基于臨床指標(biāo)和病理指標(biāo)的三種預(yù)測(cè)模型用于結(jié)直腸癌的預(yù)后分析研究
發(fā)布時(shí)間：2018-08-22 14:23
【摘要】：目的通過(guò)支持向量機(jī)模型(SVM)將臨床指標(biāo)和病理指標(biāo)進(jìn)行整合,分析其對(duì)結(jié)直腸癌(CRC)患者預(yù)后的預(yù)測(cè)價(jià)值。方法選取2002—2011年在上海市第十人民醫(yī)院胃腸外科行結(jié)直腸腫瘤切除術(shù)的患者2 951例為研究對(duì)象。收集患者的臨床指標(biāo)(性別、年齡、腫瘤大小、腫瘤位置、組織病理類型、T分期、N分期、M分期、G分期)和病理指標(biāo)[癌胚抗原(CEA)、增殖細(xì)胞核抗原(PCNA)、P53、CD_(34)、S-100、NM23、CerB-b2、P21、Ki-67]。按照隨訪信息是否缺失將患者分為兩組,第一組為臨床數(shù)據(jù)完整但隨訪信息缺失,共2 747例;第二組為臨床數(shù)據(jù)及隨訪信息均完整,共204例。記錄患者生存情況。第一組中,若某一患者有超過(guò)4個(gè)指標(biāo)缺失,則該患者被剔除;在余下的患者中,若某一指標(biāo)的缺失率30.0%,則該指標(biāo)被剔除;進(jìn)一步將少發(fā)病理類型患者剔除;計(jì)算臨床指標(biāo)和病理指標(biāo)的關(guān)聯(lián)性,隨后,將所得關(guān)聯(lián)性在第二組患者中進(jìn)行驗(yàn)證,若該關(guān)聯(lián)性在第二組中存在,則直接將第二組患者納入第三組中;若該關(guān)聯(lián)性在第二組中不存在,則采用SVM內(nèi)部算法剔除部分患者,直至該關(guān)聯(lián)性再次成立,將余下的第二組患者納入第三組。對(duì)第二組中患者的病理指標(biāo)進(jìn)行統(tǒng)計(jì),若某一指標(biāo)的缺失率50.0%,則該指標(biāo)被剔除�；赟VM對(duì)第三組數(shù)據(jù)進(jìn)行處理,建立3種預(yù)測(cè)模型:SVM1基于臨床指標(biāo)、SVM2基于病理指標(biāo)、SVM3基于臨床指標(biāo)和病理指標(biāo)的匯總。結(jié)果第一組中,共834例患者缺失指標(biāo)4個(gè),其中性別、年齡、腫瘤位置、組織病理類型、P53、CD_(34)、S-100、CerB-b2、Ki-67共9個(gè)指標(biāo)缺失率30.0%而被保留,剔除5例少見(jiàn)腫瘤患者后,共剩下829例患者。第一組患者年齡與CerB-b2表達(dá)情況存在關(guān)聯(lián)性(P0.05)。第二組剔除105例患者后,余下99例患者,患者年齡與CerB-b2表達(dá)情況存在關(guān)聯(lián)性(P0.05),將這99例患者納入第三組。第二組中PCNA、P53、CD_(34)、S-100、CerB-b2共5個(gè)指標(biāo)缺失率50.0%而被保留。第三組患者年齡與S-100、CerB-b2表達(dá)情況存在關(guān)聯(lián)性(P0.05);M分期與PCNA表達(dá)情況存在關(guān)聯(lián)性(P0.05)。第三組不同T分期、N分期患者生存曲線比較,差異有統(tǒng)計(jì)學(xué)意義(P0.05)。SVM1納入9個(gè)臨床指標(biāo)(性別、年齡、腫瘤大小、腫瘤位置、組織病理類型、T分期、N分期、M分期、G分期),準(zhǔn)確率為83.4%;SVM2納入5個(gè)病理指標(biāo)(PCNA、P53、CD_(34)、S-100、CerB-b2),準(zhǔn)確率為78.8%;初始的SVM3納入以上9個(gè)臨床指標(biāo)及5個(gè)病理指標(biāo),準(zhǔn)確率為74.8%,通過(guò)最小冗余最大相關(guān)性(MRMR)法對(duì)指標(biāo)進(jìn)行進(jìn)一步篩選,得到最終的SVM3,其納入4個(gè)臨床指標(biāo)(腫瘤位置、組織病理類型、T分期、N分期)和2個(gè)病理指標(biāo)(CD_(34)、CerB-b2),準(zhǔn)確率為81.8%。不同風(fēng)險(xiǎn)SVM1、SVM2、SVM3患者生存曲線比較,差異有統(tǒng)計(jì)學(xué)意義(P0.05)。結(jié)論臨床指標(biāo)如年齡、M分期與病理指標(biāo)如CerB-b2、S-100和PCNA存在一定的關(guān)聯(lián)性;借助SVM模型將臨床指標(biāo)和病理指標(biāo)進(jìn)行整合分析可對(duì)CRC患者預(yù)后進(jìn)行有效預(yù)測(cè)。
[Abstract]:Objective to analyze the prognostic value of support vector machine (SVM) model (SVM) in predicting the prognosis of colorectal cancer patients with (CRC) by integrating the clinical and pathological indexes. Methods from 2002 to 2011, 2 951 patients underwent colorectal tumor resection in Gastrointestinal surgery Department of the Tenth people's Hospital of Shanghai. The clinical parameters (sex, age, tumor size, tumor location, histopathological type, T staging and M stage G staging) and pathological indexes (carcinoembryonic antigen (CEA), proliferating cell nuclear antigen (PCNA) P53, CD34, S-100 NM23CerB-b2P21 Ki-67) were collected. The patients were divided into two groups according to whether the follow-up information was missing or not. The first group was complete clinical data but the follow-up information was missing in 2 747 cases, and the second group was clinical data