Does Dual-Energy Computed Tomography Material Decomposition Improve Radiomics Capability to Predict Survival in Head and Neck Squamous Cell Carcinoma Patients? A Preliminary Investigation
Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved..
OBJECTIVE: Our study objective was to explore the additional value of dual-energy CT (DECT) material decomposition for squamous cell carcinoma of the head and neck (SCCHN) survival prognostication.
METHODS: A group of 50 SCCHN patients (male, 37; female, 13; mean age, 63.6 ± 10.82 years) with baseline head and neck DECT between September 2014 and August 2020 were retrospectively included. Primary tumors were segmented, radiomics features were extracted, and DECT material decomposition was performed. We used independent train and validation datasets with cross-validation and 100 independent iterations to identify prognostic signatures applying elastic net (EN) and random survival forest (RSF). Features were ranked and intercorrelated according to their prognostic importance. We benchmarked the models against clinical parameters. Intraclass correlation coefficients were used to analyze the interreader variation.
RESULTS: The exclusively radiomics-trained models achieved similar ( P = 0.947) prognostic performance of area under the curve (AUC) = 0.784 (95% confidence interval [CI], 0.775-0.812) (EN) and AUC = 0.785 (95% CI, 0.759-0.812) (RSF). The additional application of DECT material decomposition did not improve the model's performance (EN, P = 0.594; RSF, P = 0.198). In the clinical benchmark, the top averaged AUC value of 0.643 (95% CI, 0.611-0.675) was inferior to the quantitative imaging-biomarker models ( P < 0.001). A combined imaging and clinical model did not improve the imaging-based models ( P > 0.101). Shape features revealed high prognostic importance.
CONCLUSIONS: Radiomics AI applications may be used for SCCHN survival prognostication, but the spectral information of DECT material decomposition did not improve the model's performance in our preliminary investigation.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:48 |
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| Enthalten in: |
Journal of computer assisted tomography - 48(2024), 2 vom: 13. März, Seite 323-333 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bernatz, Simon [VerfasserIn] |
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| Anmerkungen: |
Date Completed 18.03.2024 Date Revised 18.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1097/RCT.0000000000001551 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM365044164 |
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| 100 | 1 | |a Bernatz, Simon |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Does Dual-Energy Computed Tomography Material Decomposition Improve Radiomics Capability to Predict Survival in Head and Neck Squamous Cell Carcinoma Patients? A Preliminary Investigation |
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| 500 | |a Date Revised 18.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved. | ||
| 520 | |a OBJECTIVE: Our study objective was to explore the additional value of dual-energy CT (DECT) material decomposition for squamous cell carcinoma of the head and neck (SCCHN) survival prognostication | ||
| 520 | |a METHODS: A group of 50 SCCHN patients (male, 37; female, 13; mean age, 63.6 ± 10.82 years) with baseline head and neck DECT between September 2014 and August 2020 were retrospectively included. Primary tumors were segmented, radiomics features were extracted, and DECT material decomposition was performed. We used independent train and validation datasets with cross-validation and 100 independent iterations to identify prognostic signatures applying elastic net (EN) and random survival forest (RSF). Features were ranked and intercorrelated according to their prognostic importance. We benchmarked the models against clinical parameters. Intraclass correlation coefficients were used to analyze the interreader variation | ||
| 520 | |a RESULTS: The exclusively radiomics-trained models achieved similar ( P = 0.947) prognostic performance of area under the curve (AUC) = 0.784 (95% confidence interval [CI], 0.775-0.812) (EN) and AUC = 0.785 (95% CI, 0.759-0.812) (RSF). The additional application of DECT material decomposition did not improve the model's performance (EN, P = 0.594; RSF, P = 0.198). In the clinical benchmark, the top averaged AUC value of 0.643 (95% CI, 0.611-0.675) was inferior to the quantitative imaging-biomarker models ( P < 0.001). A combined imaging and clinical model did not improve the imaging-based models ( P > 0.101). Shape features revealed high prognostic importance | ||
| 520 | |a CONCLUSIONS: Radiomics AI applications may be used for SCCHN survival prognostication, but the spectral information of DECT material decomposition did not improve the model's performance in our preliminary investigation | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Böth, Ines |e verfasserin |4 aut | |
| 700 | 1 | |a Ackermann, Jörg |e verfasserin |4 aut | |
| 700 | 1 | |a Burck, Iris |e verfasserin |4 aut | |
| 700 | 1 | |a Mahmoudi, Scherwin |e verfasserin |4 aut | |
| 700 | 1 | |a Lenga, Lukas |e verfasserin |4 aut | |
| 700 | 1 | |a Martin, Simon S |e verfasserin |4 aut | |
| 700 | 1 | |a Scholtz, Jan-Erik |e verfasserin |4 aut | |
| 700 | 1 | |a Koch, Vitali |e verfasserin |4 aut | |
| 700 | 1 | |a Grünewald, Leon D |e verfasserin |4 aut | |
| 700 | 1 | |a Koch, Ina |e verfasserin |4 aut | |
| 700 | 1 | |a Stöver, Timo |e verfasserin |4 aut | |
| 700 | 1 | |a Wild, Peter J |e verfasserin |4 aut | |
| 700 | 1 | |a Winkelmann, Ria |e verfasserin |4 aut | |
| 700 | 1 | |a Vogl, Thomas J |e verfasserin |4 aut | |
| 700 | 1 | |a Pinto Dos Santos, Daniel |e verfasserin |4 aut | |
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