Application of Artificial Neural Network to Preoperative 18F-FDG PET/CT for Predicting Pathological Nodal Involvement in Non-small-cell Lung Cancer Patients - Taralli, Silvia; Scolozzi, Valentina; Boldrini, Luca; Lenkowicz, Jacopo; Pelliccioni, Armando; Lorusso, Margherita; Attieh, Ola; Ricciardi, Sara; Carleo, Francesco; Cardillo, Giuseppe; Calcagni, Maria Lucia (Frontiers in Medicine, 2021)
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 (333M; 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%; 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%; 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. 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.