Evidence map›Paper›PMID 41419585›Full record

ArticleScientific reports2025

Assessing clinician performance using a multi-modality clinical decision-support system for lung cancer prognostication.

Jaryd R Christie, Karen Eddy, Richard A Malthaner, Mehdi Qiabi, Saurav Verma, Daniel Breadner, Pencilla Lang, Viswam S Nair, Sarah A Mattonen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

9 authors.

Jaryd R ChristieDepartment of Medical Biophysics, Western University, 1151 Richmond Street, London, ON, N6A 3K7, Canada.
Karen EddyBaines Imaging Research Laboratory, London Health Sciences Centre Research Institute, London, ON, Canada.
Richard A MalthanerDivision of Thoracic Surgery, Department of Surgery, Western University, London, ON, Canada.
Mehdi QiabiDivision of Thoracic Surgery, Department of Surgery, Western University, London, ON, Canada.
Saurav VermaDepartment of Oncology, Western University, London, ON, Canada.
Daniel BreadnerDepartment of Oncology, Western University, London, ON, Canada.
Pencilla LangDepartment of Oncology, Western University, London, ON, Canada.
Viswam S NairClinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Sarah A MattonenDepartment of Medical Biophysics, Western University, 1151 Richmond Street, London, ON, N6A 3K7, Canada. sarah.mattonen@uwo.ca.

Funding

TRAINING IN CANCER BIOLOGY &TRANSPLANTATIONT32CA009515 · NCI · UNIVERSITY OF WASHINGTON · PI NANCY ELLEN DAVIDSON, Effie W Petersdorf · 1985 to 2026
$16.2M
Cancer Research Society 152218Natural Sciences and Engineering Research Council of Canada RGPIN-2020-06498NCI NIH HHS T32 CA009515NCI NIH HHS T32CA009515
6 · The paper itself

Abstract

Surgery is the primary treatment for early-stage lung cancer. Adjuvant therapy is offered to patients who are at a high risk of recurrence, however, determining the patients that would benefit from additional therapy is often difficult. In this study, we aimed to develop a clinical decision support system (CDSS) for post-surgery lung cancer prognostication integrating a multi-modality deep learning model (DLM). Pre-operative medical images and clinical, surgical, and pathological information were fed into an externally validated DLM. A CDSS was then developed to display the patient information and DLM results for potential clinical use. Four oncologists evaluated each patient's recurrence probability, their confidence level, and their post-surgery recommendations both with and without the DLM information. The CDSS DLM information demonstrated the potential to improve user prediction performance and confidence. This exploratory study is the first to integrate a multi-modality DLM for prognostication with a CDSS, as well as the first study of clinician attitudes towards CDSSs for lung cancer.

Indexed as

Decision Support Systems, ClinicalLung NeoplasmsDeep LearningFemaleHumansMaleMiddle AgedNeoplasm Recurrence, LocalPrognosisClinical decision-support systemComputed tomographyDeep learningLung cancerMulti-modalityPositron emission tomography

Identifiers

PMID41419585
PMCPMC12800322

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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.