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MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases.

ABSTRACT: Synchronous liver metastasis (SLM) remains a major challenge for rectal cancer. Early detection of SLM is a key factor to improve the survival rate of rectal cancer. In this radiomics study, we predicted the SLM based on the radiomics of primary rectal cancer. A total of 328 radiomics features were extracted from the T2WI images of 194 patients. The least absolute shrinkage and selection operator (LASSO) regression was used to reduce the feature dimension and to construct the radiomics signature. after LASSO, principal component analysis (PCA) was used to sort the features of the surplus characteristics, and selected the features of the total contribution of 85%. Then the prediction model was built by linear regression, and the decision curve analysis was used to judge the net benefit of LASSO and PCA. In addition, we used two independent cohorts for training (n?=?135) and validation (n?=?159). We found that the model based on LASSO dimensionality construction had the maximum net benefit (in the training set (AUC [95% confidence interval], 0.857 [0.787-0.912]) and in the validation set (0.834 [0.714-0.918]). The radiomics nomogram combined with clinical risk factors and LASSO features showed a good predictive performance in the training set (0.921 [0.862-0.961]) and validation set (0.912 [0.809-0.97]). Our study indicated that radiomics based on primary rectal cancer could provide a non-invasive way to predict the risk of SLM in clinical practice.

SUBMITTER: Shu Z

PROVIDER: S-EPMC6399278 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases.

Shu Zhenyu Z Fang Songhua S Ding Zhongxiang Z Mao Dewang D Cai Rui R Chen Yuanjun Y Pang Peipei P Gong Xiangyang X

Scientific reports 20190304 1

Synchronous liver metastasis (SLM) remains a major challenge for rectal cancer. Early detection of SLM is a key factor to improve the survival rate of rectal cancer. In this radiomics study, we predicted the SLM based on the radiomics of primary rectal cancer. A total of 328 radiomics features were extracted from the T2WI images of 194 patients. The least absolute shrinkage and selection operator (LASSO) regression was used to reduce the feature dimension and to construct the radiomics signature ...[more]

PMID: 30833648

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Project description:BackgroundTo 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.MethodsThis 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.ResultsIn 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.ConclusionsThe 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.

Dataset Information

MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases.

Publications

MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases.

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