Background: Antibodies that inhibit the programmed cell death protein 1 (PD-1) receptor offer a significant survival benefit, potentially cure (i.e., durable disease-free survival following treatment discontinuation), a substantial proportion of patients with advanced melanoma. Most patients however fail to respond to such treatment or acquire resistance. Previously, we reported that baseline total metabolic tumour volume (TMTV) determined by whole-body [18F]FDG PET/CT was independently correlated with survival and able to predict the futility of treatment. Manual delineation of [18F]FDG-avid lesions is however labour intensive and not suitable for routine use. A predictive survival model is proposed based on automated analysis of baseline, whole-body [18F]FDG images. Methods: Lesions were segmented on [18F]FDG PET/CT using a deep-learning approach and derived features were investigated through Kaplan–Meier survival estimates with univariate logrank test and Cox regression analyses. Selected parameters were evaluated in multivariate Cox survival regressors. Results: In the development set of 69 patients, overall survival prediction based on TMTV, lactate dehydrogenase levels and presence of brain metastases achieved an area under the curve of 0.78 at one year, 0.70 at two years. No statistically significant difference was observed with respect to using manually segmented lesions. Internal validation on 31 patients yielded scores of 0.76 for one year and 0.74 for two years. Conclusions: Automatically extracted TMTV based on whole-body [18F]FDG PET/CT can aid in building predictive models that can support therapeutic decisions in patients treated with immune-checkpoint blockade.
背景：抑制程序性细胞死亡蛋白1（PD-1）受体的抗体疗法能为晚期黑色素瘤患者带来显著的生存获益，甚至可能实现临床治愈（即停止治疗后长期无病生存）。然而多数患者对该疗法无应答或产生耐药性。既往研究表明，通过全身[18F]FDG PET/CT测定的基线总代谢肿瘤体积（TMTV）与生存期独立相关，并能预测治疗无效性。但人工勾画[18F]FDG高摄取病灶耗时费力，难以常规应用。本研究提出基于自动化分析基线全身[18F]FDG图像的生存预测模型。 方法：采用深度学习算法对[18F]FDG PET/CT图像进行病灶分割，通过Kaplan-Meier生存估计结合单变量对数秩检验及Cox回归分析评估衍生特征。筛选参数纳入多变量Cox生存回归模型。 结果：在69例患者的开发集中，基于TMTV、乳酸脱氢酶水平及脑转移状态的总体生存预测模型在一年期和两年期的曲线下面积分别达到0.78和0.70。与人工分割病灶相比无统计学显著差异。对31例患者进行内部验证，一年期和两年期预测评分分别为0.76和0.74。 结论：基于全身[18F]FDG PET/CT自动提取的TMTV可构建预测模型，为接受免疫检查点阻断治疗患者的临床决策提供支持。
肿瘤(癌症)患者之家