TY - JOUR
T1 - Exploring tumor heterogeneity in colorectal liver metastases by imaging
T2 - Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification
AU - Wang, Qiang
AU - Nilsson, Henrik
AU - Xu, Keyang
AU - Wei, Xufu
AU - Chen, Danyu
AU - Zhao, Dongqin
AU - Hu, Xiaojun
AU - Wang, Anrong
AU - Bai, Guojie
N1 - Qiang Wang receives fundings from the Analytic Imaging Diagnostic Arena (AIDA) Clinical Fellowship (No. 2232 Wang), Karolinska Institutet Erik and Edith Fernstrom Foundation (No. 2023–00979), and Karolinska Institutet Travel Grant (2023-01711).
Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/6
Y1 - 2024/6
N2 - Objectives: This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning approach to preoperative CT images. Methods: This retrospective study retrieved clinical information and CT images of 197 patients with CRLM from The Cancer Imaging Archive (TCIA) database. Radiomics features were extracted from a segmented liver lesion identified at the portal venous phase. Those features which showed high stability, non-redundancy, and indicative information were selected. An unsupervised consensus clustering analysis on these features was adopted to identify subgroups of CRLM patients. Overall survival (OS), disease-free survival (DFS), and liver-specific DFS were compared between the identified subgroups. Cox regression analysis was applied to evaluate prognostic risk factors. Results: A total of 851 radiomics features were extracted, and 56 robust features were finally selected for unsupervised clustering analysis which identified two distinct subgroups (96 and 101 patients respectively). There were significant differences in the OS, DFS, and liver-specific DFS between the subgroups (all log-rank p < 0.05). The subgroup with worse outcome using the proposed radiomics model was consistently associated with shorter OS, DFS, and liver-specific DFS, with hazard ratios of 1.78 (95 %CI: 1.12–2.83), 1.72 (95 %CI: 1.16–2.54), and 1.59 (95 %CI: 1.10–2.31), respectively. The general performance of this radiomics model outperformed the traditional Clinical Risk Score and Tumor Burden Score in the prognosis prediction after surgery for CRLM. Conclusion: Radiomics features derived from preoperative CT images can reveal the heterogeneity of CRLM and stratify the patients with CRLM into subgroups with significantly different clinical outcomes.
AB - Objectives: This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning approach to preoperative CT images. Methods: This retrospective study retrieved clinical information and CT images of 197 patients with CRLM from The Cancer Imaging Archive (TCIA) database. Radiomics features were extracted from a segmented liver lesion identified at the portal venous phase. Those features which showed high stability, non-redundancy, and indicative information were selected. An unsupervised consensus clustering analysis on these features was adopted to identify subgroups of CRLM patients. Overall survival (OS), disease-free survival (DFS), and liver-specific DFS were compared between the identified subgroups. Cox regression analysis was applied to evaluate prognostic risk factors. Results: A total of 851 radiomics features were extracted, and 56 robust features were finally selected for unsupervised clustering analysis which identified two distinct subgroups (96 and 101 patients respectively). There were significant differences in the OS, DFS, and liver-specific DFS between the subgroups (all log-rank p < 0.05). The subgroup with worse outcome using the proposed radiomics model was consistently associated with shorter OS, DFS, and liver-specific DFS, with hazard ratios of 1.78 (95 %CI: 1.12–2.83), 1.72 (95 %CI: 1.16–2.54), and 1.59 (95 %CI: 1.10–2.31), respectively. The general performance of this radiomics model outperformed the traditional Clinical Risk Score and Tumor Burden Score in the prognosis prediction after surgery for CRLM. Conclusion: Radiomics features derived from preoperative CT images can reveal the heterogeneity of CRLM and stratify the patients with CRLM into subgroups with significantly different clinical outcomes.
KW - Colorectal liver metastases
KW - Computed tomography
KW - Hepatectomy
KW - Machine learning
KW - Prognosis
KW - Radiomics
UR - http://www.scopus.com/inward/record.url?scp=85190615051&partnerID=8YFLogxK
U2 - 10.1016/j.ejrad.2024.111459
DO - 10.1016/j.ejrad.2024.111459
M3 - Journal article
C2 - 38636408
AN - SCOPUS:85190615051
SN - 0720-048X
VL - 175
JO - European Journal of Radiology
JF - European Journal of Radiology
M1 - 111459
ER -
