Developing a primary tumor and lymph node 18F-FDG PET/CT-clinical (TLPC) model to predict lymph node metastasis of resectable T2-4 NSCLC

Abstract

Purpose The goal of this study was to investigate whether the combined PET/CT radiomic features of the primary tumor and lymph node could predict lymph node metastasis (LNM) of resectable non-small cell lung cancer (NSCLC) in stage T2-4. Methods This retrospective study included 192 NSCLC patients who underwent tumor and node dissection between August 2016 and December 2017 and underwent 18F-fluorodeoxyglucose (18F-FDG) PET/CT scanning 1–3 weeks before surgery. In total, 192 primary tumors (> 3 cm) and 462 lymph nodes (LN > 0.5 cm) were analyzed. The pretreatment clinical features of these patients were recorded, and the radiomic features of their primary tumor and lymph node were extracted from PET/CT imaging. The Spearman’s relevance combined with the least absolute shrinkage and selection operator was used for radiomic feature selection. Five independent machine learning models (multi-layer perceptron, extreme Gradient Boosting, light gradient boosting machine, gradient boosting decision tree, and support vector machine) were tested as classifiers for model development. We developed the following three models to predict LNM: tumor PET/CT-clinical (TPC), lymph PET/CT-clinical (LPC), and tumor and lymph PET/CT-clinical (TLPC). The performance of the models and the clinical node (cN) staging was evaluated using the ROC curve and confusion matrix analysis. Results The ROC analysis showed that among the three models, the TLPC model had better predictive clinical utility and efficiency in predicting LNM of NSCLC (AUC = 0.93, accuracy = 85%; sensitivity = 0.93; specificity = 0.75) than both the TPC model (AUC = 0.54, accuracy = 50%; specificity = 0.38; sensitivity = 0.59) and the LPC model (AUC = 0.82, accuracy = 70%; specificity = 0.41; sensitivity = 0.92). The TLPC model also exhibited great potential in predicting the N2 stage in NSCLC (AUC = 0.94, accuracy = 79%; specificity = 0.64; sensitivity = 0.91). Conclusion The combination of CT and PET radiomic features of the primary tumor and lymph node showed great potential for predicting LNM of resectable T2-4 NSCLC. The TLPC model can non-invasively predict lymph node metastasis in NSCLC, which may be helpful for clinicians to develop more rational therapeutic strategies.

Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology

“…The clinical outcome of NSCLC is directly related to its stage at diagnosis [ 43 ]. Moreover, there were reports showing the usefulness of the 18 F-FDG PET/CT radiomics-based ML method for predicting tumor stage in lung cancer [ 44 – 46 ]. Wang et al [ 44 ] reported that the ML model with the gradient tree boosting (XGB) ML algorithm using combined clinical data and PET/CT radiomics of the primary tumor and lymph node had the highest diagnostic performance in predicting lymph node metastasis (LNM) in NSCLC (AUC: 0.93).…”

Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence—the Novel Biomarkers in Breast Cancer Immune Microenvironment

“… 70 Similar studies were also conducted based on other solid tumors such as lung cancer and gastric cancer. 71 , 72 In another study published in May 2023, Zhao et al 73 tried building a radiomic model using image features derived from different tissues including visceral metastasis, bone metastasis or nodal metastasis to predict immune therapy in advanced breast cancer. The result showed that the radiomic model obtained a high accuracy with an AUC of 0.994 (95% CI: 0.988 to 1.000) in the training cohort, and 0.920 (95% CI: 0.824 to 1.000) in the validation cohort, respectively.…”

Distinguishing Lymphoma from Benign Lymph Node Diseases in Fever of Unknown Origin using PET/CT Radiomics

“…Nonetheless, histopathological evaluation often necessitates invasive procedures, which patients may sometimes be unable to tolerate. Furthermore, even upon successful completion of surgery, false-negative results are not uncommon in such procedures [10,11]. Currently, 18 F-fuorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) is considered the most accurate imaging modality for diagnosing FUO [12][13][14][15], as it allows for diagnosis from both metabolic and morphological perspectives, particularly facilitating the diagnosis, staging, and assessment of therapeutic e cacy in lymphoma.…”