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.
- Neural network, lung cancer, 18F-FDG PET/CT