Daisuke Kawahara1, Yuji Murakami1, Shigeyuki Tani2, Yasushi Nagata1,3. 1. Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, Japan. 2. School of Medicine, Hiroshima University, Hiroshima, Japan. 3. Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan.
Abstract
OBJECTIVE: To propose the prediction model for degree of differentiation for locally advanced esophageal cancer patients from the planning CT image by radiomics analysis with machine learning. METHODS: Data of 104 patients with esophagus cancer, who underwent chemoradiotherapy followed by surgery at the Hiroshima University hospital from 2003 to 2016 were analyzed. The treatment outcomes of these tumors were known prior to the study. The data were split into 3 sets: 57/16 tumors for the training/validation and 31 tumors for model testing. The degree of differentiation of squamous cell carcinoma was classified into two groups. The first group (Group I) was a poorly differentiated (POR) patients. The second group (Group II) was well and moderately differentiated patients. The radiomics feature was extracted in the tumor and around the tumor regions. A total number of 3480 radiomics features per patient image were extracted from radiotherapy planning CT scan. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors. The radiomics features were used for the input data in the machine learning. To build predictive models with radiomics features, neural network classifiers was used. The precision, accuracy, sensitivity by generating confusion matrices, the area under the curve (AUC) of receiver operating characteristic curve were evaluated. RESULTS: By the LASSO analysis of the training data, we found 13 radiomics features from CT images for the classification. The accuracy of the prediction model was highest for using only CT radiomics features. The accuracy, specificity, and sensitivity of the predictive model were 85.4%, 88.6%, 80.0%, and the AUC was 0.92. CONCLUSION: The proposed predictive model showed high accuracy for the classification of the degree of the differentiation of esophagus cancer. Because of the good prediction ability of the method, the method may contribute to reducing the pathological examination by biopsy and predicting the local control. ADVANCES IN KNOWLEDGE: For esophageal cancer, the differentiation of degree is the import indexes reflecting the aggressiveness. The current study proposed the prediction model for the differentiation of degree with radiomics analysis.
OBJECTIVE: To propose the prediction model for degree of differentiation for locally advanced esophageal cancer patients from the planning CT image by radiomics analysis with machine learning. METHODS: Data of 104 patients with esophagus cancer, who underwent chemoradiotherapy followed by surgery at the Hiroshima University hospital from 2003 to 2016 were analyzed. The treatment outcomes of these tumors were known prior to the study. The data were split into 3 sets: 57/16 tumors for the training/validation and 31 tumors for model testing. The degree of differentiation of squamous cell carcinoma was classified into two groups. The first group (Group I) was a poorly differentiated (POR) patients. The second group (Group II) was well and moderately differentiated patients. The radiomics feature was extracted in the tumor and around the tumor regions. A total number of 3480 radiomics features per patient image were extracted from radiotherapy planning CT scan. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors. The radiomics features were used for the input data in the machine learning. To build predictive models with radiomics features, neural network classifiers was used. The precision, accuracy, sensitivity by generating confusion matrices, the area under the curve (AUC) of receiver operating characteristic curve were evaluated. RESULTS: By the LASSO analysis of the training data, we found 13 radiomics features from CT images for the classification. The accuracy of the prediction model was highest for using only CT radiomics features. The accuracy, specificity, and sensitivity of the predictive model were 85.4%, 88.6%, 80.0%, and the AUC was 0.92. CONCLUSION: The proposed predictive model showed high accuracy for the classification of the degree of the differentiation of esophagus cancer. Because of the good prediction ability of the method, the method may contribute to reducing the pathological examination by biopsy and predicting the local control. ADVANCES IN KNOWLEDGE: For esophageal cancer, the differentiation of degree is the import indexes reflecting the aggressiveness. The current study proposed the prediction model for the differentiation of degree with radiomics analysis.
Authors: James M Donahue; Francis C Nichols; Zhuo Li; David A Schomas; Mark S Allen; Stephen D Cassivi; Aminah Jatoi; Robert C Miller; Dennis A Wigle; K Robert Shen; Claude Deschamps Journal: Ann Thorac Surg Date: 2009-02 Impact factor: 4.330