Predicting pathological complete response (pCR) after stereotactic ablative radiation therapy (SABR) of lung cancer using quantitative dynamic [(18)F]FDG PET and CT perfusion: a prospective exploratory clinical study.
Predicting pathological complete response (pCR) after stereotactic ablative radiation therapy (SABR) of lung cancer using quantitative dynamic [(18)F]FDG PET and CT perfusion: a prospective exploratory clinical study.
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应用定量动态[(18)F]FDG PET和CT灌注预测肺癌立体定向消融放射治疗（SABR）后病理完全缓解（pCR）：一项前瞻性探索性临床研究。
DOI：
10.1186/s13014-021-01747-z
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发表时间：
2021-01-13
期刊：
影响因子：
--
通讯作者：
Lee TY
中科院分区：
文献类型：
--
作者：
Yang DM;Palma DA;Kwan K;Louie AV;Malthaner R;Fortin D;Rodrigues GB;Yaremko BP;Laba J;Gaede S;Warner A;Inculet R;Lee TY
立体定向消融放射治疗（SABR）是治疗不能手术的I期非小细胞肺癌（NSCLC）的有效方法，但SABR后的影像学反应评估是困难的。本前瞻性研究旨在使用来自动态[18 F]FDG-PET和CT灌注（CTP）的基于成像的生物标志物开发SABR的真实病理完全缓解（pCR）预测模型。纳入了26例接受SABR治疗后接受手术切除的早期NSCLC患者，作为一项更大研究的预先指定的次要分析。在SABR前和术后8周进行动态[18F]FDG-PET和CTP。动态[18F]FDG-PET提供了最大和平均标准化摄取值（SUV）和使用先前开发的流量修改的两个组织室模型估计的动力学参数，而CTP测量血流量，血容量和血管渗透性表面产品。使用递归分配分析（RPA）建立预测模型，测量PET和CTP成像生物标志物，用于预测pCR。将该模型与现行RECIST（实体瘤疗效评价标准1.1版）和PERCIST（实体瘤PET疗效标准1.0版）标准进行比较。RPA根据SABR前的肿瘤血容量（BVpre-SABR）和SUVmax变化（ΔSUVmax）确定了三个反应组，阈值为BVpre-SABR = 9.3 mL/100 g和ΔSUVmax = − 48.9%。在SABR后BVpre-SABR < 9.3 mL/100 g和ΔSUVmax < − 48.9%的组中观察到最高真实pCR率92%，而在BVpre-SABR ≥ 9.3 mL/100 g（0%）的组中观察到最差。RPA模型实现了极好的pCR预测（一致性：0.92; P = 0.03）。RECIST和PERCIST显示pCR预测较差（一致性分别为0.54和0.58）。在这项研究中，我们开发了一种基于动态[18 F]FDG-PET和CT灌注成像的预测模型，该模型在预测NSCLC至SABR的pCR方面明显优于RECIST和PERCIST标准。该模型使用BVpre-SABR和ΔSUVmax，分别与肿瘤微血管密度和细胞增殖相关，并保证通过更大样本量的研究进行验证。MISSILE-NSCLC，NCT 02136355（ClinicalTrials.gov）。2014年5月8日注册，https://clinicaltrials.gov/ct2/show/NCT02136355
Stereotactic ablative radiation therapy (SABR) is effective in treating inoperable stage I non-small cell lung cancer (NSCLC), but imaging assessment of response after SABR is difficult. This prospective study aimed to develop a predictive model for true pathologic complete response (pCR) to SABR using imaging-based biomarkers from dynamic [18F]FDG-PET and CT Perfusion (CTP). Twenty-six patients with early-stage NSCLC treated with SABR followed by surgical resection were included, as a pre-specified secondary analysis of a larger study. Dynamic [18F]FDG-PET and CTP were performed pre-SABR and 8-week post. Dynamic [18F]FDG-PET provided maximum and mean standardized uptake value (SUV) and kinetic parameters estimated using a previously developed flow-modified two-tissue compartment model while CTP measured blood flow, blood volume and vessel permeability surface product. Recursive partitioning analysis (RPA) was used to establish a predictive model with the measured PET and CTP imaging biomarkers for predicting pCR. The model was compared to current RECIST (Response Evaluation Criteria in Solid Tumours version 1.1) and PERCIST (PET Response Criteria in Solid Tumours version 1.0) criteria. RPA identified three response groups based on tumour blood volume before SABR (BVpre-SABR) and change in SUVmax (ΔSUVmax), the thresholds being BVpre-SABR = 9.3 mL/100 g and ΔSUVmax = − 48.9%. The highest true pCR rate of 92% was observed in the group with BVpre-SABR < 9.3 mL/100 g and ΔSUVmax < − 48.9% after SABR while the worst was observed in the group with BVpre-SABR ≥ 9.3 mL/100 g (0%). RPA model achieved excellent pCR prediction (Concordance: 0.92; P = 0.03). RECIST and PERCIST showed poor pCR prediction (Concordance: 0.54 and 0.58, respectively). In this study, we developed a predictive model based on dynamic [18F]FDG-PET and CT Perfusion imaging that was significantly better than RECIST and PERCIST criteria to predict pCR of NSCLC to SABR. The model used BVpre-SABR and ΔSUVmax which correlates to tumour microvessel density and cell proliferation, respectively and warrants validation with larger sample size studies. MISSILE-NSCLC, NCT02136355 (ClinicalTrials.gov). Registered May 8, 2014, https://clinicaltrials.gov/ct2/show/NCT02136355
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DOI：
10.1016/j.ijrobp.2007.07.2383
发表时间：
2008-03-15
影响因子：
7
作者：
Takeda, Atsuya;Kunieda, Etsuo;Kubo, Atsushi
通讯作者：
Kubo, Atsushi
影响因子：
3.8
作者：
Palma, David A.;Haasbeek, Cornelis J. A.;Senan, Suresh
通讯作者：
Senan, Suresh
影响因子：
3.8
作者：
Rouzier, Roman;Coutant, Charles;Pusztai, Lajos
通讯作者：
Pusztai, Lajos
影响因子：
7
作者：
Strobl, Carolin;Malley, James;Tutz, Gerhard
通讯作者：
Tutz, Gerhard
影响因子：
1.6
作者：
Yang, Dae-Myoung;Palma, David;Lee, Ting-Yim
通讯作者：
Lee, Ting-Yim