S. Panin, V. A. Suvorov, A. V. Zubkov, S. A. Bezborodov, A. A. Panina, N. V. Kovalenko, A. Donsckaia, I. G. Shushkova, A. V. Bykov, Ya. A. Marenkov
{"title":"在该地区实验室服务集中化的背景下,利用人工智能筛查和早期诊断胰腺肿瘤。","authors":"S. Panin, V. A. Suvorov, A. V. Zubkov, S. A. Bezborodov, A. A. Panina, N. V. Kovalenko, A. Donsckaia, I. G. Shushkova, A. V. Bykov, Ya. A. Marenkov","doi":"10.21294/1814-4861-2024-23-3-124-132","DOIUrl":null,"url":null,"abstract":"Objective. Determination of the optimal machine learning model for the creation of software for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region. Material and Methods. The clinical material was based on 1254 patients who were examined in the centralized laboratory of the Volgograd Consultative and Diagnostic Polyclinic No. 2. Of these, 139 were subsequently operated on at the Volgograd Regional Clinical Oncology Dispensary for pancreatic malignancies. In 65 (46.7 %) cases, distal pancreatic resection was performed, and in 74 (53.3 %) cases, pancreaticoduodenectomy was performed. In 28 (20.1 %) cases, at the time of tumor detection, patients did not have clinical symptoms. Statistical processing of the data was carried out using the Python programming language. Five different classifiers were used for machine learning. Results. In the course of factor analysis, 11 parameters were selected from 62 laboratory blood parameters, the dynamics of changes in which should be specifically assessed at the stages of screening and early diagnosis of pancreatic neoplasms. A comparative assessment of machine learning techniques showed that the best option for creating the appropriate software was Hist Gradient Boosting (diagnostic accuracy 0.909, sensitivity 0.642, specificity 0.965, negative predictability 0.928, positive predictability 0.794, F1 0.828, logistic regression loss function 0.352, area under the ROC curve 0.89). Conclusion. The creation of software based on the selected algorithm will make it possible to clarify the real effectiveness of machine learning on a larger population of patients with pancreatic neoplasms.","PeriodicalId":21881,"journal":{"name":"Siberian journal of oncology","volume":" 8","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region.\",\"authors\":\"S. Panin, V. A. Suvorov, A. V. Zubkov, S. A. Bezborodov, A. A. Panina, N. V. Kovalenko, A. Donsckaia, I. G. Shushkova, A. V. Bykov, Ya. A. Marenkov\",\"doi\":\"10.21294/1814-4861-2024-23-3-124-132\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Objective. Determination of the optimal machine learning model for the creation of software for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region. Material and Methods. The clinical material was based on 1254 patients who were examined in the centralized laboratory of the Volgograd Consultative and Diagnostic Polyclinic No. 2. Of these, 139 were subsequently operated on at the Volgograd Regional Clinical Oncology Dispensary for pancreatic malignancies. In 65 (46.7 %) cases, distal pancreatic resection was performed, and in 74 (53.3 %) cases, pancreaticoduodenectomy was performed. In 28 (20.1 %) cases, at the time of tumor detection, patients did not have clinical symptoms. Statistical processing of the data was carried out using the Python programming language. Five different classifiers were used for machine learning. Results. In the course of factor analysis, 11 parameters were selected from 62 laboratory blood parameters, the dynamics of changes in which should be specifically assessed at the stages of screening and early diagnosis of pancreatic neoplasms. A comparative assessment of machine learning techniques showed that the best option for creating the appropriate software was Hist Gradient Boosting (diagnostic accuracy 0.909, sensitivity 0.642, specificity 0.965, negative predictability 0.928, positive predictability 0.794, F1 0.828, logistic regression loss function 0.352, area under the ROC curve 0.89). Conclusion. The creation of software based on the selected algorithm will make it possible to clarify the real effectiveness of machine learning on a larger population of patients with pancreatic neoplasms.\",\"PeriodicalId\":21881,\"journal\":{\"name\":\"Siberian journal of oncology\",\"volume\":\" 8\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-07\",\"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/10.21294/1814-4861-2024-23-3-124-132\",\"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/10.21294/1814-4861-2024-23-3-124-132","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region.
Objective. Determination of the optimal machine learning model for the creation of software for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region. Material and Methods. The clinical material was based on 1254 patients who were examined in the centralized laboratory of the Volgograd Consultative and Diagnostic Polyclinic No. 2. Of these, 139 were subsequently operated on at the Volgograd Regional Clinical Oncology Dispensary for pancreatic malignancies. In 65 (46.7 %) cases, distal pancreatic resection was performed, and in 74 (53.3 %) cases, pancreaticoduodenectomy was performed. In 28 (20.1 %) cases, at the time of tumor detection, patients did not have clinical symptoms. Statistical processing of the data was carried out using the Python programming language. Five different classifiers were used for machine learning. Results. In the course of factor analysis, 11 parameters were selected from 62 laboratory blood parameters, the dynamics of changes in which should be specifically assessed at the stages of screening and early diagnosis of pancreatic neoplasms. A comparative assessment of machine learning techniques showed that the best option for creating the appropriate software was Hist Gradient Boosting (diagnostic accuracy 0.909, sensitivity 0.642, specificity 0.965, negative predictability 0.928, positive predictability 0.794, F1 0.828, logistic regression loss function 0.352, area under the ROC curve 0.89). Conclusion. The creation of software based on the selected algorithm will make it possible to clarify the real effectiveness of machine learning on a larger population of patients with pancreatic neoplasms.
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