Application of Artificial Neural Network to Preoperative 18F-FDG PET/CT for Predicting Pathological Nodal Involvement in Non-small-cell Lung Cancer Patients

Purpose: To evaluate the performance of artificial neural networks (aNN) applied to preoperative F-18-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 F-18-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 F-18-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.

Automated summary

The study evaluated the performance of artificial neural networks in predicting nodal involvement in non-small-cell lung cancer patients using preoperative F-18-FDG PET/CT. Results showed that the neural networks had good performance in predicting nodal involvement, especially in ruling out nodal metastases. The high negative predictive value of the neural networks and PET qualitative assessment suggest their potential for identifying unexpected nodal-metastatic patients in cases of PET-negative images.