Kaissis Georgios A, Jungmann Friederike, Ziegelmayer Sebastian, Lohöfer Fabian K, Harder Felix N, Schlitter Anna Melissa, Muckenhuber Alexander, Steiger Katja, Schirren Rebekka, Friess Helmut, Schmid Roland, Weichert Wilko, Makowski Marcus R, Braren Rickmer F
Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany.
Department of Computing, Faculty of Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BU, UK.
J Clin Med. 2020 Apr 25;9(5):1250. doi: 10.3390/jcm9051250.
Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction of patient prognosis. Many studies focus on promoting newly developed imaging biomarkers without a rigorous comparison to other established parameters. To assess the true value and leverage the potential of all efforts in this field, a multi-parametric evaluation of the available biomarkers for PDAC survival prediction is warranted. Here we present a multiparametric analysis to assess the predictive value of established parameters and the added contribution of newly developed imaging features such as biomarkers for overall PDAC patient survival.
103 patients with resectable PDAC were retrospectively enrolled. Clinical and histopathological data (age, sex, chemotherapy regimens, tumor size, lymph node status, grading and resection status), morpho-molecular and genetic data (tumor morphology, molecular subtype, tp53, kras, smad4 and p16 genetics), image-derived features and the combination of all parameters were tested for their prognostic strength based on the concordance index (CI) of multivariate Cox proportional hazards survival modelling after unsupervised machine learning preprocessing.
The average CIs of the out-of-sample data were: 0.63 for the clinical and histopathological features, 0.53 for the morpho-molecular and genetic features, 0.65 for the imaging features and 0.65 for the combined model including all parameters.
Imaging-derived features represent an independent survival predictor in PDAC and enable the multiparametric, machine learning-assisted modelling of postoperative overall survival with a high performance compared to clinical and morpho-molecular/genetic parameters. We propose that future studies systematically include imaging-derived features to benchmark their additive value when evaluating biomarker-based model performance.
胰腺导管腺癌（PDAC）仍然是一种预后极差的肿瘤实体，目前有几种生物标志物正在进行研究以预测患者预后。许多研究专注于推广新开发的成像生物标志物，而没有与其他既定参数进行严格比较。为了评估该领域所有努力的真正价值并发挥其潜力，有必要对用于PDAC生存预测的可用生物标志物进行多参数评估。在此，我们进行了一项多参数分析，以评估既定参数的预测价值以及新开发的成像特征（如生物标志物）对PDAC患者总体生存的额外贡献。
回顾性纳入103例可切除的PDAC患者。基于无监督机器学习预处理后的多变量Cox比例风险生存模型的一致性指数（CI），测试临床和组织病理学数据（年龄、性别、化疗方案、肿瘤大小、淋巴结状态、分级和切除状态）、形态分子和遗传数据（肿瘤形态、分子亚型、tp53、kras、smad4和p16基因）、图像衍生特征以及所有参数的组合的预后强度。
样本外数据的平均CI分别为：临床和组织病理学特征为0.63，形态分子和遗传特征为0.53，成像特征为0.65，包括所有参数的组合模型为0.65。
图像衍生特征是PDAC的独立生存预测指标，与临床和形态分子/遗传参数相比，能够实现多参数、机器学习辅助的术后总体生存建模，且性能较高。我们建议未来的研究在评估基于生物标志物的模型性能时，系统地纳入图像衍生特征以衡量其附加值。
