# Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT Images
## 2 Materials and Methods
### CT Image Database  
The LIDC-IDRI [4; 5] is a lung cancer screening dataset which comprises thoracic CT scans for a total of 1010 patients, alongside with annotated nodules belonging to one of three classes: (a) nodule \(\geq\) 3 mm; (b) nodule \(<\) 3 mm or (c) non-nodule \(\geq\) 3 mm, made during a two-phase annotation process by four experienced radiologists. Regarding data acquisition, slice thickness ranged from 0.6 to 5.0 mm, with X-ray current ranged from 40 to 627 mA (mean: 222.1 mA) at 120-140 kVp. Data Preparation  
To standardize the dataset, all CT scans were resampled. The pixel spacing was set to [1.00, 1.00, 1.00] mm and each CT dimension was calculated to match this new spacing, obtaining the resampled image by interpolation. Additionally, each pixel intensity value, measured in the Hounsfield Units (HU) scale, was normalized using the _min-max_ normalization method, and values under \(-\)1000 HU, which corresponds to air's radiodensity value, were transformed into 0 and values above 400 HU, representing hard tissues like bones, were transformed into 1. A linear transformation was computed to map all values in the middle into the [0, 1] intended range. To assess the nodule location, the final binary mask that included the pixels with at least 50% consensus of the provided contours was used, and an image with size (80 \(\times\) 80 \(\times\) 80) voxels centered on the nodule was extracted for each considered example in this study. From the 7371 detected nodules, only 2669 were classified as larger than 3 mm. For these examples, this database also provides nodule contours marked by each radiologist, as well as quantitative labelsfor a set of nodule related features. Although the feature learning task did not require any labels, this inclusion criterium was the one selected to facilitate the evaluation process. It was considered that a reconstructed image with a larger nodule would provide more clear information to evaluate the quality of the outputs by visualization. Considering data inclusion in the classification task, the provided malignancy risk value assessed by the radiologists was analyzed. This value corresponded to an integer ranging from 1 to 5 with the following designations related to the malignancy degree: (1) _Highly Unlikely_, (2) _Moderately Unlikely_, (3) _Indeterminate_, (4) _Moderately Suspicious_ and (5) _Highly Suspicious_. The interobserver agreement in rating malignancy was previously studied and a modest agreement was found [25,26]. For cases rated by two radiologists, they only agree in 43% of the cases. The percentage of agreement decreases for nodules annotated by more radiologists, with an agreement of 19% in the cases where the annotation was performed by three radiologists, and 12% of agreement in annotations from four radiologists. In addition, the analysis found in [5] regarding the percentage of nodules that were labelled by one, two, three or four radiologists, shows that almost 64% of the 2669 nodules were marked by a single clinician or by all of them, 29.1% and 34.8% respectively. When only assigned by one radiologist, the model only takes into account a single opinion, being susceptible to a possible error; on the other hand, if a nodule was labelled by all the four clinicians, the computed agreement rate of 12% tells that a lack of confidence in the resultant label would continue to exist. Having this analysis in mind and the fact that the subjectivity in the labelling process would not be eliminated if only the nodules annotated by all clinicians were included, this study includes the entire set of nodules marked by at least one radiologist. To take into account the different annotations provided for the same example, each nodule's malignancy value was computed by averaging the provided annotations made by all radiologists, and a mean malignancy value \(\leq\)2.0 was considered as benign, and \(\geq\)4.0 as malignant, excluding the examples with resultant mean value outside the selected ranges.