Prediction of adaptive strategies based on deformation vector field features for MR-guided adaptive radiotherapy of prostate cancer
© 2022 American Association of Physicists in Medicine..
BACKGROUND: The selection of adaptive strategies in MR-guided adaptive radiotherapy (MRgART) usually relies on subjective review of anatomical changes. However, this kind of review may lead to improper selection of adaptive strategy for some fractions.
PURPOSE: The purpose of this study was to develop prediction models based on deformation vector field (DVF) features for automatic and accurate strategy selection, using prostate cancer as an example.
METHODS: 100 fractions of 20 prostate cancer patients were retrospectively selected in this study. Treatment plans using both adapt to position (ATP) strategy and adapt to shape (ATS) strategy were generated. Optimal adaptive strategy was determined according to dosimetric evaluation. DVFs of the deformable image registration (DIR) of daily MRI and CT simulation scans were extracted. The shape, first order statistics, and spatial features were extracted from the DVFs, subjected to further selection using the minimum redundancy maximum relevance (mRMR) method. The number of features (Fn ) was hyper-tuned using bootstrapping method, and then Fn indicating a peak area under the curve (AUC) value was used to construct three prediction models.
RESULTS: According to subjective review, the ATS strategy was adopted for all 100 fractions. However, the evaluation results showed that the ATP strategy could have met the clinical requirements for 23 (23%) fractions. The three prediction models showed high prediction performance, with the best performing model achieving an AUC value of 0.896, corresponding accuracy (ACC), sensitivity (SEN) and specificity (SPC) of 0.9, 0.958, and 0.667, respectively. The features used to construct prediction models included four features extracted from y direction of DVF (DVFy ) and mask, one feature from z direction of DVF (DVFz ). It indicated that the deformation along the anterior-posterior direction had a greater impact on determining the adaptive strategy than other directions.
CONCLUSIONS: DVF-feature-based models could accurately predict the adaptive strategy and avoid unnecessary selection of time-consuming ATS strategy, which consequently improves the efficiency of the MRgART process.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:50 |
|---|---|
| Enthalten in: |
Medical physics - 50(2023), 6 vom: 01. Juni, Seite 3573-3583 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Xia, Wen-Long [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
8L70Q75FXE |
|---|
| Anmerkungen: |
Date Completed 15.06.2023 Date Revised 15.06.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1002/mp.16192 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM350952175 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM350952175 | ||
| 003 | DE-627 | ||
| 005 | 20231226050149.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1002/mp.16192 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1169.xml |
| 035 | |a (DE-627)NLM350952175 | ||
| 035 | |a (NLM)36583878 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Xia, Wen-Long |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Prediction of adaptive strategies based on deformation vector field features for MR-guided adaptive radiotherapy of prostate cancer |
| 264 | 1 | |c 2023 | |
| 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 15.06.2023 | ||
| 500 | |a Date Revised 15.06.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022 American Association of Physicists in Medicine. | ||
| 520 | |a BACKGROUND: The selection of adaptive strategies in MR-guided adaptive radiotherapy (MRgART) usually relies on subjective review of anatomical changes. However, this kind of review may lead to improper selection of adaptive strategy for some fractions | ||
| 520 | |a PURPOSE: The purpose of this study was to develop prediction models based on deformation vector field (DVF) features for automatic and accurate strategy selection, using prostate cancer as an example | ||
| 520 | |a METHODS: 100 fractions of 20 prostate cancer patients were retrospectively selected in this study. Treatment plans using both adapt to position (ATP) strategy and adapt to shape (ATS) strategy were generated. Optimal adaptive strategy was determined according to dosimetric evaluation. DVFs of the deformable image registration (DIR) of daily MRI and CT simulation scans were extracted. The shape, first order statistics, and spatial features were extracted from the DVFs, subjected to further selection using the minimum redundancy maximum relevance (mRMR) method. The number of features (Fn ) was hyper-tuned using bootstrapping method, and then Fn indicating a peak area under the curve (AUC) value was used to construct three prediction models | ||
| 520 | |a RESULTS: According to subjective review, the ATS strategy was adopted for all 100 fractions. However, the evaluation results showed that the ATP strategy could have met the clinical requirements for 23 (23%) fractions. The three prediction models showed high prediction performance, with the best performing model achieving an AUC value of 0.896, corresponding accuracy (ACC), sensitivity (SEN) and specificity (SPC) of 0.9, 0.958, and 0.667, respectively. The features used to construct prediction models included four features extracted from y direction of DVF (DVFy ) and mask, one feature from z direction of DVF (DVFz ). It indicated that the deformation along the anterior-posterior direction had a greater impact on determining the adaptive strategy than other directions | ||
| 520 | |a CONCLUSIONS: DVF-feature-based models could accurately predict the adaptive strategy and avoid unnecessary selection of time-consuming ATS strategy, which consequently improves the efficiency of the MRgART process | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a MR-guided adaptive radiotherapy | |
| 650 | 4 | |a adaptive strategy | |
| 650 | 4 | |a deformation vector field | |
| 650 | 4 | |a machine learning | |
| 650 | 7 | |a Adenosine Triphosphate |2 NLM | |
| 650 | 7 | |a 8L70Q75FXE |2 NLM | |
| 700 | 1 | |a Liang, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Men, Kuo |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Ke |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ming-Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Ning-Ning |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ye-Xiong |e verfasserin |4 aut | |
| 700 | 1 | |a Dai, Jian-Rong |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Medical physics |d 1974 |g 50(2023), 6 vom: 01. Juni, Seite 3573-3583 |w (DE-627)NLM000294748 |x 2473-4209 |7 nnns |
| 773 | 1 | 8 | |g volume:50 |g year:2023 |g number:6 |g day:01 |g month:06 |g pages:3573-3583 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1002/mp.16192 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 50 |j 2023 |e 6 |b 01 |c 06 |h 3573-3583 | ||