Article(id=1198558270425231874, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558265329152414, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.0355.2023.1031, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1678204800000, receivedDateStr=2023-03-08, revisedDate=null, revisedDateStr=null, acceptedDate=1684857600000, acceptedDateStr=2023-05-24, onlineDate=1763688159804, onlineDateStr=2025-11-21, pubDate=1722096000000, pubDateStr=2024-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1763688159804, onlineIssueDateStr=2025-11-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1763688159804, creator=13701087609, updateTime=1763688159804, updator=13701087609, issue=Issue{id=1198558265329152414, tenantId=1146029695717560320, journalId=1189873630562394117, year='2024', volume='49', issue='7', pageStart='733', pageEnd='854', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1763688158589, creator=13701087609, updateTime=1763689196450, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1198562618517581944, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558265329152414, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1198562618517581945, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558265329152414, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=739, endPage=746, ext={EN=ArticleExt(id=1198558270706250258, articleId=1198558270425231874, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=The diagnostic value of computerized tomography‑based radiomics features in pulmonary alveolar proteinosis, columnId=1190310109000602400, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Clinical Research, runingTitle=null, highlight=null, articleAbstract=

Objective To investigate the diagnostic value of CT radiomics features in pulmonary alveolar proteinosis (PAP). Methods The general data and clinical characteristics of 24 patients with PAP in the Chinese PLA General Hospital from November 2008 to August 2022 were retrospectively collected and analyzed. Another 53 patients with other diffuse lung diseases except for PAP during the same period served as control group. The differences in the 10 conventional CT signs (semantic features) and 107 radiomics features between the two groups were compared. All patients were randomly divided into the training group (n=53) and the validation group (n=24) at a ratio of 7:3. CT semantic feature model, radiomics model and combined model to diagnose PAP were constructed in training group, and the diagnostic efficacy of models was compared using the receiver operating characteristic (ROC) curve in validation group. Decision curve analysis (DCA) was used to assess the value of models for practical clinical application. Radscore was calculated for the model with the highest diagnostic efficacy. Results A total of 24 patients with pathologically confirmed PAP were enrolled, with a male to female ratio of 3:1 and an average age of (44.6±15.2) years. The main clinical symptoms of patients with PAP included shortness of breath, cough, sputum and chest tightness. Compared with control group, the incidence of pleural effusion in PAP group was significantly lower (P<0.05), while no significant differences were observed in other CT features (P>0.05). The areas under the curve (AUC) of the semantic feature model for diagnosing PAP in training and validation group were 0.590 and 0.594, respectively, and in validation group, the accuracy, sensitivity, and specificity for diagnosis of PAP were 0.188, 1.000, and 0.188, respectively. The AUCs of the radiomics model in training group and validation group were 0.845 and 0.867, respectively, and in validation group, the accuracy, sensitivity, and specificity were 0.641, 0.938, and 0.703, respectively. The AUCs of the combined model in training group and validation group were 0.850 and 0.883, respectively, and in validation group, the accuracy, sensitivity, and specificity were 0.688, 0.750, and 0.938, respectively. The AUCs of the radiomics model and the combined model were significantly greater than that of the semantic feature model, but there was no significant difference in the AUCs between the first two models. The decision curve analysis showed that both the radiomics model and the combined model had high application value for predicting PAP. Conclusion CT radiomics shows higher clinical value in the diagnosis of PAP compared with conventional CT features.

