Ultra-low dose computed tomography protocols using spectral shaping for lung cancer screening : Comparison with low-dose for volumetric LungRADS classification
Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved..
PURPOSE: To compare Low-Dose Computed Tomography (LDCT) with four different Ultra-Low-Dose Computed Tomography (ULDCT) protocols for PN classification according to the Lung Reporting and Data System (LungRADS).
METHODS: Three hundred sixty-one participants of an ongoing lung cancer screening (LCS) underwent single-breath-hold double chest Computed Tomography (CT), including LDCT (120kVp, 25mAs; CTDIvol 1,62 mGy) and one ULDCT among: fully automated exposure control ("ULDCT1"); fixed tube-voltage and current according to patient size ("ULDCT2"); hybrid approach with fixed tube-voltage ("ULDCT3") and tube current automated exposure control ("ULDCT4"). Two radiologists (R1, R2) assessed LungRADS 2022 categories on LDCT, and then after 2 weeks on ULDCT using two different kernels (R1: Qr49ADMIRE 4; R2: Br49ADMIRE 3). Intra-subject agreement for LungRADS categories between LDCT and ULDCT was measured by the k-Cohen Index with Fleiss-Cohen weights.
RESULTS: LDCT-dominant PNs were detected in ULDCT in 87 % of cases on Qr49ADMIRE 4 and 88 % on Br49ADMIRE 3. The intra-subject agreement was: κULDCT1 = 0.89 [95 %CI 0.82-0.96]; κULDCT2 = 0.90 [0.81-0.98]; κULDCT3 = 0.91 [0.84-0.99]; κULDCT4 = 0.88 [0.78-0.97] on Qr49ADMIRE 4, and κULDCT1 = 0.88 [0.80-0.95]; κULDCT2 = 0.91 [0.86-0.96]; κULDCT3 = 0.87 [0.78-0.95]; and κULDCT4 = 0.88 [0.82-0.94] on Br49ADMIRE 3. LDCT classified as LungRADS 4B were correctly identified as LungRADS 4B at ULDCT3, with the lowest radiation exposure among the tested protocols (median effective doses were 0.31, 0.36, 0.27 and 0.37 mSv for ULDCT1, ULDCT2, ULDCT3, and ULDCT4, respectively).
CONCLUSIONS: ULDCT by spectral shaping allows the detection and characterization of PNs with an excellent agreement with LDCT and can be proposed as a feasible approach in LCS.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:161 |
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| Enthalten in: |
European journal of radiology - 161(2023) vom: 01. Apr., Seite 110760 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Milanese, Gianluca [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 20.03.2023 Date Revised 20.03.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ejrad.2023.110760 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM353839744 |
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| 041 | |a eng | ||
| 100 | 1 | |a Milanese, Gianluca |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Ultra-low dose computed tomography protocols using spectral shaping for lung cancer screening |b Comparison with low-dose for volumetric LungRADS classification |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
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| 500 | |a Date Completed 20.03.2023 | ||
| 500 | |a Date Revised 20.03.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved. | ||
| 520 | |a PURPOSE: To compare Low-Dose Computed Tomography (LDCT) with four different Ultra-Low-Dose Computed Tomography (ULDCT) protocols for PN classification according to the Lung Reporting and Data System (LungRADS) | ||
| 520 | |a METHODS: Three hundred sixty-one participants of an ongoing lung cancer screening (LCS) underwent single-breath-hold double chest Computed Tomography (CT), including LDCT (120kVp, 25mAs; CTDIvol 1,62 mGy) and one ULDCT among: fully automated exposure control ("ULDCT1"); fixed tube-voltage and current according to patient size ("ULDCT2"); hybrid approach with fixed tube-voltage ("ULDCT3") and tube current automated exposure control ("ULDCT4"). Two radiologists (R1, R2) assessed LungRADS 2022 categories on LDCT, and then after 2 weeks on ULDCT using two different kernels (R1: Qr49ADMIRE 4; R2: Br49ADMIRE 3). Intra-subject agreement for LungRADS categories between LDCT and ULDCT was measured by the k-Cohen Index with Fleiss-Cohen weights | ||
| 520 | |a RESULTS: LDCT-dominant PNs were detected in ULDCT in 87 % of cases on Qr49ADMIRE 4 and 88 % on Br49ADMIRE 3. The intra-subject agreement was: κULDCT1 = 0.89 [95 %CI 0.82-0.96]; κULDCT2 = 0.90 [0.81-0.98]; κULDCT3 = 0.91 [0.84-0.99]; κULDCT4 = 0.88 [0.78-0.97] on Qr49ADMIRE 4, and κULDCT1 = 0.88 [0.80-0.95]; κULDCT2 = 0.91 [0.86-0.96]; κULDCT3 = 0.87 [0.78-0.95]; and κULDCT4 = 0.88 [0.82-0.94] on Br49ADMIRE 3. LDCT classified as LungRADS 4B were correctly identified as LungRADS 4B at ULDCT3, with the lowest radiation exposure among the tested protocols (median effective doses were 0.31, 0.36, 0.27 and 0.37 mSv for ULDCT1, ULDCT2, ULDCT3, and ULDCT4, respectively) | ||
| 520 | |a CONCLUSIONS: ULDCT by spectral shaping allows the detection and characterization of PNs with an excellent agreement with LDCT and can be proposed as a feasible approach in LCS | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Lung cancer screening | |
| 650 | 4 | |a LungRADS | |
| 650 | 4 | |a Pulmonary nodules | |
| 650 | 4 | |a Ultra-low dose computed tomography | |
| 700 | 1 | |a Ledda, Roberta Eufrasia |e verfasserin |4 aut | |
| 700 | 1 | |a Sabia, Federica |e verfasserin |4 aut | |
| 700 | 1 | |a Ruggirello, Margherita |e verfasserin |4 aut | |
| 700 | 1 | |a Sestini, Stefano |e verfasserin |4 aut | |
| 700 | 1 | |a Silva, Mario |e verfasserin |4 aut | |
| 700 | 1 | |a Sverzellati, Nicola |e verfasserin |4 aut | |
| 700 | 1 | |a Marchianò, Alfonso Vittorio |e verfasserin |4 aut | |
| 700 | 1 | |a Pastorino, Ugo |e verfasserin |4 aut | |
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