Automatic quantification of calcifications in the coronary arteries and thoracic aorta on radiotherapy planning CT scans of Western and Asian breast cancer patients
Copyright © 2018 Elsevier B.V. All rights reserved..
PURPOSE: This study automatically quantified calcifications in coronary arteries (CAC) and thoracic aorta (TAC) on breast planning computed tomography (CT) scans and assessed its reproducibility compared to manual scoring.
MATERIAL AND METHODS: Dutch (n = 1199) and Singaporean (n = 1090) breast cancer patients with radiotherapy planning CT scan were included. CAC and TAC were automatically scored using deep learning algorithm. CVD risk categories were based on Agatson CAC: 0, 1-10, 11-100, 101-400 and >400. Reliability between automatic and manual scoring was assessed in 120 randomly selected CT scans from each population, with linearly weighted kappa for CAC categories and intraclass correlation coefficient for TAC.
RESULTS: Median age was higher in Dutch patients than Singaporean patients: 57 versus 52 years. CAC and TAC increased with age and were more present in Dutch patients than Singaporean patients: 24.2% versus 17.3% and 73.0% versus 62.2%, respectively. Reliability of CAC categories and TAC was excellent in the Netherlands (0.85 (95% confidence interval (CI) = 0.77-0.93) and 0.98 (95% CI = 0.96-0.98) respectively) and Singapore (0.90 (95% CI = 0.84-0.96) and 0.99 (95% CI = 0.98-0.99) respectively).
CONCLUSIONS: CAC and TAC prevalence was considerable and increased with age. Deep learning software is a reliable method to automatically measure CAC and TAC on radiotherapy breast CT scans.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2018 |
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| Erschienen: |
2018 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:127 |
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| Enthalten in: |
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology - 127(2018), 3 vom: 01. Juni, Seite 487-492 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gernaat, Sofie A M [VerfasserIn] |
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| Links: |
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| Themen: |
Automatic scoring |
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| Anmerkungen: |
Date Completed 07.11.2018 Date Revised 07.11.2018 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.radonc.2018.04.011 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM283510196 |
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| 041 | |a eng | ||
| 100 | 1 | |a Gernaat, Sofie A M |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Automatic quantification of calcifications in the coronary arteries and thoracic aorta on radiotherapy planning CT scans of Western and Asian breast cancer patients |
| 264 | 1 | |c 2018 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
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| 500 | |a Date Completed 07.11.2018 | ||
| 500 | |a Date Revised 07.11.2018 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2018 Elsevier B.V. All rights reserved. | ||
| 520 | |a PURPOSE: This study automatically quantified calcifications in coronary arteries (CAC) and thoracic aorta (TAC) on breast planning computed tomography (CT) scans and assessed its reproducibility compared to manual scoring | ||
| 520 | |a MATERIAL AND METHODS: Dutch (n = 1199) and Singaporean (n = 1090) breast cancer patients with radiotherapy planning CT scan were included. CAC and TAC were automatically scored using deep learning algorithm. CVD risk categories were based on Agatson CAC: 0, 1-10, 11-100, 101-400 and >400. Reliability between automatic and manual scoring was assessed in 120 randomly selected CT scans from each population, with linearly weighted kappa for CAC categories and intraclass correlation coefficient for TAC | ||
| 520 | |a RESULTS: Median age was higher in Dutch patients than Singaporean patients: 57 versus 52 years. CAC and TAC increased with age and were more present in Dutch patients than Singaporean patients: 24.2% versus 17.3% and 73.0% versus 62.2%, respectively. Reliability of CAC categories and TAC was excellent in the Netherlands (0.85 (95% confidence interval (CI) = 0.77-0.93) and 0.98 (95% CI = 0.96-0.98) respectively) and Singapore (0.90 (95% CI = 0.84-0.96) and 0.99 (95% CI = 0.98-0.99) respectively) | ||
| 520 | |a CONCLUSIONS: CAC and TAC prevalence was considerable and increased with age. Deep learning software is a reliable method to automatically measure CAC and TAC on radiotherapy breast CT scans | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Automatic scoring | |
| 650 | 4 | |a Breast cancer | |
| 650 | 4 | |a Coronary artery calcifications | |
| 650 | 4 | |a Radiotherapy planning CT scans | |
| 650 | 4 | |a Thoracic aorta calcifications | |
| 700 | 1 | |a van Velzen, Sanne G M |e verfasserin |4 aut | |
| 700 | 1 | |a Koh, Vicky |e verfasserin |4 aut | |
| 700 | 1 | |a Emaus, Marleen J |e verfasserin |4 aut | |
| 700 | 1 | |a Išgum, Ivana |e verfasserin |4 aut | |
| 700 | 1 | |a Lessmann, Nikolas |e verfasserin |4 aut | |
| 700 | 1 | |a Moes, Shinta |e verfasserin |4 aut | |
| 700 | 1 | |a Jacobson, Anouk |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Poey W |e verfasserin |4 aut | |
| 700 | 1 | |a Grobbee, Diederick E |e verfasserin |4 aut | |
| 700 | 1 | |a van den Bongard, Desiree H J |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Johann I |e verfasserin |4 aut | |
| 700 | 1 | |a Verkooijen, Helena M |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology |d 1993 |g 127(2018), 3 vom: 01. Juni, Seite 487-492 |w (DE-627)NLM012598305 |x 1879-0887 |7 nnns |
| 773 | 1 | 8 | |g volume:127 |g year:2018 |g number:3 |g day:01 |g month:06 |g pages:487-492 |
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