I$\text{'}$m working on a survival analysis project for non$-$small cell lung cancer $($NSCLC$)$ using the NSCLC$-$Radiomics dataset. This dataset contains two types of DICOM $(.$dcm$)$ files:

Regular CT scan images

Segmentation files $($containing manual $3$D tumor volume delineations$)$

I$\text{'}$m struggling to understand how to process these two types of DICOM files and integrate them into a Convolutional Neural Network $($CNN$)$ for survival analysis. Could you provide a detailed, step$-$by$-$step guide with code examples on how to:

Load and preprocess both types of DICOM files $($regular CT scans and segmentation files$)?$

Combine the information from the CT scans and segmentation files?

Prepare the processed data as input for a CNN model?

Design a CNN architecture that can effectively use both the image and segmentation data for survival analysis?

Please include Python code snippets for each step, using relevant libraries such as pydicom, numpy, and keras$/$tensorflow$.$ I$\text{'}$m particularly interested in:

How to read and interpret the segmentation DICOM files

Techniques to overlay or combine the segmentation data with the CT scan images

Any necessary data transformations or augmentations specific to medical imaging

How to structure the input data for the CNN $($e$.$g$.,$ multi$-$channel input, $3$D convolutions, etc.$)$

Your detailed explanation and code examples will greatly help me understand how to properly handle these different types of DICOM files and incorporate them into a CNN model for survival analysis. Thank you!