Volume Histograms(볼륨 히스토그램)란 무엇입니까?
Volume Histograms 볼륨 히스토그램 - In patients with adjVCFs, dose-volume histograms for adjacent-level endplates were calculated. [1] Absolute average Dose-Volume Histograms (DVH) of SL2 were calculated for patients with severe (G3) and severe/moderate (G3/G2) skin acute toxicities. [2] Methods The clinical outcomes, dose–volume histograms (DVH), and PF parameters of 122 patients (forced expiratory volume in 1 s [FEV1%]: 60–69%) receiving dCCRT between 2013 and 2019 were recorded. [3] The planning feasibility in each scenario and dose–volume histograms (DVHs) were analyzed and compared with the InitPlan (delivered 33 Gy only to PTV1) by paired t‐test. [4] Dose‐volume histograms (DVHs) for TDmean and percentages of thyroid volume exceeding 10, 20, 30, 40, and 50 Gy (V 10, V 20, V 30, V 40, and V 50, respectively) were also analyzed. [5] Dosimetric parameters including percentage volume of PTV receiving 100% of the prescription dose, percentage volume of PTV receiving 93% of the prescription dose, and consistency of the dose-volume histograms of the target volumes were assessed. [6] The automatically generated reports evaluated the organ volume changes, the actual dose received during a single fraction, and the projected dose to each organ at the completion of the treatment course via comparative cumulative dose-volume histograms (DVHs). [7] Dosimetric evaluation involves computing the mean and 95% confidence intervals for the differences in cumulative dose-volume histograms for propagated and expert contours. [8] 03 cc of reconstructed breast or overlying breast skin was obtained from dose-volume histograms. [9] Dose-volume histograms (DVH) were calculated for critical structures to calculate organ equivalent doses (OED) to obtain excess absolute risk (EAR), life-time attributable risk (LAR) and other risk relevant parameters. [10] This meta-analysis was performed to compare IMRT and 3D-CRT in the treatment of breast cancer in terms of dose-volume histograms and outcomes, including survival and toxicity. [11] 3D dose distributions and dose-volume histograms in lungs of mice were simulated and analyzed. [12] Mean and maximum splenic doses should be kept on mind while evaluating the treatment dose-volume histograms (DVHs). [13] 0cc of the brachial plexus were collected from the dose-volume histograms (DVH) and recalculated to the biologically effective dose (BED) using α/β = 3 Gy. [14] Analysis of dose‐volume histograms and clinical goal fulfillment revealed that DCA can generate satisfactory and near equivalent dosimetric quality to VMAT, except for complex tumor geometries. [15] The dose-volume histograms of both planned and accumulated IMPT doses showed better sparing of OARs than that of the IMRT. [16] Dose-volume histograms were generated to determine the dose of radiation to the pituitary gland. [17] Dose–volume histograms were created for nearby normal organs, with dose constraints set at Dmax <45 Gy, V30 Gy <120 cc, V20 <30%, V30 <30% and Dmax <40 Gy in the spinal cord, small intestine, functional kidney, liver and stomach, respectively (Fig. [18] The thyroid gland was contoured on each slice of the planning computed tomography scan when available (hypothyroid: n = 18; euthyroid > 2 years: n = 16), and dose-volume histograms based on physical dose and biologically equivalent dose (BED) were compared systematically to find the significant dose-volume thresholds that distinguish the patients who developed clinical hypothyroidism. [19] Plan quality was assessed by means of dose-volume histograms and scored with conventional metrics. [20] The TLN risk was evaluated with radiation dose-volume histograms (a dosimetric risk indicator of organ injury) and the dynamics of blood circulating neutrophil-to-lymphocyte ratios (a clinical indicator of systemic inflammation) by linear and logistic regression models. [21] The purpose of this study is to report the results of optimization of the fractionation scheme by evaluating the radiation effect on target and OARs with a modified linear-quadratic model, universal survival curve (USC), based on dose-volume histograms (DVHs). [22] The OEDs were calculated from differential dose-volume histograms (dDVHs) on the basis of the "linear-exponential," "plateau," and "full mechanistic" dose-response models. [23] Dose-volume histograms were used to calculate the volume of brainstem/trigeminal nerve receiving 20%, 30%, and 50% of the prescribed radiation doses. [24] In addition, graphics and dose-volume histograms (DVH) were developed. [25] Through comparing the dose-volume histograms of the two plan types, the differences of the homogeneity index, conformity index and the doses to organs at risk (OARs) were analyzed. [26] The dose-volume histograms of the INTRABEAM and Axxent were computed with measured percent depth dose data and with TG-43 parameters, respectively. [27] Differential dose–volume histograms were generated, and the excess absolute risks were calculated for each plan of each patient. [28] Dose-volume histograms (DVHs) summarize the distribution of radiation doses in the irradiated structures. [29] Execution time and treatment plan quality were evaluated based on the dose-volume histograms of prostate (PTV), its sub-volume U-P, and organs at risk (OARs). [30] The evaluation of the techniques was carried out with dose-volume histograms and isodoses in target volumes and healthy tissues, and with conformity (CI) and homogeneity (HI) indexes. [31] Dose-volume histograms were calculated for the rectum and anal sphincter complex, and associations between the dose-volume parameters and anorectal function were assessed. [32] Difference maps of the dose distributions and dose-volume histograms were evaluated. [33] Based on these geometrical variations, anatomies were generated to create population-based dose-volume histograms (DVH) per patient, which were also compared to clinical values. [34] Dose-volume histograms for the target volume coverage and organs at risk were evaluated and analyzed. [35] Dose distribution was analyzed through mean dose, metrics extracted from dose-volume histograms, and Dice similarity coefficients applied on isodoses. [36] Dose-volume histograms were compared for all plans. [37] Eventually, dosimetric evaluations based on dose-volume histograms were studied and analyzed by Wilcoxon signed rank test for each plan. [38] Dosimetric indices of HPA, extracted using cumulative