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Abstract：
UNASSIGNED: We explored imaging and blood bio-markers for survival prediction in a cohort of patients with metastatic melanoma treated with immune checkpoint inhibition.
UNASSIGNED: 94 consecutive metastatic melanoma patients treated with immune checkpoint inhibition were included into this study. PET/CT imaging was available at baseline (Tp0), 3 months (Tp1) and 6 months (Tp2) after start of immunotherapy. Radiological response at Tp2 was evaluated using iRECIST. Total tumor burden (TB) at each time-point was measured and relative change of TB compared to baseline was calculated. LDH, CRP and S-100B were also analyzed. Cox proportional hazards model and logistic regression were used for survival analysis.
UNASSIGNED: iRECIST at Tp2 was significantly associated with overall survival (OS) with C-index=0.68. TB at baseline was not associated with OS, whereas TB at Tp1 and Tp2 provided similar predictive power with C-index of 0.67 and 0.71, respectively. Appearance of new metastatic lesions during follow-up was an independent prognostic factor (C-index=0.73). Elevated LDH and S-100B ratios at Tp2 were significantly associated with worse OS: C-index=0.73 for LDH and 0.73 for S-100B. Correlation of LDH with TB was weak (r=0.34). A multivariate model including TB change, S-100B, and appearance of new lesions showed the best predictive performance with C-index=0.83.
UNASSIGNED: Our analysis shows only a weak correlation between LDH and TB. Additionally, baseline TB was not a prognostic factor in our cohort. A multivariate model combining early blood and imaging biomarkers achieved the best predictive power with regard to survival, outperforming iRECIST.
UNASSIGNED: 94 consecutive metastatic melanoma patients treated with immune checkpoint inhibition were included into this study. PET/CT imaging was available at baseline (Tp0), 3 months (Tp1) and 6 months (Tp2) after start of immunotherapy. Radiological response at Tp2 was evaluated using iRECIST. Total tumor burden (TB) at each time-point was measured and relative change of TB compared to baseline was calculated. LDH, CRP and S-100B were also analyzed. Cox proportional hazards model and logistic regression were used for survival analysis.
UNASSIGNED: iRECIST at Tp2 was significantly associated with overall survival (OS) with C-index=0.68. TB at baseline was not associated with OS, whereas TB at Tp1 and Tp2 provided similar predictive power with C-index of 0.67 and 0.71, respectively. Appearance of new metastatic lesions during follow-up was an independent prognostic factor (C-index=0.73). Elevated LDH and S-100B ratios at Tp2 were significantly associated with worse OS: C-index=0.73 for LDH and 0.73 for S-100B. Correlation of LDH with TB was weak (r=0.34). A multivariate model including TB change, S-100B, and appearance of new lesions showed the best predictive performance with C-index=0.83.
UNASSIGNED: Our analysis shows only a weak correlation between LDH and TB. Additionally, baseline TB was not a prognostic factor in our cohort. A multivariate model combining early blood and imaging biomarkers achieved the best predictive power with regard to survival, outperforming iRECIST.
摘要：
UNASSIGNED：我们在一组接受免疫检查点抑制治疗的转移性黑色素瘤患者中探索了成像和血液生物标记物用于生存预测。
UNASSIGNED：94例接受免疫检查点抑制治疗的转移性黑色素瘤患者被纳入本研究。PET/CT成像在基线（Tp0）可用，免疫疗法开始后3个月(Tp1)和6个月(Tp2)。使用iRECIST评估Tp2的放射学响应。测量每个时间点的总肿瘤负荷(TB),并计算TB与基线相比的相对变化。LDH,同时对CRP和S-100B进行分析。采用Cox比例风险模型和logistic回归进行生存分析。
未经证实：在Tp2时的iRECIST与总生存期（OS）显著相关，C指数=0.68。基线时的TB与OS无关，而Tp1和Tp2的TB具有相似的预测能力，C指数分别为0.67和0.71。随访期间新转移灶的出现是独立的预后因素（C指数=0.73）。Tp2时LDH和S-100B比率升高与LDH较差的OS:C指数=0.73和S-100B较差显著相关。LDH与TB的相关性较弱(r=0.34)。包括TB变化的多变量模型，S-100B,新病变的出现显示出最佳的预测性能，C指数=0.83。
UNASSIGNED：我们的分析显示LDH与TB之间的相关性较弱。此外,基线TB不是我们队列中的预后因素.结合早期血液和成像生物标志物的多变量模型实现了关于生存的最佳预测能力。表现优于IRECIST。
UNASSIGNED：94例接受免疫检查点抑制治疗的转移性黑色素瘤患者被纳入本研究。PET/CT成像在基线（Tp0）可用，免疫疗法开始后3个月(Tp1)和6个月(Tp2)。使用iRECIST评估Tp2的放射学响应。测量每个时间点的总肿瘤负荷(TB),并计算TB与基线相比的相对变化。LDH,同时对CRP和S-100B进行分析。采用Cox比例风险模型和logistic回归进行生存分析。
未经证实：在Tp2时的iRECIST与总生存期（OS）显著相关，C指数=0.68。基线时的TB与OS无关，而Tp1和Tp2的TB具有相似的预测能力，C指数分别为0.67和0.71。随访期间新转移灶的出现是独立的预后因素（C指数=0.73）。Tp2时LDH和S-100B比率升高与LDH较差的OS:C指数=0.73和S-100B较差显著相关。LDH与TB的相关性较弱(r=0.34)。包括TB变化的多变量模型，S-100B,新病变的出现显示出最佳的预测性能，C指数=0.83。
UNASSIGNED：我们的分析显示LDH与TB之间的相关性较弱。此外,基线TB不是我们队列中的预后因素.结合早期血液和成像生物标志物的多变量模型实现了关于生存的最佳预测能力。表现优于IRECIST。
