TY - JOUR
T1 - Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models
AU - Wesdorp, Nina
AU - Zeeuw, Michiel
AU - van der Meulen, Delanie
AU - van ‘t Erve, Iris
AU - Bodalal, Zuhir
AU - Roor, Joran
AU - van Waesberghe, Jan Hein
AU - Moos, Shira
AU - van den Bergh, Janneke
AU - Nota, Irene
AU - van Dieren, Susan
AU - Stoker, Jaap
AU - Meijer, Gerrit
AU - Swijnenburg, Rutger-Jan
AU - Punt, Cornelis
AU - Huiskens, Joost
AU - Beets-Tan, Regina
AU - Fijneman, Remond
AU - on behalf of the Dutch Colorectal Cancer Group Liver Expert Panel
AU - Marquering, Henk
AU - Kazemier, Geert
N1 - Funding Information: This research has received funding by the Dutch Cancer Society (KWF Kankerbestrijding), project number 14002/2021-2 and by KWF project number 10438, and an unrestricted grant from the Cancer Center Amsterdam Foundation. The CAIRO5 study is supported by unrestricted scientific grants from Roche and Amgen. Publisher Copyright: © 2023 by the authors.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - For patients with colorectal cancer liver metastases (CRLM), the genetic mutation status is important in treatment selection and prognostication for survival outcomes. This study aims to investigate the relationship between radiomics imaging features and the genetic mutation status (KRAS mutation versus no mutation) in a large multicenter dataset of patients with CRLM and validate these findings in an external dataset. Patients with initially unresectable CRLM treated with systemic therapy of the randomized controlled CAIRO5 trial (NCT02162563) were included. All CRLM were semi-automatically segmented in pre-treatment CT scans and radiomics features were calculated from these segmentations. Additionally, data from the Netherlands Cancer Institute (NKI) were used for external validation. A total of 255 patients from the CAIRO5 trial were included. Random Forest, Gradient Boosting, Gradient Boosting + LightGBM, and Ensemble machine-learning classifiers showed AUC scores of 0.77 (95%CI 0.62–0.92), 0.77 (95%CI 0.64–0.90), 0.72 (95%CI 0.57–0.87), and 0.86 (95%CI 0.76–0.95) in the internal test set. Validation of the models on the external dataset with 129 patients resulted in AUC scores of 0.47–0.56. Machine-learning models incorporating CT imaging features could identify the genetic mutation status in patients with CRLM with a good accuracy in the internal test set. However, in the external validation set, the models performed poorly. External validation of machine-learning models is crucial for the assessment of clinical applicability and should be mandatory in all future studies in the field of radiomics.
AB - For patients with colorectal cancer liver metastases (CRLM), the genetic mutation status is important in treatment selection and prognostication for survival outcomes. This study aims to investigate the relationship between radiomics imaging features and the genetic mutation status (KRAS mutation versus no mutation) in a large multicenter dataset of patients with CRLM and validate these findings in an external dataset. Patients with initially unresectable CRLM treated with systemic therapy of the randomized controlled CAIRO5 trial (NCT02162563) were included. All CRLM were semi-automatically segmented in pre-treatment CT scans and radiomics features were calculated from these segmentations. Additionally, data from the Netherlands Cancer Institute (NKI) were used for external validation. A total of 255 patients from the CAIRO5 trial were included. Random Forest, Gradient Boosting, Gradient Boosting + LightGBM, and Ensemble machine-learning classifiers showed AUC scores of 0.77 (95%CI 0.62–0.92), 0.77 (95%CI 0.64–0.90), 0.72 (95%CI 0.57–0.87), and 0.86 (95%CI 0.76–0.95) in the internal test set. Validation of the models on the external dataset with 129 patients resulted in AUC scores of 0.47–0.56. Machine-learning models incorporating CT imaging features could identify the genetic mutation status in patients with CRLM with a good accuracy in the internal test set. However, in the external validation set, the models performed poorly. External validation of machine-learning models is crucial for the assessment of clinical applicability and should be mandatory in all future studies in the field of radiomics.
KW - CT scan
KW - KRAS mutation
KW - colorectal cancer
KW - genetic mutation
KW - liver metastases
KW - radiomics
UR - http://www.scopus.com/inward/record.url?scp=85179301021&partnerID=8YFLogxK
U2 - https://doi.org/10.3390/cancers15235648
DO - https://doi.org/10.3390/cancers15235648
M3 - Article
C2 - 38067353
SN - 2072-6694
VL - 15
JO - Cancers
JF - Cancers
IS - 23
M1 - 5648
ER -