and follow-up information complete, 204 cases. The patient's survival was recorded. In the first group, if more than 4 indexes were missing in one patient, the patient was eliminated; in the remaining group, if the missing rate of one index was 30.0, the index was removed. The correlation between clinical and pathological indexes was calculated, and then the correlation was verified in the second group. If the correlation existed in the second group, the second group was directly included in the third group. If the correlation does not exist in the second group, the SVM internal algorithm is used to remove some patients until the association is established again, and the remaining group of patients are included in the third group. The pathological indexes of the patients in the second group were counted, and if the missing rate of one index was 50. 0%, the index was eliminated. The third group of data was processed based on SVM, and three prediction models: SVM1 were established based on clinical index, SVM2 based on pathological index and SVM3 based on clinical and pathological indexes. Results in the first group, there were 4 missing indexes in 834 patients, including sex, age, tumor location, histopathologic type and the deletion rate of 9 indexes (30.0%). After 5 rare tumor patients were excluded, 829 patients were left. In the first group, there was a correlation between age and CerB-b2 expression (P0.05). The second group excluded 105 patients, the remaining 99 patients, the patient age and CerB-b2 expression were correlated (P0.05), the 99 patients were included in the third group. In the second group, the deletion rate of five indexes of PCNAn P53, CD34, S-100 and CerB-b2 was 50.0%. The age of patients in the third group was correlated with the expression of CerB-b2 (P0.05) and the expression of PCNA was correlated with the stage of M (P0.05). In the third group, the survival curve of patients with different T stage and N stage was compared, the difference was statistically significant (P0.05) .SVM1 included 9 clinical indexes (sex, age, tumor size, tumor location), The accuracy was 83.4% and SVM2 was included in five pathological indexes (PCNAn P53 CD34 / S-100 CerB-b2), the accuracy was 78.8%, the initial SVM3 included the above 9 clinical indexes and 5 pathological indexes. The accuracy rate was 74.8. The final SVM3 was obtained by (MRMR) with minimal redundancy and maximum correlation. It included four clinical indexes (tumor location, histopathological type, T staging) and two pathological indexes (CD34 / CerB-b2). The accuracy of SVM3 was 81.8%. There was significant difference in survival curve between SVM1 and SVM2 SVM3 patients with different risk (P0.05). Conclusion there is a certain correlation between the clinical parameters such as age M staging and pathological indexes such as CerB-b2P S-100 and PCNA, and the prognosis of CRC patients can be predicted effectively by integrating the clinical and pathological indexes with SVM model.
【作者單位】： 安徽醫(yī)科大學(xué)上海臨床學(xué)院;上海市第十人民醫(yī)院胃腸外科;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(81230057)
【分類號(hào)】：R735.34
本文編號(hào)：2197360
[Abstract]:Objective to analyze the prognostic value of support vector machine (SVM) model (SVM) in predicting the prognosis of colorectal cancer patients with (CRC) by integrating the clinical and pathological indexes. Methods from 2002 to 2011, 2 951 patients underwent colorectal tumor resection in Gastrointestinal surgery Department of the Tenth people's Hospital of Shanghai. The clinical parameters (sex, age, tumor size, tumor location, histopathological type, T staging and M stage G staging) and pathological indexes (carcinoembryonic antigen (CEA), proliferating cell nuclear antigen (PCNA) P53, CD34, S-100 NM23CerB-b2P21 Ki-67) were collected. The patients were divided into two groups according to whether the follow-up information was missing or not. The first group was complete clinical data but the follow-up information was missing in 2 747 cases, and the second group was clinical data and follow-up information complete, 204 cases. The patient's survival was recorded. In the first group, if more than 4 indexes were missing in one patient, the patient was eliminated; in the remaining group, if the missing rate of one index was 30.0, the index was removed. The correlation between clinical and pathological indexes was calculated, and then the correlation was verified in the second group. If the correlation existed in the second group, the second group was directly included in the third group. If the correlation does not exist in the second group, the SVM internal algorithm is used to remove some patients until the association is established again, and the remaining group of patients are included in the third group. The pathological indexes of the patients in the second group were counted, and if the missing rate of one index was 50. 0%, the index was eliminated. The third group of data was processed based on SVM, and three prediction models: SVM1 were established based on clinical index, SVM2 based on pathological index and SVM3 based on clinical and pathological indexes. Results in the first group, there were 4 missing indexes in 834 patients, including sex, age, tumor location, histopathologic type and the deletion rate of 9 indexes (30.0%). After 5 rare tumor patients were excluded, 829 patients were left. In the first group, there was a correlation between age and CerB-b2 expression (P0.05). The second group excluded 105 patients, the remaining 99 patients, the patient age and CerB-b2 expression were correlated (P0.05), the 99 patients were included in the third group. In the second group, the deletion rate of five indexes of PCNAn P53, CD34, S-100 and CerB-b2 was 50.0%. The age of patients in the third group was correlated with the expression of CerB-b2 (P0.05) and the expression of PCNA was correlated with the stage of M (P0.05). In the third group, the survival curve of patients with different T stage and N stage was compared, the difference was statistically significant (P0.05) .SVM1 included 9 clinical indexes (sex, age, tumor size, tumor location), The accuracy was 83.4% and SVM2 was included in five pathological indexes (PCNAn P53 CD34 / S-100 CerB-b2), the accuracy was 78.8%, the initial SVM3 included the above 9 clinical indexes and 5 pathological indexes. The accuracy rate was 74.8. The final SVM3 was obtained by (MRMR) with minimal redundancy and maximum correlation. It included four clinical indexes (tumor location, histopathological type, T staging) and two pathological indexes (CD34 / CerB-b2). The accuracy of SVM3 was 81.8%. There was significant difference in survival curve between SVM1 and SVM2 SVM3 patients with different risk (P0.05). Conclusion there is a certain correlation between the clinical parameters such as age M staging and pathological indexes such as CerB-b2P S-100 and PCNA, and the prognosis of CRC patients can be predicted effectively by integrating the clinical and pathological indexes with SVM model.
【作者單位】： 安徽醫(yī)科大學(xué)上海臨床學(xué)院;上海市第十人民醫(yī)院胃腸外科;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(81230057)
【分類號(hào)】：R735.34
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