我们的目标是探索人体测量参数在接受免疫治疗的大规模患者群体中的预后价值。我们回顾性纳入了623例接受免疫检查点抑制剂治疗的晚期非小细胞肺癌（NSCLC）（n=318）或黑色素瘤（n=305）患者，这些患者进行了治疗前（胸部-腹部-骨盆）CT扫描。我们使用了一组55例NSCLC患者进行了外部验证。使用深度学习软件（Anthropometer3DNet）进行三维（3D）人体测量参数的测量，该软件能够自动进行瘦体重、脂肪体重（FBM）、肌肉体重（MBM）、内脏脂肪量（VFM）和皮下脂肪量（SFM）的多层面测量。还收集了体重指数（BMI）和体重减轻（WL）数据。使用接受者操作特征曲线（ROC曲线）分析，并使用Kaplan-Meier（KM）曲线和Cox回归分析计算整体生存率。 在整个队列中，309例患者的1年死亡率为0.496（95% CI：0.457至0.537），477例患者的5年死亡率为0.196（95% CI：0.165至0.233）。在单变量Kaplan-Meier分析中，SFM低（<3.95 kg/m2）、FBM低（<3.26 kg/m2）、VFM低（<0.91 kg/m2）、MBM低（<5.85 kg/m2）和BMI低（<24.97 kg/m2）的患者预后较差（p<0.001）。这些参数在Cox单变量分析中也显示出显著性（p<0.001），在多元逐步Cox分析中，显著参数为MBM（p<0.0001）、SFM（0.013）和WL（0.0003）。按肿瘤类型进行子分析时，对于NSCLC，所有身体组成参数在ROC、KM和Cox单变量分析中均具有统计学意义，而对于黑色素瘤，除了MBM外，其他参数均不具有统计学意义。在多元Cox分析中，NSCLC的显著参数为MBM（HR=0.81，p=0.0002）、SFM（HR=0.94，p=0.02）和WL（HR=1.06，p=0.004）。对于NSCLC，结合SFM和MBM的KM分析能够将患者分为三类，并显示出同时具有双低SFM（<5.22 kg/m2）和MBM（<6.86 kg/m2）的患者预后更差（p<0.0001）。在外部验证队列中，低SFM和低MBM的组合具有负面影响，1年时死亡率为63%，而非此组合组为25%（p=0.0029）。三维测量的低SFM和低MBM是NSCLC接受免疫检查点抑制剂治疗的显著预后因子，可以结合使用以改善预后价值。© 作者（或其雇主）2023年。在CC BY-NC下允许再使用。不允许商业再使用。由BMJ出版。

Our aim was to explore the prognostic value of anthropometric parameters in a large population of patients treated with immunotherapy.We retrospectively included 623 patients with advanced non-small cell lung cancer (NSCLC) (n=318) or melanoma (n=305) treated by an immune-checkpoint-inhibitor having a pretreatment (thorax-)abdomen-pelvis CT scan. An external validation cohort of 55 patients with NSCLC was used. Anthropometric parameters were measured three-dimensionally (3D) by a deep learning software (Anthropometer3DNet) allowing an automatic multislice measurement of lean body mass, fat body mass (FBM), muscle body mass (MBM), visceral fat mass (VFM) and sub-cutaneous fat mass (SFM). Body mass index (BMI) and weight loss (WL) were also retrieved. Receiver operator characteristic (ROC) curve analysis was performed and overall survival was calculated using Kaplan-Meier (KM) curve and Cox regression analysis.In the overall cohort, 1-year mortality rate was 0.496 (95% CI: 0.457 to 0.537) for 309 events and 5-year mortality rate was 0.196 (95% CI: 0.165 to 0.233) for 477 events. In the univariate Kaplan-Meier analysis, prognosis was worse (p<0.001) for patients with low SFM (<3.95 kg/m2), low FBM (<3.26 kg/m2), low VFM (<0.91 kg/m2), low MBM (<5.85 kg/m2) and low BMI (<24.97 kg/m2). The same parameters were significant in the Cox univariate analysis (p<0.001) and, in the multivariate stepwise Cox analysis, the significant parameters were MBM (p<0.0001), SFM (0.013) and WL (0.0003). In subanalyses according to the type of cancer, all body composition parameters were statistically significant for NSCLC in ROC, KM and Cox univariate analysis while, for melanoma, none of them, except MBM, was statistically significant. In multivariate Cox analysis, the significant parameters for NSCLC were MBM (HR=0.81, p=0.0002), SFM (HR=0.94, p=0.02) and WL (HR=1.06, p=0.004). For NSCLC, a KM analysis combining SFM and MBM was able to separate the population in three categories with the worse prognostic for the patients with both low SFM (<5.22 kg/m2) and MBM (<6.86 kg/m2) (p<0001). On the external validation cohort, combination of low SFM and low MBM was pejorative with 63% of mortality at 1 year versus 25% (p=0.0029).3D measured low SFM and MBM are significant prognosis factors of NSCLC treated by immune checkpoint inhibitors and can be combined to improve the prognostic value.© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.