dose-volume histograms, were correlated with worsening endocrine function using logistic regression analysis. [39] The resulting plans are compared in terms of dose‐volume histograms and estimated treatment times with those produced by a conventional beam arrangement. [40] Compared to organ-level dose-volume histograms (DVHs), using derived RM structures permits a greater level of control over the shapes and anatomical regions that are studied and ensures that all new structures are consistently identified. [41] A proof-of-concept method was proposed to derive the PTV against both the plan- and the machine-specific delivery errors directly from the clinically relevant dose-volume histograms (DVHs) using measured-guided dose reconstruction (MGDR) during DQA. [42] Conclusions Significant differences were found between dose–volume histograms from FP and IP methods. [43] RESULTS Doses to the organs at risk were all maintained at the maximal tolerance in both protocols; however, the interinstitutional variation of the PTVevl dose-volume histograms was significantly decreased with Protocol 2. [44] The radiation dose to be applied was calculated from the dose-volume histograms of the target and at risk organs of the patients with tailor-made plans. [45] The quality of each treatment plan was estimated by dose-volume histograms and gamma index maps. [46]adjVCF가 있는 환자에서 인접 수준 종판에 대한 용량-부피 히스토그램이 계산되었습니다. [1] 중증(G3) 및 중증/중등도(G3/G2) 피부 급성 독성이 있는 환자에 대해 SL2의 절대 평균 용량-부피 히스토그램(DVH)을 계산했습니다. [2] 방법 2013년에서 2019년 사이에 dCCRT를 받은 122명의 환자(강제 호기량 1초[FEV1%]: 60–69%)의 임상 결과, 용량-체적 히스토그램(DVH) 및 PF 매개변수가 기록되었습니다. [3] 각 시나리오의 계획 타당성과 DVH(선량-체적 히스토그램)를 분석하고 paired t-test를 통해 InitPlan(PTV1에만 33Gy 제공)과 비교했습니다. [4] TDmean 및 10, 20, 30, 40 및 50Gy를 초과하는 갑상선 용적 백분율(각각 V 10, V 20, V 30, V 40 및 V 50)에 대한 선량-체적 히스토그램(DVH)도 분석되었습니다. [5] 처방선량의 100%를 받는 PTV의 체적 백분율, 처방 선량의 93%를 받는 PTV의 체적 백분율 및 목표 체적의 선량-체적 히스토그램의 일관성을 포함하는 선량 측정 매개변수를 평가했습니다. [6] 자동으로 생성된 보고서는 비교 누적 선량-부피 히스토그램(DVH)을 통해 장기 부피 변화, 단일 분할 동안 받은 실제 선량 및 치료 과정 완료 시 각 장기에 대한 예상 선량을 평가했습니다. [7] 선량계측 평가에는 전파 및 전문가 등고선에 대한 누적 선량-부피 히스토그램의 차이에 대한 평균 및 95% 신뢰 구간 계산이 포함됩니다. [8] 재건된 유방 또는 그 위에 있는 유방 피부 03cc를 용량-부피 히스토그램에서 얻었다. [9] 선량-체적 히스토그램(DVH)은 초과 절대 위험(EAR), 평생 기여 위험(LAR) 및 기타 위험 관련 매개변수를 얻기 위해 장기 등가선량(OED)을 계산하기 위해 중요한 구조에 대해 계산되었습니다. [10] 이 메타 분석은 유방암 치료에서 IMRT와 3D-CRT를 생존 및 독성을 포함한 용량-부피 히스토그램 및 결과 측면에서 비교하기 위해 수행되었습니다. [11] 쥐의 폐에서 3D 선량 분포와 선량-체적 히스토그램을 시뮬레이션하고 분석했습니다. [12] 치료 용량-부피 히스토그램(DVH)을 평가하는 동안 평균 및 최대 비장 용량을 염두에 두어야 합니다. [13] 상완 신경총의 0cc를 용량-부피 히스토그램(DVH)에서 수집하고 α/β = 3 Gy를 사용하여 생물학적 유효량(BED)으로 다시 계산했습니다. [14] 용량-체적 히스토그램 및 임상 목표 달성 분석은 DCA가 복잡한 종양 기하 구조를 제외하고 VMAT에 대해 만족스럽고 거의 동등한 선량 측정 품질을 생성할 수 있음을 보여주었습니다. [15] 계획된 IMPT 선량과 누적 IMPT 선량의 선량-체적 히스토그램은 IMRT보다 OAR을 더 잘 보존하는 것으로 나타났습니다. [16] 선량-체적 히스토그램은 뇌하수체에 대한 방사선량을 결정하기 위해 생성되었습니다. [17] 척수, 소장, 기능성에서 Dmax <45 Gy, V30 Gy <120cc, V20 <30%, V30 <30% 및 Dmax <40 Gy로 설정된 선량 제약으로 인근 정상 장기에 대한 선량-체적 히스토그램이 생성되었습니다. 각각 신장, 간, 위(Fig. [18] 갑상선은 가능한 경우 계획된 컴퓨터 단층 촬영 스캔의 각 조각에서 윤곽을 잡았고(갑상선 기능 저하: n=18; 정상 갑상선 > 2년: n=16), 물리적 선량 및 생물학적 등가 선량(BED)을 기반으로 한 선량-체적 히스토그램은 다음과 같습니다. 