Department of Radiology
Neoplasms | Radiology | Respiratory Tract Diseases | Statistical Models
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.
Cancer, Cancer imaging, Lung cancer, Predictive markers
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DOI of Published Version
de Margerie-Mellon C, Gill RR, Salazar P, Oikonomou A, Nguyen ET, Heidinger BH, Medina MA, VanderLaan PA, Bankier AA. Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models. Sci Rep. 2020 Sep 3;10(1):14585. doi: 10.1038/s41598-020-70316-3. PMID: 32883973; PMCID: PMC7471897. Link to article on publisher's site
de Margerie-Mellon C, Gill RR, Salazar P, Oikonomou A, Nguyen ET, Heidinger BH, Medina MA, VanderLaan PA, Bankier AA. (2020). Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models. Open Access Publications by UMMS Authors. https://doi.org/10.1038/s41598-020-70316-3. Retrieved from https://escholarship.umassmed.edu/oapubs/4378
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.