A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [18F]FDG-PET/CT parameters

Abstract

Background In patients with non-small cell lung cancer (NSCLC), accuracy of [18F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment. Methods Monocentric retrospective analysis of pretherapeutic [18F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1 vs. N2/3 disease were compared. Ten-fold nested cross-validation was used to derive the mean area under the ROC curve of the ten test folds (“test AUC”) and AUC in the validation cohort. Reference standard was the final N stage from interdisciplinary consensus (histological results for N2/3 LNs in 96%). Results N2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87–0.94). Validation AUC was 0.94 (0.89–0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on “mediastinal LN uptake > mediastinum” (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM. Conclusions A machine learning model based on routinely available variables from [18F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available.

Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer

“…Firstly, this retrospective study was conducted in a single center, which was the main cause of the decrease in RQS and also led to patient selection bias. It is necessary to design another prospective, multi-center, and large-cohort study to further validate the performance and generalization ability of the CBR Model in the real-world clinical settings [ 40 ]. Secondly, there is no significant statistical difference in primary tumor size, histologic type and metabolic parameters between LN− and LN + patients, consistent with the report [ 41 ].…”

Lung Cancer Staging: Imaging and Potential Pitfalls

“…Machine learning models are in development to differentiate the clinically relevant categories N0/N1 and N2/N3 using variables from FDG PET/CT images and clinical and pathological data that can be obtained in any patient with routinely available tools. The features include lymph node SUVmax, lymph node short axis diameter, primary tumor diameter, and patient age [26].…”

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

“… 119 Other scholars have used different ML and DL means to identify metastasis of mediastinal lymph node in NSCLC patients with PET/CT and achieved ideal results. 120 At present, CNN is extensively used in the staging of malignant tumor of the lung. The CNN algorithm was used in some studies with PET/CT image data to divide 472 cases into stages T1-T2 or T3-T4.…”