Late gadolinium MRI enhancement of colorectal liver metastases is associated with overall survival among nonsurgical patients

A radiomic biomarker for prognosis of resected colorectal cancer liver metastases generalizes across MRI contrast agents

Introduction

Contrast-enhanced MRI is routinely performed as part of preoperative work-up for patients with Colorectal Cancer Liver Metastases (CRLM). Radiomic biomarkers depicting the characteristics of CRLMs in MRI have been associated with overall survival (OS) of patients, but the reproducibility and clinical applicability of these biomarkers are limited due to the variations in MRI protocols between hospitals.

Methods

In this work, we propose a generalizable radiomic model for predicting OS of CRLM patients who received preoperative chemotherapy and delayed-phase contrast enhanced (DPCE) MRIs prior to hepatic resection. This retrospective two-center study included three DPCE MRI cohorts (n=221) collected between January 2006 and December 2012. A 10-minute delayed Gd-DO3A-butrol enhanced MRI discovery cohort was used to select features based on robustness across contrast agents, correlation with OS and pairwise Pearson correlation, and to train a logistic regression model that predicts 3-year OS.

Results

The model was evaluated on a 10-minute delayed Gd-DO3A-butrol enhanced MRI validation cohort (n=121), a 20-minute delayed Gd-EOB-DTPA (n=72) cohort from the same institute, and a 5-minute delayed Gd-DTPA cohort (n=28) from an independent institute. Two features were selected: minor axis length and dependence variance. The radiomic signature model stratified high-risk and low-risk CRLM groups in the Gd-DO3Abutrol (HR = 6.29, p = .007), Gd-EOB-DTPA (HR = 3.54, p = .003) and Gd-DTPA (HR = 3.16, p = .04) validation cohorts.

Discussion

While most existing MRI findings focus on a specific contrast agent, our study shows the potential of MRI features to be generalizable across main-stream contrast agents at delayed phase.

Comprehensive Imaging Characterization of Colorectal Liver Metastases

Colorectal liver metastases (CRLM) have heterogenous histopathological and immunohistochemical phenotypes, which are associated with variable responses to treatment and outcomes. However, this information is usually only available after resection, and therefore of limited value in treatment planning. Improved techniques for in vivo disease assessment, which can characterise the variable tumour biology, would support further personalization of management strategies. Advanced imaging of CRLM including multiparametric MRI and functional imaging techniques have the potential to provide clinically-actionable phenotypic characterisation. This includes assessment of the tumour-liver interface, internal tumour components and treatment response. Advanced analysis techniques, including radiomics and machine learning now have a growing role in assessment of imaging, providing high-dimensional imaging feature extraction which can be linked to clinical relevant tumour phenotypes, such as a the Consensus Molecular Subtypes (CMS). In this review, we outline how imaging techniques could reproducibly characterize the histopathological features of CRLM, with several matched imaging and histology examples to illustrate these features, and discuss the oncological relevance of these features. Finally, we discuss the future challenges and opportunities of CRLM imaging, with a focus on the potential value of advanced analytics including radiomics and artificial intelligence, to help inform future research in this rapidly moving field.

Delayed MRI Enhancement of Colorectal Cancer Liver Metastases Is Associated With Metastatic Mutational Profile

BACKGROUND/AIM

Individual tumor genomics plays a key role in determining patient prognosis, response to chemotherapy and in guiding therapy. In prior studies, it was shown that the degree of late enhancement of colorectal liver metastases (CRCLM) target tumor enhancement (TTE) as seen on magnetic resonance imaging (MRI) was associated with overall survival. In order to better understand the relationship between MRI enhancement and survival, the aim of this study was to characterize genomic profiles of tumors clustered by MRI TTE, and investigate the association between TTE and genetic mutations.

MATERIALS AND METHODS

Matched tumor and normal tissue samples from patients with weak TTE and strong TTE were analyzed by Next-generation sequencing (NGS) technology using a custom colorectal cancer panel.

RESULTS

We discovered a total of 42 non-synonymous somatic mutations from 10 patients with weak TTE and 26 with 10 patients with strong TTE. Adenomatosis Polyposis Coli (APC) was the most commonly altered gene, 18 of those APC mutations were found in the weak TTE and 9 in the strong TTE group.

CONCLUSION

An association exists between TTE and mutational status of CRCLM, which may offer some explanation as to why TTE is associated with overall survival in patients with CRCLM.

Firework Optimization Algorithm-Based Diagnosis of Hepatocellular Carcinoma and Hepatic Cavernous Hemangioma Using MRI Images

This study was aimed to explore the diagnostic features of magnetic resonance imaging (MRI) on hepatocellular carcinoma (HCC) and hepatic cavernous hemangioma (HCH). A fireworks algorithm optimization (FAO) was proposed based on the fireworks algorithm (FA), and it was compared with the maximum between-class variance method (OTSU) and the maximum entropy threshold method (KSW) for analysis. In addition, it was applied to the diagnosis of MRI images of 55 HCC patients in the experimental group (group E) and 55 HCH patients in the control group (group C). It was found that the FAO showed a greatly lower difference function (DF) and a shorter running time in contrast to the OTSU and KSW algorithms (P < 0.05); the diagnostic accuracy (DA) of the T1-weighted image (T1WI) for patients in groups E and C was 85.31% and 95.85%, respectively, and the DA of the T2-weighted image (T2WI) was 97.84% (group E) and 89.71% (group C), respectively. In short, FAO showed an excellent performance in segmentation and reconstruction of MRI images for liver tissue, and T1WI and T2WI of MRI images showed high accuracy in diagnosing the HCC and HCH, respectively.

WITHDRAWN: Risks and Benefits of Gadolinium-Based Contrast Enhanced MRI

The Publisher regrets that this article is an accidental duplication of an article that has already been published in [Seminars in Ultrasound, CT, and MRI, 41/2 (2020) 170–182], https://dx.doi.org/10.1053/j.sult.2019.12.005. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal