ArticleScientific reports2023
Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05968898 (Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules), which is not on this map. Cited by 15 papers, 1 of them a synthesis that pooled 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.
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.
Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Scientific evidence of commercial artificial intelligence products for pulmonary nodule assessment on CT scans: a systematic review.European radiology · 2026Article
- Theoretical Clinical Utility of Advanced Practice Provider Use of an Artificial Intelligence Radiomics-based Tool for Pulmonary Nodule Evaluation and Management.CHEST pulmonary · 2026Article
- Imaging and AI in tertiary prevention of lung cancer: Narrative review and clinical perspectives.Multidisciplinary respiratory medicine · 2026Article
- Performance validation of a closed loop fully automated AI model for lung nodule stratification in screening cases.Respiratory investigation · 2026Article
- Differences in lung cancer screening outcomes and follow-up by patient, provider and place-based characteristics in Missouri and Illinois: A cross-sectional study.Research square · 2025Article
- Biomarkers for the diagnosis of indeterminate pulmonary nodules: are we there yet?Journal of thoracic disease · 2025Review
- Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review.Narra J · 2025Review
- Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime?Cancer biomarkers : section A of Disease markers · 2025Review
- Imaging features and clinical markers for predicting postoperative recurrence in early-stage lung cancer.American journal of cancer research · 2025Article
- A Cross Spatio-Temporal Pathology-based Lung Nodule Dataset.Scientific data · 2024Article
- Growth dynamics of lung nodules: implications for classification in lung cancer screening.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Usefulness of longitudinal nodule-matching algorithm in computer-aided diagnosis of new pulmonary metastases on cancer surveillance CT scans.Quantitative imaging in medicine and surgery · 2024Article
- Application value of a computer-aided diagnosis and management system for the detection of lung nodules.Quantitative imaging in medicine and surgery · 2023Article
- Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment.Cancers · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
A deep learning model (LCP CNN) for the stratification of indeterminate pulmonary nodules (IPNs) demonstrated better discrimination than commonly used clinical prediction models. However, the LCP CNN score is based on a single timepoint that ignores longitudinal information when prior imaging studies are available. Clinically, IPNs are often followed over time and temporal trends in nodule size or morphology inform management. In this study we investigated whether the change in LCP CNN scores over time was different between benign and malignant nodules. This study used a prospective-specimen collection, retrospective-blinded-evaluation (PRoBE) design. Subjects with incidentally or screening detected IPNs 6-30 mm in diameter with at least 3 consecutive CT scans prior to diagnosis (slice thickness ≤ 1.5 mm) with the same nodule present were included. Disease outcome was adjudicated by biopsy-proven malignancy, biopsy-proven benign disease and absence of growth on at least 2-year imaging follow-up. Lung nodules were analyzed using the Optellum LCP CNN model. Investigators performing image analysis were blinded to all clinical data. The LCP CNN score was determined for 48 benign and 32 malignant nodules. There was no significant difference in the initial LCP CNN score between benign and malignant nodules. Overall, the LCP CNN scores of benign nodules remained relatively stable over time while that of malignant nodules continued to increase over time. The difference in these two trends was statistically significant. We also developed a joint model that incorporates longitudinal LCP CNN scores to predict future probability of cancer. Malignant and benign nodules appear to have distinctive trends in LCP CNN score over time. This suggests that longitudinal modeling may improve radiomic prediction of lung cancer over current models. Additional studies are needed to validate these early findings.
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.