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基于锥形束 CT 的自适应强度调制质子治疗对头颈癌的自动计划评估。

Cone beam CT-based adaptive intensity modulated proton therapy assessment using automated planning for head-and-neck cancer.

机构信息

Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA.

Department of Biomedical Engineering, College of Engineering, University of Miami, Coral Gables, FL, USA.

出版信息

Radiat Oncol. 2024 Jan 23;19(1):13. doi: 10.1186/s13014-024-02406-9.

DOI:10.1186/s13014-024-02406-9
PMID:38263237
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10804468/
Abstract

BACKGROUND

To assess the feasibility of CBCT-based adaptive intensity modulated proton therapy (IMPT) using automated planning for treatment of head and neck (HN) cancers.

METHODS

Twenty HN cancer patients who received radiotherapy and had pretreatment CBCTs were included in this study. Initial IMPT plans were created using automated planning software for all patients. Synthetic CTs (sCT) were then created by deforming the planning CT (pCT) to the pretreatment CBCTs. To assess dose calculation accuracy on sCTs, repeat CTs (rCTs) were deformed to the pretreatment CBCT obtained on the same day to create deformed rCT (rCT), serving as gold standard. The dose recalculated on sCT and on rCT were compared by using Gamma analysis. The accuracy of DIR generated contours was also assessed. To explore the potential benefits of adaptive IMPT, two sets of plans were created for each patient, a non-adapted IMPT plan and an adapted IMPT plan calculated on weekly sCT images. The weekly doses for non-adaptive and adaptive IMPT plans were accumulated on the pCT, and the accumulated dosimetric parameters of two sets were compared.

RESULTS

Gamma analysis of the dose recalculated on sCT and rCT resulted in a passing rate of 97.9% ± 1.7% using 3 mm/3% criteria. With the physician-corrected contours on the sCT, the dose deviation range of using sCT to estimate mean dose for the most organ at risk (OARs) can be reduced to (- 2.37%, 2.19%) as compared to rCT, while for V95 of primary or secondary CTVs, the deviation can be controlled within (- 1.09%, 0.29%). Comparison of the accumulated doses from the adaptive planning against the non-adaptive plans reduced mean dose to constrictors (- 1.42 Gy ± 2.79 Gy) and larynx (- 2.58 Gy ± 3.09 Gy). The reductions result in statistically significant reductions in the normal tissue complication probability (NTCP) of larynx edema by 7.52% ± 13.59%. 4.5% of primary CTVs, 4.1% of secondary CTVs, and 26.8% tertiary CTVs didn't meet the V > 95% constraint on non-adapted IMPT plans. All adaptive plans were able to meet the coverage constraint.

CONCLUSION

sCTs can be a useful tool for accurate proton dose calculation. Adaptive IMPT resulted in better CTV coverage, OAR sparing and lower NTCP for some OARs as compared with non-adaptive IMPT.

摘要

背景

评估基于 CBCT 的自适应强度调制质子治疗 (IMPT) 的可行性,使用自动规划治疗头颈部 (HN) 癌症。

方法

本研究纳入了 20 名接受放射治疗并在治疗前接受 CBCT 的 HN 癌症患者。所有患者均使用自动规划软件创建初始 IMPT 计划。然后通过将计划 CT (pCT) 变形到预处理 CBCT 来创建合成 CT (sCT)。为了评估 sCT 上剂量计算的准确性,将重复 CT (rCT) 变形到同一天获得的预处理 CBCT 上,以创建变形 rCT (rCT),作为金标准。使用伽马分析比较 sCT 和 rCT 上重新计算的剂量。还评估了 DIR 生成轮廓的准确性。为了探索自适应 IMPT 的潜在益处,为每个患者创建了两组计划,一组是无自适应 IMPT 计划,一组是每周 sCT 图像上计算的自适应 IMPT 计划。将非自适应和自适应 IMPT 计划的每周剂量累积到 pCT 上,并比较两组的累积剂量学参数。

结果

使用 3 毫米/3%标准,sCT 和 rCT 上重新计算的剂量伽马分析通过率为 97.9%±1.7%。使用 sCT 上医生校正的轮廓,使用 sCT 估计最危及器官 (OAR) 的平均剂量的剂量偏差范围可以缩小至 (-2.37%,2.19%),而对于原发性或继发性CTVs 的 V95,偏差可以控制在 (-1.09%,0.29%)。与非自适应计划相比,自适应计划累积剂量的比较使食管收缩剂 (-1.42Gy±2.79Gy) 和喉 (-2.58Gy±3.09Gy) 的平均剂量降低。这些减少导致喉水肿的正常组织并发症概率 (NTCP) 降低了 7.52%±13.59%,具有统计学意义。4.5%的原发性 CTV、4.1%的继发性 CTV 和 26.8%的三级 CTV 未能满足非自适应 IMPT 计划的 V>95%约束。所有自适应计划都能够满足覆盖范围的约束。

结论

sCT 可以成为准确质子剂量计算的有用工具。与非自适应 IMPT 相比,自适应 IMPT 可实现更好的 CTV 覆盖、OAR 保护和某些 OAR 的 NTCP 降低。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7308/10804468/0dcb1d79241c/13014_2024_2406_Fig8_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7308/10804468/74bd0e645429/13014_2024_2406_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7308/10804468/92042c4da50e/13014_2024_2406_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7308/10804468/a6e63cf95dd8/13014_2024_2406_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7308/10804468/a5b9aebdad83/13014_2024_2406_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7308/10804468/4d03ec570c80/13014_2024_2406_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7308/10804468/0dcb1d79241c/13014_2024_2406_Fig8_HTML.jpg

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