Abstract
The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification.
Highlights
The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ, 64 minimally invasive adenocarcinomas and 132 invasive adenocarcinomas
31 nodules were of type H1, 64 nodules were of type H2 and 132 nodules
We proposed two parsimonious models for the prediction of lung adenocarcinoma subtypes presenting as subsolid nodules on CT
Summary
The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). By classifying nodules as either non-invasive or invasive adenocarcinomas, these models were limited by their binary output Because this binary output hardly reflects the histological complexity of the adenocarcinoma spectrum, we aimed to expand this binary into a multiclass model classification that would better match the current histological adenocarcinoma categories[3]. Information about how CT input features would affect outcome categories of degrees of invasiveness If successful, this approach would enable radiologists to assess the probability of invasiveness of individual cancers as defined by CT characteristics of lung nodules. The aim of our study, was to develop and test multiclass predictive models based on ordinal regressions for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on CT examinations
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