• The Herder model showed a ROC AUC of 0.807 on 180 SPNs. • The BIMC model showed a ROC AUC of 0.822 on 180 SPNs. • Decision analysis is more favourable to the BIMC model.

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SummaryBackgroundThe solitary pulmonary nodule (SPN) is a common radiologic abnormality on chest x-rays or computed tomography (CT) scans of the lungs. The differential diagnosis of SPNs is particularly wide as it includes a multitude of benign as well as malignant entities. Nodule location within the lungs has been proposed as a predictive feature in the literature. This study aims at illustrating the distribution within the lungs of a large current series of consecutive SPNs according to their histological subtype, which was definitely proved at core biopsy.Material/MethodsTwo hundred-eight SPNs referred to our center for characterization were reviewed in this single-centre retrospective study. Histological subtypes were defined following the IASLC/ATS/ERS and WHO (2004) histological classification.ResultsThis study provides evidence with respect to the prevalence of adenocarcinomas and other non-neuroendocrine primary lung cancer types in the right upper lobe. It also provides new evidence with respect to the prevalence of carcinoid tumors in the middle and right lower lobe, with a tendency to occur in the central lung parenchyma.ConclusionsThis work updates existing knowledge of solid SPNs location within the lungs by providing a current picture of SPN distribution according to their nature.

• The BIMC model can accurately discriminate malignancies in the clinical setting • The BIMC model showed ROC AUC of 0.880 in this multicentre study • The BIMC model compares favourably with the Mayo Clinic model.

• The BIMC and PKUPH models offer better characterization than older prediction models • Both the PKUPH and BIMC models completely avoided false negative results • The Mayo model suffers from a large number of indeterminate results.

Surgeons should be aware that current data shows how the Mayo Clinic model is of little use in preoperative nodule characterization.

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