Application of Automated Machine Learning Based on Radiomics Features of T2WI and RS-EPI DWI to Predict Preoperative T Staging of Rectal Cancer

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{wen:2021:SichuanUniversity,

• author = "Da-Guang Wen and Si-Xian Hu and Zhen-Lin Li and Xiang-Bing Deng and Chuan Tian and Xin Li and Xin-Rong Wang and Qi Leng and Chun-Chao Xia",
• title = "Application of Automated Machine Learning Based on Radiomics Features of {T2WI} and {RS-EPI} {DWI} to Predict Preoperative {T} Staging of Rectal Cancer",
• journal = "Journal of Sichuan University. Medical science edition",
• year = "2021",
• volume = "52",
• number = "4",
• pages = "698--705",
• month = jul,
• keywords = "genetic algorithms, genetic programming, TPOT, China, Diffusion Magnetic Resonance Imaging, Echo-Planar Imaging, Humans, Machine Learning, Rectal Neoplasms/diagnostic imaging/surgery, Retrospective Studies, Automated machine learning, Radiomics, Rectal cancer, T stage",
• ISSN = "1672-173X",
• DOI = "doi:10.12182/20210460201",
• abstract = "OBJECTIVE: To explore the radiomics features of T2 weighted image (T2WI) and readout-segmented echo-planar imaging (RS-EPI) plus diffusion-weighted imaging (DWI), to develop an automated machine-learning model based on the said radiomics features, and to test the value of this model in predicting preoperative T staging of rectal cancer. METHODS: The study retrospectively reviewed 131 patients who were diagnosed with rectal cancer confirmed by the pathology results of their surgical specimens at West China Hospital of Sichuan University between October, 2017 and December, 2018. In addition, these patients had preoperative rectal MRI. Tumor regions from preoperative MRI were manually segmented by radiologists with the ITK-SNAP software from T2WI and RS-EPI DWI images. PyRadiomics was used to extract 200 features-100 from T2WI and 100 from the apparent diffusion coefficient (ADC) calculated from the RS-EPI DWI. MWMOTE and NEATER were used to resample and balance the dataset, and 13 cases of T (1-2) stage simulation cases were added. The overall dataset was divided into a training set (111 cases) and a test set (37 cases) by a ratio of 3∶1. Tree-based Pipeline Optimization Tool (TPOT) was applied on the training set to optimize model parameters and to select the most important radiomics features for modeling. Five independent T stage models were developed accordingly. Accuracy and the area under the curve ( AUC) of receiver operating characteristic (ROC) were used to pick out the optimal model, which was then applied on the training set and the original dataset to predict the T stage of rectal cancer. RESULTS: The performance of the the five T staging models recommended by automated machine learning were as follows: The accuracy for the training set ranged from 0.802 to 0.838, sensitivity, from 0.762 to 0.825, specificity, from 0.833 to 0.896, AUC, from 0.841 to 0.893, and average precision (AP) from 0.870 to 0.901. After comparison, an optimal model was picked out, with sensitivity, specificity and AUC for the training set reaching 0.810, 0.875, and 0.893, respectively. The sensitivity, specificity and AUC for the test set were 0.810, 0.813, and 0.810, respectively. The sensitivity, specificity and AUC for the original dataset were 0.810, 0.830, and 0.860, respectively. CONCLUSION: Based on the radiomics data of T2WI and RS-EPI DWI, the model established by automated machine learning showed a fairly high accuracy in predicting rectal cancer T stage.",
• notes = "PMID: 34323052",

}

Genetic Programming entries for Da-Guang Wen Si-Xian Hu Zhen-Lin Li Xiang-Bing Deng Chuan Tian Xin Li Xin-Rong Wang Qi Leng Chun-Chao Xia

Citations