<p>Multi-Window CT Based Radiological Traits for Improving Early Detection in Lung Cancer Screening</p>

Abstract

Rationale and Objectives: Evaluate ability of radiological semantic traits assessed on multi-window computed tomography (CT) to predict lung cancer risk. Materials and Methods: A total of 199 participants were investigated, including 60 incident lung cancers and 139 benign positive controls. Twenty lung window features and 2 mediastinal window features were extracted and scored on a point scale in three screening rounds. Multivariate logistic regression analysis was used to explore the association of these radiological traits with the risk of developing lung cancer. The areas under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value (PPV) were computed to evaluate the best predictive model. Results: Combining mediastinal window-specific features with the lung window featuresbased model significantly improves performance compared to individual window features. Model performance is consistent both at baseline and the first follow-up scan, with an AUROC increased from 0.822 to 0.871 (p = 0.009) and from 0.877 to 0.917 (p = 0.008), respectively, for single to multi-window feature models. We also find that the multi-window CT based model showed better specificity and PPV, with PPV at the second follow-up scan improved to 0.953. Conclusion: We find combining window semantic features improves model performance in identifying cancerous nodules. We also find that lung window features are more informative compared to mediastinal features in predicting malignancy.

Performance of radiomics models derived from different CT reconstruction parameters for lung cancer risk prediction

“…Our results are consistent with those of Yang et al, who showed that radiomics signatures from both plain and vein-phase CT images were not superior to either plain signatures or vein signatures in differentiating solitary granulomas and solid lung adenocarcinomas [ 14 ]. However, two recent studies on lung cancer indicated that multiple-window-based models performed better than single-window-based models [ 20 , 21 ]. For example, Lu et al found that when predicting lung tumour growth patterns from radiomics features, the models constructed based on the lung window or the difference region (subtracting the mediastinal window region from the lung window region) were inferior to the model established based on both of them [ 21 ].…”

Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer

“…Most existing thresholding-based lung nodule detection techniques handle CT scan images consisting of lung windows only and do not consider the mediastinal windows in the detection of the disease. Research such as [33] has shown that the detection rate is improved if both windows are analyzed for lung nodules. On the other hand, deep learning requires a huge amount of data to train a model perfectly; it is challenging to obtain such an extensive dataset in the medical field, where imaging and tagging by experts is a tedious and expensive task.…”