Küstner Thomas, Vogel Jonas, Hepp Tobias, Forschner Andrea, Pfannenberg Christina, Schmidt Holger, Schwenzer Nina F, Nikolaou Konstantin, la Fougère Christian, Seith Ferdinand
MIDAS.Lab, Department of Radiology, University Hospital of Tübingen, 72076 Tubingen, Germany.
Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, University Hospital Tübingen, 72076 Tubingen, Germany.
Diagnostics (Basel). 2022 Aug 30;12(9):2102. doi: 10.3390/diagnostics12092102.
Besides tremendous treatment success in advanced melanoma patients, the rapid development of oncologic treatment options comes with increasingly high costs and can cause severe life-threatening side effects. For this purpose, predictive baseline biomarkers are becoming increasingly important for risk stratification and personalized treatment planning. Thus, the aim of this pilot study was the development of a prognostic tool for the risk stratification of the treatment response and mortality based on PET/MRI and PET/CT, including a convolutional neural network (CNN) for metastasized-melanoma patients before systemic-treatment initiation. The evaluation was based on 37 patients (19 f, 62 ± 13 y/o) with unresectable metastasized melanomas who underwent whole-body 18F-FDG PET/MRI and PET/CT scans on the same day before the initiation of therapy with checkpoint inhibitors and/or BRAF/MEK inhibitors. The overall survival (OS), therapy response, metastatically involved organs, number of lesions, total lesion glycolysis, total metabolic tumor volume (TMTV), peak standardized uptake value (SULpeak), diameter (Dmlesion) and mean apparent diffusion coefficient (ADCmean) were assessed. For each marker, a Kaplan−Meier analysis and the statistical significance (Wilcoxon test, paired t-test and Bonferroni correction) were assessed. Patients were divided into high- and low-risk groups depending on the OS and treatment response. The CNN segmentation and prediction utilized multimodality imaging data for a complementary in-depth risk analysis per patient. The following parameters correlated with longer OS: a TMTV < 50 mL; no metastases in the brain, bone, liver, spleen or pleura; ≤4 affected organ regions; no metastases; a Dmlesion > 37 mm or SULpeak < 1.3; a range of the ADCmean < 600 mm2/s. However, none of the parameters correlated significantly with the stratification of the patients into the high- or low-risk groups. For the CNN, the sensitivity, specificity, PPV and accuracy were 92%, 96%, 92% and 95%, respectively. Imaging biomarkers such as the metastatic involvement of specific organs, a high tumor burden, the presence of at least one large lesion or a high range of intermetastatic diffusivity were negative predictors for the OS, but the identification of high-risk patients was not feasible with the handcrafted parameters. In contrast, the proposed CNN supplied risk stratification with high specificity and sensitivity.
除了在晚期黑色素瘤患者中取得巨大的治疗成功外，肿瘤治疗方案的快速发展伴随着越来越高的成本，并且可能导致严重的危及生命的副作用。为此，预测性基线生物标志物对于风险分层和个性化治疗规划变得越来越重要。因此，本试点研究的目的是开发一种基于PET/MRI和PET/CT的治疗反应和死亡率风险分层的预后工具，包括用于全身治疗开始前转移性黑色素瘤患者的卷积神经网络（CNN）。评估基于37例（19例女性，62±13岁）不可切除转移性黑色素瘤患者，这些患者在开始使用检查点抑制剂和/或BRAF/MEK抑制剂治疗前的同一天接受了全身18F-FDG PET/MRI和PET/CT扫描。评估了总生存期（OS）、治疗反应、转移累及的器官、病灶数量、总病灶糖酵解、总代谢肿瘤体积（TMTV）、峰值标准化摄取值（SULpeak）、直径（Dmlesion）和平均表观扩散系数（ADCmean）。对于每个标志物，进行了Kaplan-Meier分析和统计学显著性评估（Wilcoxon检验、配对t检验和Bonferroni校正）。根据OS和治疗反应将患者分为高风险组和低风险组。CNN分割和预测利用多模态成像数据对每位患者进行补充性深入风险分析。以下参数与较长的OS相关：TMTV<50 mL；脑、骨、肝、脾或胸膜无转移；≤4个受影响的器官区域；无转移；Dmlesion>37 mm或SULpeak<1.3；ADCmean范围<600 mm2/s。然而，没有一个参数与患者分层为高风险组或低风险组有显著相关性。对于CNN，敏感性、特异性、阳性预测值和准确性分别为92%、96%、92%和95%。特定器官的转移累及、高肿瘤负荷、至少一个大病灶的存在或高范围的转移间扩散率等影像学生物标志物是OS的阴性预测指标，但通过手工制作的参数识别高风险患者是不可行的。相比之下，所提出的CNN提供了具有高特异性和敏感性的风险分层。
