Liver metastases: The role of magnetic resonance imaging

Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases

RATIONALE AND OBJECTIVES

To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases.

MATERIALS AND METHODS

In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC).

RESULTS

The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively.

CONCLUSION

The DLR model is an effective method for identifying the primary source of liver metastases.

Complementary comments on metastatic liver lesions with exceptional and rare cases

Liver metastases can appear in different forms in magnetic resonance imaging. Contrary to popular belief, while radiologists report hypovascular or hypervascular metastatic lesions, exceptional examples may be detected in various tumors. The aim of this article is to improve this review by presenting rare and atypical examples of liver metastasis, as well as cases that might potentially be misdiagnosed as metastases during the process of differential diagnosis.

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

PURPOSE

We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases.

METHODS

Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon-Mann-Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered.

RESULTS

The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%.

CONCLUSIONS

Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

Effect of magnetic resonance imaging in liver metastases

This letter to the editor is a commentary on a study titled "Liver metastases: The role of magnetic resonance imaging." Exploring a noninvasive imaging evaluation system for the biological behavior of hepatocellular carcinoma (HCC) is the key to achieving precise diagnosis and treatment and improving prognosis. This review summarizes the role of magnetic resonance imaging in the detection and evaluation of liver metastases, describes its main imaging features, and focuses on the added value of the latest imaging tools (such as T1 weighted in phase imaging, T1 weighted out of phase imaging; diffusion-weighted imaging, T2 weighted imaging). In this study, I investigated the necessity and benefits of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid for HCC diagnostic testing and prognostic evaluation.

Effect of magnetic resonance imaging in liver metastases

This letter to the editor is a commentary on a study titled "Liver metastases: The role of magnetic resonance imaging." Exploring a noninvasive imaging evaluation system for the biological behavior of hepatocellular carcinoma (HCC) is the key to achieving precise diagnosis and treatment and improving prognosis. This review summarizes the role of magnetic resonance imaging in the detection and evaluation of liver metastases, describes its main imaging features, and focuses on the added value of the latest imaging tools (such as T1 weighted in phase imaging, T1 weighted out of phase imaging; diffusion-weighted imaging, T2 weighted imaging). In this study, I investigated the necessity and benefits of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid for HCC diagnostic testing and prognostic evaluation.