Machine learning prediction of prostate cancer from transrectal ultrasound video clips
文献类型：期刊论文
| 作者 | Wang, Kai2; Chen, Peizhe1; Feng, Bojian5,6; Tu, Jing2; Hu, Zhengbiao2; Zhang, Maoliang2; Yang, Jie2 ; Zhan, Ying2; Yao, Jincao4,5,6; Xu, Dong3,4,5,6
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| 刊名 | FRONTIERS IN ONCOLOGY
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| 出版日期 | 2022-08-26 |
| 卷号 | 12 |
| ISSN号 | 2234-943X |
| 关键词 | artificial intelligence prostate cancer ultrasound machine learning support vector machine |
| DOI | 10.3389/fonc.2022.948662 |
| 通讯作者 | Yao, Jincao(yaojc@zjcc.org.cn) ; Xu, Dong(xudong@zjcc.org.cn) |
| 英文摘要 | ObjectiveTo build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI). MethodsWe systematically collated data from 501 patients-276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model. We extracted 851 features from ultrasound video clips. After dimensionality reduction with the least absolute shrinkage and selection operator (LASSO) regression, 14 features were finally selected and the support vector machine (SVM) and random forest (RF) algorithms were used to establish radiomics models based on those features. In addition, we creatively proposed a machine learning models aided diagnosis algorithm (MLAD) composed of SVM, RF, and radiologists' diagnosis based on MRI to evaluate the performance of ML models in computer-aided diagnosis (CAD). We evaluated the area under the curve (AUC) as well as the sensitivity, specificity, and precision of the ML models and radiologists' diagnosis based on MRI by employing receiver operator characteristic curve (ROC) analysis. ResultsThe AUC, sensitivity, specificity, and precision of the SVM in the diagnosis of PCa in the validation set and the test set were 0.78, 63%, 80%; 0.75, 65%, and 67%, respectively. Additionally, the SVM model was found to be superior to senior radiologists' (SR, more than 10 years of experience) diagnosis based on MRI (AUC, 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set), and the difference was statistically significant (p< 0.05). ConclusionThe prediction model constructed by the ML algorithm has good diagnostic efficiency for prostate cancer. The SVM model's diagnostic efficiency is superior to that of MRI, as it has a more focused application value. Overall, these prediction models can aid radiologists in making better diagnoses. |
| WOS关键词 | PERIPHERAL ZONE |
| 资助项目 | National Natural Science Foundation of China[82071946] ; Natural Science Foundation of Zhejiang Province[LZY21F030001] ; Natural Science Foundation of Zhejiang Province[LSD19H180001] ; Medical and Health Research Project of Zhejiang Province[2021KY099] ; Medical and Health Research Project of Zhejiang Province[2022KY110] ; University Cancer Foundation via the Sister Institution Network Fund at the University of Texas MD Anderson Cancer Center ; Key Science and Technology Project of Jinhua, Zhejiang Province[2022-3-017] |
| WOS研究方向 | Oncology |
| 语种 | 英语 |
| 出版者 | FRONTIERS MEDIA SA |
| WOS记录号 | WOS:000858628700001 |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Zhejiang Province ; Medical and Health Research Project of Zhejiang Province ; University Cancer Foundation via the Sister Institution Network Fund at the University of Texas MD Anderson Cancer Center ; Key Science and Technology Project of Jinhua, Zhejiang Province |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/129438]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Yao, Jincao; Xu, Dong |
| 作者单位 | 1.Zhejiang Univ, Coll Opt Sci & Engn, Hangzhou, Peoples R China 2.Wenzhou Med Univ, Affiliated Dongyang Hosp, Dept Ultrasound, Dongyang, Peoples R China 3.Zhejiang Prov Res Ctr Canc Intelligent Diag & Mol, Hangzhou, Peoples R China 4.Key Lab Head & Neck Canc Translat Res Zhejiang Pro, Hangzhou, Peoples R China 5.Chinese Acad Sci, Inst Basic Med & Canc IBMC, Hangzhou, Peoples R China 6.Univ Chinese Acad Sci, Canc Hosp, Zhejiang Canc Hosp, Dept Ultrasound, Hangzhou, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Kai,Chen, Peizhe,Feng, Bojian,et al. Machine learning prediction of prostate cancer from transrectal ultrasound video clips[J]. FRONTIERS IN ONCOLOGY,2022,12. |
| APA | Wang, Kai.,Chen, Peizhe.,Feng, Bojian.,Tu, Jing.,Hu, Zhengbiao.,...&Xu, Dong.(2022).Machine learning prediction of prostate cancer from transrectal ultrasound video clips.FRONTIERS IN ONCOLOGY,12. |
| MLA | Wang, Kai,et al."Machine learning prediction of prostate cancer from transrectal ultrasound video clips".FRONTIERS IN ONCOLOGY 12(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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