-masked) and regions toward the yellow part of the spectrum have lower weights (masked). The third column represents the final masked PET/CT images computed by multiplying the mask with the original PET/ CT. The image was correctly classified as containing the adenoma in the lower right region.

Figure 3

Some examples of masking of hyperactive parathyroid tissue (HPTT), which is indicated by an arrow in column (I). The images are shown in the same format as in Figure 2. Rows (D), (F) and (G) represent the only 3 cases where HPTT was not completely masked.
Some examples of masking of hyperactive parathyroid tissue (HPTT), which is indicated by an arrow in column (I). The images are shown in the same format as in Figure 2. Rows (D), (F) and (G) represent the only 3 cases where HPTT was not completely masked.

Confusion matrices for CPr (A) and CLoc (B) for both RN10 and mRN10 models_ Note that the confusion matrices for CLoc have more samples (360 in total), as they were computed by summing the confusion matrices for each of the three included locations (UL, LL, LR)

CPr task with RN10 CPr task with mRN10
HPTT present HPTT present not sum HPTT present HPTT present not sum
Model HPTT present output 79 8 87 Model HPTT present output 90 11 101
Model output HPTT not present 20 13 33 Model output HPTT not present 9 10 19
sum 99 21 120 sum 99 21 120

Diagnostic performance metrics of RN10 and mRN10 as well as p-values as determined by McNemar test comparing both models for each task (except AUCROC)

CPr RN10 CPr mRN10 CPr p-value CLoc RN10 CLoc mRN10 CLoc p-value
Sensitivity [95% CI] 0.800 [0.719; 0.877] 0.909 [0.852; 0.965] 0.028 0.365 [0.268; 0.460] 0.552 [0.453; 0.652] 0.018
Specificity [95% CI] 0.619 [0.411; 0.827] 0.476 [0.263; 0.690] 0.257 0.807 [0.759; 0.854] 0.811 [0.763; 0.858] 0.910
Positive predictive value [95% CI] 0.908 [0.847; 0.969] 0.891 [0.830; 0.951] 0.507 0.407 [0.303; 0.511] 0.515 [0.418; 0.611] 0.089
Negative predictive value [95% CI] 0.394 [0.227; 0.560] 0.526 [0.302; 0.751] 0.205 0.777 [0.728; 0.827] 0.833 [0.787; 0.878] 0.021
Accuracy [95% CI] 0.767 [0.681; 0.839] 0.833 [0.756; 0.895] 0.050 0.689 [0.638; 0.736] 0.742 [0.693 0.786] 0.031
AUCROC 0.815 0.849 / 0.702 0.770 /

Comparison of mRN10 and human performance for the CLoc task_ p-values were determined by using the McNemar test

CLoc mRN10 CLoc human p-value
Sensitivity [95% CI] 0.552 [0.453; 0.652] 0.917 [0.857; 0.958] < 0.001
Specificity [95% CI] 0.811 [0.763; 0.858] 0.997 [0.986; 0.999] < 0.001
Positive predictive value [95% CI] 0.515 [0.418; 0.611] 0.992 [0.945; 0.999] < 0.001
Negative predictive value [95% CI] 0.833 [0.787; 0.878] 0.972 [0.952; 0.984] < 0.001
Accuracy [95% CI] 0.742 [0.693; 0.786] 0.977 [0.960; 0.988] < 0.001

Performance of several models on CPr task

Model name mRN10 RN10 Resnet50 Resnet101 Densenet101 PreActResnet101 WideResnet101
parameters # Trainable (millions) 33.5 14.3 46.2 85.2 112.9 85.2 85.2
Optimal learning initial rate 0.0136 0.0136 2.15*10-3 1.47*10-4 0.316 1.47*10-4 2.15*10-3
Mean CPr AUCROC [95% CI] 0.850 [0.734; 0.998] 0.812 [0.716; 0.994] 0.754 [0.624; 0.980] 0.527 [0.410; 0.639] 0.703 [0.606; 0.905] 0.739 [0.486; 0.998] 0.752 [0.653; 0.966]
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Médecine, Médecine clinique, Médecine interne, Hématologie, oncologie, Radiologie
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