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References from Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics. Local targets link to admitted publications; unresolved targets remain external evidence.
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries
2021 · External reference
Standard-dose versus high-dose conformal radiotherapy with concurrent and consolidation carboplatin plus paclitaxel with or without cetuximab for patients with stage IIIA or IIIB non-small-cell lung cancer (RTOG 0617): a randomised, two-by-two factorial phase 3 study
10.1016/s1470-2045(14)71207-0 · 2015 · External reference
Novel biomarkers that assist in accurate discrimination of squamous cell carcinoma from adenocarcinoma of the lung
10.1186/s12885-016-2792-1 · 2016 · External reference
Cancer in Canada: Stage at diagnosis
2018 · External reference
What’s new in non-small cell lung cancer for pathologists the importance of accurate subtyping, EGFR mutations and ALK rearrangements
10.1097/pat.0b013e328342629d · 2011 · External reference
The lymph node status and histologic subtypes influenced the effect of postoperative radiotherapy on patients with N2 positive IIIA non-small cell lung cancer
10.1002/jso.25308 · 2019 · External reference
A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics
10.21037/tlcr-21-44 · 2021 · External reference
The Indicative Value of Serum Tumor Markers for Metastasis and Stage of Non-Small Cell Lung Cancer
10.3390/cancers14205064 · 2022 · External reference
Improved differentiation between primary lung cancer and pulmonary metastasis by combining dual-energy CT–derived biomarkers with conventional CT attenuation
10.1007/s00330-020-07195-9 · 2021 · External reference
Radiomics and deep learning in lung cancer
10.1007/s00066-020-01625-9 · 2020 · External reference
Radiomics and radiogenomics in lung cancer: A review for the clinician
10.1016/j.lungcan.2017.10.015 · 2018 · External reference
Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer
10.1007/s00330-017-5221-1 · 2018 · External reference
Non-invasive classification of non-small cell lung cancer: a comparison between random forest models utilising radiomic and semantic features
10.1259/bjr.20190159 · 2019 · External reference
Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET
2020 · External reference
Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types
10.1007/s11307-019-01336-3 · 2019 · External reference
A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer
10.1097/rlu.0000000000002810 · 2019 · External reference
Correlations Study Between 18F-FDG PET/CT Metabolic Parameters Predicting Epidermal Growth Factor Receptor Mutation Status and Prognosis in Lung Adenocarcinoma
2019 · External reference
Optimal standardized uptake value threshold for auto contouring of gross tumor volume using positron emission tomography/computed tomography in patients with operable nonsmall-cell lung cancer: Comparison with pathological tumor size
10.4103/ijnm.ijnm_134_20 · 2021 · External reference
The Utility of PET/CT Metabolic Parameters Measured Based on Fixed Percentage Threshold of SUVmax and Adaptive Iterative Algorithm in the New Revised FIGO Staging System for Stage III Cervical Cancer
2021 · External reference
Intratumoral heterogeneity as measured using the tumor-stroma ratio and PET texture analyses in females with lung adenocarcinomas differs from that of males with lung adenocarcinomas or squamous cell carcinomas
10.1097/md.0000000000014876 · 2019 · External reference
Association Between Younger Age and Targetable Genomic Alterations and Prognosis in Non–Small-Cell Lung Cancer
2016 · External reference
Characteristics of Lung Cancer in Patients Younger than 40 Years: A Prospective Multicenter Analysis in France
10.1159/000489784 · 2018 · External reference
Clinical and Imaging Features of Non–Small-Cell Lung Cancer in Young Patients
10.1016/j.cllc.2020.10.012 · 2021 · External reference
Molecular features and clinical outcome of lung malignancies in very young people
10.2217/fon.15.10 · 2015 · External reference
Clinical Characterization of Node-Negative Lung Adenocarcinoma: Results of a Prospective Investigation
10.1016/s1556-0864(15)30412-3 · 2006 · External reference
Correlation between FDG-PET/CT findings and solid type non-small cell cancer prognostic factors: are there differences between adenocarcinoma and squamous cell carcinoma?
10.1007/s12149-015-1025-z · 2015 · External reference
Correlation of quantified metabolic activity in nonsmall cell lung cancer with tumor size and tumor pathological characteristics
10.1097/md.0000000000011628 · 2018 · External reference
Relationship between primary lesion FDG uptake and clinical stage at PET–CT for non-small cell lung cancer patients: An observation
10.1016/j.lungcan.2009.07.009 · 2010 · External reference
Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing
10.1016/j.ejmp.2019.04.009 · 2019 · External reference
Association of Fluorodeoxyglucose Positron Emission Tomography Radiomics Features with Clinicopathological Factors and Prognosis in Lung Squamous Cell Cancer
10.1007/s13139-022-00774-2 · 2022 · External reference
Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas
10.1007/s00330-023-09412-7 · 2023 · External reference
Comprehensive 18F-FDG PET-based radiomics in elevating the pathological response to neoadjuvant immunochemotherapy for resectable stage III non-small-cell lung cancer: A pilot study
2022 · External reference
Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach
10.1007/s00259-021-05220-7 · 2021 · External reference
Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT
10.1007/s12149-019-01414-0 · 2020 · External reference
Correction to: Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung
10.1007/s00259-021-05226-1 · 2021 · External reference
A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes
10.21037/qims-20-1182 · 2021 · External reference
Machine Learning methods for Quantitative Radiomic Biomarkers
10.1038/srep13087 · 2015 · External reference
Three-Dimensional Convolutional Neural Network-Based Prediction of Epidermal Growth Factor Receptor Expression Status in Patients With Non-Small Cell Lung Cancer
2022 · External reference
A novel machine learning method for multiaxial fatigue life prediction: Improved adaptive neuro-fuzzy inference system
10.1016/j.ijfatigue.2023.108007 · 2024 · External reference
Classification of Pulmonary Nodules in 2-[18F]FDG PET/CT Images with a 3D Convolutional Neural Network
10.1007/s13139-023-00821-6 · 2024 · External reference
