ArticleTranslational lung cancer research2020
Establishment and validation of a mathematical diagnosis model to distinguish benign pulmonary nodules from early non-small cell lung cancer in Chinese people.
Article in Translational lung cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules.BMC medical informatics and decision making · 2025Article
- Construction of a risk screening and visualization system for pulmonary nodule in physical examination population based on feature self-recognition machine learning model.Frontiers in medicine · 2024Article
- Research progress and value of albumin-related inflammatory markers in the prognosis of non-small cell lung cancer: a review of clinical evidence.Annals of medicine · 2023Review
- A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics.Bioengineered · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn this study, we aimed to establish and validate a mathematical diagnosis model to distinguish benign pulmonary nodules (BPNs) from early non-small cell lung cancer (eNSCLC) based on clinical characteristics, radiomics features, and hematological biomarkers.
methodsMedical records from 81 patients (27 BPNs, 54 eNSCLC) were used to establish a novel mathematical diagnosis model and an additional 61 patients (21 BPNs, 40 eNSCLC) were used to validate this new model. To establish a clinical diagnosis model, a least absolute shrinkage and selection operator (LASSO) regression was applied to select predictors for eNSCLC, then multivariate logistic regression analysis was performed to determine independent predictors of the probability of eNSCLC, and to establish a clinical diagnosis model. The diagnostic accuracy and discriminative ability of our model were compared with the PKUPH and Mayo models using the following 4 indices: area under the receiver-operating characteristics curve (ROC), net reclassification improvement index (NRI), integrated discrimination improvement index (IDI), and decision curve analysis (DCA).
resultsMultivariate logistic regression analysis identified age, border, and albumin (ALB) as independent diagnostic markers of eNSCLC. In the training cohort, the AUC of our model was 0.740, which was larger than the AUCs for the PKUPH model (0.717, P=0.755) and the Mayo model (0.652, P=0.275). Compared with the PKUPH and Mayo models, the NRI of our model increased by 3.7% (P=0.731) and 27.78% (P=0.008), respectively, while the IDI changed -4.77% (P=0.437) and 11.67% (P=0.015), respectively. Moreover, the DCA demonstrated that our model had a higher overall net benefit compared to previously published models. Importantly, similar findings were confirmed in the validation cohort.
conclusionsAge, border, and serum ALB levels were independent diagnostic markers of eNSCLC. Thus, our model could more accurately distinguish BPNs from eNSCLC and outperformed previously published models.
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.