基于流行病學(xué)、臨床癥狀、腫瘤標(biāo)志和影像學(xué)特征的肺癌診斷模型的建立
發(fā)布時(shí)間：2018-08-02 19:05
【摘要】：肺癌是一種涉及了基因和表觀遺傳改變的復(fù)雜疾病,是世界范圍內(nèi)癌癥死亡的主要原因。近年來(lái),中國(guó)地區(qū)的肺癌發(fā)病率較前明顯增加,成為一個(gè)重大的公共衛(wèi)生挑戰(zhàn)。盡管隨著醫(yī)療水平的提高和臨床藥物應(yīng)用的發(fā)展,肺癌的治療效果得到有效的改善,但是晚期肺癌患者的生存率和總體預(yù)后仍然在相對(duì)較低的水平。因此,提高早期診斷效率是改善肺癌患者預(yù)后的關(guān)鍵。流行病學(xué)表明肺癌和吸煙有強(qiáng)大的統(tǒng)計(jì)學(xué)關(guān)聯(lián)。據(jù)估計(jì),全世界有12.5億煙民,每年超過(guò)一百萬(wàn)人死于煙草導(dǎo)致的肺癌。吸煙與肺癌的發(fā)生發(fā)展密切相關(guān),85%的肺癌與吸煙有關(guān),并且2年生存率10%。美國(guó)肺癌篩查中心推薦對(duì)吸煙史30年包,且禁煙15年的患者進(jìn)行肺癌篩查。腫瘤標(biāo)志是反映存在于腫瘤中的生物化學(xué)物質(zhì)。它們不存在于正常成人組織中,或只存在于胚胎組織。它們?cè)谀[瘤組織中的含量顯著超過(guò)正常組織。它們量或質(zhì)的改變也許預(yù)示了腫瘤的本質(zhì),以便了解腫瘤組織、細(xì)胞分化和細(xì)胞功能,并幫助進(jìn)行腫瘤的診斷、分類(lèi)、判斷預(yù)后和治療。然而,單一腫瘤標(biāo)志檢測(cè)的敏感性和特異性通常低于由不同特性、敏感性和互補(bǔ)性的多個(gè)腫瘤標(biāo)志組成的腫瘤標(biāo)志群。因此,目前多采用多種腫瘤標(biāo)志聯(lián)合診斷來(lái)提高早期肺癌的檢測(cè)。影像學(xué)是臨床肺癌診斷的一種重要的方法。但是低靈敏度的X線片也是肺癌診斷延誤的主要原因。近來(lái),在美國(guó)肺癌篩查試驗(yàn)研究表明,在肺癌高危人群中進(jìn)行低劑量CT掃描相比X線降低了20%肺癌死亡率。因此,這項(xiàng)檢查被美國(guó)預(yù)防服務(wù)中心、美國(guó)癌癥協(xié)會(huì)和其他咨詢(xún)機(jī)構(gòu)推薦。現(xiàn)在,低劑量CT被用于高度懷疑肺癌患者的檢測(cè),而且擁有高度敏感性來(lái)幫助發(fā)現(xiàn)和確定早期肺癌。然而,CT對(duì)肺癌診斷的特異度太差。腫瘤標(biāo)志比動(dòng)態(tài)CT成像表現(xiàn)出較低的敏感度,而特異性高于CT掃描。因此,CT成像掃描結(jié)合腫瘤標(biāo)志能夠有助于區(qū)分肺癌與良性肺疾病。數(shù)據(jù)挖掘技術(shù)作為建模工具已經(jīng)證明了其從多個(gè)來(lái)源吸收信息并精確分析及建立復(fù)雜模型的能力�，F(xiàn)在,許多研究將腫瘤特征同數(shù)據(jù)挖掘技術(shù)結(jié)合來(lái)診斷腫瘤。盡管肺癌診斷有很多因素并且它們之間有復(fù)雜的關(guān)系,數(shù)據(jù)挖掘技術(shù)能夠?qū)W習(xí)不能通過(guò)數(shù)學(xué)方法描述的模糊評(píng)價(jià),并且能夠解決一些復(fù)雜的、不確定和非線性問(wèn)題,特別是當(dāng)面對(duì)大樣品、多媒體、多變量時(shí),數(shù)據(jù)挖掘技術(shù)在解決非線性和未知數(shù)據(jù)分布問(wèn)題上顯示了更優(yōu)秀的能力。目的:本研究在課題組前期成果的基礎(chǔ)上,將血清腫瘤標(biāo)志與流行病、臨床癥狀、影像學(xué)特征聯(lián)合,利用數(shù)據(jù)挖掘技術(shù)來(lái)建立肺癌-肺良性疾病輔助診斷模型,以期進(jìn)一步提高肺癌診斷的準(zhǔn)確率,并為肺癌診斷提供參考和輔助方法,改善肺癌患者的生存率和預(yù)后。方法:1.收集2014年10月至2016年3月鄭州大學(xué)第一附屬醫(yī)院呼吸內(nèi)科423例住院患者的血清學(xué)標(biāo)本并測(cè)定血清腫瘤標(biāo)志水平。從住院醫(yī)師或主治醫(yī)師修改并完成的住院病歷中提取住院患者的流行病學(xué)和臨床信息。包括性別、年齡、吸煙史、飲酒史、家族史(腫瘤方面);是否咳嗽、咳痰、痰中帶血、乏力、發(fā)熱出汗、聲音嘶啞。2.使用Fisher判別分析和Logistic回歸分析方法對(duì)血清腫瘤標(biāo)志、流行病學(xué)和臨床癥狀指標(biāo)進(jìn)行篩選優(yōu)化。3.將篩選優(yōu)化后指標(biāo)通過(guò)數(shù)據(jù)挖掘技術(shù)(ANN、SVM、決策樹(shù)C5.0)和Fisher判別分析建立肺癌診斷模型。4.同時(shí)收集423例患者的CT影像學(xué)資料,并根據(jù)病例的納入和排除標(biāo)準(zhǔn)選取其中214例患者的CT影像作為研究對(duì)象。5.請(qǐng)3位高年資呼吸科主治醫(yī)師分別對(duì)214例患者的CT影像進(jìn)行判斷,提取19項(xiàng)特征并評(píng)分。各個(gè)影像學(xué)特征的最后評(píng)分取3位醫(yī)師的平均分。6.將提取的19項(xiàng)影像學(xué)指標(biāo)通過(guò)Fisher判別分析和Logistic逐步回歸分析的方法來(lái)篩選優(yōu)化,并通過(guò)數(shù)據(jù)挖掘技術(shù)(ANN、SVM、決策樹(shù)C5.0)和Fisher判別分析建立肺癌診斷模型。7.采用Fisher判別分析和Logistic逐步回歸分析的方法對(duì)血清腫瘤標(biāo)志、流行病及臨床癥狀、CT影像學(xué)特征一系列指標(biāo)進(jìn)行篩選優(yōu)化并通過(guò)數(shù)據(jù)挖掘技術(shù)(ANN、SVM、決策樹(shù)C5.0)和Fisher判別分析建立肺癌診斷模型。結(jié)果:1.腫瘤標(biāo)志聯(lián)合流行病學(xué)及臨床指標(biāo)建立的各模型對(duì)預(yù)測(cè)集預(yù)測(cè)結(jié)果的靈敏度、特異度、準(zhǔn)確度、陽(yáng)性預(yù)測(cè)值和陰性預(yù)測(cè)值和AUC要明顯高于單獨(dú)腫瘤標(biāo)志建立的各模型。2.腫瘤標(biāo)志、流行病學(xué)、臨床癥狀指標(biāo)建立的模型中,ANN模型的的靈敏度、特異度、準(zhǔn)確度、陽(yáng)性預(yù)測(cè)值和陰性預(yù)測(cè)值和AUC均高于其他3種模型,ROC曲線下面積對(duì)比差異有統(tǒng)計(jì)學(xué)意義(P0.05)。3.腫瘤標(biāo)志和流行病學(xué)及臨床癥狀聯(lián)合后各組指標(biāo)建立的ANN模型之間AUC差異無(wú)統(tǒng)計(jì)學(xué)意義,但10種腫瘤標(biāo)志和流行病學(xué)及臨床癥狀全部Logistic逐步回歸分析優(yōu)化后的13項(xiàng)指標(biāo),即年齡、性別、吸煙史、咳痰、痰中帶血、發(fā)熱出汗和DNMT3B、DNMT1、HDAC1、胃泌素、NSE、CEA和鈣離子,建立的ANN模型訓(xùn)練集準(zhǔn)確度為100%,預(yù)測(cè)集準(zhǔn)確度為94.33%,特異度95.5%,陽(yáng)性預(yù)測(cè)值93.8%,均高于其他模型。4.Logistic逐步回歸分析篩選出的空洞征、棘突征和氣管狹窄3個(gè)變量建立的SVM模型對(duì)預(yù)測(cè)集預(yù)測(cè)結(jié)果的靈敏度為92.3%、特異度81.8%、準(zhǔn)確度86.9%、陽(yáng)性預(yù)測(cè)值90.6%、陰性預(yù)測(cè)值91.8%、AUC 0.857。5.將血清腫瘤標(biāo)志、流行病學(xué)、臨床癥狀、影像學(xué)聯(lián)合,利用Logistic逐步回歸分析篩選出16項(xiàng)指標(biāo)建立的SVM模型對(duì)肺癌預(yù)測(cè)結(jié)果的特異度、準(zhǔn)確度、陽(yáng)性預(yù)測(cè)值、AUC分別為95.5%、97.2%、95.4%、0.969,靈敏度和陰性預(yù)測(cè)值為99.0%和95.4%。6.血清腫瘤標(biāo)志、流行病學(xué)、臨床癥狀、影像學(xué)指標(biāo)聯(lián)合建立的SVM和決策樹(shù)C5.0模型對(duì)肺癌診斷效能優(yōu)于單獨(dú)影像學(xué)建立的SVM模型和決策樹(shù)C5.0模型,AUC相比差異有統(tǒng)計(jì)學(xué)意義(P0.05)。結(jié)論:1.用Fisher判別分析和Logistic逐步回歸分析分別對(duì)流行病學(xué)、臨床癥狀和血清腫瘤標(biāo)志指標(biāo)進(jìn)行篩選優(yōu)化,優(yōu)化后的指標(biāo)聯(lián)合建立肺癌ANN診斷模型,其靈敏度、特異度、準(zhǔn)確度、陽(yáng)性預(yù)測(cè)值、陰性預(yù)測(cè)值和AUC明顯高于單純血清腫瘤標(biāo)志聯(lián)合建立的數(shù)據(jù)挖掘模型,能夠更好的對(duì)肺癌進(jìn)行臨床輔助診斷。2.Logistic回歸分析篩選出的空洞征、棘突征和氣管狹窄3個(gè)變量建立的SVM肺癌診斷模型可作為肺癌臨床影像學(xué)診斷的一種方法。3.血清腫瘤標(biāo)志、流行病學(xué)、臨床癥狀、影像學(xué)指標(biāo)聯(lián)合建立的SVM模型和決策樹(shù)C5.0模型對(duì)肺癌診斷效能優(yōu)于單獨(dú)影像學(xué)建立的SVM模型和決策樹(shù)C5.0模型,可作為肺癌臨床輔助診斷的一種優(yōu)選方法。
[Abstract]:Lung cancer is a complex disease involving gene and epigenetic changes. It is the main cause of cancer death worldwide. In recent years, the incidence of lung cancer in China has increased significantly and has become a major public health challenge. Although with the improvement of medical level and the development of clinical drug application, the treatment effect of lung cancer Effective improvement is achieved, but the survival and overall prognosis of patients with advanced lung cancer are still at a relatively low level. Therefore, improving early diagnostic efficiency is the key to improving the prognosis of lung cancer patients. Epidemiology shows a strong statistical link between lung cancer and smoking. It is estimated that there are 1 billion 250 million smokers in the world and more than one million people die each year. Smoking is closely related to lung cancer. Smoking is closely related to the development of lung cancer. 85% of lung cancer is associated with smoking, and the 2 year survival rate of 10%. American Lung Screening Center recommends lung cancer screening for patients with a history of smoking for 30 years and 15 years of smoking. The tumor markers reflect the biological chemicals existing in the tumor. They do not exist in normal adults. In human tissues, or only in embryonic tissues. They are significantly higher in tumor tissues than in normal tissues. Their quantity or quality changes may predict the nature of the tumor to understand tumor tissue, cell differentiation and cell function, and help to diagnose, classify, judge prognosis and treat the tumor. However, single tumor markers are detected. The sensitivity and specificity are usually lower than the tumor markers consisting of multiple tumor markers with different characteristics, sensitivity and complementarity. Therefore, multiple tumor markers are used together to improve the detection of early lung cancer. Imaging is an important method for the diagnosis of lung cancer. However, the low sensitivity X-ray film is also a lung cancer. The main reason for the delay in diagnosis. Recently, the American lung cancer screening test showed that low dose CT scan in high risk people for lung cancer decreased the mortality of lung cancer by 20% compared with X ray. Therefore, this examination was recommended by the American preventive service center, the American Cancer Association and other advisory bodies. Now, low dose CT is used to highly suspect lung cancer. Patients were tested and highly sensitive to help identify and determine early lung cancer. However, the specificity of CT for lung cancer diagnosis was too poor. The tumor markers showed a lower sensitivity than the dynamic CT imaging, and the specificity was higher than the CT scan. Therefore, the CT imaging scan combined with the tumor markers could help to distinguish between lung cancer and benign lung disease. Mining technology as a modeling tool has proven its ability to absorb information from multiple sources and to accurately analyze and build complex models. Now, many studies combine tumor features with data mining techniques to diagnose tumors. Although there are many factors in the diagnosis of lung cancer and there are complex relationships among them, data mining techniques can be learned. The fuzzy evaluation can not be described by mathematical methods, and can solve some complicated, uncertain and nonlinear problems, especially when facing large samples, multimedia and multivariable, data mining technology shows better ability to solve the problem of nonlinear and unknown data distribution. At the same time, we combine the serum tumor markers with the epidemic, clinical symptoms and imaging features, and use data mining to establish the auxiliary diagnosis model of lung cancer and lung disease, in order to further improve the accuracy of lung cancer diagnosis, and provide reference and auxiliary methods for lung cancer diagnosis, and improve the survival rate and prognosis of lung cancer patients. Method: 1. collection of 20 A serological specimen of 423 hospitalized patients in the Department of respiratory medicine, the First Affiliated Hospital of Zhengzhou University, from October to March 2016, 14 years, and the level of serum tumor markers were measured. The epidemiological and clinical information of hospitalized patients was extracted from the hospitalized physician or the physician who was modified and completed. Tumor): whether coughing, phlegm, sputum, blood, fatigue, fever and sweating, hoarseness.2. using Fisher discriminant analysis and Logistic regression analysis methods for screening and optimizing serum tumor markers, epidemiological and clinical symptoms,.3. will be screened through data mining technology (ANN, SVM, decision tree C5.0) and Fisher discrimination The lung cancer diagnosis model.4. was established to collect the CT imaging data of 423 patients. According to the inclusion and exclusion criteria of the cases, the CT images of 214 patients were selected as the research object. 3 senior senior Department of respiration doctors were asked to judge 214 patients' CT images and extract 19 features and score. The final score of the feature is taken by the average score of 3 physicians to select the 19 image indexes extracted by Fisher discriminant analysis and Logistic stepwise regression analysis, and establish the lung cancer diagnosis model.7. using Fisher discriminant analysis and Logistic step by step through the data mining technology (ANN, SVM, decision tree C5.0) and Fisher discriminant analysis to establish the lung cancer diagnosis model.7. Regression analysis was used to select and optimize a series of indicators for serum tumor markers, epidemics and clinical symptoms, CT imaging features and to establish a diagnostic model for lung cancer by data mining (ANN, SVM, decision tree C5.0) and Fisher discriminant analysis. Results 1. the model of the combined epidemiology and clinical indicators of the tumor markers combined with the prediction set. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value and AUC of the predicted results were significantly higher than that of the model.2. tumor markers, epidemiology, and clinical symptoms established by individual tumor markers, and the sensitivity, specificity, accuracy, positive predictive value, negative predictive value and AUC of the ANN model were higher than that of the model. There were 3 models, the difference in area contrast under ROC curves was statistically significant (P0.05), and there was no statistical difference between the ANN models established by the combination of.3. tumor markers and epidemiological and clinical symptoms, but the 10 tumor markers and the 13 index of all Logistic stepwise regression analysis of all the tumor markers and epidemiological and clinical symptoms, that is, year Age, sex, smoking history, expectoration, phlegm, blood, fever and sweating and DNMT3B, DNMT1, HDAC1, gastrin, NSE, CEA and calcium ions, the accuracy of the established ANN model training set was 100%, the accuracy of the prediction set was 94.33%, the specificity was 95.5%, and the positive predictive value was 93.8%, which were higher than those of the stepwise regression analysis of his model.4.Logistic. The sensitivity of the SVM model established by 3 variables of tracheal stenosis was 92.3%, the specificity was 81.8%, the accuracy was 86.9%, the positive predictive value was 90.6%, the negative predictive value was 91.8%. The serum tumor markers, epidemiology, clinical symptoms, and imaging were combined with the AUC 0.857.5., and the SVM of 16 indexes was selected by Logistic stepwise regression analysis. The specificity, accuracy, and positive predictive values of the predicted results of lung cancer were 95.5%, 97.2%, 95.4%, 0.969, respectively, and the sensitivity and negative predictive values were 99% and 95.4%.6. serum tumor markers, the epidemiological, clinical symptoms, and imaging indexes combined with SVM and the decision tree C5.0 model were better than the single imaging diagnosis for lung cancer. The SVM model and the decision tree C5.0 model were statistically significant (P0.05) compared with AUC. Conclusion: 1. the Fisher discriminant analysis and Logistic stepwise regression analysis were used to select and optimize the epidemiology, clinical symptoms and serum tumor markers respectively, and the optimized indexes were combined to establish the lung cancer ANN diagnostic model. The sensitivity, specificity and accuracy of the model were combined. The positive predictive value, negative predictive value and AUC are significantly higher than the data mining model combined with simple serum tumor markers. It can be better for the clinical diagnosis of lung cancer by clinical auxiliary diagnosis of.2.Logistic regression analysis of the cavity sign, spinous process and tracheal stenosis, 3 variables of SVM lung cancer diagnosis can be used as clinical imaging diagnosis of lung cancer A method of.3. serum tumor markers, epidemiology, clinical symptoms, the combined SVM model and the decision tree C5.0 model for the diagnosis of lung cancer is better than the SVM model and the decision tree C5.0 model, which can be used as a preferred method for the clinical diagnosis of lung cancer.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：R734.2
本文編號(hào)：2160431
[Abstract]:Lung cancer is a complex disease involving gene and epigenetic changes. It is the main cause of cancer death worldwide. In recent years, the incidence of lung cancer in China has increased significantly and has become a major public health challenge. Although with the improvement of medical level and the development of clinical drug application, the treatment effect of lung cancer Effective improvement is achieved, but the survival and overall prognosis of patients with advanced lung cancer are still at a relatively low level. Therefore, improving early diagnostic efficiency is the key to improving the prognosis of lung cancer patients. Epidemiology shows a strong statistical link between lung cancer and smoking. It is estimated that there are 1 billion 250 million smokers in the world and more than one million people die each year. Smoking is closely related to lung cancer. Smoking is closely related to the development of lung cancer. 85% of lung cancer is associated with smoking, and the 2 year survival rate of 10%. American Lung Screening Center recommends lung cancer screening for patients with a history of smoking for 30 years and 15 years of smoking. The tumor markers reflect the biological chemicals existing in the tumor. They do not exist in normal adults. In human tissues, or only in embryonic tissues. They are significantly higher in tumor tissues than in normal tissues. Their quantity or quality changes may predict the nature of the tumor to understand tumor tissue, cell differentiation and cell function, and help to diagnose, classify, judge prognosis and treat the tumor. However, single tumor markers are detected. The sensitivity and specificity are usually lower than the tumor markers consisting of multiple tumor markers with different characteristics, sensitivity and complementarity. Therefore, multiple tumor markers are used together to improve the detection of early lung cancer. Imaging is an important method for the diagnosis of lung cancer. However, the low sensitivity X-ray film is also a lung cancer. The main reason for the delay in diagnosis. Recently, the American lung cancer screening test showed that low dose CT scan in high risk people for lung cancer decreased the mortality of lung cancer by 20% compared with X ray. Therefore, this examination was recommended by the American preventive service center, the American Cancer Association and other advisory bodies. Now, low dose CT is used to highly suspect lung cancer. Patients were tested and highly sensitive to help identify and determine early lung cancer. However, the specificity of CT for lung cancer diagnosis was too poor. The tumor markers showed a lower sensitivity than the dynamic CT imaging, and the specificity was higher than the CT scan. Therefore, the CT imaging scan combined with the tumor markers could help to distinguish between lung cancer and benign lung disease. Mining technology as a modeling tool has proven its ability to absorb information from multiple sources and to accurately analyze and build complex models. Now, many studies combine tumor features with data mining techniques to diagnose tumors. Although there are many factors in the diagnosis of lung cancer and there are complex relationships among them, data mining techniques can be learned. The fuzzy evaluation can not be described by mathematical methods, and can solve some complicated, uncertain and nonlinear problems, especially when facing large samples, multimedia and multivariable, data mining technology shows better ability to solve the problem of nonlinear and unknown data distribution. At the same time, we combine the serum tumor markers with the epidemic, clinical symptoms and imaging features, and use data mining to establish the auxiliary diagnosis model of lung cancer and lung disease, in order to further improve the accuracy of lung cancer diagnosis, and provide reference and auxiliary methods for lung cancer diagnosis, and improve the survival rate and prognosis of lung cancer patients. Method: 1. collection of 20 A serological specimen of 423 hospitalized patients in the Department of respiratory medicine, the First Affiliated Hospital of Zhengzhou University, from October to March 2016, 14 years, and the level of serum tumor markers were measured. The epidemiological and clinical information of hospitalized patients was extracted from the hospitalized physician or the physician who was modified and completed. Tumor): whether coughing, phlegm, sputum, blood, fatigue, fever and sweating, hoarseness.2. using Fisher discriminant analysis and Logistic regression analysis methods for screening and optimizing serum tumor markers, epidemiological and clinical symptoms,.3. will be screened through data mining technology (ANN, SVM, decision tree C5.0) and Fisher discrimination The lung cancer diagnosis model.4. was established to collect the CT imaging data of 423 patients. According to the inclusion and exclusion criteria of the cases, the CT images of 214 patients were selected as the research object. 3 senior senior Department of respiration doctors were asked to judge 214 patients' CT images and extract 19 features and score. The final score of the feature is taken by the average score of 3 physicians to select the 19 image indexes extracted by Fisher discriminant analysis and Logistic stepwise regression analysis, and establish the lung cancer diagnosis model.7. using Fisher discriminant analysis and Logistic step by step through the data mining technology (ANN, SVM, decision tree C5.0) and Fisher discriminant analysis to establish the lung cancer diagnosis model.7. Regression analysis was used to select and optimize a series of indicators for serum tumor markers, epidemics and clinical symptoms, CT imaging features and to establish a diagnostic model for lung cancer by data mining (ANN, SVM, decision tree C5.0) and Fisher discriminant analysis. Results 1. the model of the combined epidemiology and clinical indicators of the tumor markers combined with the prediction set. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value and AUC of the predicted results were significantly higher than that of the model.2. tumor markers, epidemiology, and clinical symptoms established by individual tumor markers, and the sensitivity, specificity, accuracy, positive predictive value, negative predictive value and AUC of the ANN model were higher than that of the model. There were 3 models, the difference in area contrast under ROC curves was statistically significant (P0.05), and there was no statistical difference between the ANN models established by the combination of.3. tumor markers and epidemiological and clinical symptoms, but the 10 tumor markers and the 13 index of all Logistic stepwise regression analysis of all the tumor markers and epidemiological and clinical symptoms, that is, year Age, sex, smoking history, expectoration, phlegm, blood, fever and sweating and DNMT3B, DNMT1, HDAC1, gastrin, NSE, CEA and calcium ions, the accuracy of the established ANN model training set was 100%, the accuracy of the prediction set was 94.33%, the specificity was 95.5%, and the positive predictive value was 93.8%, which were higher than those of the stepwise regression analysis of his model.4.Logistic. The sensitivity of the SVM model established by 3 variables of tracheal stenosis was 92.3%, the specificity was 81.8%, the accuracy was 86.9%, the positive predictive value was 90.6%, the negative predictive value was 91.8%. The serum tumor markers, epidemiology, clinical symptoms, and imaging were combined with the AUC 0.857.5., and the SVM of 16 indexes was selected by Logistic stepwise regression analysis. The specificity, accuracy, and positive predictive values of the predicted results of lung cancer were 95.5%, 97.2%, 95.4%, 0.969, respectively, and the sensitivity and negative predictive values were 99% and 95.4%.6. serum tumor markers, the epidemiological, clinical symptoms, and imaging indexes combined with SVM and the decision tree C5.0 model were better than the single imaging diagnosis for lung cancer. The SVM model and the decision tree C5.0 model were statistically significant (P0.05) compared with AUC. Conclusion: 1. the Fisher discriminant analysis and Logistic stepwise regression analysis were used to select and optimize the epidemiology, clinical symptoms and serum tumor markers respectively, and the optimized indexes were combined to establish the lung cancer ANN diagnostic model. The sensitivity, specificity and accuracy of the model were combined. The positive predictive value, negative predictive value and AUC are significantly higher than the data mining model combined with simple serum tumor markers. It can be better for the clinical diagnosis of lung cancer by clinical auxiliary diagnosis of.2.Logistic regression analysis of the cavity sign, spinous process and tracheal stenosis, 3 variables of SVM lung cancer diagnosis can be used as clinical imaging diagnosis of lung cancer A method of.3. serum tumor markers, epidemiology, clinical symptoms, the combined SVM model and the decision tree C5.0 model for the diagnosis of lung cancer is better than the SVM model and the decision tree C5.0 model, which can be used as a preferred method for the clinical diagnosis of lung cancer.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：R734.2
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 趙爽;李鐳;邱志新;程越;景玉婷;周永召;李為民;;四川地區(qū)2008年-2013年3,663例肺癌臨床病理特征及流行趨勢(shì)分析[J];中國(guó)肺癌雜志;2016年02期
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