Đáp ứng sớm của điều trị xạ trị lập thể định vị thân ở bệnh nhân ung thư phổi không tế bào nhỏ giai đoạn I

Mục tiêu: Đánh giá đáp ứng sớm điều trị xạ trị lập thể định vị thân ở bệnh nhân ung thư phổi không tế bào nhỏ giai đoạn I (T1-T2aN0M0). Đối tượng và phương pháp: Nghiên cứu tiến cứu, theo dõi dọc 25 bệnh nhân ung thư phổi không tế bào nhỏ giai đoạn T1-T2aN0M0 được điều trị xạ trị lập thể định vị thân và đánh giá mỗi 3 tháng từ tháng 01/2015 đến tháng 12/2020. Đáp ứng điều trị sớm sau 3 tháng được đánh giá theo tiêu chuẩn RECIST 1.1 và PERCIST 1.0, đánh giá tác dụng không mong muốn theo tiêu chuẩn của Viện Ung thư quốc gia Mỹ. Kết quả: Tuổi trung bình là 65,32 tuổi, kích thước trung bình của khối u trên CT ngực là 3,33cm, trên PET/CT 3,21cm, giá trị FDG trung bình 8,01. Giai đoạn của khối u đa số là T2a (56%). Bệnh nhân được chỉ định SBRT do COPD chiếm 60%. Liều điều trị trung bình 4208cGy, 40% điều trị 1 phân liều, còn lại là 3 - 5 phân liều. Theo RECIST, không có đáp ứng hoàn toàn, 44% đáp ứng 1 phần, 36% bệnh ổn định, 5 bệnh nhân có bệnh tiến triển, tỉ lệ đáp ứng khách quan là 44%, tỷ lệ kiểm soát bệnh là 80%. Theo PERCIST, có 1 bệnh nhân đạt đáp ứng hoàn toàn, các tỷ lệ khác lần lượt là 68%, 24%, 8%, 68% và 92%, sự khác biệt giữa 2 tiêu chuẩn có ý nghĩa thống kê với p<0,05. CEA và giá trị SUVmax có mối liên quan đến đáp ứng sau điều trị (p<0,05). Tác dụng không mong muốn hay gặp là viêm phổi do xạ: 11 bệnh nhân, chủ yếu là độ 1, không có viêm phổi do xạ độ 4, 5. Không có sự thay đổi về chức năng hô hấp của bệnh nhân sau điều trị SBRT. Kết luận: SBRT là phương pháp điều trị cho đáp ứng tốt ở bệnh nhân ung thư phổi không tế bào nhỏ giai đoạn I với tỷ lệ kiểm soát bệnh 92%, đồng thời đây là một biện pháp điều trị an toàn cho người bệnh.

A method for evaluation of patient-specific lean body mass from limited-coverage CT images and its application in PERCIST: comparison with predictive equation

Background Standardized uptake value (SUV) normalized by lean body mass ([LBM] SUL) is recommended as metric by PERCIST 1.0. The James predictive equation (PE) is a frequently used formula for LBM estimation, but may cause substantial error for an individual. The purpose of this study was to introduce a novel and reliable method for estimating LBM by limited-coverage (LC) CT images from PET/CT examinations and test its validity, then to analyse whether SUV normalised by LC-based LBM could change the PERCIST 1.0 response classifications, based on LBM estimated by the James PE. Methods First, 199 patients who received whole-body PET/CT examinations were retrospectively retrieved. A patient-specific LBM equation was developed based on the relationship between LC fat volumes (FVLC) and whole-body fat mass (FMWB). This equation was cross-validated with an independent sample of 97 patients who also received whole-body PET/CT examinations. Its results were compared with the measurement of LBM from whole-body CT (reference standard) and the results of the James PE. Then, 241 patients with solid tumours who underwent PET/CT examinations before and after treatment were retrospectively retrieved. The treatment responses were evaluated according to the PE-based and LC-based PERCIST 1.0. Concordance between them was assessed using Cohen’s κ coefficient and Wilcoxon’s signed-ranks test. The impact of differing LBM algorithms on PERCIST 1.0 classification was evaluated. Results The FVLC were significantly correlated with the FMWB (r=0.977). Furthermore, the results of LBM measurement evaluated with LC images were much closer to the reference standard than those obtained by the James PE. The PE-based and LC-based PERCIST 1.0 classifications were discordant in 27 patients (11.2%; κ = 0.823, P=0.837). These discordant patients’ percentage changes of peak SUL (SULpeak) were all in the interval above or below 10% from the threshold (±30%), accounting for 43.5% (27/62) of total patients in this region. The degree of variability is related to changes in LBM before and after treatment. Conclusions LBM algorithm-dependent variability in PERCIST 1.0 classification is a notable issue. SUV normalised by LC-based LBM could change PERCIST 1.0 response classifications based on LBM estimated by the James PE, especially for patients with a percentage variation of SULpeak close to the threshold.

