基于磁共振氫譜和彌散成像的診斷模型在顱內(nèi)常見(jiàn)腫瘤的應(yīng)用
發(fā)布時(shí)間：2018-08-07 10:41
【摘要】：目的： 在臨床工作中對(duì)于膠質(zhì)瘤、腦膜瘤和轉(zhuǎn)移瘤等顱內(nèi)常見(jiàn)腦腫瘤的鑒別診斷常常遇到困難，而不同腫瘤的臨床治療方法和預(yù)后又有很大差異。本項(xiàng)目擬開(kāi)發(fā)一種顱內(nèi)常見(jiàn)腫瘤的智能診斷軟件，結(jié)合磁共振技術(shù)和人工智能技術(shù)兩方面的優(yōu)勢(shì)，使臨床常見(jiàn)顱內(nèi)腫瘤的診斷正確率得到提高，使診斷程序更加簡(jiǎn)捷。 方法： 選自山東省醫(yī)學(xué)影像學(xué)研究所2012年11月-2013年11月期間腦腫瘤病人60例，術(shù)前均行磁共振常規(guī)檢查和磁共振波譜檢查，包括膠質(zhì)瘤25例(低級(jí)別膠質(zhì)瘤10例，高級(jí)別膠質(zhì)瘤15例)、腦膜瘤20例和轉(zhuǎn)移瘤15例，部分轉(zhuǎn)移瘤經(jīng)臨床證實(shí)，其余病例均經(jīng)手術(shù)病理證實(shí)，另選20例正常志愿者。 在術(shù)前采用西門子公司SKYRA3.0T超導(dǎo)磁共振行MR常規(guī)檢查(軸位TSE序列T2WI、T1WI和FLAIR)、1H-MRS、DWI檢查，分別在腫瘤實(shí)質(zhì)區(qū)、瘤周水腫區(qū)和正常對(duì)照區(qū)選取感興趣區(qū)(ROI)，測(cè)定ROI區(qū)域的代謝物比值和ADC值，記錄各ROI的NAA/Cr、Cho/Cr、NAA/Cho比值及ADC值。統(tǒng)計(jì)學(xué)分析使用SPSS13.0，計(jì)算三種腫瘤感興趣區(qū)各代謝物比值及ADC值的平均值，用均數(shù)±標(biāo)準(zhǔn)差表示；采用雙樣本t檢驗(yàn)，對(duì)比三種腫瘤實(shí)質(zhì)區(qū)之間、三種腫瘤水腫區(qū)之間、不同級(jí)別膠質(zhì)瘤之間各代謝物比值及ADC值有無(wú)差異，P值小于0.05為差異具有統(tǒng)計(jì)學(xué)意義。 對(duì)波譜數(shù)據(jù)集行遺傳算法分析，選擇與優(yōu)化特征值，將提取出的20個(gè)顯著特征值作為最優(yōu)特征子集輸入分類器；將代謝物比值和ADC值組成的典型特征值，直接作為特征值輸入分類器。用于病人樣本分類的分類器采用Fisher判別法和支持向量機(jī)(SVM)兩種。然后根據(jù)每種單分類器的權(quán)重和四個(gè)結(jié)果的差異對(duì)分類結(jié)果進(jìn)行評(píng)估，最后確定診斷結(jié)果。在實(shí)際醫(yī)學(xué)診斷過(guò)程中，將新病例輸入最優(yōu)化的多分類器組進(jìn)行分類，將分類結(jié)果作為人工智能診斷結(jié)果。 結(jié)果： 1.三種顱內(nèi)腫瘤實(shí)質(zhì)區(qū)之間NAA/Cr值差異有統(tǒng)計(jì)學(xué)意義(p0.05)，腦膜瘤實(shí)質(zhì)區(qū)與膠質(zhì)瘤、轉(zhuǎn)移瘤實(shí)質(zhì)區(qū)之間Cho/Cr值差異有統(tǒng)計(jì)學(xué)意義(p0.05)，腦膜瘤實(shí)質(zhì)區(qū)與膠質(zhì)瘤、轉(zhuǎn)移瘤實(shí)質(zhì)區(qū)之間的NAA/Cho值差異有統(tǒng)計(jì)學(xué)意義(p0.01)，腦膜瘤實(shí)質(zhì)區(qū)與膠質(zhì)瘤、轉(zhuǎn)移瘤實(shí)質(zhì)區(qū)之間ADC值差異有統(tǒng)計(jì)學(xué)意義(p0.01)，膠質(zhì)瘤瘤周水腫區(qū)與腦膜瘤、轉(zhuǎn)移瘤瘤周水腫區(qū)之間ADC值差異有統(tǒng)計(jì)學(xué)意義(p0.05)，見(jiàn)表3；高、低級(jí)別膠質(zhì)瘤瘤周水腫區(qū)之間Cho/Cr、 NAA/Cho及ADC值差異有統(tǒng)計(jì)學(xué)意義(p0.05)，見(jiàn)表4；高級(jí)別膠質(zhì)瘤與轉(zhuǎn)移瘤之間瘤周水腫區(qū)NAA/Cho、Cho/Cr、NAA/Cr及ADC值均有統(tǒng)計(jì)學(xué)差異(p0.01)，見(jiàn)表5。 2.1H-MRS波譜數(shù)據(jù)經(jīng)遺傳算法(Genetic Algorithms，GA)特征提取，得到20個(gè)特征值，得到的經(jīng)典特征值包括彌散加權(quán)成像后處理得到ADC值，波譜后處理測(cè)得的NAA、Cho、Cr、Lac、Lip濃度值以及NAA/Cr、Cho/Cr、NAA/Cho等濃度的比值。經(jīng)交叉驗(yàn)證實(shí)驗(yàn)后，將提取的特征及經(jīng)典特征值進(jìn)入Fisher分類器、SVM分類器，得到分類結(jié)果。評(píng)估計(jì)算機(jī)診斷模型的診斷正確率，見(jiàn)表6。 結(jié)論： 1．在腫瘤實(shí)質(zhì)區(qū)，腦膜瘤與其他兩種腫瘤的代謝物比值及ADC值存在明顯差別；高、低級(jí)別膠質(zhì)瘤瘤周水腫區(qū)的代謝物比值及ADC值差異明顯；瘤周的NAA/Cho及ADC值可用于鑒別高級(jí)別膠質(zhì)瘤與轉(zhuǎn)移瘤。利用腫瘤和瘤周水腫區(qū)的1H-MRS代謝物比值及ADC值，可以較好的對(duì)三種顱內(nèi)腫瘤作出鑒別診斷。 2．以1H-MRS和DWI為基礎(chǔ)建立的人工智能診斷模型通過(guò)數(shù)據(jù)挖掘，，特征向量提取，將不同腫瘤的代謝和彌散特征分辨出來(lái)，診斷正確率高，達(dá)到鑒別診斷的目的。在一定程度上起到了非侵入性活檢的效果,對(duì)臨床治療方案的制定具有更強(qiáng)的指導(dǎo)意義，具有良好的臨床應(yīng)用價(jià)值。
[Abstract]:Objective:
In clinical work, the differential diagnosis of intracranial common brain tumors, such as glioma, meningioma and metastatic tumor, often encountered difficulties, and the clinical treatment methods and prognosis of different tumors are very different. This project intends to develop an intelligent diagnosis software for intracranial common tumors, combined with the two aspects of magnetic resonance technology and artificial intelligence technology. The diagnostic accuracy of clinical common intracranial tumors has been improved, making the diagnostic procedure simpler.
Method:
60 patients with brain tumors were selected from the Shandong medical imaging Institute in November 2012 -2013 November. Preoperative magnetic resonance imaging and magnetic resonance spectroscopy were performed, including 25 gliomas (10 cases of low grade gliomas, 15 high grade gliomas), 20 meningiomas and 15 metastatic tumors. Some metastatic tumors were clinically confirmed, and the rest of the cases were all confirmed. All the remaining cases were all confirmed cases were all cases were all cases were all confirmed cases were all cases were all cases were all confirmed cases were all cases all cases were confirmed by clinical After the operation and pathology, 20 normal volunteers were selected.
The SIEMENS SKYRA3.0T superconducting magnetic resonance (MR) routine examination (axial TSE sequence T2WI, T1WI and FLAIR), 1H-MRS, DWI examination were used before the operation to select the region of interest (ROI) in the tumor parenchyma, the peritumoral edema area and the normal control area, respectively, to determine the metabolite ratio and ADC value of the ROI region. Statistical analysis was used to calculate the average value of the ratio of metabolites and ADC values in three regions of interest with SPSS13.0, and the mean values of average number of ADC were calculated, and the ratio of metabolites and ADC of different grade gliomas were compared between the three tumor edema areas by double sample t test, and the value of P was less than 0.05. The difference was statistically significant.