임상적 갑상선 기능 저하증이 발생한 환자를 구별하는 유의한 용량-용량 역치를 찾기 위해 체계적으로 비교했습니다. [19] 계획 품질은 용량-부피 히스토그램을 사용하여 평가하고 기존 메트릭으로 점수를 매겼습니다. [20] TLN 위험은 선형 및 로지스틱 회귀 모델에 의한 방사선량-부피 히스토그램(장기 손상의 선량 측정 위험 지표) 및 혈액 순환 호중구 대 림프구 비율(전신 염증의 임상 지표)의 역학으로 평가되었습니다. [21] 본 연구의 목적은 선량-체적 히스토그램(DVH)을 기반으로 수정된 선형-2차 모델인 보편적 생존 곡선(USC)을 사용하여 표적 및 OAR에 대한 방사선 영향을 평가하여 분류 방식을 최적화한 결과를 보고하는 것입니다. [22] OED는 "선형 지수", "고원" 및 "전체 기계론적" 용량 반응 모델을 기반으로 하는 차등 용량-부피 히스토그램(dDVH)에서 계산되었습니다. [23] 선량-체적 히스토그램은 처방된 방사선량의 20%, 30% 및 50%를 받는 뇌간/삼차신경의 체적을 계산하는 데 사용되었습니다. [24] 또한 그래픽 및 DVH(선량 체적 히스토그램)가 개발되었습니다. [25] 두 가지 계획 유형의 선량-체적 히스토그램을 비교하여 균질성 지수, 적합성 지수 및 위험장기선량(OAR)의 차이를 분석했습니다. [26] INTRABEAM과 Axxent의 선량-부피 히스토그램은 각각 측정된 퍼센트 깊이 선량 데이터와 TG-43 매개변수로 계산되었습니다. [27] 차등 선량-체적 히스토그램을 생성하고 각 환자의 각 계획에 대해 초과 절대 위험을 계산했습니다. [28] 선량-체적 히스토그램(DVH)은 조사된 구조에서 방사선량 분포를 요약합니다. [29] 실행 시간 및 치료 계획 품질은 전립선(PTV), 하위 볼륨 U-P 및 위험 장기(OAR)의 용량-체적 히스토그램을 기반으로 평가되었습니다. [30] 기술 평가는 표적 부피 및 건강한 조직의 용량-부피 히스토그램 및 등선량, 그리고 적합성(CI) 및 균질성(HI) 지수로 수행되었습니다. [31] 직장 및 항문 괄약근 복합체에 대한 용량-체적 히스토그램을 계산하고 용량-체적 매개변수와 항문직장 기능 간의 연관성을 평가했습니다. [32] 선량 분포와 선량-부피 히스토그램의 차이 맵을 평가했습니다. [33] 이러한 기하학적 변화를 기반으로 해부 구조를 생성하여 환자당 인구 기반 DVH(선량 체적 히스토그램)를 생성했으며, 이는 임상 값과도 비교되었습니다. [34] 대상 체적 범위 및 위험 기관에 대한 용량-체적 히스토그램을 평가 및 분석했습니다. [35] 선량 분포는 평균 선량, 선량-부피 히스토그램에서 추출한 메트릭, 등선량에 적용된 주사위 유사성 계수를 통해 분석되었습니다. [36] 모든 계획에 대해 용량-부피 히스토그램을 비교했습니다. [37] 결국, 각 계획에 대해 Wilcoxon 서명 순위 검정에 의해 선량-부피 히스토그램을 기반으로 하는 선량 측정 평가가 연구되고 분석되었습니다. [38] 누적 선량-부피 히스토그램을 사용하여 추출한 HPA의 선량 측정 지수는 로지스틱 회귀 분석을 사용하여 내분비 기능 악화와 상관관계가 있었습니다. [39] 결과 계획은 선량-체적 히스토그램 및 예상 치료 시간 측면에서 기존 빔 배열로 생성된 계획과 비교됩니다. [40] 장기 수준의 DVH(선량-체적 히스토그램)와 비교하여 파생된 RM 구조를 사용하면 연구 대상인 모양과 해부학적 영역을 더 잘 제어할 수 있으며 모든 새로운 구조가 일관되게 식별됩니다. [41] DQA 중 MGDR(측정 유도 선량 재구성)을 사용하여 임상적으로 관련된 DVH(선량 부피 히스토그램)에서 직접 계획 및 기계별 전달 오류 모두에 대해 PTV를 유도하기 위해 개념 증명 방법이 제안되었습니다. [42] 결론 FP와 IP 방법의 선량-부피 히스토그램 간에 상당한 차이가 발견되었습니다. [43] 결과 위험에 처한 장기에 대한 선량은 두 프로토콜 모두에서 최대 내성으로 유지되었습니다. 그러나 PTVevl 용량-부피 히스토그램의 기관 간 변동은 프로토콜 2에서 유의하게 감소했습니다. [44] 적용할 방사선량은 맞춤형 계획에 따라 대상 및 위험 장기의 선량-체적 히스토그램에서 계산되었습니다. [45] 각 치료 계획의 품질은 선량-체적 히스토그램과 감마 지수 맵에 의해 추정되었습니다. [46]
planning target volume 계획 목표 볼륨
Planning target volumes (PTVs), CTVs, and organs at risks (OARs) doses were analyzed with dose–volume histograms (DVHs). [1] Dose volume histograms (DVH) and other dose statistics of planning target volumes (PTV) and organ-at-risk (OAR) were analyzed and compared between plans. [2] The dose indexes (D98, D95, D2, mean dose) in a planning target volume were evaluated from dose volume histograms for the original plan, BH, and RTT offset plans. [3] Drawing the dose volume histograms (DVHs) was done for planning target volumes (PTVs), including Prostate PTV & nodal PTV, and organs at risk including rectum, bladder, femoral heads, and bowel bag for the plans. [4] RESULTS The dose-volume histograms (DVHs) for all plans exhibited a decrease in planning target volume (PTV) dose uniformity with increasing sag magnification, whereas dose to organs at risk exhibited no coherent trend. [5] Means of dose-volume histograms (DVHs) for both planning target volume (PTV) and organs at risk (OARs) were used for the quantitative plan evaluation. [6] Dose-volume histograms (DVHs) of the planning target volume (PTV), OARs such as the rectum, bladder, left and right femur were determined in each plan. [7] Both techniques were compared on the basis of Dose-Volume Histograms (DVH) for the Planning Target Volume (PTV), Organs At Risk (OAR) as well as homogeneity and conformity indices. [8]계획 목표 체적(PTV), CTV 및 위험 장기(OAR) 선량은 선량-체적 히스토그램(DVH)으로 분석되었습니다. [1] 선량 체적 히스토그램(DVH) 및 계획 목표 체적(PTV) 및 위험 장기(OAR)의 기타 선량 통계를 분석하고 계획 간에 비교했습니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8]
