Abstract
Objective
To develop and evaluate a radiomics approach for classifying histological subtypes and epidermal growth factor receptor (EGFR) mutation status in lung cancer on PET/CT images.
Methods
PET/CT images of lung cancer patients were obtained from public databases and used to establish two datasets, respectively to classify histological subtypes (156 adenocarcinomas and 32 squamous cell carcinomas) and EGFR mutation status (38 mutant and 100 wild-type samples). Seven types of imaging features were obtained from PET/CT images of lung cancer. Two types of machine learning algorithms were used to predict histological subtypes and EGFR mutation status: random forest (RF) and gradient tree boosting (XGB). The classifiers used either a single type or multiple types of imaging features. In the latter case, the optimal combination of the seven types of imaging features was selected by Bayesian optimization. Receiver operating characteristic analysis, area under the curve (AUC), and tenfold cross validation were used to assess the performance of the approach.
Results
In the classification of histological subtypes, the AUC values of the various classifiers were as follows: RF, single type: 0.759; XGB, single type: 0.760; RF, multiple types: 0.720; XGB, multiple types: 0.843. In the classification of EGFR mutation status, the AUC values were: RF, single type: 0.625; XGB, single type: 0.617; RF, multiple types: 0.577; XGB, multiple types: 0.659.
Conclusions
The radiomics approach to PET/CT images, together with XGB and Bayesian optimization, is useful for classifying histological subtypes and EGFR mutation status in lung cancer.
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Funding
The present study was supported by JSPS KAKENHI (Grant Number JP16K19883 and JP19K17232). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Appendix
Appendix
Optimal hyperparameters of RF in the classification of histological subtype were as follows.
max_depth: 315.0
max_features: 0.2753668962684457
min_samples_leaf: 0.05751743706118054
min_samples_split: 0.058694587169320536
min_weight_fraction_leaf’: 0.0953635294579193
n_estimators: 160.0,
Optimal hyperparameters of XGB in the classification of histological subtype were as follows.
colsample_bytree: 0.7027440354853365
gamma: 7.050213660755347
learning_rate: 0.7977229209740264
max_depth: 2.0
min_child_weight: 2.4182773699264475
n_estimators: 321.0
subsample: 0.9103390721909822
Optimal hyperparameters of RF in the classification of EGFR mutation status were as follows.
max_depth: 193.0
max_features: 0.6572677943338099
min_samples_leaf: 0.12015049574522058
min_samples_split: 0.13611438197316517
min_weight_fraction_leaf: 0.20518268083328728
n_estimators: 160.0
Optimal hyperparameters of XGB in the classification of EGFR mutation status were as follows.
colsample_bytree: 0.674785098701497
gamma: 11.61630121641136
learning_rate: 0.6246833186520859
max_depth: 7.0
min_child_weight: 6.224162304714124
n_estimators: 400.0
subsample: 0.7311708739539864
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Koyasu, S., Nishio, M., Isoda, H. et al. Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT. Ann Nucl Med 34, 49–57 (2020). https://doi.org/10.1007/s12149-019-01414-0
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DOI: https://doi.org/10.1007/s12149-019-01414-0