Deep Learning Analysis Using 18F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma

Abstract

IntroductionThe aim of this work was to determine the feasibility of using a deep learning approach to predict occult lymph node metastasis (OLM) based on preoperative FDG-PET/CT images in patients with clinical node-negative (cN0) lung adenocarcinoma.Materials and MethodsDataset 1 (for training and internal validation) included 376 consecutive patients with cN0 lung adenocarcinoma from our hospital between May 2012 and May 2021. Dataset 2 (for prospective test) used 58 consecutive patients with cN0 lung adenocarcinoma from June 2021 to February 2022 at the same center. Three deep learning models: PET alone, CT alone, and combined model, were developed for the prediction of OLM. The performance of the models was evaluated on internal validation and prospective test in terms of accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve (AUCs).ResultsThe combined model incorporating PET and CT showed the best performance, achieved an AUC of 0.81 [95% confidence interval (CI): 0.61, 1.00] in the prediction of OLM in internal validation set (n = 60) and an AUC of 0.87 (95% CI: 0.75, 0.99) in the prospective test set (n = 58). The model achieved 87.50% sensitivity, 80.00% specificity, and 81.00% accuracy in the internal validation set and achieved 75.00% sensitivity, 88.46% specificity, and 86.60% accuracy in the prospective test set.ConclusionThis study presented a deep learning approach to enable the prediction of occult nodal involvement based on the PET/CT images before surgery in cN0 lung adenocarcinoma, which would help clinicians select patients who would be suitable for sublobar resection.

Construction and Evaluation of a Preoperative Prediction Model for Lymph Node Metastasis of cIA Lung Adenocarcinoma Using Random Forest

“…Compared with previous prediction models of the same type, our model has a larger data volume and a more refined data collection in that it included tumors in all locations rather than only tumors in the peripheral location [ 12 , 38 , 39 ]. Similar to Wu et al [ 12 ], we did not exclude pure GGO and GGO-dominant part-solid tumors in this model construction, because the CTR in this study was different from CTR in other studies.…”

A comparison of 18F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study

“…Therefore, an accurate pre-operative nodal staging is essential. To date, 18 F-fluorodeoxyglucose ( 18 F-FDG) PET is increasingly used for lung cancer staging. 18 F-FDG PET is more accurate than computed tomography (CT) for detecting involved regional lymph nodes; however, its sensitivity and specificity vary [3,6,7].…”

“…To date, 18 F-fluorodeoxyglucose ( 18 F-FDG) PET is increasingly used for lung cancer staging. 18 F-FDG PET is more accurate than computed tomography (CT) for detecting involved regional lymph nodes; however, its sensitivity and specificity vary [3,6,7]. Besides, the sensitivity of 18 F-FDG PET for detecting regional nodal metastasis of lung cancer is lower in adenocarcinoma than in other histopathology [8].…”

“…18 F-FDG PET is more accurate than computed tomography (CT) for detecting involved regional lymph nodes; however, its sensitivity and specificity vary [3,6,7]. Besides, the sensitivity of 18 F-FDG PET for detecting regional nodal metastasis of lung cancer is lower in adenocarcinoma than in other histopathology [8]. False positivity owning to lymphadenitis is also a pitfall of 18 F-FDG PET when evaluating regional nodal status [9].…”

“…Besides, the sensitivity of 18 F-FDG PET for detecting regional nodal metastasis of lung cancer is lower in adenocarcinoma than in other histopathology [8]. False positivity owning to lymphadenitis is also a pitfall of 18 F-FDG PET when evaluating regional nodal status [9]. Therefore, improving the accuracy of 18 F-FDG PET in detecting regional nodal metastasis of lung adenocarcinoma is an unmet need.…”