ArticleBMJ open respiratory research2025
Radiomic 'Stress Test': exploration of a deep learning radiomic model in a high-risk prospective lung nodule cohort.
Article in BMJ open respiratory research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundIndeterminate pulmonary nodules (IPNs) are commonly biopsied to ascertain a diagnosis of lung cancer, but many are ultimately benign. The Lung Cancer Prediction (LCP) score is a commercially available deep learning radiomic model with strong diagnostic performance in incidentally identified IPNs, but its potential use to reduce the need for invasive procedures has not been evaluated in patients with nodules for which a biopsy has been recommended.
methodsIn this prospectively collected, retrospective blinded evaluation, the probability of cancer in consecutively biopsied IPNs at a tertiary care centre was calculated using the Mayo Clinic prediction model and categorised into low, intermediate and high-probability groups by applying <10% no-test and >70% treatment thresholds per British Thoracic Society guidelines. We evaluated the diagnostic performance of the Mayo Clinic model, the LCP radiomic model and an integrated model combining the LCP score with statistically selected clinical variables (age, spiculation and upper lobe location) using stepwise logistic regression. Performance was assessed using area under the receiver operating characteristic curve (AUC), F1 score and reclassification analysis based on the bias-corrected clinical net reclassification index.
resultsThe study population included 196 malignant and 125 benign IPNs (61% prevalence of malignancy). The Mayo Clinic model's AUC was 0.69 (0.63-0.75), LCP's AUC was 0.67 (0.61-0.73) and the integrated model combining LCP with statistically selected clinical variables (age, spiculation and upper lobe location) had the highest AUC of 0.75 (0.69-0.80). The integrated model demonstrated improved classification, with an F1 score of 0.645 (0.572-0.716) and a significantly higher AUC compared with the Mayo Clinic model (p=0.046). Reclassification analysis showed a clinical net reclassification index of 0.36 (0.21-0.53) for benign IPNs with eight correctly downgraded intermediate-risk benign nodules and no malignant nodules misclassified into the low-risk category.
conclusionIncorporating LCP with select clinical variables results in an improvement in malignancy risk prediction and nodule classification and could reduce unnecessary invasive biopsies for IPNs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.