Quantitative US Delta Radiomics to Predict Radiation Response in Individuals with Head and Neck Squamous Cell Carcinoma
Purpose To investigate the role of quantitative US (QUS) radiomics data obtained after the 1st week of radiation therapy (RT) in predicting treatment response in individuals with head and neck squamous cell carcinoma (HNSCC). Materials and Methods This prospective study included 55 participants (21 with complete response [median age, 65 years {IQR: 47-80 years}, 20 male, one female; and 34 with incomplete response [median age, 59 years {IQR: 39-79 years}, 33 male, one female) with bulky node-positive HNSCC treated with curative-intent RT from January 2015 to October 2019. All participants received 70 Gy of radiation in 33-35 fractions over 6-7 weeks. US radiofrequency data from metastatic lymph nodes were acquired prior to and after 1 week of RT. QUS analysis resulted in five spectral maps from which mean values were extracted. We applied a gray-level co-occurrence matrix technique for textural analysis, leading to 20 QUS texture and 80 texture-derivative parameters. The response 3 months after RT was used as the end point. Model building and evaluation utilized nested leave-one-out cross-validation. Results Five delta (Δ) parameters had statistically significant differences (P < .05). The support vector machines classifier achieved a sensitivity of 71% (15 of 21), a specificity of 76% (26 of 34), a balanced accuracy of 74%, and an area under the receiver operating characteristic curve of 0.77 on the test set. For all the classifiers, the performance improved after the 1st week of treatment. Conclusion A QUS Δ-radiomics model using data obtained after the 1st week of RT from individuals with HNSCC predicted response 3 months after treatment completion with reasonable accuracy. Keywords: Computer-Aided Diagnosis (CAD), Ultrasound, Radiation Therapy/Oncology, Head/Neck, Radiomics, Quantitative US, Radiotherapy, Head and Neck Squamous Cell Carcinoma, Machine Learning Clinicaltrials.gov registration no. NCT03908684 Supplemental material is available for this article. © RSNA, 2024.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:6 |
|---|---|
| Enthalten in: |
Radiology. Imaging cancer - 6(2024), 2 vom: 02. März, Seite e230029 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Osapoetra, Laurentius Oscar [VerfasserIn] |
|---|
| Links: |
|---|
| Anmerkungen: |
Date Completed 26.02.2024 Date Revised 05.04.2024 published: Print ClinicalTrials.gov: NCT03908684 Citation Status MEDLINE |
|---|
| doi: |
10.1148/rycan.230029 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM368812952 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM368812952 | ||
| 003 | DE-627 | ||
| 005 | 20240405233624.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240229s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1148/rycan.230029 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1366.xml |
| 035 | |a (DE-627)NLM368812952 | ||
| 035 | |a (NLM)38391311 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Osapoetra, Laurentius Oscar |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Quantitative US Delta Radiomics to Predict Radiation Response in Individuals with Head and Neck Squamous Cell Carcinoma |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 26.02.2024 | ||
| 500 | |a Date Revised 05.04.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a ClinicalTrials.gov: NCT03908684 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Purpose To investigate the role of quantitative US (QUS) radiomics data obtained after the 1st week of radiation therapy (RT) in predicting treatment response in individuals with head and neck squamous cell carcinoma (HNSCC). Materials and Methods This prospective study included 55 participants (21 with complete response [median age, 65 years {IQR: 47-80 years}, 20 male, one female; and 34 with incomplete response [median age, 59 years {IQR: 39-79 years}, 33 male, one female) with bulky node-positive HNSCC treated with curative-intent RT from January 2015 to October 2019. All participants received 70 Gy of radiation in 33-35 fractions over 6-7 weeks. US radiofrequency data from metastatic lymph nodes were acquired prior to and after 1 week of RT. QUS analysis resulted in five spectral maps from which mean values were extracted. We applied a gray-level co-occurrence matrix technique for textural analysis, leading to 20 QUS texture and 80 texture-derivative parameters. The response 3 months after RT was used as the end point. Model building and evaluation utilized nested leave-one-out cross-validation. Results Five delta (Δ) parameters had statistically significant differences (P < .05). The support vector machines classifier achieved a sensitivity of 71% (15 of 21), a specificity of 76% (26 of 34), a balanced accuracy of 74%, and an area under the receiver operating characteristic curve of 0.77 on the test set. For all the classifiers, the performance improved after the 1st week of treatment. Conclusion A QUS Δ-radiomics model using data obtained after the 1st week of RT from individuals with HNSCC predicted response 3 months after treatment completion with reasonable accuracy. Keywords: Computer-Aided Diagnosis (CAD), Ultrasound, Radiation Therapy/Oncology, Head/Neck, Radiomics, Quantitative US, Radiotherapy, Head and Neck Squamous Cell Carcinoma, Machine Learning Clinicaltrials.gov registration no. NCT03908684 Supplemental material is available for this article. © RSNA, 2024 | ||
| 650 | 4 | |a Clinical Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Computer-Aided Diagnosis (CAD) | |
| 650 | 4 | |a Head and Neck Squamous Cell Carcinoma | |
| 650 | 4 | |a Head/Neck | |
| 650 | 4 | |a Machine Learning | |
| 650 | 4 | |a Quantitative US | |
| 650 | 4 | |a Radiation Therapy/Oncology | |
| 650 | 4 | |a Radiomics | |
| 650 | 4 | |a Radiotherapy | |
| 650 | 4 | |a Ultrasound | |
| 700 | 1 | |a Dasgupta, Archya |e verfasserin |4 aut | |
| 700 | 1 | |a DiCenzo, Daniel |e verfasserin |4 aut | |
| 700 | 1 | |a Fatima, Kashuf |e verfasserin |4 aut | |
| 700 | 1 | |a Quiaoit, Karina |e verfasserin |4 aut | |
| 700 | 1 | |a Saifuddin, Murtuza |e verfasserin |4 aut | |
| 700 | 1 | |a Karam, Irene |e verfasserin |4 aut | |
| 700 | 1 | |a Poon, Ian |e verfasserin |4 aut | |
| 700 | 1 | |a Husain, Zain |e verfasserin |4 aut | |
| 700 | 1 | |a Tran, William T |e verfasserin |4 aut | |
| 700 | 1 | |a Sannachi, Lakshmanan |e verfasserin |4 aut | |
| 700 | 1 | |a Czarnota, Gregory J |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Radiology. Imaging cancer |d 2019 |g 6(2024), 2 vom: 02. März, Seite e230029 |w (DE-627)NLM308856082 |x 2638-616X |7 nnns |
| 773 | 1 | 8 | |g volume:6 |g year:2024 |g number:2 |g day:02 |g month:03 |g pages:e230029 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1148/rycan.230029 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 6 |j 2024 |e 2 |b 02 |c 03 |h e230029 | ||