ArticleAnnals of surgical oncology2026
Machine Learning for Classification in Lung Cancer Using Routine Clinical and Laboratory Data.
Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Hybrid Deep Learning-Machine Learning Fusion of Clinical, Radiomic and Deep Learning Features for Preoperative Differentiation of Solitary Pulmonary Mucinous Adenocarcinoma.Diagnostics (Basel, Switzerland) · 2026Article
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
backgroundAccurate pathological classification of lung cancer is essential for informing treatment strategies. However, invasive biopsy procedures are not feasible for high-risk patients or those with inaccessible lesions. This study aimed to develop a machine learning model utilizing routine clinical and laboratory data for classification of non-invasive lung cancer.
methodsData from patients admitted to Sichuan Provincial Cancer Hospital were retrospectively analyzed. Key features were determined using LASSO and Boruta algorithms. Four machine learning models, including logistic regression, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and random forest (RandomForest), were trained and optimized through five-fold cross-validation. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, and F1 score. An online calculator was developed using R Shiny for clinical deployment.
resultsA total of 1122 patients with lung cancer were included and randomly assigned to the training and test sets. In the training set, 16 features were incorporated into the models. The RandomForest model demonstrated superior performance compared with the other models, achieving an AUC of 0.999, an accuracy of 0.984, and an F1 score of 1.000. Notably, sex and tumor markers were identified as significant predictors. In the test set, the RandomForest model attained a micro-averaged AUC of 0.969 and macro-averaged AUC of 0.940. Sensitivity and specificity varied from 0.667 to 0.995 across subtypes. A web-based tool was implemented to facilitate real-time clinical application ( https://nkuwangkai.shinyapps.io/lung-cancer-v1/ ).
conclusionThis study presented a robust, non-invasive machine learning model for lung cancer subtype classification, addressing critical gaps in clinical practice for biopsy-ineligible patients. A web-based calculator was developed to facilitate clinical application. Nonetheless, future multicenter validation is warranted to expand the generalizability of this model and promote adoption in diverse healthcare settings.
Indexed as
Identifiers
What Socratic 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 Socratic graph.