, correspAuthors=Zhen Yang, Liang-An Chen, authorNote=null, correspAuthorsNote=
Chen Liang-An, E-mail:
Yang Zhen, E-mail:
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目的 探讨CT影像组学特征在肺泡蛋白沉积症(PAP)中的诊断价值。方法 回顾性分析2008年11月-2022年8月在解放军总医院就诊的24例PAP患者的一般资料及临床特征;另选取同期该院就诊的53例非PAP弥漫性肺疾病患者作为对照组;比较两组间10个常规胸部CT征象(语义特征)和107个CT影像组学特征的差异。将所有患者再按7:3随机分为训练组53例及验证组24例,采用训练组构建PAP诊断的CT语义特征模型、影像组学模型及联合模型,并在验证组利用受试者工作特征(ROC)曲线进行诊断效能比较。采用临床决策分析法检验各模型的临床PAP诊断应用价值。对PAP诊断效能最高的模型,计算影像学特征评分。结果 共纳入PAP患者24例,男女比例3:1,年龄(44.6±15.2)岁,主要临床症状为气促、咳嗽、咳痰及胸闷等。与对照组比较,PAP组胸腔积液发生率明显降低(P<0.05),其余CT特征差异无统计学意义(P>0.05)。CT语义特征模型在训练组和验证组诊断PAP的曲线下面积(AUC)分别为0.590和0.594,在验证组诊断PAP的准确度、敏感度、特异度分别为0.188、1.000、0.188。影像组学模型在训练组和验证组诊断PAP的AUC分别为0.845和0.867,其在验证组诊断PAP的准确度、敏感度、特异度分别为0.641、0.938、0.703。联合模型在训练组和验证组诊断PAP的AUC分别为0.850和0.883,其在训练组诊断PAP的准确度、敏感度、特异度分别为0.688、0.750、0.938。联合模型或影像组学模型诊断PAP的AUC均明显大于CT语义特征模型(P<0.05),但联合模型与影像组学模型诊断PAP的AUC差异无统计学意义(P>0.05)。临床决策曲线分析结果显示,使用联合模型或影像组学模型预测PAP的临床应用价值均较高。结论 与传统CT特征比较,CT影像组学在PAP诊断中有较高的临床应用价值。

, correspAuthors=杨震, 陈良安, authorNote=null, correspAuthorsNote=
陈良安,E-mail:
杨震,E-mail:
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王月明,博士研究生,主要从事肺部疾病的临床与基础研究

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AJR Am J Roentgenol, 2016, 207(3): 544-551., articleTitle=Pulmonary fibrosis on high-resolution CT of patients with pulmonary alveolar proteinosis, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1198589365715825127, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, xref=1, ext=[AuthorCompanyExt(id=1198589365728408040, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, companyId=1198589365715825127, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1School of Medicine, Nankai University, Tianjin 300071, China), AuthorCompanyExt(id=1198589365736796649, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, companyId=1198589365715825127, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1南开大学医学院,天津 300071)]), AuthorCompany(id=1198589365812294123, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, xref=2, ext=[AuthorCompanyExt(id=1198589365820682732, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, companyId=1198589365812294123, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2Department of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing 100853, China), AuthorCompanyExt(id=1198589365829071341, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, companyId=1198589365812294123, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2解放军总医院第一医学中心呼吸与危重症医学科,北京 100853)]), AuthorCompany(id=1198589365900374511, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, xref=3, ext=[AuthorCompanyExt(id=1198589365917151728, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, companyId=1198589365900374511, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3Medical School of Chinese PLA, Beijing 100853, China), AuthorCompanyExt(id=1198589365925540338, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, companyId=1198589365900374511, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3解放军医学院,北京 100853)])], figs=[ArticleFig(id=1198589370346336939, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=EN, label=Fig.1, caption=ROC curves of different radiomics models in the training group (left) and validation group (right), figureFileSmall=dXHvqIV1WI2IixalWy/C9w==, figureFileBig=BdwfyV7Gjt9J1olBgVMF6Q==, tableContent=null), ArticleFig(id=1198589370455388850, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=CN, label=图1, caption=不同影像组学模型在训练组(左)和验证组(右)诊断PAP的ROC曲线

PAP. 肺泡蛋白沉积症;LR. 逻辑回归;SVM. 支持向量机;AB. 自适应增强;DT. 决策树

, figureFileSmall=dXHvqIV1WI2IixalWy/C9w==, figureFileBig=BdwfyV7Gjt9J1olBgVMF6Q==, tableContent=null), ArticleFig(id=1198589370564440763, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=EN, label=Fig.2, caption=ROC curves of CT model, radiomics model and combined model in the training group (left) and validation group (right), figureFileSmall=+kckGhRII5LBVWjFlsf0dQ==, figureFileBig=BhpP/lHZNlgWA9gcw5rDUw==, tableContent=null), ArticleFig(id=1198589370681881282, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=CN, label=图2, caption=CT语义特征模型、影像组学模型和联合模型在训练组(左)和验证组(右)诊断PAP的ROC曲线

