Evidence map›Paper›PMID 40935443›Full record

ReviewSeminars in radiation oncology2025

Clinical Applications of Quantitative Imaging and Artificial Intelligence for Pancreatic Cance.

Yeseul Kim, David Martinus, Taydan T Tran, Michael K Rooney, Anya Pant, Rance B Tino, Eugene J Koay

Abstract readReview
In one paragraph

Review in Seminars in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yeseul KimDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX; UT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX.
David MartinusDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX; UT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX.
Taydan T TranDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Michael K RooneyDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Anya PantEpiscopal High School in Bellaire, Houston, TX.
Rance B TinoDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Eugene J KoayDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX. Electronic address: ekoay@mdanderson.org.

Funding

Project 3: Enhanced Sensitivity of Tumors to Proton Beam Therapy: Mechanisms and Biomarkers.P01CA261669 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI TITT, UWE · 2021 to 2025
$14.0M
Clinical Validation Center for Early Detection of Pancreatic CancerU01CA200468 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI ANIRBAN MAITRA · 2016 to 2026
$11.0M
Circulating Biomarkers and Imaging for Early Detection of Pancreatic CancerU01CA214263 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI EUGENE J KOAY, Subrata Sen · 2018 to 2026
$7.2M
Early Detection of Hepatocellular CarcinomaR01CA195524 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LAURA BERETTA, David Fuentes · 2016 to 2026
$6.5M
Risk Stratification for and Early Detection of Liver CancerU01CA230997 · NCI · BAYLOR COLLEGE OF MEDICINE · PI Jagpreet Chhatwal, Hashem B El-Serag · 2018 to 2026
$6.4M
Optimization and Evaluation of Anatomical Models of Liver Radiation ResponseR01CA221971 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BROCK, KRISTY · 2018 to 2022
$1.8M
NCI NIH HHS P01 CA261669NCI NIH HHS R01 CA195524NCI NIH HHS R01 CA221971NCI NIH HHS U01 CA200468NCI NIH HHS U01 CA214263NCI NIH HHS U01 CA230997
6 · The paper itself

Abstract

Pancreatic cancer remains as a leading cause of cancer death in the United States due to the disease's deadly combination of evasiveness to detection, aggressive biology, and resistance to treatment. Quantitative imaging and artificial intelligence (AI) methods are emerging as promising and innovative techniques to combat the extensive challenges facing the clinic in the diagnosis and treatment of pancreatic ductal adenocarcinoma. These methods extract data from the fabric of clinical images that allow for earlier diagnosis, improved prognostication, automation of treatment planning, and increased reliability for response assessment. This review examines quantitative imaging techniques from 2013 to 2025 and summarizes them into three parts: differential diagnosis for pancreatic disease, grading and staging of pancreatic tumors, and treatment response assessment and prognosis prediction. We outline key challenges specific to pancreatic cancer and potential mitigations for future direction. We also highlight developing areas such as MRI-guided adaptive radiotherapy, automated target delineation, and integrated radiomic-omics tools that may help incorporate quantitative imaging into routine care of pancreatic cancer. Altogether, the current investigation suggests that quantitative imaging will become an integral tool for this disease across the oncologic journey of a patient.

Indexed as

Artificial IntelligenceCarcinoma, Pancreatic DuctalPancreatic NeoplasmsDiagnosis, DifferentialHumansPrognosis

Identifiers

PMID40935443
PMCPMC13539480

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.