Abstract
Purpose: To evaluate the performance of artificial neural networks (aNN) applied to preoperative 18F-FDG PET/CT for predicting nodal involvement in non-small-cell lung cancer (NSCLC) patients.Methods: We retrospectively analyzed data from 540 clinically resectable NSCLC patients (333 M; 67.4 ± 9 years) undergone preoperative 18F-FDG PET/CT and pulmonary resection with hilo-mediastinal lymphadenectomy. A 3-layers NN model was applied (dataset randomly splitted into 2/3 training and 1/3 testing). Using histopathological reference standard, NN performance for nodal involvement (N0/N+ patient) was calculated by ROC analysis in terms of: area under the curve (AUC), accuracy (ACC), sensitivity (SE), specificity (SP), positive and negative predictive values (PPV, NPV). Diagnostic performance of PET visual analysis (N+ patient: at least one node with uptake ≥ mediastinal blood-pool) and of logistic regression (LR) was evaluated.Results: Histology proved 108/540 (20%) nodal-metastatic patients. Among all collected data, relevant features selected as input parameters were: patients' age, tumor parameters (size, PET visual and semiquantitative features, histotype, grading), PET visual nodal result (patient-based, as N0/N+ and N0/N1/N2). Training and testing NN performance (AUC = 0.849, 0.769): ACC = 80 and 77%; SE = 72 and 58%; SP = 81 and 81%; PPV = 50 and 44%; NPV = 92 and 89%, respectively. Visual PET performance: ACC = 82%, SE = 32%, SP = 94%; PPV = 57%, NPV = 85%. Training and testing LR performance (AUC = 0.795, 0.763): ACC = 75 and 77%; SE = 68 and 55%; SP = 77 and 82%; PPV = 43 and 43%; NPV = 90 and 88%, respectively.Conclusions: aNN application to preoperative 18F-FDG PET/CT provides overall good performance for predicting nodal involvement in NSCLC patients candidate to surgery, especially for ruling out nodal metastases, being NPV the best diagnostic result; a high NPV was also reached by PET qualitative assessment. Moreover, in such population with low a priori nodal involvement probability, aNN better identify the relatively few and unexpected nodal-metastatic patients than PET analysis, so supporting the additional aNN use in case of PET-negative images.
Highlights
The evaluation of lymph nodal status is of paramount importance for selecting the optimal therapeutic approach in patients with non-small-cell lung cancer (NSCLC), with N0 and N1 patients addressed to surgery, and N3 ones to non-surgical approaches, while N2 patients still have more controversial therapeutic options [1, 2]. 18-Fluorine-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (18F-FDG PET/CT) is widely used for nodal staging in NSCLC patients, being recommended by the National Comprehensive Cancer Network (NCCN) guidelines [1]. 18F-FDG PET/CT shows an overall good accuracy for nodal evaluation with sensitivity and specificity values ranging from 72 to 90% and from 81 to 95%, respectively [1,2,3,4,5,6]
Neural Networks (NN) represent an application of machine learning based on an artificial reinterpretation of the human brain structure, that relies on the use of numerous layers of “neurons.” Each neuron is characterized by a specific weight and importance in the context of the whole network
Still limited, literature evidence has explored the application of NN to 18F-FDG PET/CT for predicting nodal involvement in NSCLC patients, but burdened by differences in clinical and procedural aspects [14,15,16,17]
Summary
The evaluation of lymph nodal status is of paramount importance for selecting the optimal therapeutic approach in patients with non-small-cell lung cancer (NSCLC), with N0 and N1 patients addressed to surgery (when clinically feasible), and N3 ones to non-surgical approaches, while N2 patients still have more controversial therapeutic options [1, 2]. 18-Fluorine-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (18F-FDG PET/CT) is widely used for nodal staging in NSCLC patients, being recommended by the National Comprehensive Cancer Network (NCCN) guidelines [1]. 18F-FDG PET/CT shows an overall good accuracy for nodal evaluation with sensitivity and specificity values ranging from 72 to 90% and from 81 to 95%, respectively [1,2,3,4,5,6]. Machine learning methods have been applied to 18F-FDG PET/CT as an advanced and innovative analysis tool in NSCLC patients for staging, treatment evaluation and prognostic stratification [7,8,9,10]. Each layer receives data, calculates scores and passes the output of the analysis to the layer in a self-learning process. This architecture has been recently widely used in the context of biomedical imaging research and radiation oncology, aiming to predict clinical outcomes and enrich diagnostic information, describing the interactions and complex simultaneous relationships of variables belonging to different domains [11,12,13]. Still limited, literature evidence has explored the application of NN to 18F-FDG PET/CT for predicting nodal involvement in NSCLC patients, but burdened by differences in clinical and procedural aspects [14,15,16,17]
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