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Dosimetric Comparison of Deep Inspiration Breath-Hold and Free-Breathing Techniques in Stereotactic Body Radiotherapy for Localized Lung Tumors at a Tertiary Care Center.

기술보고 1/5 보강
Cureus 📖 저널 OA 99.9% 2021: 42/43 OA 2022: 79/79 OA 2023: 181/181 OA 2024: 284/284 OA 2025: 774/774 OA 2026: 506/506 OA 2021~2026 2026 Vol.18(3) p. e104466
Retraction 확인
출처

PICO 자동 추출 (휴리스틱, conf 2/4)

유사 논문
P · Population 대상 환자/모집단
환자: localized lung tumors
I · Intervention 중재 / 시술
computed tomography (CT) simulation using both four-dimensional CT (4DCT) FB and DIBH techniques
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
추출되지 않음

Arora S, Sathyanarayanan MS, Kajamohideen S, Chilukuri S, Grover K

📝 환자 설명용 한 줄

Background Respiratory motion is a major source of geometric uncertainty in lung stereotactic body radiotherapy (SBRT) and may lead to increased irradiation of normal lung tissue and adjacent organs a

🔬 핵심 임상 통계 (초록에서 자동 추출 — 원문 검증 권장)
  • p-value p < 0.05

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↓ .bib ↓ .ris
APA Arora S, Sathyanarayanan MS, et al. (2026). Dosimetric Comparison of Deep Inspiration Breath-Hold and Free-Breathing Techniques in Stereotactic Body Radiotherapy for Localized Lung Tumors at a Tertiary Care Center.. Cureus, 18(3), e104466. https://doi.org/10.7759/cureus.104466
MLA Arora S, et al.. "Dosimetric Comparison of Deep Inspiration Breath-Hold and Free-Breathing Techniques in Stereotactic Body Radiotherapy for Localized Lung Tumors at a Tertiary Care Center.." Cureus, vol. 18, no. 3, 2026, pp. e104466.
PMID 41924679 ↗

Abstract

Background Respiratory motion is a major source of geometric uncertainty in lung stereotactic body radiotherapy (SBRT) and may lead to increased irradiation of normal lung tissue and adjacent organs at risk (OARs). Deep inspiration breath-hold (DIBH) is an advanced motion management technique that minimizes tumor motion by immobilizing the target during treatment delivery and increasing lung volume. This study aimed to evaluate the dosimetric advantages of DIBH compared with free breathing (FB) in SBRT for patients with localized lung tumors. Methods Twenty patients with localized lung tumors underwent computed tomography (CT) simulation using both four-dimensional CT (4DCT) FB and DIBH techniques. SBRT plans were generated for each dataset using 6 MV flattening filter-free (FFF) beams with two coplanar partial arcs (180°-220° offset), and dose calculation was performed using the anisotropic analytical algorithm (AAA) in the Eclipse treatment planning system. Identical dose constraints and plan normalization to the planning target volume (PTV) mean dose were applied for both techniques. Image guidance was performed using cone-beam CT (CBCT). Dosimetric parameters for lung volumes, target coverage, and OAR doses were extracted from dose-volume histograms (DVHs). Statistical comparisons were carried out using paired t-tests. Results DIBH significantly increased lung volumes compared with FB, with mean expansion factors of 1.41 and 1.37 for the ipsilateral and contralateral lungs, respectively, and resulted in a 1.45-fold reduction in internal target volume. The mean ipsilateral lung dose was reduced by 36.84% in DIBH plans (7.48 ± 3.57 Gy) compared with FB plans (10.23 ± 4.58 Gy). Statistically significant reductions were also observed in lung V10, V15, and V20 parameters (p < 0.05). DIBH plans demonstrated improved dose conformity (CI: 1.05 ± 0.08) without significant differences in homogeneity or target coverage indices. In addition, doses to critical OARs including the heart, spinal cord, and chest wall were significantly lower in DIBH-SBRT plans. Conclusion DIBH-based SBRT provides significant dosimetric advantages over FB SBRT for localized lung tumors by enhancing target immobilization, increasing lung volume, and reducing radiation exposure to the lungs and surrounding critical organs without compromising target coverage. DIBH is a feasible and effective respiratory motion management strategy in lung SBRT, although appropriate patient selection and training are essential for successful implementation.

🏷️ 키워드 / MeSH 📖 같은 키워드 OA만

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🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반

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