Jingyuan Chen, Yunze Yang, Hongying Feng, Lian Zhang, Zhengliang Liu, Tianming Liu, Carlos E Vargas, Nathan Y Yu, Jean-Claude M Rwigema, Sameer R Keole, Samir H Patel, Sujay A Vora, Jiajian Shen, Wei Liu
{"title":"基于剂量-线性能量转移(LET)体积约束的点扫描质子治疗稳健优化。","authors":"Jingyuan Chen, Yunze Yang, Hongying Feng, Lian Zhang, Zhengliang Liu, Tianming Liu, Carlos E Vargas, Nathan Y Yu, Jean-Claude M Rwigema, Sameer R Keole, Samir H Patel, Sujay A Vora, Jiajian Shen, Wei Liu","doi":"10.1016/j.ijrobp.2024.11.068","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Historically, spot scanning proton therapy (SSPT) treatment planning utilizes dose volume constraints and linear-energy-transfer (LET) volume constraints separately to balance tumor control and organs-at-risk (OARs) protection. We propose a novel dose-LET volume constraint (DLVC)-based robust optimization (DLVCRO) method for SSPT in treating prostate cancer to obtain a desirable joint dose and LET distribution to minimize adverse events (AEs).</p><p><strong>Methods: </strong>DLVCRO treats DLVC as soft constraints that control the shapes of the dose-LET volume histogram (DLVH) curves. It minimizes the overlap of high LET and high dose in OARs and redistributes high LET from OARs to targets in a user defined way. Ten prostate cancer patients were included in this retrospective study. Rectum and bladder were considered as OARs. DLVCRO was compared with the conventional robust optimization (RO) method. Plan robustness was quantified using the worst-case analysis method. Besides the dose-volume histogram (DVH) indices, the analogous LET-volume histogram (LETVH), extra-biological-dose (the product of per voxel dose and LET)-volume histogram (xBDVH) indices characterizing the joint dose/LET distributions and DLVH indices were also used. The Wilcoxon signed rank test was performed to measure statistical significance.</p><p><strong>Results: </strong>In the nominal scenario, DLVCRO significantly improved joint distribution of dose and LET to protect OARs compared with RO. The physical dose distributions in targets and OARs are comparable. In the worst-case scenario, DLVCRO markedly enhanced OAR protection (more robust) while maintaining almost the same plan robustness in target dose coverage and homogeneity.</p><p><strong>Conclusion: </strong>DLVCRO upgrades 2D DVH-based to 3D DLVH-based treatment planning to adjust dose/LET distributions simultaneously and robustly. DLVCRO is potentially a powerful tool to improve patient outcomes in SSPT.</p>","PeriodicalId":14215,"journal":{"name":"International Journal of Radiation Oncology Biology Physics","volume":" ","pages":""},"PeriodicalIF":6.4000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust Optimization for Spot Scanning Proton Therapy based on Dose-Linear Energy Transfer (LET) Volume Constraints.\",\"authors\":\"Jingyuan Chen, Yunze Yang, Hongying Feng, Lian Zhang, Zhengliang Liu, Tianming Liu, Carlos E Vargas, Nathan Y Yu, Jean-Claude M Rwigema, Sameer R Keole, Samir H Patel, Sujay A Vora, Jiajian Shen, Wei Liu\",\"doi\":\"10.1016/j.ijrobp.2024.11.068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Historically, spot scanning proton therapy (SSPT) treatment planning utilizes dose volume constraints and linear-energy-transfer (LET) volume constraints separately to balance tumor control and organs-at-risk (OARs) protection. We propose a novel dose-LET volume constraint (DLVC)-based robust optimization (DLVCRO) method for SSPT in treating prostate cancer to obtain a desirable joint dose and LET distribution to minimize adverse events (AEs).</p><p><strong>Methods: </strong>DLVCRO treats DLVC as soft constraints that control the shapes of the dose-LET volume histogram (DLVH) curves. It minimizes the overlap of high LET and high dose in OARs and redistributes high LET from OARs to targets in a user defined way. Ten prostate cancer patients were included in this retrospective study. Rectum and bladder were considered as OARs. DLVCRO was compared with the conventional robust optimization (RO) method. Plan robustness was quantified using the worst-case analysis method. Besides the dose-volume histogram (DVH) indices, the analogous LET-volume histogram (LETVH), extra-biological-dose (the product of per voxel dose and LET)-volume histogram (xBDVH) indices characterizing the joint dose/LET distributions and DLVH indices were also used. The Wilcoxon signed rank test was performed to measure statistical significance.</p><p><strong>Results: </strong>In the nominal scenario, DLVCRO significantly improved joint distribution of dose and LET to protect OARs compared with RO. The physical dose distributions in targets and OARs are comparable. In the worst-case scenario, DLVCRO markedly enhanced OAR protection (more robust) while maintaining almost the same plan robustness in target dose coverage and homogeneity.</p><p><strong>Conclusion: </strong>DLVCRO upgrades 2D DVH-based to 3D DLVH-based treatment planning to adjust dose/LET distributions simultaneously and robustly. 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Robust Optimization for Spot Scanning Proton Therapy based on Dose-Linear Energy Transfer (LET) Volume Constraints.
Purpose: Historically, spot scanning proton therapy (SSPT) treatment planning utilizes dose volume constraints and linear-energy-transfer (LET) volume constraints separately to balance tumor control and organs-at-risk (OARs) protection. We propose a novel dose-LET volume constraint (DLVC)-based robust optimization (DLVCRO) method for SSPT in treating prostate cancer to obtain a desirable joint dose and LET distribution to minimize adverse events (AEs).
Methods: DLVCRO treats DLVC as soft constraints that control the shapes of the dose-LET volume histogram (DLVH) curves. It minimizes the overlap of high LET and high dose in OARs and redistributes high LET from OARs to targets in a user defined way. Ten prostate cancer patients were included in this retrospective study. Rectum and bladder were considered as OARs. DLVCRO was compared with the conventional robust optimization (RO) method. Plan robustness was quantified using the worst-case analysis method. Besides the dose-volume histogram (DVH) indices, the analogous LET-volume histogram (LETVH), extra-biological-dose (the product of per voxel dose and LET)-volume histogram (xBDVH) indices characterizing the joint dose/LET distributions and DLVH indices were also used. The Wilcoxon signed rank test was performed to measure statistical significance.
Results: In the nominal scenario, DLVCRO significantly improved joint distribution of dose and LET to protect OARs compared with RO. The physical dose distributions in targets and OARs are comparable. In the worst-case scenario, DLVCRO markedly enhanced OAR protection (more robust) while maintaining almost the same plan robustness in target dose coverage and homogeneity.
Conclusion: DLVCRO upgrades 2D DVH-based to 3D DLVH-based treatment planning to adjust dose/LET distributions simultaneously and robustly. DLVCRO is potentially a powerful tool to improve patient outcomes in SSPT.
期刊介绍:
International Journal of Radiation Oncology • Biology • Physics (IJROBP), known in the field as the Red Journal, publishes original laboratory and clinical investigations related to radiation oncology, radiation biology, medical physics, and both education and health policy as it relates to the field.
This journal has a particular interest in original contributions of the following types: prospective clinical trials, outcomes research, and large database interrogation. In addition, it seeks reports of high-impact innovations in single or combined modality treatment, tumor sensitization, normal tissue protection (including both precision avoidance and pharmacologic means), brachytherapy, particle irradiation, and cancer imaging. Technical advances related to dosimetry and conformal radiation treatment planning are of interest, as are basic science studies investigating tumor physiology and the molecular biology underlying cancer and normal tissue radiation response.