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

• Published in: Abdominal imaging Vol. 49; no. 12; pp. 4295 - 4306
• Main Authors: Pietsch, Friedrich L., Haag, Florian, Ayx, Isabelle, Grawe, Freba, Vellala, Abhinay K., Schoenberg, Stefan O., Froelich, Matthias F., Tharmaseelan, Hishan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2024; Springer Nature B.V
• Subjects: Adult; Aged; Aged, 80 and over; Cancer; Cancer therapies; Coefficient of variation; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - diagnostic imaging; Colorectal Neoplasms - pathology; Computed tomography; Contrast Media; Drug resistance; Female; Gastroenterology; Hepatobiliary; Hepatology; Heterogeneity; Humans; Image contrast; Image enhancement; Imaging; Lesions; Liver; Liver cancer; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - secondary; Male; Malignancy; Medical imaging; Medicine; Medicine & Public Health; Metastases; Metastasis; Middle Aged; Pancreatic cancer; Pancreatic Neoplasms - diagnostic imaging; Pancreatic Neoplasms - pathology; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Radiomics; Retrospective Studies; Tomography, X-Ray Computed - methods; Tumor/Oncology; Computed tomography; Pancreas; Colorectum; Tumoral Heterogeneity; Radiomics
• ISSN: 2366-0058; 2366-004X; 2366-0058
• DOI: 10.1007/s00261-024-04511-5

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). Materials and methods In this retrospective single-center study 73 subjects (42 CRC, 31 PDAC) with 1291 liver metastases (430 CRC, 861 PDAC) were segmented fully automated on contrast-enhanced CT images by a UNet for medical images. Radiomics features were extracted using the Python package Pyradiomics. The mean coefficient of variation (CV) was calculated patient-wise for each feature to quantify the heterogeneity. An unpaired t-test identified features with significant differences in feature variability between CRC and PDAC metastases. Results In both colorectal and pancreatic liver metastases, interlesional heterogeneity in imaging can be observed using quantitative imaging features. 75 second-order features were extracted to compare the varying textural characteristics. In total, 18 radiomics features showed a significant difference ( p  < 0.05) in their expression between the two malignancies. Out of these, 16 features showed higher levels of variability within the cohort of pancreatic metastases, which, as illustrated in a radar plot, suggests greater textural heterogeneity for this entity. Conclusions Radiomics has the potential to identify the interlesional heterogeneity of CT texture among individual liver metastases. In this proof-of-concept study for the quantification and comparison of imaging-related heterogeneity in liver metastases a variation in the extent of heterogeneity levels in CRC and PDAC liver metastases was shown.