Evidence map›Paper›PMID 35213359›Full record

ArticleCancer biomarkers : section A of Disease markers2022

Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.

Touseef Ahmad Qureshi, Srinivas Gaddam, Ashley Max Wachsman, Lixia Wang, Linda Azab, Vahid Asadpour, Wansu Chen, Yibin Xie, Bechien Wu, Stephen Jacob Pandol and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer biomarkers : section A of Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

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

  1. Pooled it
  2. Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025
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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

11 authors.

Touseef Ahmad QureshiBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Srinivas GaddamGastroenterology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Ashley Max WachsmanDepartment of Radiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Lixia WangDepartment of Radiology, Chaoyang Hospital, Beijing, China.
Linda AzabBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Vahid AsadpourSouthern California Kaiser Permanente Medical Center, Los Angeles, CA, USA.
Wansu ChenSouthern California Kaiser Permanente Medical Center, Los Angeles, CA, USA.
Yibin XieBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Bechien WuSouthern California Kaiser Permanente Medical Center, Los Angeles, CA, USA.
Stephen Jacob PandolGastroenterology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Debiao LiBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Funding

Predicting Pancreatic Ductal Adenocarcinoma PDAC Through Artificial Intelligence Analysis of Pre Diagnostic CT Images in African AmericansR01CA260955 · NCI · CEDARS-SINAI MEDICAL CENTER · PI LI, DEBIAO, PANDOL, STEPHEN J. · 2021 to 2025
$4.8M
NCI NIH HHS R01 CA260955
6 · The paper itself

Abstract

backgroundEarly stage diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is challenging due to the lack of specific diagnostic biomarkers. However, stratifying individuals at high risk of PDAC, followed by monitoring their health conditions on regular basis, has the potential to allow diagnosis at early stages.

objectiveTo stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans.

methodsA set of CT features, potentially predictive of PDAC, was identified in the analysis of 4000 raw radiomic parameters extracted from pancreases in pre-diagnostic scans. The naïve Bayes classifier was then developed for automatic classification of CT scans of the pancreas with high risk for PDAC. A set of 108 retrospective CT scans (36 scans from each healthy control, pre-diagnostic, and diagnostic group) from 72 subjects was used for the study. Model development was performed on 66 multiphase CT scans, whereas external validation was performed on 42 venous-phase CT scans.

resultsThe system achieved an average classification accuracy of 86% on the external dataset.

conclusionsRadiomic analysis of abdominal CT scans can unveil, quantify, and interpret micro-level changes in the pre-diagnostic pancreas and can efficiently assist in the stratification of high risk individuals for PDAC.

Indexed as

Artificial IntelligenceAbdomenBayes TheoremCarcinoma, Pancreatic DuctalEarly Detection of CancerHumansPancreatic NeoplasmsTomography, X-Ray Computedabdominal CT scansmachine learningpancreatic cancerPancreatic Ductal Adenocarcinoma (PDAC)PDAC predictionradiomics

Identifiers

PMID35213359
PMCPMC9255993

What OpenQuestion holds

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Registered trials

None linked

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.