Literature DB >> 33151087

Validation of Prognostic Radiomic Features From Resectable Pancreatic Ductal Adenocarcinoma in Patients With Advanced Disease Undergoing Chemotherapy.

Emmanuel Salinas-Miranda1,2, Farzad Khalvati1,3, Kashayar Namdar1, Dominik Deniffel1, Xin Dong1, Engy Abbas3, Julie M Wilson2, Grainne M O'Kane2,4, Jennifer Knox2,4, Steven Gallinger2,5, Masoom A Haider1,2,3.   

Abstract

BACKGROUND: Radiomic features in pancreatic ductal adenocarcinoma (PDAC) often lack validation in independent test sets or are limited to early or late stage disease. Given the lethal nature of PDAC it is possible that there are similarities in radiomic features of both early and advanced disease reflective of aggressive biology.
PURPOSE: To assess the performance of prognostic radiomic features previously published in patients with resectable PDAC in a test set of patients with unresectable PDAC undergoing chemotherapy.
METHODS: The pre-treatment CT of 108 patients enrolled in a prospective chemotherapy trial were used as a test cohort for 2 previously published prognostic radiomic features in resectable PDAC (Sum Entropy and Cluster Tendency with square-root filter[Sqrt]). We assessed the performance of these 2 radiomic features for the prediction of overall survival (OS) and time to progression (TTP) using Cox proportional-hazard models.
RESULTS: Sqrt Cluster Tendency was significantly associated with outcome with a hazard ratio (HR) of 1.27(for primary pancreatic tumor plus local nodes), (Confidence Interval(CI):1.01 -1.6, P-value = 0.039) for OS and a HR of 1.25(CI:1.00 -1.55, P-value = 0.047) for TTP. Sum entropy was not associated with outcomes. Sqrt Cluster Tendency remained significant in multivariate analysis.
CONCLUSION: The CT radiomic feature Sqrt Cluster Tendency, previously demonstrated to be prognostic in resectable PDAC, remained a significant prognostic factor for OS and TTP in a test set of unresectable PDAC patients. This radiomic feature warrants further investigation to understand its biologic correlates and CT applicability in PDAC patients.

Entities:  

Keywords:  computed tomography; machine learning; pancreatic ductal adenocarcinoma; prognosis; radiomic analysis

Mesh:

Year:  2020        PMID: 33151087     DOI: 10.1177/0846537120968782

Source DB:  PubMed          Journal:  Can Assoc Radiol J        ISSN: 0846-5371            Impact factor:   2.248


  3 in total

1.  Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study.

Authors:  Liang Liang; Ying Ding; Yiyi Yu; Kai Liu; Shengxiang Rao; Yingqian Ge; Mengsu Zeng
Journal:  BMC Med Imaging       Date:  2021-04-26       Impact factor: 1.930

Review 2.  The impact of radiomics in diagnosis and staging of pancreatic cancer.

Authors:  Calogero Casà; Antonio Piras; Andrea D'Aviero; Francesco Preziosi; Silvia Mariani; Davide Cusumano; Angela Romano; Ivo Boskoski; Jacopo Lenkowicz; Nicola Dinapoli; Francesco Cellini; Maria Antonietta Gambacorta; Vincenzo Valentini; Gian Carlo Mattiucci; Luca Boldrini
Journal:  Ther Adv Gastrointest Endosc       Date:  2022-03-16

Review 3.  Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications.

Authors:  Kiersten Preuss; Nate Thach; Xiaoying Liang; Michael Baine; Justin Chen; Chi Zhang; Huijing Du; Hongfeng Yu; Chi Lin; Michael A Hollingsworth; Dandan Zheng
Journal:  Cancers (Basel)       Date:  2022-03-24       Impact factor: 6.639

  3 in total

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