PAP. 肺泡蛋白沉积症

, figureFileSmall=+kckGhRII5LBVWjFlsf0dQ==, figureFileBig=BhpP/lHZNlgWA9gcw5rDUw==, tableContent=null), ArticleFig(id=1198589370761573063, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=EN, label=Fig.3, caption=Decision curve analysis of radiomics model and combined model in diagnosis of PAP, figureFileSmall=v+eF6fVk99u5hcwc6H3l5g==, figureFileBig=7J31SsvarddzxJqdamp0iQ==, tableContent=null), ArticleFig(id=1198589370841264843, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=CN, label=图3, caption=影像组学和联合模型用于PAP诊断的临床决策曲线

PAP. 肺泡蛋白沉积症

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PAP. 肺泡蛋白沉积症

, figureFileSmall=Yafsm03TviOI/vLCZo2lFg==, figureFileBig=WP7W861hHz84PakxUYAHKA==, tableContent=null), ArticleFig(id=1198589371181003483, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=EN, label=Tab.1, caption=

Comparison of general data and clinical characteristics between PAP group and control group

, figureFileSmall=null, figureFileBig=null, tableContent=
项目PAP组 (n=24)对照组 (n=53)P
性别 [例(%)]0.898
18(75.0)39(73.6)
6(25.0)14(26.4)
年龄(岁, $\bar{x}±s$)44.6±15.248.4±12.30.245
吸烟史 [例(%)]0.470
11(45.8)29(54.7)
13(54.2)24(45.3)
症状 [例(%)]
无症状4(16.7)6(11.3)0.518
气促17(70.8)40(75.5)0.667
咳嗽13(54.2)42(79.2)0.024
咳痰10(41.7)34(64.2)0.065
胸闷10(41.7)18(34.0)0.515
胸痛3(12.5)7(13.2)0.932
发热1(4.2)36(67.9)<0.001
体征 [例(%)]<0.001
无异常19(79.2)17(32.1)
湿啰音4(16.7)34(64.1)
干啰音1(4.2)2(3.8)
肺功能(%, $\bar{x}±s$)
VC%86.4±13.588.4±12.80.611
TLC%81.1±12.883.0±11.90.590
FVC%88.4±11.889.0±11.90.860
FEV1%88.4±12.688.2±12.00.959
FEV1/FVC85.0±8.185.5±7.30.829
DLCO%68.2±12.770.6±13.20.545
血气分析($\bar{x}±s$)
PaO2(mmHg)76.7±12.278.5±10.20.555
PaCO2(mmHg)41.1±5.239.7±4.40.266
SaO2(%)93.8±2.993.5±2.60.658
), ArticleFig(id=1198589371373941471, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=CN, label=表1, caption=

PAP组与对照组的一般资料与临床特征比较

, figureFileSmall=null, figureFileBig=null, tableContent=
项目PAP组 (n=24)对照组 (n=53)P
性别 [例(%)]0.898
18(75.0)39(73.6)
6(25.0)14(26.4)
年龄(岁, $\bar{x}±s$)44.6±15.248.4±12.30.245
吸烟史 [例(%)]0.470
11(45.8)29(54.7)
13(54.2)24(45.3)
症状 [例(%)]
无症状4(16.7)6(11.3)0.518
气促17(70.8)40(75.5)0.667
咳嗽13(54.2)42(79.2)0.024
咳痰10(41.7)34(64.2)0.065
胸闷10(41.7)18(34.0)0.515
胸痛3(12.5)7(13.2)0.932
发热1(4.2)36(67.9)<0.001
体征 [例(%)]<0.001
无异常19(79.2)17(32.1)
湿啰音4(16.7)34(64.1)
干啰音1(4.2)2(3.8)
肺功能(%, $\bar{x}±s$)
VC%86.4±13.588.4±12.80.611
TLC%81.1±12.883.0±11.90.590
FVC%88.4±11.889.0±11.90.860
FEV1%88.4±12.688.2±12.00.959
FEV1/FVC85.0±8.185.5±7.30.829
DLCO%68.2±12.770.6±13.20.545
血气分析($\bar{x}±s$)
PaO2(mmHg)76.7±12.278.5±10.20.555
PaCO2(mmHg)41.1±5.239.7±4.40.266
SaO2(%)93.8±2.993.5±2.60.658
), ArticleFig(id=1198589371503964901, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=EN, label=Tab.2, caption=

