Abdominal radiology (New York) · 2021
A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer.
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Hu Yumin, Weng Qiaoyou, Xia Haihong, Chen Tao, Kong Chunli, Chen Weiyue, Pang Peipei, Xu Min, Lu Chenying, Ji Jiansong
Аннотация
PURPOSE: To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs).
METHODS: A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated.
RESULTS: Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram.
CONCLUSION: The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.
METHODS: A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated.
RESULTS: Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram.
CONCLUSION: The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.