A method for evaluation of patient-specific lean body mass from limited-coverage CT images and its application in PERCIST: comparison with predictive equation

Background: Standardized uptake value (SUV) normalized by lean body mass ([LBM] SUL) is recommended as metric by PERCIST 1.0. The James predictive equation (PE) is a frequently used formula for LBM estimation, but may cause substantial error for an individual. The purpose of this study was to introduce a novel and reliable method for estimating LBM by limited-coverage (LC) CT images from PET/CT examinations and test its validity, then to analyse whether SUV normalised by LC-based LBM could change the PERCIST 1.0 response classifications, based on LBM estimated by the James PE. Methods: First, 199 patients who received whole-body PET/CT examinations were retrospectively retrieved. A patient-specific LBM equation was developed based on the relationship between LC fat volumes (FVLC) and whole-body fat mass (FMWB). This equation was cross-validated with an independent sample of 97 patients who also received whole-body PET/CT examinations. Its results were compared with the measurement of LBM from whole-body CT (reference standard) and the results of the James PE. Then, 241 patients with solid tumours who underwent PET/CT examinations before and after treatment were retrospectively retrieved. The treatment responses were evaluated according to the PE-based and LC-based PERCIST 1.0. Concordance between them was assessed using Cohen’s κ coefficient and Wilcoxon’s signed-ranks test. The impact of differing LBM algorithms on PERCIST 1.0 classification was evaluated.Results: The FVLC were significantly correlated with the FMWB (r=0.977). Furthermore, the results of LBM measurement evaluated with LC images were much closer to the reference standard than those obtained by the James PE. The PE-based and LC-based PERCIST 1.0 classifications were discordant in 27 patients (11.2 %; κ = 0.823, P=0.837). These discordant patients’ percentage changes of peak SUL (SULpeak) were all in the interval above or below 10 % from the threshold (± 30 %), accounting for 43.5 % (27/62) of total patients in this region. The degree of variability is related to changes in LBM before and after treatment. Conclusions: LBM algorithm-dependent variability in PERCIST 1.0 classification is a notable issue. SUV normalised by LC-based LBM could change PERCIST 1.0 response classifications based on LBM estimated by the James PE, especially for patients with a percentage variation of SULpeak close to the threshold.

A method for evaluation of patient-specific lean body mass from limited-coverage CT images and its application in PERCIST: comparison with predictive equation

Background : To introduce a novel and reliable method for estimating LBM by limited-coverage (LC) CT images from PET/CT examinations and test its validity, then to analyse whether SUV normalised by LC-based LBM could change the PERCIST 1.0 response classifications, based on LBM estimated by the James predictive equation (PE). Methods: First, 199 patients who received whole-body PET/CT examinations were retrospectively retrieved. A patient-specific LBM equation was developed based on the relationship between LC fat volumes (FV LC ) and whole-body fat mass (FM WB ). This equation was cross-validated with an independent sample of 97 patients who also received whole-body PET/CT examinations. Its results were compared with the measurement of LBM from whole-body CT (reference standard) and the results of the James PE. Then, 241 patients with solid tumours who underwent PET/CT examinations before and after treatment were retrospectively retrieved. The treatment responses were evaluated according to the PE-based and LC-based PERCIST 1.0. Concordance between them was assessed using Cohen’s κ coefficient and Wilcoxon’s signed-ranks test. The impact of differing LBM algorithms on PERCIST 1.0 classification was evaluated. Results: The FV LC were significantly correlated with the FM WB (r=0.977). Furthermore, the results of LBM measurement evaluated with LC images were much closer to the reference standard than those obtained by the James PE. The PE-based and LC-based PERCIST 1.0 classifications were discordant in 27 patients (11.2 %; κ = 0.823, P =0.837). These discordant patients’ percentage changes of SUL peak were all in the interval above or below 10 % from the threshold (± 30 %), accounting for 43.5 % (27/62) of total patients in this region. The degree of variability is related to changes in LBM before and after treatment. Conclusions: LBM algorithm-dependent variability in PERCIST 1.0 classification is a notable issue. SUV normalised by LC-based LBM could change PERCIST 1.0 response classifications based on LBM estimated by the James PE, especially for patients with a percentage variation of SUL peak close to the threshold.