Comparison of chest CT signs between PAP group and non-PAP group [n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
项目PAP 组(n=24)对照组(n=53)P
磨玻璃影0.498
24(100.0)52(98.1)
01(1.9)
小叶间隔增厚0.876
22(91.7)48(90.6)
2(8.3)5(9.4)
结节影0.131
13(54.2)19(35.8)
11(45.8)34(64.2)
实变影0.087
10(41.7)12(22.6)
14(58.3)41(77.4)
条索影0.236
7(29.2)23(43.4)
17(70.8)30(56.6)
囊状影0.315
2(8.3)9(17.0)
22(91.7)44(83.0)
蜂窝影0.224
4(16.7)4(7.5)
20(83.3)49(92.5)
支气管充气征0.322
8(33.3)12(22.6)
16(66.7)41(77.4)
淋巴结肿大0.965
6(25.0)13(24.5)
18(75.0)40(75.5)
胸腔积液0.045
1(4.2)12(22.6)
23(95.8)41(77.4)
), ArticleFig(id=1198589371604628198, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=CN, label=表2, caption=

PAP患者与非PAP患者(对照组)胸部CT征象比较 [例(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
项目PAP 组(n=24)对照组(n=53)P
磨玻璃影0.498
24(100.0)52(98.1)
01(1.9)
小叶间隔增厚0.876
22(91.7)48(90.6)
2(8.3)5(9.4)
结节影0.131
13(54.2)19(35.8)
11(45.8)34(64.2)
实变影0.087
10(41.7)12(22.6)
14(58.3)41(77.4)
条索影0.236
7(29.2)23(43.4)
17(70.8)30(56.6)
囊状影0.315
2(8.3)9(17.0)
22(91.7)44(83.0)
蜂窝影0.224
4(16.7)4(7.5)
20(83.3)49(92.5)
支气管充气征0.322
8(33.3)12(22.6)
16(66.7)41(77.4)
淋巴结肿大0.965
6(25.0)13(24.5)
18(75.0)40(75.5)
胸腔积液0.045
1(4.2)12(22.6)
23(95.8)41(77.4)
), ArticleFig(id=1198589371734651629, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=EN, label=Tab.3, caption=

The radiomics parameters selected by univariate analysis and LASSO regression

, figureFileSmall=null, figureFileBig=null, tableContent=
序号影像组学参数名称
X1original_glcm_Idn
X2original_glcm_ClusterProminence
X3original_gldm_LargeDependenceHighGrayLevelEmphasis
), ArticleFig(id=1198589372854530802, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558270425231874, language=CN, label=表3, caption=

单因素分析和LASSO回归筛选出的影像组学参数

, figureFileSmall=null, figureFileBig=null, tableContent=
序号影像组学参数名称
X1original_glcm_Idn
X2original_glcm_ClusterProminence
X3original_gldm_LargeDependenceHighGrayLevelEmphasis
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The diagnostic value of different radiomics models for PAP in training group and validation group

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影像组学模型训练组(n=53)验证组(n=24)
AUC准确度敏感度特异度

阳性
预测值

阴性
预测值

AUC准确度敏感度特异度

阳性
预测值

阴性
预测值

逻辑回归(LR)0.8450.6600.9380.5410.4690.9520.8670.6670.8750.5630.5000.900
支持向量机(SVM)0.8330.7291.0000.3210.6891.0000.8590.6770.9470.2500.6670.750
自适应增强(AB)0.8390.8490.8130.8650.7220.9140.7500.7920.6250.8750.7140.824
决策树(DT)0.8970.8111.0000.7300.6151.0000.7110.5420.7500.4380.4000.778
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不同影像组学模型在训练组和模型组的PAP诊断效能

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影像组学模型训练组(n=53)验证组(n=24)
AUC准确度敏感度特异度

阳性
预测值

阴性
预测值

AUC准确度敏感度特异度

阳性
预测值

阴性
预测值

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支持向量机(SVM)0.8330.7291.0000.3210.6891.0000.8590.6770.9470.2500.6670.750
自适应增强(AB)0.8390.8490.8130.8650.7220.9140.7500.7920.6250.8750.7140.824
决策树(DT)0.8970.8111.0000.7300.6151.0000.7110.5420.7500.4380.4000.778
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CT影像组学特征在肺泡蛋白沉积症中的诊断价值
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王月明 1, 2 , 喻航 3 , 王淼煜 3 , 史文佳 3 , 魏元辉 1 , 杨震 2, * , 陈良安 1, 2, *
解放军医学杂志 | 临床研究 2024,49(7): 739-746
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解放军医学杂志 | 临床研究 2024, 49(7): 739-746
CT影像组学特征在肺泡蛋白沉积症中的诊断价值
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王月明1, 2, 喻航3, 王淼煜3, 史文佳3, 魏元辉1, 杨震2, * , 陈良安1, 2, *
作者信息
  • 1南开大学医学院,天津 300071
  • 2解放军总医院第一医学中心呼吸与危重症医学科,北京 100853
  • 3解放军医学院,北京 100853
  • 王月明,博士研究生,主要从事肺部疾病的临床与基础研究

