The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer
Abstract
Purpose: We sought to distinguish lung adenocarcinoma (ADC) from squamous cell carcinoma using a machine-learning algorithm with PET-based radiomic features. Methods: A total of 396 patients with 210 ADCs and 186 squamous cell carcinomas who underwent FDG PET/CT prior to treatment were retrospectively analyzed. Four clinical features (age, sex, tumor size, and smoking status) and 40 radiomic features were investigated in terms of lung ADC subtype prediction. Radiomic features were extracted from the PET images of segmented tumors using the LIFEx package. The clinical and radiomic features were ranked, and a subset of useful features was selected based on Gini coefficient scores in terms of associations with histological class. The areas under the receiver operating characteristic curves (AUCs) of classifications afforded by several machine-learning algorithms (random forest, neural network, naive Bayes, logistic regression, and a support vector machine) were compared and validated via random sampling. Results: We developed and validated a PET-based radiomic model predicting the histological subtypes of lung cancer. Sex, SUVmax, gray-level zone length nonuniformity, gray-level nonuniformity for zone, and total lesion glycolysis were the 5 best predictors of lung ADC. The logistic regression model outperformed all other classifiers (AUC = 0.859, accuracy = 0.769, F1 score = 0.774, precision = 0.804, recall = 0.746) followed by the neural network model (AUC = 0.854, accuracy = 0.772, F1 score = 0.777, precision = 0.807, recall = 0.750). Conclusions: A machine-learning approach successfully identified the histological subtypes of lung cancer. A PET-based radiomic features may help clinicians improve the histopathologic diagnosis in a noninvasive manner.
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“Radiological data has been combined with other data types for predictive AI systems in various clinical disciplines like oncology21–24 or neurology25,26.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [24]
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“The complexity of the non-imaging data in multimodal fusion work was limited, particularly in the context of available feature-rich and time-series data in the EHR. Instead, most studies focused primarily on basic demographic information such as age and gender25,27,39, a limited range of categorical clinical history such as hypertension or smoking status32,34 or disease-specific clinical features”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [32]
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Metadata
- Authors
- Hyun, S. H., Ahn, M. S., Koh, Y. W., & Lee, S. J.
- Affiliations
- Samsung Medical Center (KR)
- Hematology-Oncology
- Pathology
- Ajou University (KR)
- Corresponding author
- Hyun, Seung Hyup
- Venue
- Clinical Nuclear Medicine
- Published
- 2019-10-31
- Volume
- 44
- Issue
- 12
- Pages
- 956-960
- ISSN
- 0363-9762 (ISSN-L)
- 1536-0229
- OpenAlex
- W2985928856
- Cited by
- 185
- References
- 17
- Citations by year
- 2026: 13 · 2025: 30 · 2024: 34 · 2023: 26 · 2022: 40 · 2021: 27 · 2020: 15
- FWCI
- 13.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Nuclear Medicine (publishedVersion)
- PubMed (publishedVersion)
- AJOU Open Repository (Ajou University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- lung cancer, lung adenocarcinoma, squamous cell carcinoma, PET radiomics, FDG PET/CT, radiomic features, SUVmax, total lesion glycolysis, logistic regression, non-invasive diagnosis
- MeSH terms
- Machine Learning, Positron Emission Tomography Computed Tomography, Aged, Bayes Theorem, Carcinoma, Squamous Cell, Female, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Male, Middle Aged, Retrospective Studies, ROC Curve, Area Under Curve
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