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Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?

Aleksander Marin

Aleksander Marin

  • Edinburgh Imaging facility Queens Medical Research Institute, University of EdinburghEdinburgh, United Kingdom
  • Faculty of Medicine, University of LjubljanaLjubljana, Slovenia
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Marin, Aleksander
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John T. Murchison

John T. Murchison

Department of Radiology, Royal Infirmary of EdinburghEdinburgh, United Kingdom
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Murchison, John T.
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Kristopher M. Skwarski

Kristopher M. Skwarski

Department of Respiratory Medicine, Royal Infirmary of EdinburghEdinburgh, United Kingdom
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Skwarski, Kristopher M.
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Adriana A.S. Tavares

Adriana A.S. Tavares

Edinburgh Imaging facility Queens Medical Research Institute, University of EdinburghEdinburgh, United Kingdom
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Tavares, Adriana A.S.
, 
Alison Fletcher

Alison Fletcher

Edinburgh Imaging facility Queens Medical Research Institute, University of EdinburghEdinburgh, United Kingdom
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Fletcher, Alison
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William A. Wallace

William A. Wallace

Department of Pathology, Royal Infirmary of EdinburghEdinburgh, United Kingdom
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Wallace, William A.
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Vladka Salapura

Vladka Salapura

Faculty of Medicine, University of LjubljanaLjubljana, Slovenia
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Salapura, Vladka
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Edwin J.R. van Beek

Edwin J.R. van Beek

Edinburgh Imaging facility Queens Medical Research Institute, University of EdinburghEdinburgh, United Kingdom
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van Beek, Edwin J.R.
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Saeed Mirsadraee

Saeed Mirsadraee

  • Edinburgh Imaging facility Queens Medical Research Institute, University of EdinburghEdinburgh, United Kingdom
  • National Heart and Lung Institute, Imperial College LondonLondon, United Kingdom
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Mirsadraee, Saeed
  
31 may 2021
Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?'s Cover Image
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Categoría del artículo: Research Article
Publicado en línea: 31 may 2021
Páginas: 259 - 267
Recibido: 26 mar 2021
Aceptado: 24 abr 2021
DOI: https://doi.org/10.2478/raon-2021-0024
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© 2021 Aleksander Marin, John T. Murchison, Kristopher M. Skwarski, Adriana A.S. Tavares, Alison Fletcher, William A. Wallace, Vladka Salapura, Edwin J.R. van Beek, Saeed Mirsadraee, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Dynamic PET images of a small pulmonary nodule in the left upper lobe and corresponding time activity curve (TAC) of the nodule displayed by PMOD 3.409 software.
Dynamic PET images of a small pulmonary nodule in the left upper lobe and corresponding time activity curve (TAC) of the nodule displayed by PMOD 3.409 software.

Figure 2

Patlak plot derived from the tissue time activity curve (TAC) and the input function (plasma TAC). The Patlak plot becomes linear after the tracer concentrations in reversible compartments and in plasma are in steady state.
Patlak plot derived from the tissue time activity curve (TAC) and the input function (plasma TAC). The Patlak plot becomes linear after the tracer concentrations in reversible compartments and in plasma are in steady state.

Figure 3

(A) standardized uptake value (SUVmax), (B) Dynamic 18F-FDG PET/CT influx constant (Ki), (C) Perfusion CT parameters blood volume (BV) and (D) Average arterial flow (AF) of the benign and malignant nodules.
(A) standardized uptake value (SUVmax), (B) Dynamic 18F-FDG PET/CT influx constant (Ki), (C) Perfusion CT parameters blood volume (BV) and (D) Average arterial flow (AF) of the benign and malignant nodules.

Figure 4

Comparison of AUCs on ROC curves (A) SUVmax, SURBLOOD, SURLIVER, PET grade and (B) SURLIVER, Ki, BV and AF. 95% CI, p values for SUVmax / SURBLOOD / SURLIVER / PET grade / Ki / BV / AF: 0.6264 to 1.000, 0.0157/ 0.6486 to 1.000, 0.0105/ 0.6550 to 1.000, 0.0105/ 0.507 to 0.956, 0.0756/ 0.602 to 1.000, 0.0300/ 0.6322 to 1.000, 0.0248/ 0.4342 to 0.9944, 0.1432.
Comparison of AUCs on ROC curves (A) SUVmax, SURBLOOD, SURLIVER, PET grade and (B) SURLIVER, Ki, BV and AF. 95% CI, p values for SUVmax / SURBLOOD / SURLIVER / PET grade / Ki / BV / AF: 0.6264 to 1.000, 0.0157/ 0.6486 to 1.000, 0.0105/ 0.6550 to 1.000, 0.0105/ 0.507 to 0.956, 0.0756/ 0.602 to 1.000, 0.0300/ 0.6322 to 1.000, 0.0248/ 0.4342 to 0.9944, 0.1432.

The demographic data, average nodule size, standardized uptake value (SUVmax), metabolic parameter relating to the pulmonary nodules through dynamic 18F-FDG PET/CT, and perfusion parameters through perfusion CT for the benign and malignant nodules

Benign Nodules Malignant Nodules p value
Total Number of nodules 9 12
Number of male patients (%) 5/9 (55 %) 6/12 (50 %)
Average patient age (years ± SD) 63 ± 7.5 68 ± 6.7
Average nodule size, range (mm) 18, 9–29 22, 12–30
Average SUVmax 18F-FDG PET/CT ± SD 2.2 ± 1.7 7.0 ± 4.5 0.0148
Number of nodules analysed for dynamic 18F-FDG PET/CT 7 9
Average Ki ± SD (min-1) 0.0057 ± 0.0071 0.0230 ± 0.0155 0.0311
Number of nodules analysed for perfusion CT parameters 7 10
Average BV ± SD (Patlak, ml/100ml) 11.6857 ± 6.7347 28.3400 ± 15.9672 0.0250
Average AF ± SD (ml/100g/min) 74.4571 ± 89.0321 89.2000 ± 49.8883 0.1613

Comparison of the diagnostic accuracy of different techniques and parameters with pre-specified and derived cut-point values for malignancy

Parameter Cut-point value/grade Sensitivity (95% CI) Specificity (95% CI) Accuracy
SUVmax Pre-specified Derived ≥ 2.5* ≥ 3.4 75.0% (46.8 to 91.1%) 75.0% (46.8 to 91.1%) 66.7% (35.4 to 87.9%) 88.9% (56.5 to 99.43%) 71.4% 81.0%
SURBLOOD Pre-Derived specified ≥ 1.56 83.3% (55.2 to 97.0%) 88.9% (56.5 to 99.4%) 85.7%
SURLIVER Pre-specified Derived ≥ 1.12 ≥ 1.65 83.3% (55.2 to 97.0%) 75% (46.8 to 91.1%) 66.7% (35.4 to 87.9%) 88.9% (56.5 to 99.4%) 76.2% 81.0%
SUV grade Pre-specified & Derived ≥ 3 66.7% (39.0 to 86.2%) 77.8% (45.3 to 96.0%) 71.4%
Ki Derived ≥ 0.01 min-1 77.8% (45.2 to 96.0%) 85.7% (48.7 to 99.3%) 81.3%
BV Derived ≥ 21 ml/100ml 70% (39.7 to 89.2%) 100% (64.6 to 100%) 82.4%
AF Derived ≥ 65 ml/100g/min 70% (39.7 to 89.2%) 85.7% (48.7 to 99.3%) 76.5%
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Medicina, Medicina Clínica, Medicina Interna, Hematología, oncología, Radiología
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