通讯作者:

陈良安,E-mail:
杨震,E-mail:
The diagnostic value of computerized tomography‑based radiomics features in pulmonary alveolar proteinosis
Yue-Ming Wang1, 2, Hang Yu3, Miao-Yu Wang3, Wen-Jia Shi3, Yuan-Hui Wei1, Zhen Yang2, * , Liang-An Chen1, 2, *
Affiliations
  • 1School of Medicine, Nankai University, Tianjin 300071, China
  • 2Department of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing 100853, China
  • 3Medical School of Chinese PLA, Beijing 100853, China
出版时间: 2024-07-28 doi: 10.11855/j.issn.0577-7402.0355.2023.1031
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目的 探讨CT影像组学特征在肺泡蛋白沉积症(PAP)中的诊断价值。方法 回顾性分析2008年11月-2022年8月在解放军总医院就诊的24例PAP患者的一般资料及临床特征;另选取同期该院就诊的53例非PAP弥漫性肺疾病患者作为对照组;比较两组间10个常规胸部CT征象(语义特征)和107个CT影像组学特征的差异。将所有患者再按7:3随机分为训练组53例及验证组24例,采用训练组构建PAP诊断的CT语义特征模型、影像组学模型及联合模型,并在验证组利用受试者工作特征(ROC)曲线进行诊断效能比较。采用临床决策分析法检验各模型的临床PAP诊断应用价值。对PAP诊断效能最高的模型,计算影像学特征评分。结果 共纳入PAP患者24例,男女比例3:1,年龄(44.6±15.2)岁,主要临床症状为气促、咳嗽、咳痰及胸闷等。与对照组比较,PAP组胸腔积液发生率明显降低(P<0.05),其余CT特征差异无统计学意义(P>0.05)。CT语义特征模型在训练组和验证组诊断PAP的曲线下面积(AUC)分别为0.590和0.594,在验证组诊断PAP的准确度、敏感度、特异度分别为0.188、1.000、0.188。影像组学模型在训练组和验证组诊断PAP的AUC分别为0.845和0.867,其在验证组诊断PAP的准确度、敏感度、特异度分别为0.641、0.938、0.703。联合模型在训练组和验证组诊断PAP的AUC分别为0.850和0.883,其在训练组诊断PAP的准确度、敏感度、特异度分别为0.688、0.750、0.938。联合模型或影像组学模型诊断PAP的AUC均明显大于CT语义特征模型(P<0.05),但联合模型与影像组学模型诊断PAP的AUC差异无统计学意义(P>0.05)。临床决策曲线分析结果显示,使用联合模型或影像组学模型预测PAP的临床应用价值均较高。结论 与传统CT特征比较,CT影像组学在PAP诊断中有较高的临床应用价值。

肺泡蛋白沉积症  /  CT  /  影像组学  /  诊断模型

Objective To investigate the diagnostic value of CT radiomics features in pulmonary alveolar proteinosis (PAP). Methods The general data and clinical characteristics of 24 patients with PAP in the Chinese PLA General Hospital from November 2008 to August 2022 were retrospectively collected and analyzed. Another 53 patients with other diffuse lung diseases except for PAP during the same period served as control group. The differences in the 10 conventional CT signs (semantic features) and 107 radiomics features between the two groups were compared. All patients were randomly divided into the training group (n=53) and the validation group (n=24) at a ratio of 7:3. CT semantic feature model, radiomics model and combined model to diagnose PAP were constructed in training group, and the diagnostic efficacy of models was compared using the receiver operating characteristic (ROC) curve in validation group. Decision curve analysis (DCA) was used to assess the value of models for practical clinical application. Radscore was calculated for the model with the highest diagnostic efficacy. Results A total of 24 patients with pathologically confirmed PAP were enrolled, with a male to female ratio of 3:1 and an average age of (44.6±15.2) years. The main clinical symptoms of patients with PAP included shortness of breath, cough, sputum and chest tightness. Compared with control group, the incidence of pleural effusion in PAP group was significantly lower (P<0.05), while no significant differences were observed in other CT features (P>0.05). The areas under the curve (AUC) of the semantic feature model for diagnosing PAP in training and validation group were 0.590 and 0.594, respectively, and in validation group, the accuracy, sensitivity, and specificity for diagnosis of PAP were 0.188, 1.000, and 0.188, respectively. The AUCs of the radiomics model in training group and validation group were 0.845 and 0.867, respectively, and in validation group, the accuracy, sensitivity, and specificity were 0.641, 0.938, and 0.703, respectively. The AUCs of the combined model in training group and validation group were 0.850 and 0.883, respectively, and in validation group, the accuracy, sensitivity, and specificity were 0.688, 0.750, and 0.938, respectively. The AUCs of the radiomics model and the combined model were significantly greater than that of the semantic feature model, but there was no significant difference in the AUCs between the first two models. The decision curve analysis showed that both the radiomics model and the combined model had high application value for predicting PAP. Conclusion CT radiomics shows higher clinical value in the diagnosis of PAP compared with conventional CT features.

