AUTHOR=Taralli Silvia , Scolozzi Valentina , Boldrini Luca , Lenkowicz Jacopo , Pelliccioni Armando , Lorusso Margherita , Attieh Ola , Ricciardi Sara , Carleo Francesco , Cardillo Giuseppe , Calcagni Maria Lucia 

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

JOURNAL=Frontiers in Medicine

VOLUME=8

YEAR=2021

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.664529

DOI=10.3389/fmed.2021.664529

ISSN=2296-858X

ABSTRACT=<p><bold>Purpose:</bold> To evaluate the performance of artificial neural networks (aNN) applied to preoperative <sup>18</sup>F-FDG PET/CT for predicting nodal involvement in non-small-cell lung cancer (NSCLC) patients.</p><p><bold>Methods:</bold> We retrospectively analyzed data from 540 clinically resectable NSCLC patients (333 M; 67.4 ± 9 years) undergone preoperative <sup>18</sup>F-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.</p><p><bold>Results:</bold> 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.</p><p><bold>Conclusions:</bold> aNN application to preoperative <sup>18</sup>F-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 <italic>a priori</italic> 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.</p>