当前位置： SCI文献检索 > Journal of Clinical Medicine期刊下所有文献 > Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma.

Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma.

Abstract：

:To bridge the translational gap between recent discoveries of distinct molecular phenotypes of pancreatic cancer and tangible improvements in patient outcome, there is an urgent need to develop strategies and tools informing and improving the clinical decision process. Radiomics and machine learning approaches can offer non-invasive whole tumor analytics for clinical imaging data-based classification. The retrospective study assessed baseline computed tomography (CT) from 207 patients with proven pancreatic ductal adenocarcinoma (PDAC). Following expert level manual annotation, Pyradiomics was used for the extraction of 1474 radiomic features. The molecular tumor subtype was defined by immunohistochemical staining for KRT81 and HNF1a as quasi-mesenchymal (QM) vs. non-quasi-mesenchymal (non-QM). A Random Forest machine learning algorithm was developed to predict the molecular subtype from the radiomic features. The algorithm was then applied to an independent cohort of histopathologically unclassifiable tumors with distinct clinical outcomes. The classification algorithm achieved a sensitivity, specificity and ROC-AUC (area under the receiver operating characteristic curve) of 0.84 ± 0.05, 0.92 ± 0.01 and 0.93 ± 0.01, respectively. The median overall survival for predicted QM and non-QM tumors was 16.1 and 20.9 months, respectively, log-rank-test p = 0.02, harzard ratio (HR) 1.59. The application of the algorithm to histopathologically unclassifiable tumors revealed two groups with significantly different survival (8.9 and 39.8 months, log-rank-test p < 0.001, HR 4.33). The machine learning-based analysis of preoperative (CT) imaging allows the prediction of molecular PDAC subtypes highly relevant for patient survival, allowing advanced pre-operative patient stratification for precision medicine applications.

journal_name

J Clin Med

journal_title

Journal of clinical medicine

authors

Kaissis GA,Ziegelmayer S,Lohöfer FK,Harder FN,Jungmann F,Sasse D,Muckenhuber A,Yen HY,Steiger K,Siveke J,Friess H,Schmid R,Weichert W,Makowski MR,Braren RF

doi

10.3390/jcm9030724

subject

Has Abstract

pub_date

2020-03-07 00:00:00

issue

issn

2077-0383

pii

jcm9030724

journal_volume

pub_type

杂志文章

在线工具

Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma.