利用机器学习预测胰十二指肠切除术后的胰腺囊肿

V. A. Suvorov, S. Panin, N. V. Kovalenko, V. Zhavoronkova, M. Postolov, S. Tolstopyatov, A. E. Bublikov, A. V. Panova, V. O. Popova
{"title":"利用机器学习预测胰十二指肠切除术后的胰腺囊肿","authors":"V. A. Suvorov, S. Panin, N. V. Kovalenko, V. Zhavoronkova, M. Postolov, S. Tolstopyatov, A. E. Bublikov, A. V. Panova, V. O. Popova","doi":"10.21294/1814-4861-2023-22-6-25-34","DOIUrl":null,"url":null,"abstract":"Objective: to analyze the results of pancreatoduodenectomy (PD) and identify predictive risk factors for postoperative pancreatic fistula (PF) using machine learning (ML) technology.Material and Methods. A nonrandomized study of treatment outcomes in 128 patients, who underwent PD for periampullary carcinoma between 2018 and 2023, was conducted. To predict PF, the ML models based on the multilayer perceptron and binary logistic regression (BLR) in SPSS Statistics v.26, were used. The Receiver Operator Characteristics (ROC) analysis was used to assess the accuracy of the models. To compare ROC curves, the DeLong test was used.Results. Clinically significant PF occurred in 19 (14.8 %) patients (grade B according to ISGPS 2016 – in 16 (12.5 %), grade C – in 3 (2.3 %)). The data of 90 (70.3 %) patients were used to train the neural network, and 38 (29.7 %) were used to test the predictive model. In multivariate analysis, the predictors of PF were a comorbidity level above 7 points on the age-adjusted Charlson scale, a diameter of the main pancreatic duct less than 3 mm, and a soft pancreatic consistency. The diagnostic accuracy of the ML model estimated using the area under the ROC curve was 0.939 ± 0.027 (95 % CI: 0.859–0.998, sensitivity: 84.2 %, specificity; 96.3 %). The predictive model, which was developed using BLR, demonstrated lower accuracy: 0.918±0.039 (95 % CI: 0.842–0.994, sensitivity: 78.9 %, specificity: 94.5 %) (p=0.02).Conclusion. The use of machine learning technologies makes it possible to increase the probability of a correct prediction of the occurrence of pancreatic fistula after pancreatoduodenectomy.","PeriodicalId":21881,"journal":{"name":"Siberian journal of oncology","volume":" 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of pancreatic fstula after pancreatoduodenectomy using machine learning\",\"authors\":\"V. A. Suvorov, S. Panin, N. V. Kovalenko, V. Zhavoronkova, M. Postolov, S. Tolstopyatov, A. E. Bublikov, A. V. Panova, V. O. Popova\",\"doi\":\"10.21294/1814-4861-2023-22-6-25-34\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Objective: to analyze the results of pancreatoduodenectomy (PD) and identify predictive risk factors for postoperative pancreatic fistula (PF) using machine learning (ML) technology.Material and Methods. A nonrandomized study of treatment outcomes in 128 patients, who underwent PD for periampullary carcinoma between 2018 and 2023, was conducted. To predict PF, the ML models based on the multilayer perceptron and binary logistic regression (BLR) in SPSS Statistics v.26, were used. The Receiver Operator Characteristics (ROC) analysis was used to assess the accuracy of the models. To compare ROC curves, the DeLong test was used.Results. Clinically significant PF occurred in 19 (14.8 %) patients (grade B according to ISGPS 2016 – in 16 (12.5 %), grade C – in 3 (2.3 %)). The data of 90 (70.3 %) patients were used to train the neural network, and 38 (29.7 %) were used to test the predictive model. In multivariate analysis, the predictors of PF were a comorbidity level above 7 points on the age-adjusted Charlson scale, a diameter of the main pancreatic duct less than 3 mm, and a soft pancreatic consistency. The diagnostic accuracy of the ML model estimated using the area under the ROC curve was 0.939 ± 0.027 (95 % CI: 0.859–0.998, sensitivity: 84.2 %, specificity; 96.3 %). The predictive model, which was developed using BLR, demonstrated lower accuracy: 0.918±0.039 (95 % CI: 0.842–0.994, sensitivity: 78.9 %, specificity: 94.5 %) (p=0.02).Conclusion. The use of machine learning technologies makes it possible to increase the probability of a correct prediction of the occurrence of pancreatic fistula after pancreatoduodenectomy.\",\"PeriodicalId\":21881,\"journal\":{\"name\":\"Siberian journal of oncology\",\"volume\":\" 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Siberian journal of oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21294/1814-4861-2023-22-6-25-34\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Siberian journal of oncology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21294/1814-4861-2023-22-6-25-34","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

