Dataset Information

Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma.

ABSTRACT:

Objective

To build and validate a CT radiomic model for pre-operatively predicting lymph node metastasis in early cervical carcinoma.

Methods and materials

A data set of 150 patients with Stage IB1 to IIA2 cervical carcinoma was retrospectively collected from the Nanfang hospital and separated into a training cohort (n = 104) and test cohort (n = 46). A total of 348 radiomic features were extracted from the delay phase of CT images. Mann-Whitney U test, recursive feature elimination, and backward elimination were used to select key radiomic features. Ridge logistics regression was used to build a radiomic model for prediction of lymph node metastasis (LNM) status by combining radiomic and clinical features. The area under the receiver operating characteristic curve (AUC) and κ test were applied to verify the model.

Results

Two radiomic features from delay phase CT images and one clinical feature were associated with LNM status: log-sigma-2-0 mm-3D_glcm_Idn (p = 0.01937), wavelet-HL_firstorder_Median (p = 0.03592), and Stage IB (p = 0.03608). Radiomic model was built consisting of the three features, and the AUCs were 0.80 (95% confidence interval: 0.70 ~ 0.90) and 0.75 (95% confidence intervalI: 0.53 ~ 0.93) in training and test cohorts, respectively. The κ coefficient was 0.84, showing excellent consistency.

Conclusion

A non-invasive radiomic model, combining two radiomic features and a International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma. This model could serve as a pre-operative tool.

Advances in knowledge

A noninvasive CT radiomic model, combining two radiomic features and the International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma.

SUBMITTER: Chen J

PROVIDER: S-EPMC7362918 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma.

Chen Jiaming J He Bingxi B Dong Di D Liu Ping P Duan Hui H Li Weili W Li Pengfei P Wang Lu L Fan Huijian H Wang Siwen S Zhang Liwen L Tian Jie J Huang Zhipei Z Chen Chunlin C

The British journal of radiology 20200130 1108

<h4>Objective</h4>To build and validate a CT radiomic model for pre-operatively predicting lymph node metastasis in early cervical carcinoma.<h4>Methods and materials</h4>A data set of 150 patients with Stage IB1 to IIA2 cervical carcinoma was retrospectively collected from the Nanfang hospital and separated into a training cohort (<i>n</i> = 104) and test cohort (<i>n</i> = 46). A total of 348 radiomic features were extracted from the delay phase of CT images. Mann-Whitney <i>U</i> test, recurs ...[more]

PMID: 31957473

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Project description:PURPOSE:To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS:A total of 197 esophageal cancer patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS:Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell's Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION:The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.

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Dataset Information

Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma.

Objective

Methods and materials

Results

Conclusion

Advances in knowledge

Publications

Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma.

Similar Datasets

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