Zaid Mohamed, Widmann Lauren, Dai Annie, Sun Kevin, Zhang Jie, Zhao Jun, Hurd Mark W, Varadhachary Gauri R, Wolff Robert A, Maitra Anirban, Katz Matthew H G, Herman Joseph M, Wang Huamin, Knopp Michael V, Williams Terence M, Bhosale Priya, Tamm Eric P, Koay Eugene J
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Cancers (Basel). 2020 Dec 5;12(12):3656. doi: 10.3390/cancers12123656.
Previously, we characterized qualitative imaging-based subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed tomography (CT) scans. Conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we developed a quantitative classification of this imaging-based subtype (quantitative delta; q-delta). Retrospectively, baseline pancreatic protocol CT scans of three cohorts (cohort#1 = 101, cohort#2 = 90 and cohort#3 = 16 [external validation]) of patients with PDAC were qualitatively classified into high and low delta. We used a voxel-based method to volumetrically quantify tumor enhancement while referencing normal-pancreatic-parenchyma and used machine learning-based analysis to build a predictive model. In addition, we quantified the stromal content using hematoxylin- and eosin-stained treatment-naïve PDAC sections. Analyses revealed that PDAC quantitative enhancement values are predictive of the qualitative delta scoring and were used to build a classification model (q-delta). Compared to high q-delta, low q-delta tumors were associated with improved outcomes, and the q-delta class was an independent prognostic factor for survival. In addition, low q-delta tumors had higher stromal content and lower cellularity compared to high q-delta tumors. Our results suggest that q-delta classification provides a clinically and biologically relevant tool that may be integrated into ongoing and future clinical trials.
此前，我们在计算机断层扫描（CT）图像上对胰腺导管腺癌（PDAC）基于成像的定性亚型进行了特征描述。与不明显（低δ）的肿瘤相比，明显（高δ）的PDAC肿瘤更有可能具有侵袭性生物学行为和更差的临床结局。在此，我们开发了一种基于这种成像亚型的定量分类方法（定量δ；q-δ）。回顾性地，对三个队列（队列#1 = 101例、队列#2 = 90例和队列#3 = 16例[外部验证]）的PDAC患者的基线胰腺增强扫描进行定性分类，分为高δ和低δ。我们使用基于体素的方法在参考正常胰腺实质的情况下对肿瘤强化进行体积定量，并使用基于机器学习的分析来建立预测模型。此外，我们使用苏木精和伊红染色的未经治疗的PDAC切片对基质含量进行了定量。分析显示，PDAC定量强化值可预测定性δ评分，并用于建立分类模型（q-δ）。与高q-δ相比，低q-δ肿瘤与更好的预后相关，并且q-δ分类是生存的独立预后因素。此外，与高q-δ肿瘤相比，低q-δ肿瘤的基质含量更高，细胞密度更低。我们的结果表明，q-δ分类提供了一种临床和生物学相关的工具，可整合到正在进行和未来的临床试验中。
