Evidence map›Paper›PMID 30931954›Full record

ArticleScientific reports2019

Prognostic Value of CT Radiomic Features in Resectable Pancreatic Ductal Adenocarcinoma.

Farzad Khalvati, Yucheng Zhang, Sameer Baig, Edrise M Lobo-Mueller, Paul Karanicolas, Steven Gallinger, Masoom A Haider

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  3. Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Farzad KhalvatiDepartment of Medical Imaging, University of Toronto, Toronto, ON, Canada.
Yucheng ZhangDepartment of Medical Imaging, University of Toronto, Toronto, ON, Canada.
Sameer BaigDepartment of Medical Imaging, University of Toronto, Toronto, ON, Canada.
Edrise M Lobo-MuellerCross Cancer Institute, University of Alberta, Edmonton, AB, Canada.
Paul KaranicolasDepartment of Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
Steven GallingerLunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Masoom A HaiderDepartment of Medical Imaging, University of Toronto, Toronto, ON, Canada. mahaider@radfiler.com.ORCID http://orcid.org/0000-0002-7165-8315

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this work, we assess the reproducibility and prognostic value of CT-derived radiomic features for resectable pancreatic ductal adenocarcinoma (PDAC). Two radiologists contoured tumour regions on pre-operative CT of two cohorts from two institutions undergoing curative-intent surgical resection for PDAC. The first (n = 30) and second cohorts (n = 68) were used for training and validation of proposed prognostic model for overall survival (OS), respectively. Radiomic features were extracted using PyRadiomics library and those with weak inter-reader reproducibility were excluded. Through Cox regression models, significant features were identified in the training cohort and retested in the validation cohort. Significant features were then fused via Cox regression to build a single radiomic signature in the training cohort, which was validated across readers in the validation cohort. Two radiomic features derived from Sum Entropy and Cluster Tendency features were both robust to inter-reader reproducibility and prognostic of OS across cohorts and readers. The radiomic signature showed prognostic value for OS in the validation cohort with hazard ratios of 1.56 (P = 0.005) and 1.35 (P = 0.022), for the first and second reader, respectively. CT-based radiomic features were shown to be prognostic in patients with resectable PDAC. These features may help stratify patients for neoadjuvant or alternative therapies.

Indexed as

AgedCarcinoma, Pancreatic DuctalFemaleHumansKaplan-Meier EstimateMaleMiddle AgedPancreatic NeoplasmsPrognosisReproducibility of ResultsRetrospective StudiesTomography, X-Ray Computed

Identifiers

PMID30931954
PMCPMC6443807

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.