A Functional Spatial Analysis Platform for Discovery of Immunological Interactions Predictive of Low-Grade to High-Grade Transition of Pancreatic Intraductal Papillary Mucinous Neoplasms.
A Functional Spatial Analysis Platform for Discovery of Immunological Interactions Predictive of Low-Grade to High-Grade Transition of Pancreatic Intraductal Papillary Mucinous Neoplasms.
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DOI：
10.1177/1176935118782880
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发表时间：
2018
影响因子：
2
通讯作者：
Rao A
中科院分区：
文献类型：
--
作者：
Barua S;Solis L;Parra ER;Uraoka N;Jiang M;Wang H;Rodriguez-Canales J;Wistuba I;Maitra A;Sen S;Rao A
导管内乳头状黏液性肿瘤(IPMN)是胰腺癌(PDAC)的重要前驱物，但在胰腺癌界知之甚少。研究人员已经证明，高度异型增生的IPMN患者比低级别异型增生的患者有更大的风险在残馀胰腺发生PDAC。在这项研究中，我们建立了一个计算预测模型，该模型封装了IPMN中的空间细胞相互作用，这些空间细胞相互作用在低级别IPMN包囊向高级别包囊转化的过程中起着关键作用。利用IPMN包囊的多重免疫荧光图像，我们采用空间统计学和功能数据分析中的算法来创建总结IPMN中空间相互作用的度量标准。我们表明，使用这些空间度量学习的模型集合可以以高精度可靠地预测(1)不典型增生级别(低级别与高级别)和(2)低级别囊肿进展为高级别囊肿的风险。我们在这两个任务上都获得了很高的分类精度，任务1的曲线下面积为0.81(95%可信区间：0.71-0.9)，任务2的曲线下面积为0.81(95%可信区间：0.7-0.94)。据我们所知，这是首次应用集成机器学习方法来利用成像数据发现IPMN中的关键细胞空间相互作用。我们设想，我们的工作可以作为诊断为IPMNS的患者的风险评估工具，并促进对导致IPMNS向PDAC过渡的细胞相互作用的更好理解和研究。
Intraductal papillary mucinous neoplasms (IPMNs), critical precursors of the devastating tumor pancreatic ductal adenocarcinoma (PDAC), are poorly understood in the pancreatic cancer community. Researchers have shown that IPMN patients with high-grade dysplasia have a greater risk of subsequent development of PDAC in the remnant pancreas than do patients with low-grade dysplasia. In this study, we built a computational prediction model that encapsulates the spatial cellular interactions in IPMNs that play key roles in the transformation of low-grade IPMN cysts to high-grade cysts en route to PDAC. Using multiplex immunofluorescent images of IPMN cysts, we adopted algorithms from spatial statistics and functional data analysis to create metrics that summarize the spatial interactions in IPMNs. We showed that an ensemble of models learned using these spatial metrics can robustly predict, with high accuracy, (1) the dysplasia grade (low vs high grade) and (2) the risk of a low-grade cyst progressing to a high-grade cyst. We obtained high classification accuracies on both tasks, with areas under the curve of 0.81 (95% confidence interval: 0.71-0.9) for task 1 and 0.81 (95% confidence interval: 0.7-0.94) for task 2. To the best of our knowledge, this is the first application of an ensemble machine learning approach for discovering critical cellular spatial interactions in IPMNs using imaging data. We envision that our work can be used as a risk assessment tool for patients diagnosed with IPMNs and facilitate greater understanding and investigation of the cellular interactions that cause transition of IPMNs to PDAC.