tumor control probability 종양 조절 확률
Niemierko and on the computed differential dose volume histograms, the investigators modeled local tumor control probability (TCP) values, by taking into account the uncertainties of main radiobiological parameters, and estimated normal tissue complication probabilities (NTCP) for the anterior rectal wall as the organ most at risk of irradiation. [1] (b) Calculate tumor control probability (TCP) and normal tissue complication probability (NTCP) from the motion-convolved dose-volume histograms. [2] In addition, radiobiological parameters such as tumor control probability (TCP), normal tissue complication probability (NTCP), and equivalent uniform dose (EUD) of the targets and OAR were determined based on their dose-volume histograms (DVHs). [3]Niemierko와 계산된 차등 선량 부피 히스토그램에서 연구자들은 주요 방사선생물학적 매개변수의 불확실성을 고려하여 국소 종양 조절 확률(TCP) 값을 모델링하고 장기인 직장 전벽에 대한 정상 조직 합병증 확률(NTCP)을 추정했습니다. 방사선의 위험이 가장 큽니다. [1] (b) 모션 컨볼루션 용량-부피 히스토그램에서 종양 조절 확률(TCP) 및 정상 조직 합병증 확률(NTCP)을 계산합니다. [2] nan [3]
normal tissue complication 정상 조직 합병증
The dose calculation on the PPIR is based on Eudmodel for dose volume histograms (DVH), which in radiotherapy treatment plan evaluation relies on an implicit estimation of the tumor normal tissue complication probability (NTCP) and control probability (TCP). [1] Dose-volume histograms (DVH's) and normal tissue complication probabilities (NTCP) for the heart, left ventricle (LV), left anterior descending artery (LAD) and left lung were calculated for all four techniques. [2]PPIR에 대한 선량 계산은 방사선 치료 계획 평가에서 종양 정상 조직 합병증 확률(NTCP) 및 제어 확률(TCP)의 암시적 추정에 의존하는 선량 부피 히스토그램(DVH)에 대한 Eudmodel을 기반으로 합니다. [1] 심장, 좌심실(LV), 좌전하행동맥(LAD) 및 좌폐에 대한 용량-체적 히스토그램(DVH's) 및 정상 조직 합병증 확률(NTCP)이 4가지 기술 모두에 대해 계산되었습니다. [2]
Dose Volume Histograms 선량 부피 히스토그램
Dose volume histograms (DVH), different dose values, conformity index (CI), homogeneity index (HI), gradient index (GI) and a new “better than average score” were used to analyse the dose distributions. [1] Dose volume histograms (DVH) and other dose statistics of planning target volumes (PTV) and organ-at-risk (OAR) were analyzed and compared between plans. [2] The dose calculation on the PPIR is based on Eudmodel for dose volume histograms (DVH), which in radiotherapy treatment plan evaluation relies on an implicit estimation of the tumor normal tissue complication probability (NTCP) and control probability (TCP). [3] Materials and Methods The target dose was studied with 4-and 6-field 3D-CRT, 7-field IMRT and tomotherapy techniques used to treat ten patients for prostate cancer and the dose volume histograms of critical organs were analyzed. [4] Rectal dose volume histograms were extracted for all patients in both cohorts and used as input to two different NTCP models, with up to six different published photon-based parameter sets. [5] The HT Planned Adaptive Software was used to