A personalized DVH prediction model for HDR brachytherapy in cervical cancer treatment
Copyright © 2022 Li, Chen, Yang, Zhu, Yang, Li and Fu..
Purpose: Although the knowledge-based dose-volume histogram (DVH) prediction has been largely researched and applied in External Beam Radiation Therapy, it is still less investigated in the domain of brachytherapy. The purpose of this study is to develop a reliable DVH prediction method for high-dose-rate brachytherapy plans.
Method: A DVH prediction workflow combining kernel density estimation (KDE), k-nearest neighbor (kNN), and principal component analysis (PCA) was proposed. PCA and kNN were first employed together to select similar patients based on principal component directions. 79 cervical cancer patients with different applicators inserted was included in this study. The KDE model was built based on the relationship between distance-to-target (DTH) and the dose in selected cases, which can be subsequently used to estimate the dose probability distribution in the validation set. Model performance of bladder and rectum was quantified by |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the form of mean and standard deviation. The model performance between KDE only and the combination of kNN, PCA, and KDE was compared.
Result: 20, 30 patients were selected for rectum and bladder based on KNN and PCA, respectively. The absolute residual between the actual plans and the predicted plans were 0.38 ± 0.29, 0.4 ± 0.32, 0.43 ± 0.36, 0.97 ± 0.66, and 0.13 ± 0.99 for |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the bladder, respectively. For rectum, the corresponding results were 0.34 ± 0.27, 0.38 ± 0.33, 0.63 ± 0.57, 1.41 ± 0.99 and 0.23 ± 0.17, respectively. The combination of kNN, PCA, and KDE showed a significantly better prediction performance than KDE only, with an improvement of 30.3% for the bladder and 33.3% for the rectum.
Conclusion: In this study, a knowledge-based machine learning model was proposed and verified to accurately predict the DVH for new patients. This model is proved to be effective in our testing group in the workflow of HDR brachytherapy.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
|---|---|
| Enthalten in: |
Frontiers in oncology - 12(2022) vom: 08., Seite 967436 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Li, Zhen [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Brachytherapy |
|---|
| Anmerkungen: |
Date Revised 17.09.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3389/fonc.2022.967436 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM346277140 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM346277140 | ||
| 003 | DE-627 | ||
| 005 | 20231226205253.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3389/fonc.2022.967436 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1154.xml |
| 035 | |a (DE-627)NLM346277140 | ||
| 035 | |a (NLM)36110960 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Li, Zhen |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A personalized DVH prediction model for HDR brachytherapy in cervical cancer treatment |
| 264 | 1 | |c 2022 | |
| 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 Revised 17.09.2022 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright © 2022 Li, Chen, Yang, Zhu, Yang, Li and Fu. | ||
| 520 | |a Purpose: Although the knowledge-based dose-volume histogram (DVH) prediction has been largely researched and applied in External Beam Radiation Therapy, it is still less investigated in the domain of brachytherapy. The purpose of this study is to develop a reliable DVH prediction method for high-dose-rate brachytherapy plans | ||
| 520 | |a Method: A DVH prediction workflow combining kernel density estimation (KDE), k-nearest neighbor (kNN), and principal component analysis (PCA) was proposed. PCA and kNN were first employed together to select similar patients based on principal component directions. 79 cervical cancer patients with different applicators inserted was included in this study. The KDE model was built based on the relationship between distance-to-target (DTH) and the dose in selected cases, which can be subsequently used to estimate the dose probability distribution in the validation set. Model performance of bladder and rectum was quantified by |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the form of mean and standard deviation. The model performance between KDE only and the combination of kNN, PCA, and KDE was compared | ||
| 520 | |a Result: 20, 30 patients were selected for rectum and bladder based on KNN and PCA, respectively. The absolute residual between the actual plans and the predicted plans were 0.38 ± 0.29, 0.4 ± 0.32, 0.43 ± 0.36, 0.97 ± 0.66, and 0.13 ± 0.99 for |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the bladder, respectively. For rectum, the corresponding results were 0.34 ± 0.27, 0.38 ± 0.33, 0.63 ± 0.57, 1.41 ± 0.99 and 0.23 ± 0.17, respectively. The combination of kNN, PCA, and KDE showed a significantly better prediction performance than KDE only, with an improvement of 30.3% for the bladder and 33.3% for the rectum | ||
| 520 | |a Conclusion: In this study, a knowledge-based machine learning model was proposed and verified to accurately predict the DVH for new patients. This model is proved to be effective in our testing group in the workflow of HDR brachytherapy | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a brachytherapy | |
| 650 | 4 | |a cervical cancer | |
| 650 | 4 | |a dose prediction | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a radiation oncology | |
| 700 | 1 | |a Chen, Kehui |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Zhenyu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Qingyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Xiaojing |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhaobin |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Jie |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Frontiers in oncology |d 2011 |g 12(2022) vom: 08., Seite 967436 |w (DE-627)NLM218189583 |x 2234-943X |7 nnns |
| 773 | 1 | 8 | |g volume:12 |g year:2022 |g day:08 |g pages:967436 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3389/fonc.2022.967436 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 12 |j 2022 |b 08 |h 967436 | ||