18F-FDG PET/CT-based Machine Learning For Lung Nodule Characterization And Prognosis Prediction Of Lung Cancer

Background and Purpose:Accurate differentiation of malignant from benign pulmonary nodules on 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography scan(18F-FDG PET/CT)is crucial for triaging patients for more extensive work-up or less aggressive management of an indeterminate lung nodule.Machine learning has been a powerful tool in evaluating lung nodules but has focused primarily on lung CT scans,and those aimed at prognostication have mostly relied on handcrafted features rather than deep learning.Study 1 evaluated the effectiveness of convolutional neural network(CNN)in distinguishing malignant from benign lesions using images from 18F-FDG PET/CT scans.Study 2 explored the value of using machine learning to analyze pre-treatment 18F-FDG PET/CT scans to predict lung cancer progression and overall survival(OS).Materials and methods:Study 1:A retrospective review was conducted across three institutions identifying patients that had received a 18F-FDG PET/CT as a part of pre-procedure work-up of an indeterminate lung nodule.679 patients met the inclusion criteria and 868 lesions,602 malignant and 262 benign,were manually segmented from their 18F-FDG PET/CT scans.Lesions were split 7:2:1 between training,validation,and test sets,and an ensemble model containing convolutional neural nets with Efficient Net B4 architectures was trained using CT and PET inputs.Model performance on the test set was evaluated on area under the receiver operating curve(AUC),accuracy,sensitivity,and specificity and compared to the performance of five experts.Study 2:A retrospective review across three institutions identified patients who had a pre-procedure 18F-FDG PET/CT and an associated lung cancer diagnosis.Lesions were manually and automatically segmented,and CNNs were trained using 18F-FDG PET/CT inputs to predict cancer progression.Performance was evaluated using AUC,accuracy,sensitivity,and specificity.Image features were extracted from CNNs and by radiomics feature extraction,and random survival forests(RSF)were constructed to predict OS.Concordance index(C-index)and Integrated Brier score(IBS)were used to evaluate OS prediction.Results:Study 1:The PET/CT ensemble model achieved a test AUC of 0.81,accuracy of 0.74 [95% confidence interval(CI): 0.64,0.82],sensitivity of 0.72(95% CI: 0.60,0.82),and specificity of 0.78(95% CI: 0.59,0.90),PPV of0.88,and NPV of 0.55.The model outperformed two out of five experts on accuracy(0.74 vs.0.59,0.58;P = 0.04,0.03)and sensitivity(0.72 vs.0.51,0.56;P = 0.01,0.04).Study 2:1168 tumors(965 patients)were identified.792 tumors had progression and 376 were progression-free.The most common subtypes were adenocarcinoma(n = 740)and squamous cell carcinoma(n = 179).For progression risk,the PET + CT ensemble model with manual segmentation(accuracy = 0.790,AUC = 0.876)performed similarly to the CT only(accuracy = 0.723,AUC = 0.888)and better compared to the PET only(accuracy = 0.664,AUC = 0.669)model.For OS prediction with deep learning features,the PET + CT + clinical RSF ensemble model(C-index = 0.737)performed similarly to the CT only(C-index =0.730)and better than the PET only(C-index = 0.595),and clinical only(C-index = 0.595)models.RSF models constructed with radiomics features had comparable performance to those with deep learning features.Conclusion:CNNs trained using 18F-FDG PET/CT data can achieve good performance in differentiation of malignant from benign lung nodules that is comparable or better than expert performance.These models have the potential to improve clinician’s ability to triage and diagnose patients.CNNs trained using pre-treatment 18F-FDG PET/CT performed well in predicting lung cancer progression and OS.The prognostic models could inform treatment options and improve patient care.