Predicting the risk of pancreatic cancer with a CT-based ensemble AI algorithm

• URL: http://arxiv.org/abs/2004.01388v1
• Date: Fri, 3 Apr 2020 06:06:43 GMT
• Title: Predicting the risk of pancreatic cancer with a CT-based ensemble AI algorithm
• Authors: Chenjie Zhou MD, Jianhua Ma Ph.D, Xiaoping Xu MD, Lei Feng MD, Adilijiang Yimamu MD, Xianlong Wang MD, Zhiming Li MD, Jianhua Mo MS, Chengyan Huang MS, Dexia Kong MS, Yi Gao MD, Shulong Li Ph.D
• Abstract summary: Pancreatic cancer is a lethal disease, hard to diagnose and results in poor prognosis and high mortality. We propose an ensemble AI algorithm to predict universally cancer risk of all kinds of pancreatic lesions with noncontrast CT.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objectives: Pancreatic cancer is a lethal disease, hard to diagnose and usually results in poor prognosis and high mortality. Developing an artificial intelligence (AI) algorithm to accurately and universally predict the early cancer risk of all kinds of pancreatic cancer is extremely important. We propose an ensemble AI algorithm to predict universally cancer risk of all kinds of pancreatic lesions with noncontrast CT. Methods: Our algorithm combines the radiomics method and a support tensor machine (STM) by the evidence reasoning (ER) technique to construct a binary classifier, called RadSTM-ER. RadSTM-ER takes advantage of the handcrafted features used in radiomics and learning features learned automatically by the STM from the CTs for presenting better characteristics of lesions. The patient cohort consisted of 135 patients with pathological diagnosis results where 97 patients had malignant lesions. Twenty-seven patients were randomly selected as independent test samples, and the remaining patients were used in a 5-fold cross validation experiment to confirm the hyperparameters, select optimal handcrafted features and train the model. Results: RadSTM-ER achieved independent test results: an area under the receiver operating characteristic curve of 0.8951, an accuracy of 85.19%, a sensitivity of 88.89%, a specificity of 77.78%, a positive predictive value of 88.89% and a negative predictive value of 77.78%. Conclusions: These results are better than the diagnostic performance of the five experimental radiologists, four conventional AI algorithms, which initially demonstrate the potential of noncontrast CT-based RadSTM-ER in cancer risk prediction for all kinds of pancreatic lesions.

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