A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques

Alshohoumi, Fatma; Al-Hamdani, Abdullah; Hedjam, Rachid; AlAbdulsalam, AbdulRahman Khalifa; AlZaabi, Adhari

doi:10.3390/healthcare10102075

Cited by 4 publications

(2 citation statements)

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“…Radiomics aims to extract large amounts of high-dimensional data from traditional imaging sequences to mine the underlying pathophysiological information at the microscopic level [26][27][28] and has demonstrated important roles in diagnosing and treating tumors [29][30][31][32]. Most existing studies on the radiomic prediction of CRLM are based on imaging data from the liver parenchyma [33][34][35][36]; few studies have used radiomic models based on baseline MRI of the primary lesion in CRC patients to predict CRLM.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of Colorectal Cancer Liver Metastasis through an MRI Radiomic Model

Wu,

Liu,

Tao

et al. 2023

Preprint

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Objective: To investigate the efficacy of a magnetic resonance imaging (MRI) radiomics model in predicting colorectal cancer liver metastasis (CRLM). Methods: A total of 120 patients who underwent baseline MRI examination at the Affiliated Hospital of North Sichuan Medical College from June 2016 to August 2022 and were pathologically confirmed to have colorectal cancer (CRC) were randomly divided into a training group and a validation group. The clinical risk factors and MRI data of all patients were collected. Univariate and multivariate analysis were used to screen the clinically independent risk factors for CRLM. The radiomic features of each sequence were extracted from oblique axial or axial fat-free T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences. Least absolute shrinkage and selection operator (LASSO) regression was used to screen the optimal radiomic features of each sequence. Logistic regression was used to establish a prediction model of each sequence (T2WI and DWI models), a combined radiomics model (M) integrating the features of T2WI and DWI sequences, and a combined imaging-clinical model (U) combining the radiomic features of each sequence with clinically independent risk factors. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the predictive performance of each model. Results: Among the 120 CRC patients enrolled, 57 had liver metastasis, and 63 did not. The tumor markers carcinoembryonic antigen and carbohydrate antigen 19-9 were clinically independent risk factors for CRLM. Three optimal radiomic features were screened from T2WI and DWI sequences through LASSO regression analysis, respectively. The AUC values of the T2WI, DWI, M, and U models were 0.811, 0.803, 0.824, and 0.899 in the training group and 0.795, 0.798, 0.813, and 0.889 in the validation group, respectively. The predictive performance of the combined models was better than that of the single-sequence models. The U model performed best at predicting CRLM. Conclusion: An MRI radiomics model based on CRC primary lesions can predict CRLM well. Our combined model integrating the radiomic features of each sequence and clinically independent risk factors had the best predictive performance.

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Section: Discussionmentioning

confidence: 99%

“…(2) This study used single-center data and lacked external validation. Integrating data from multiple sources can capture more accurate information, resulting in more robust predictions [26]; ideally, model validation should be performed using external data [44]. Multicenter data should also be employed in future studies.…”

Section: Discussionmentioning

confidence: 99%