ArticleEuropean journal of nuclear medicine and molecular imaging2023
Predicting pathological highly invasive lung cancer from preoperative [
Article in European journal of nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- A methodological framework for integrating generalisable deep learning and radiomics fusion model for early lung cancer detection across multi-centre imaging datasets.BMC medical informatics and decision making · 2026Pooled it
- Wedge resection versus segmentectomy in peripheral clinical stage IA lung cancer concerning ground-glass opacity.Surgery today · 2026Article
- Ensemble model for neoadjuvant chemotherapy response prediction and treatment sensitivity in TNBC based on DNA replication stress signatures.Scientific reports · 2025Article
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- Optimizing Strategy for Lung Cancer Screening: From Risk Prediction to Clinical Decision Support.JCO clinical cancer informatics · 2025Article
- Predicting the toxic side effects of drug interactions using chemical structures and protein sequences.Scientific reports · 2024Article
- A Machine Learning-Based Clustering Using Radiomics of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for the Prediction of Prognosis in Patients with Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2024Article
- Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.Journal of applied clinical medical physics · 2024Article
- Exploring the diagnostic value of ultrasound radiomics for neonatal respiratory distress syndrome.BMC pediatrics · 2024Article
- Fusion of laser-induced breakdown spectroscopy technology and deep learning: a new method to identify malignant and benign lung tumors with high accuracy.Analytical and bioanalytical chemistry · 2024Article
- Imaging phenotyping usingInsights into imaging · 2024Article
- Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024Review
- Peri- and intra-nodular radiomic features based onFrontiers in medicine · 2024Article
- Advances in artificial intelligence applications in the field of lung cancer.Frontiers in oncology · 2024Review
- Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification.PloS one · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
- Multiple Machine-Learning Fusion Model Based on Gd-EOB-DTPA-Enhanced MRI and Aminotransferase-to-Platelet Ratio and Gamma-Glutamyl Transferase-to-Platelet Ratio to Predict Microvascular Invasion in Solitary Hepatocellular Carcinoma: A Multicenter Study.Journal of hepatocellular carcinoma · 2024Article
- Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis.Heliyon · 2023Article
- Development of a Model Based on Delta-Radiomic Features for the Optimization of Head and Neck Squamous Cell Carcinoma Patient Treatment.Medicina (Kaunas, Lithuania) · 2023Article
- See Lung Cancer with an AI.Cancers · 2023Review
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe efficacy of sublobar resection of primary lung cancer have been proven in recent years. However, sublobar resection for highly invasive lung cancer increases local recurrence. We developed and validated multiple machine learning models predicting pathological invasiveness of lung cancer based on preoperative [
methodsOverall, 873 patients who underwent lobectomy or segmentectomy for primary lung cancer were enrolled. Radiomics features were extracted from preoperative PET/CT images with the PyRadiomics package. Seven machine learning models and an ensemble of all models (ENS) were evaluated after 100 iterations. In addition, the probability of highly invasive lung cancer was calculated in a nested cross-validation to assess the calibration plot and clinical usefulness and to compare to consolidation tumour ratio (CTR) on CT images, one of the generally used diagnostic criteria.
resultsIn the training set, when PET and CT features were combined, all models achieved an area under the curve (AUC) of ≥ 0.880. In the test set, ENS showed the highest mean AUC of 0.880 and smallest standard deviation of 0.0165, and when the cutoff was 0.5, accuracy of 0.804, F1 of 0.851, precision of 0.821, and recall of 0.885. In the nested cross-validation, the AUC of 0.882 (95% CI: 0.860-0.905) showed a high discriminative ability, and the calibration plot indicated consistency with a Brier score of 0.131. A decision curve analysis showed that the ENS was valid with a threshold probability ranging from 3 to 98%. Accuracy showed an improvement of more than 8% over the CTR.
conclusionThe machine learning model based on preoperative [
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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.