The growth pattern of liver metastases on MRI predicts early recurrence in patients with colorectal cancer: a multicenter study

Apr 14, 2022·
Qian Cai
1st Author
,
Yi-Ze Mao
Co-1st Author
,
Si-Qi Dai
Co-1st Author
Feng GAO
Feng GAO
Co-1st Author
,
Qian Xiao
,
Wan-Ming Hu
,
Tao Qin
,
Qiu-Xia Yang
,
Zhao-Zhou Li
Du CAI
Du CAI
Min-Er ZHONG
Min-Er ZHONG
,
Ke-Feng Ding
Co-corresponding Author
,
Xiaojian Wu
Co-corresponding Author
,
Rong Zhang
Corresponding Author
· 0 min read
Abstract

Objectives: The multicenter study aimed to explore the relationship between the growth pattern of liver metastases on preoperative MRI and early recurrence in patients with colorectal cancer liver metastases (CRCLM) after surgery.

Methods: A total of 348 CRCLM patients from 3 independent centers were enrolled, including 130 patients with 339 liver metastases in the primary cohort and 218 patients in validation cohorts. Referring to the gross classification of hepatocellular carcinoma (HCC), the growth pattern of each liver metastasis on MRI was classified into four types: rough, smooth, focal extranodular protuberant (FEP), and nodular confluent (NC). Disease-free survival (DFS) curve was constructed using the Kaplan-Meier method.

Results: In primary cohort, 42 (12.4%) of the 339 liver metastases were rough type, 237 (69.9%) were smooth type, 29 (8.6%) were FEP type, and 31 (9.1%) were NC type. Those patients with FEP- and/or NC-type liver metastases had shorter DFS than those without such metastases (p < 0.05). However, there were no significant differences in DFS between patients with rough- and smooth-type liver metastases and those without such metastases. The patients with FEP- and/or NC-type liver metastases also had shorter DFS than those without such metastases in two external validation cohorts. In addition, 40.5% of high-risk-type (FEP and NC) liver metastases converted to low-risk types (rough and smooth) after neoadjuvant chemotherapy.

Conclusions: The FEP- and NC-type liver metastases were associated with early recurrence, which may facilitate the clinical treatment of CRCLM patients.

Type
Publication
European Radiology
publication
Feng GAO
Authors
Professor
My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.
Du CAI
Authors
Postdoc
I focus on leveraging explainable AI and large foundation models to advance medical imaging and digital pathology in colorectal cancer research.
Min-Er ZHONG
Authors
Postdoc
I am a surgeon and clinical researcher focused on deep learning and translational studies in colorectal cancer.