By analyzing the genetic algorithm of spectral data set, selecting and optimizing the eigenvalues, 20 significant eigenvalues are extracted as the optimal subset to input the classifier, and the typical eigenvalues composed of the ratio of metabolites and ADC values are used directly as the eigenvalue input classifier. The classifier used in the classification of patient samples adopts the Fisher discriminant method and support. Two kinds of vector machines (SVM). Then the classification results are evaluated according to the weight of each single classifier and the difference between four results. Finally, the diagnosis results are determined. In the actual medical diagnosis process, the new multiple classifier group is classified into the optimized multiple classifier group, and the classification results are made as artificial intelligent diagnosis results.
Result:
1. the difference of NAA/Cr value between the three kinds of intracranial tumor parenchyma was statistically significant (P0.05). The difference of Cho/Cr between the parenchymal area of meningioma and glioma and the parenchymal area of metastatic tumor was statistically significant (P0.05). The difference of NAA/Cho between the parenchymal area of meningioma and glioma and the parenchymal area of the metastatic tumor was statistically significant (P0.01), and the parenchyma area of meningioma and glioma were and glia. The difference of ADC value between tumor and metastatic tumor was statistically significant (P0.01). The difference of ADC between the edema area of glioma and meningioma and the edema area of metastatic tumor was statistically significant (P0.05), see Table 3; high, Cho/Cr, NAA /Cho and ADC between the edema regions of low grade glioma, and NAA /Cho and ADC value (P0.05), see Table 4; high grade The values of NAA/Cho, Cho/Cr, NAA/Cr and ADC in the peritumoral edema area between glioma and metastatic tumor were statistically different (P0.01), see table 5..
The 2.1H-MRS spectral data are extracted by genetic algorithm (Genetic Algorithms, GA), and 20 eigenvalues are obtained. The classical eigenvalues obtained are ADC value after diffusion weighted imaging, NAA, Cho, Cr, Lac, Lip concentration measured after spectral processing, and the ratio of concentration to NAA/Cr, Cho/Cr, and etc. after cross validation. After cross validation experiments, the extract will be extracted. Classical eigenvalues are incorporated into Fisher classifier and SVM classifier to obtain classification results. The diagnostic accuracy of the computer diagnostic model is evaluated in Table 6.
Conclusion:
1. in the tumor parenchyma, the metabolite ratio and ADC value of the meningioma and other two kinds of tumor were significantly different. The metabolite ratio and the ADC value of the high, low grade glioma edema area were significantly different. The NAA/Cho and ADC values of the tumor peritumor could be used to identify the high grade glioma and metastatic tumor. The 1H-MRS metabolites of the tumor and peritumoral edema area were used. The ratio and ADC value can be used for differential diagnosis of three kinds of intracranial tumors.
2. the artificial intelligent diagnostic model based on 1H-MRS and DWI is established by data mining and feature vector extraction to distinguish the metabolic and dispersion characteristics of different tumors. The diagnostic accuracy is high and the purpose of differential diagnosis is achieved. To a certain extent, the results of non invasive biopsies have been achieved, and the formulation of clinical treatment schemes is stronger. It is of guiding significance and has good clinical application value.
【學(xué)位授予單位】：泰山醫(yī)學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R739.41;R445.2
本文編號(hào)：2169770
[Abstract]:Objective:
In clinical work, the differential diagnosis of intracranial common brain tumors, such as glioma, meningioma and metastatic tumor, often encountered difficulties, and the clinical treatment methods and prognosis of different tumors are very different. This project intends to develop an intelligent diagnosis software for intracranial common tumors, combined with the two aspects of magnetic resonance technology and artificial intelligence technology. The diagnostic accuracy of clinical common intracranial tumors has been improved, making the diagnostic procedure simpler.
Method:
60 patients with brain tumors were selected from the Shandong medical imaging Institute in November 2012 -2013 November. Preoperative magnetic resonance imaging and magnetic resonance spectroscopy were performed, including 25 gliomas (10 cases of low grade gliomas, 15 high grade gliomas), 20 meningiomas and 15 metastatic tumors. Some metastatic tumors were clinically confirmed, and the rest of the cases were all confirmed. All the remaining cases were all confirmed cases were all cases were all cases were all confirmed cases were all cases were all cases were all confirmed cases were all cases all cases were confirmed by clinical After the operation and pathology, 20 normal volunteers were selected.
The SIEMENS SKYRA3.0T superconducting magnetic resonance (MR) routine examination (axial TSE sequence T2WI, T1WI and FLAIR), 1H-MRS, DWI examination were used before the operation to select the region of interest (ROI) in the tumor parenchyma, the peritumoral edema area and the normal control area, respectively, to determine the metabolite ratio and ADC value of the ROI region. Statistical analysis was used to calculate the average value of the ratio of metabolites and ADC values in three regions of interest with SPSS13.0, and the mean values of average number of ADC were calculated, and the ratio of metabolites and ADC of different grade gliomas were compared between the three tumor edema areas by double sample t test, and the value of P was less than 0.05. The difference was statistically significant.
By analyzing the genetic algorithm of spectral data set, selecting and optimizing the eigenvalues, 20 significant eigenvalues are extracted as the optimal subset to input the classifier, and the typical eigenvalues composed of the ratio of metabolites and ADC values are used directly as the eigenvalue input classifier. The classifier used in the classification of patient samples adopts the Fisher discriminant method and support. Two kinds of vector machines (SVM). Then the classification results are evaluated according to the weight of each single classifier and the difference between four results. Finally, the diagnosis results are determined. In the actual medical diagnosis process, the new multiple classifier group is classified into the optimized multiple classifier group, and the classification results are made as artificial intelligent diagnosis results.
Result:
1. the difference of NAA/Cr value between the three kinds of intracranial tumor parenchyma was statistically significant (P0.05). The difference of Cho/Cr between the parenchymal area of meningioma and glioma and the parenchymal area of metastatic tumor was statistically significant (P0.05). The difference of NAA/Cho between the parenchymal area of meningioma and glioma and the parenchymal area of the metastatic tumor was statistically significant (P0.01), and the parenchyma area of meningioma and glioma were and glia. The difference of ADC value between tumor and metastatic tumor was statistically significant (P0.01). The difference of ADC between the edema area of glioma and meningioma and the edema area of metastatic tumor was statistically significant (P0.05), see Table 3; high, Cho/Cr, NAA /Cho and ADC between the edema regions of low grade glioma, and NAA /Cho and ADC value (P0.05), see Table 4; high grade The values of NAA/Cho, Cho/Cr, NAA/Cr and ADC in the peritumoral edema area between glioma and metastatic tumor were statistically different (P0.01), see table 5..
The 2.1H-MRS spectral data are extracted by genetic algorithm (Genetic Algorithms, GA), and 20 eigenvalues are obtained. The classical eigenvalues obtained are ADC value after diffusion weighted imaging, NAA, Cho, Cr, Lac, Lip concentration measured after spectral processing, and the ratio of concentration to NAA/Cr, Cho/Cr, and etc. after cross validation. After cross validation experiments, the extract will be extracted. Classical eigenvalues are incorporated into Fisher classifier and SVM classifier to obtain classification results. The diagnostic accuracy of the computer diagnostic model is evaluated in Table 6.
Conclusion:
1. in the tumor parenchyma, the metabolite ratio and ADC value of the meningioma and other two kinds of tumor were significantly different. The metabolite ratio and the ADC value of the high, low grade glioma edema area were significantly different. The NAA/Cho and ADC values of the tumor peritumor could be used to identify the high grade glioma and metastatic tumor. The 1H-MRS metabolites of the tumor and peritumoral edema area were used. The ratio and ADC value can be used for differential diagnosis of three kinds of intracranial tumors.
2. the artificial intelligent diagnostic model based on 1H-MRS and DWI is established by data mining and feature vector extraction to distinguish the metabolic and dispersion characteristics of different tumors. The diagnostic accuracy is high and the purpose of differential diagnosis is achieved. To a certain extent, the results of non invasive biopsies have been achieved, and the formulation of clinical treatment schemes is stronger. It is of guiding significance and has good clinical application value.
【學(xué)位授予單位】：泰山醫(yī)學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R739.41;R445.2
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 周宏偉;孔博玉;蘭文婧;劉洋;谷艷英;;氫質(zhì)子磁共振波譜在顱內(nèi)常見(jiàn)腫瘤診斷中的應(yīng)用價(jià)值[J];吉林大學(xué)學(xué)報(bào)(醫(yī)學(xué)版);2010年02期
本文編號(hào)：2169770
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