摘要

目的:分析胰十二指肠切除术(PD)的结果,并利用机器学习(ML)技术识别术后胰瘘(PF)的预测风险因素。对2018年至2023年间因胰周腺癌接受胰十二指肠切除术的128名患者的治疗结果进行了非随机研究。为了预测 PF,使用了基于 SPSS Statistics v.26 中的多层感知器和二元逻辑回归(BLR)的 ML 模型。采用接收器特征(ROC)分析来评估模型的准确性。为了比较 ROC 曲线,使用了 DeLong 检验。19例(14.8%)患者出现了有临床意义的PF(根据2016年ISGPS标准,16例(12.5%)为B级,3例(2.3%)为C级)。90名(70.3%)患者的数据被用于训练神经网络,38名(29.7%)患者的数据被用于测试预测模型。在多变量分析中,预测 PF 的因素包括合并症水平在年龄调整后的夏尔森量表中超过 7 分、主胰管直径小于 3 毫米以及胰腺软一致性。根据 ROC 曲线下面积估算,ML 模型的诊断准确率为 0.939 ± 0.027(95 % CI:0.859-0.998,灵敏度:84.2 %,特异性:96.3 %)。使用 BLR 开发的预测模型准确率较低:0.918±0.039(95 % CI:0.842-0.994,灵敏度:78.9 %,特异性:94.5 %)(P=0.02)。使用机器学习技术可以提高胰十二指肠切除术后胰瘘发生率的正确预测概率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Prediction of pancreatic fstula after pancreatoduodenectomy using machine learning
Objective: to analyze the results of pancreatoduodenectomy (PD) and identify predictive risk factors for postoperative pancreatic fistula (PF) using machine learning (ML) technology.Material and Methods. A nonrandomized study of treatment outcomes in 128 patients, who underwent PD for periampullary carcinoma between 2018 and 2023, was conducted. To predict PF, the ML models based on the multilayer perceptron and binary logistic regression (BLR) in SPSS Statistics v.26, were used. The Receiver Operator Characteristics (ROC) analysis was used to assess the accuracy of the models. To compare ROC curves, the DeLong test was used.Results. Clinically significant PF occurred in 19 (14.8 %) patients (grade B according to ISGPS 2016 – in 16 (12.5 %), grade C – in 3 (2.3 %)). The data of 90 (70.3 %) patients were used to train the neural network, and 38 (29.7 %) were used to test the predictive model. In multivariate analysis, the predictors of PF were a comorbidity level above 7 points on the age-adjusted Charlson scale, a diameter of the main pancreatic duct less than 3 mm, and a soft pancreatic consistency. The diagnostic accuracy of the ML model estimated using the area under the ROC curve was 0.939 ± 0.027 (95 % CI: 0.859–0.998, sensitivity: 84.2 %, specificity; 96.3 %). The predictive model, which was developed using BLR, demonstrated lower accuracy: 0.918±0.039 (95 % CI: 0.842–0.994, sensitivity: 78.9 %, specificity: 94.5 %) (p=0.02).Conclusion. The use of machine learning technologies makes it possible to increase the probability of a correct prediction of the occurrence of pancreatic fistula after pancreatoduodenectomy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Siberian journal of oncology
Siberian journal of oncology Medicine-Oncology
CiteScore
0.40
自引率
0.00%
发文量
117
审稿时长
8 weeks
期刊介绍: The main objectives of the journal are: -to promote the establishment of Russia’s leading worldwide positions in the field of experimental and clinical oncology- to create the international discussion platform intended to cover all aspects of basic and clinical cancer research, including carcinogenesis, molecular biology, epidemiology, cancer prevention, diagnosis and multimodality treatment (surgery, chemotherapy, radiation therapy, hormone therapy), anesthetic management, medical and social rehabilitation, palliative care as well as the improvement of life quality of cancer patients- to encourage promising young scientists to be actively involved in cancer research programs- to provide a platform for researches and doctors all over the world to promote, share, and discuss various new issues and developments in cancer related problems. (to create a communication platform for the expansion of cooperation between Russian and foreign professional associations).- to provide the information about the latest worldwide achievements in different fields of oncology The most important tasks of the journal are: -to encourage scientists to publish their research results- to offer a forum for active discussion on topics of major interest - to invite the most prominent Russian and foreign authors to share their latest research findings with cancer research community- to promote the exchange of research information, clinical experience, current trends and the recent developments in the field of oncology as well as to review interesting cases encountered by colleagues all over the world- to expand the editorial board and reviewers with the involvement of well-known Russian and foreign experts- to provide open access to full text articles- to include the journal into the international database- to increase the journal’s impact factor- to promote the journal to the International and Russian markets
期刊最新文献
Small cell cancer of the esophagus, treatment outcomes of a rare histological type A rare tumour – metastatic digital papillary adenocarcinoma: literature review, clinical case of successful therapy Vulvar cancer. The contribution of radiotherapy to the treatment of the disease Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region. Histological trends and molecular genetic features of non-small cell lung cancer in the Khanty-Mansiysk autonomous district – Ugra
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1