see the differences in the planning and verification doses at dose volume histograms (DVH). [6] The dose distributions were analyzed by dose volume histograms, dose differences, and dose indices. [7] The dose indexes (D98, D95, D2, mean dose) in a planning target volume were evaluated from dose volume histograms for the original plan, BH, and RTT offset plans. [8] Drawing the dose volume histograms (DVHs) was done for planning target volumes (PTVs), including Prostate PTV & nodal PTV, and organs at risk including rectum, bladder, femoral heads, and bowel bag for the plans. [9] We developed a weight-tuning policy network (WTPN) that observes dose volume histograms of a plan and outputs an action to adjust organ weighting factors, similar to the behaviors of a human planner. [10] The dose distributions were applied on the manually delineated OARs, their dose volume histograms and dose constraints compliances were analyzed. [11] To demonstrate the proof of principle, we first compared the quality of dose volume histograms (DVHs) of MP - VMAT to the ones calculated from 36 DEIMs following step 1 of MP - VMAT model. [12] Three dimensional absorbed dose maps, dose profiles and Dose Volume Histograms (DVHs) were produced for liver through MC simulations and convolution method implemented in STRATOS software. [13] The TCP and NTCP values were calculated based on differential dose volume histograms using the Niemierko model for both TCP and NTCP, and the Källman-s model for NTCP calculations. [14] Monitor Units(MU), Modulation Factor, Dose Volume Histograms(DVH) and quality indices were used to evaluate the effect of heterogeneity correction on dose calculation and investigate the mechanism of this effect in the low and high energies. [15] ResultsWhen comparing the dose volume histograms, a significant difference was found exclusively between the D2-values. [16] For each disease site, detailed isodoseline distributions and dose volume histograms for a randomly selected representative case were compared among the three research plans and manual plan. [17] Dose volume histograms (DVH) for regional organs at risk (OARs) and periprostatic regions were calculated with and without expansions to account for contouring uncertainty. [18] The radiation oncologist identifies the optimal RT plan for each patient by comparing dose volume histograms (DVH) for different plans to determine how much radiation dose will be delivered to different volumes of the tumor target and each adjacent normal organ. [19] METHODS: Plans were created using four different approaches (single beam, parallel opposed pair, single plane arcs, couch rotation arcs) and dose volume histograms (DVH) for the tumour and the relevant organs at risk (OARs) (mouth, ipsilateral brain, contralateral brain, brain stem) were compared for a sample mouse subject. [20] The calculated dose distributions were then compared to the planned dose by comparison of dose volume histograms and dosimetric volumetric indices. [21] The single fraction irradiations resulted in target contour dose volume histograms (DVH) created by Adaptivo™ that were in close agreement with those determined by gel dosimeter measurements for doses similar to and higher than the