Radiomics analysis on PET/CT scans to discriminate malignant (adenocarcinoma) and benign pulmonary nodules

Background-Aim: Artificial intelligence techniques (machine
learning, deep learning, and radiomics) play an increasing role in
nuclear medicine to diagnose oncological diseases. Radiomics provides the possibility to build classification and/or regression models
based on quantitative features extracted from positron emission
tomography (PET) scan data. The aim of our work was the discrimination between benign and malignant solitary pulmonary nodules
(SPN), as it may provide an in vivo classification of the disease, thus
avoiding invasive diagnostic techniques.
Methods: Baseline PET/CT scans (Discovery 710, GE Healthcare) of
83 histologically-confirmed lung nodules (44 adenocarcinomas, 39
benign nodules) from as many patients (46 males, 37 females,
age = 67.2 ± 9.0 [44–83] year) undergoing examination at the Unit
of Nuclear Medicine of the University of Sassari, (Italy), between
November 2014 and July 2019 were retrospectively analysed. For the
radiomics analysis we considered 5 conventional features, 8 firstorder texture features, 9 s-order texture features and 6 shape features
computed on the PET and CT signal separately for a total of 42
features. A subset of 25 features that showed statistically-significant
differences (determined via Mann–Whitney U test) between the two
phenotypes were eventually retained for the classification step. Four
classification models (k-NN, Logistic Regression, Random Forest and
Support Vector Machines) were fed with the selected features to test
their ability to discriminate the adenocarcinomas from the benign
lesions. Accuracy estimation was based on split-sample validation
with stratified sampling at 70% train rate, and the results were averaged over 250 random splits into train and test set for a
stable estimation.
Results: The performance of the four classification models was very
close; the best classifier achieved an accuracy of 80.2% (sensitivity = 75.1%, specificity = 94.4%).
Conclusions: Our results suggest that adenocarcinoma has a radiomics signature that allows to differentiate it from benign nodules,
therefore representing an ‘‘identity card’’ of this type of lung cancer.
This confirms the potential benefits of radiomics in the management
of patients with indeterminate pulmonary nodules. This work is
however not exempt from limitations, in particular the relatively
contained sample size and the retrospective nature of the study. The
results should be further validated in larger, ideally prospective
studies.