Radiomics-driven spectral profiling of six kidney stone types with monoenergetic CT reconstructions in photon-counting CT.

Objectives: Urolithiasis, a common and painful urological condition, is influenced by factors such as lifestyle, genetics, and medication. Differentiating between different types of kidney stones is crucial for personalized therapy. The purpose of this study is to investigate the use of photon-counting computed tomography (PCCT) in combination with radiomics and machine learning to develop a method for automated and detailed characterization of kidney stones.

Textural heterogeneity of liver lesions in CT imaging - comparison of colorectal and pancreatic metastases.

Purpose: Tumoral heterogeneity poses a challenge for personalized cancer treatments. Especially in metastasized cancer, it remains a major limitation for successful targeted therapy, often leading to drug resistance due to tumoral escape mechanisms. This work explores a non-invasive radiomics-based approach to capture textural heterogeneity in liver lesions and compare it between colorectal cancer (CRC) and pancreatic cancer (PDAC).

Comparing quantitative image parameters between animal and clinical CT-scanners: a translational phantom study analysis.

Purpose: This study compares phantom-based variability of extracted radiomics features from scans on a photon counting CT (PCCT) and an experimental animal PET/CT-scanner (Albira II) to investigate the potential of radiomics for translation from animal models to human scans. While oncological basic research in animal PET/CT has allowed an intrinsic comparison between PET and CT, but no 1:1 translation to a human CT scanner due to resolution and noise limitations, Radiomics as a statistical and thus scale-independent method can potentially close the critical gap.

Methods: Two phantoms were scanned on a PCCT and animal PET/CT-scanner with different scan parameters and then the radiomics parameters were extracted.

Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning.

Objectives: The goal of this study is to demonstrate the performance of radiomics and CNN-based classifiers in determining the primary origin of gastrointestinal liver metastases for visually indistinguishable lesions.

Methods: In this retrospective, IRB-approved study, 31 pancreatic cancer patients with 861 lesions (median age [IQR]: 65.39 [56.