planned target dose, with two of the three cases matching to within 5%. [22] Optical properties of pig and human lungs were determined, and dose volume histograms determined. [23] Comparisons were made utilizing dose volume histograms of HR-CTVs, conformation number (CN), and the equivalent total dose in 2 Gy fractions (EQD2) to 2 cm3 of the normal structures. [24] To reduce the complexity associated with VMAT planning, we developed a model which can predict the dose volume histograms (DVHs) of organ-at-risk using the prior knowledge of the high quality esophageal VMAT plans and the distance to target histograms (DTHs). [25] ), and dose volume histograms (DVHs) were compared for CP, NCP and cARC techniques. [26] The 3D dose distributions and dose volume histograms calculated with RAPID were similar for the PET/CT and SPECT/CT. [27] Niemierko and on the computed differential dose volume histograms, the investigators modeled local tumor control probability (TCP) values, by taking into account the uncertainties of main radiobiological parameters, and estimated normal tissue complication probabilities (NTCP) for the anterior rectal wall as the organ most at risk of irradiation. [28] RESULTS In the phantom, the method exhibits a geometrical accuracy within the voxel size and Dose Volume Histograms deviations up to 3. [29] The planned target volume dose structure was registered to the physical target region present in an optical CT data array and the dose volume histograms of the target volume while static and undergoing deformation are investigated and reported. [30] 1cc) spinal cord was evaluated from the Dose Volume Histograms (DVHs). [31] Targets and OARs dose volume histograms and irradiation time were compared; data were analyzed with paired t-test; p value < 0. [32] Dose volume histograms (DVH) are estimated by Monte Carlo simulation. [33] Moreover the code algorithm is validated by comparing 3D dose distributions and dose volume histograms (DVH) calculated by FDC with Geant4. [34]선량 부피 히스토그램(DVH), 다양한 선량 값, 적합성 지수(CI), 균질성 지수(HI), 구배 지수(GI) 및 새로운 "평균보다 나은 점수"를 사용하여 선량 분포를 분석했습니다. [1] 선량 체적 히스토그램(DVH) 및 계획 목표 체적(PTV) 및 위험 장기(OAR)의 기타 선량 통계를 분석하고 계획 간에 비교했습니다. [2] PPIR에 대한 선량 계산은 방사선 치료 계획 평가에서 종양 정상 조직 합병증 확률(NTCP) 및 제어 확률(TCP)의 암시적 추정에 의존하는 선량 부피 히스토그램(DVH)에 대한 Eudmodel을 기반으로 합니다. [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14] nan [15] nan [16] nan [17] nan [18] nan [19] nan [20] nan [21] nan [22] nan [23] nan [24] nan [25] nan [26] RAPID로 계산된 3D 선량 분포와 선량 부피 히스토그램은 PET/CT 및 SPECT/CT에서 유사했습니다. [27] Niemierko와 계산된 차등 선량 부피 히스토그램에서 연구자들은 주요 방사선생물학적 매개변수의 불확실성을 고려하여 국소 종양 조절 확률(TCP) 값을 모델링하고 장기인 직장 전벽에 대한 정상 조직 합병증 확률(NTCP)을 추정했습니다. 방사선의 위험이 가장 큽니다. [28] nan [29] nan [30] nan [31] nan [32] nan [33] nan [34]
volume histograms calculated 계산된 볼륨 히스토그램
Dosimetric endpoints were based on dose-volume histograms calculated from the CTref and the pCTs for various volumes of interest and on 3-dimensional gamma analyses. [1] The 3D dose distributions and dose volume histograms calculated with RAPID were similar for the PET/CT and SPECT/CT. [2]선량 측정 종점은 CTref와 다양한 관심 체적에 대한 pCT 및 3차원 감마 분석에서 계산된 선량-체적 히스토그램을 기반으로 했습니다. [1] RAPID로 계산된 3D 선량 분포와 선량 부피 히스토그램은 PET/CT 및 SPECT/CT에서 유사했습니다. [2]