Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis

Wang, Yun; Ma, Luyao; Yin, Xiaoping; Gao, Bu-Lang

doi:10.3389/fonc.2021.689509

Cited by 19 publications

(16 citation statements)

References 88 publications

(135 reference statements)

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“…Due to its anatomical condition in relation to its portal circulation, colorectal cancer (CRC) cells are most likely to metastasize to the liver [ 1 , 2 ]. The high incidence of liver metastases (LM) is the major factor leading poor prognoses in patients with CRC [ 1 – 3 ].…”

Section: Introductionmentioning

confidence: 99%

Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery

Liang

Wang

et al. 2022

Cancer Imaging

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Background To develop a radiomics model based on pretreatment whole-liver portal venous phase (PVP) contrast-enhanced CT (CE-CT) images for predicting metachronous liver metastases (MLM) within 24 months after rectal cancer (RC) surgery. Methods This study retrospectively analyzed 112 RC patients without preoperative liver metastases who underwent rectal surgery between January 2015 and December 2017 at our institution. Volume of interest (VOI) segmentation of the whole-liver was performed on the PVP CE-CT images. All 1316 radiomics features were extracted automatically. The maximum-relevance and minimum-redundancy and least absolute shrinkage and selection operator methods were used for features selection and radiomics signature constructing. Three models based on radiomics features (radiomics model), clinical features (clinical model), and radiomics combined with clinical features (combined model) were built by multivariable logistic regression analysis. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of models, and calibration curve and the decision curve analysis were performed to evaluate the clinical application value. Results In total, 52 patients in the MLM group and 60 patients in the non-MLM group were enrolled in this study. The radscore was built using 16 selected features and the corresponding coefficients. Both the radiomics model and the combined model showed higher diagnostic performance than clinical model (AUCs of training set: radiomics model 0.84 (95% CI, 0.76–0.93), clinical model 0.65 (95% CI, 0.55–0.75), combined model 0.85 (95% CI, 0.77–0.94); AUCs of validation set: radiomics model 0.84 (95% CI, 0.70–0.98), clinical model 0.58 (95% CI, 0.40–0.76), combined model 0.85 (95% CI, 0.71–0.99)). The calibration curves showed great consistency between the predicted value and actual event probability. The DCA showed that both the radiomics and combined models could add a net benefit on a large scale. Conclusions The radiomics model based on preoperative whole-liver PVP CE-CT could predict MLM within 24 months after RC surgery. Clinical features could not significantly improve the prediction efficiency of the radiomics model.

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Section: Introductionmentioning

confidence: 99%

Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery

Liang

Wang

et al. 2022

Cancer Imaging

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“…A patient’s prognosis is greatly affected by their liver metastases, and the average survival time for patients with hepatic metastases from gastrointestinal cancers is only 6 months without appropriate treatment. Therefore, it is essential to accurately predict and differentiate liver metastases from CRC to make a proper therapeutic plan and improve the patient’s prognosis [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…Quantitative imaging features define the texture of tumors [ 39 ]. The quantitative imaging features include shape, intensity, volume, size, and texture, which provide detailed information on tumor microenvironment and phenotype compared to laboratory results, clinical reports, and genomic or proteomics analyses [ 22 ]. Additionally, radiomics measures spatial and temporal intramural heterogeneity non-invasively [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

“…For example, entropy and heterogeneity, especially after contrast enhancement, indicate active disease with heterogeneous clones, whereas homogeneity after chemotherapy indicates tumor necrosis [ 28 ]. In addition to exploring subtle changes in tumor and liver texture before and after treatment, radiomics can also be used to evaluate the response of CRLM lesions to chemotherapy [ 22 ]. To better understand the occurrence and development of diseases, radiogenomics can be used to discover radiomic features indicative of gene expression or polymorphism.…”

Section: Introductionmentioning

confidence: 99%

“…Noninvasive and conventional imaging methods are used in radiogenomics to understand gene expression in diseases. At present, radiogenomics studies of liver tumors are limited [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2022

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An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.

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A nomogram was developed using clinicopathological features to predict postoperative liver metastasis in patients with colorectal cancer

Dou,

Xi,

Zheng

et al. 2023

J Cancer Res Clin Oncol

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The objective of this study is to examine the risk factors that contribute to the development of liver metastasis (LM) in patients who have suffered radical resection for colorectal cancer (CRC), and to establish a nomogram model that can be used to predict the occurrence of the LM. MethodsThe present study enrolled 1377 patients diagnosed with CRC between January 2010 and July 2021. The datasets were allocated to training (n = 965) and validation (n = 412) sets in a randomly strati ed manner.The study utilized univariate and multivariate logistic regression analysis to establish a nomogram for predicting LM in patients with CRC. ResultsMultivariate analysis revealed that T stage, N stage, number of harvested lymph nodes (LNH), mismatch repair (MMR) status, neutrophil count, monocyte count, postoperative carcinoembryonic antigen (CEA) levels, postoperative cancer antigen 125 (CA125) levels, and postoperative carbohydrate antigen 19 − 9 (CA19-9) levels were independent predictive factors for LM after radical resection. These factors were then utilized to construct a comprehensive nomogram for predicting LM. The nomogram demonstrated great discrimination, with an area under the curve (AUC) of 0.782 for the training set and 0.768 for the validation set. Additionally, the nomogram exhibited excellent calibration and signi cant clinical bene t as con rmed by the calibration curves and the decision curve analysis, respectively. ConclusionThis nomogram has the potential to support clinicians in identifying high-risk patients who may develop LM post-surgery. Clinicians can devise personalized treatment and follow-up plans, ultimately leading to an improved prognosis for patients.

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Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis

Cited by 19 publications

References 88 publications

Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery

Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery

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

A nomogram was developed using clinicopathological features to predict postoperative liver metastasis in patients with colorectal cancer

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