pulmonary alveolar proteinosis  /  CT  /  radiomics  /  diagnostic model
王月明, 喻航, 王淼煜, 史文佳, 魏元辉, 杨震, 陈良安. CT影像组学特征在肺泡蛋白沉积症中的诊断价值. 解放军医学杂志, 2024 , 49 (7) : 739 -746 . DOI: 10.11855/j.issn.0577-7402.0355.2023.1031
Yue-Ming Wang, Hang Yu, Miao-Yu Wang, Wen-Jia Shi, Yuan-Hui Wei, Zhen Yang, Liang-An Chen. The diagnostic value of computerized tomography‑based radiomics features in pulmonary alveolar proteinosis[J]. Medical Journal of Chinese People’s Liberation Army, 2024 , 49 (7) : 739 -746 . DOI: 10.11855/j.issn.0577-7402.0355.2023.1031
肺泡蛋白沉积症(pulmonary alveolar proteinosis,PAP)是一种罕见的弥漫性肺部疾病,患病率约为百万分之七[1]。确诊PAP的主要依据为肺泡灌洗液或肺活检组织病理学检查发现过碘酸希夫(periodic acid-schiff,PAS)染色阳性的蛋白样物质。无创的影像学检查结果是PAP诊断的重要参考依据。PAP患者典型的胸部CT表现为双肺弥漫分布的磨玻璃影伴小叶间隔增厚,形成铺路石征[2],但其也可见于其他肺部疾病[3]。因此,仅依靠常规CT征象较难将PAP与其他肺部疾病进行鉴别。影像组学的概念于2012年首次被提出,是指从医学影像中高通量地提取大量定量图像特征[4]。将这些定量图像特征与患者的其他临床数据相结合,通过生物信息学工具进行更深层次的挖掘,可提高疾病诊断、疗效评估及预后预测的准确性,协助临床决策[5-6]。基于CT的影像组学已被应用于多种疾病的研究[7-13]。在肺部疾病,CT影像组学主要被用于肺癌的鉴别诊断、分子分型、疗效评估和预后等研究[14-15]。有研究显示,CT影像组学模型在COVID-19肺炎的诊断和疗效预测中表现良好[16-17],但在其他肺部良性疾病中的应用研究相对较少,也少见PAP的CT影像组学特征相关研究报道。本研究旨在回顾性分析PAP患者的临床资料及CT影像组学特征,探讨CT影像组学对PAP诊断的临床价值。
回顾性分析2008年11月-2022年8月解放军总医院收治的24例PAP患者的临床资料及CT影像组学特征。入选标准:病例最终诊断为PAP。排除标准:胸部CT影像学资料不全。另选取同期就诊于该院的具有弥漫性磨玻璃样改变、影像特征接近的良性肺部疾病(非PAP)患者53例作为对照组,包括肺孢子菌肺炎、COVID-19肺炎及间质性肺病患者。比较两组患者常规CT和影像组学特征。本研究方案通过解放军总医院医学伦理委员会审批(S2022-764-01)。
肺孢子菌肺炎的诊断标准参照欧洲癌症治疗研究组织/真菌感染协作组(EORTC/MSGERC)发布的共识[18]:(1)临床表现为发热、咳嗽、呼吸困难等;(2)影像学表现为弥漫性磨玻璃样改变或肺间质弥漫浸润病变等;(3)从呼吸道标本(痰、支气管肺泡灌洗液或肺活检组织)中通过传统染色镜检、PCR等方法检测出肺孢子菌。
COVID-19肺炎的诊断标准参照国家卫健委发布的《新型冠状病毒感染诊疗方案(试行第十版)》:(1)具有新冠病毒感染的相关临床表现;(2)具有以下一种或以上病原学、血清学检查结果(新冠病毒核酸检测阳性;新冠病毒抗原检测阳性;新冠病毒分离、培养阳性;恢复期新冠病毒特异性IgG抗体水平较急性期升高4倍或以上)。
间质性肺疾病的分类及诊断标准参照美国胸科协会/欧洲呼吸协会(ATS/ERS)发布的共识[19]
收集并记录入选PAP患者的人口学及临床资料,包括姓名、年龄、性别、吸烟史、职业暴露史、基础疾病、症状、体征、胸部CT检查、肺功能检查、支气管镜检查、病理学结果、血气分析等。收集对照组患者胸部CT资料作为对照分析影像学特征。
所有患者均接受胸部高分辨率CT检查。肺CT检查仪器为Philips Brilliance iCT(荷兰飞利浦公司)或Siemens Somatom Definition(德国西门子公司)。患者取仰卧位,双手抱头,于深吸气末接受CT扫描。CT参数:管电流110 mA,管电压120 kV,准直器0.75×128,螺距1,层厚1.0~1.5 mm,层间距1.0~1.5 mm,矩阵大小512×512,重建算法iDose3(Philips)或B35f(Siemens)。
影像学特征分为语义特征和影像组学特征。语义特征是指传统影像学可直观判断的密度、形态、大小、类型、位置等特征。本研究观察的语义特征共10个,包括磨玻璃影、小叶间隔增厚、结节影、实变影、条索影、囊状影、蜂窝状影、支气管充气征、淋巴结肿大、胸腔积液。影像组学特征共107个,分为14个形态特征(Shape)、18个一阶特征(First Order)、24个灰度共生矩阵特征(GLCM)、16个灰度区域大小矩阵特征(GLSZM)、16个灰度行程矩阵特征(GLRLM)、5个邻域灰度差距矩阵特征(NGTDM)及14个灰度依赖矩阵特征(GLDM)。
(1)病灶区域分割:将原始Dicom图像导入数字肺影像分析系统(FACT数字肺,陕西神州德信公司),系统自动识别全肺病灶区域(region of interest,ROI),由本院放射科高年资医师进行人工检查和修正,获得全肺PAP病灶ROI。(2)语义特征获取:由本院呼吸科两名医师对所有CT影像进行阅片,评估是否存在上述语义特征。对于无法确定的语义特征,由呼吸科1名主任医师进行确认。(3)影像组学特征提取及标准化:通过3D Slicer 5.0.3软件进行,首先对病灶ROI进行1 mm×1 mm×1 mm重采样,然后利用SlicerRadiomics功能模块提取影像组学特征。
通过R软件将所有患者按7:3随机分为训练组53例和验证组24例;训练组用于模型建立,验证组用于模型验证。(1)模型:包括语义特征模型(仅纳入语义特征)、影像组学模型(仅纳入影像组学特征)、联合模型(纳入语义特征+影像组学特征);(2)建模方法:特征数据采用Z-score 标准化处理,特征筛选采用单因素分析+最小绝对收缩算子(LASSO回归)法,模型建立先采用逻辑回归(logistic regression,LR)、支持向量机(support vector machine,SVM)、决策树(decision tree,DT)、自适应增强(adaptive boosting,AB)4种机器学习模型进行,根据ROC曲线下面积(area under curve,AUC)确定最终模型;(3)模型评价:在验证组中进行,采用ROC曲线的AUC比较不同模型的诊断效能,并比较不同模型的敏感度、特异度、准确度、阳性预测值、阴性预测值;(4)临床应用价值评估:采用临床决策分析(decision curve analysis,DCA)法检验模型的临床实际应用价值;(5)对诊断效能最高的模型,计算影像学特征评分(Radscore)并以瀑布图表示。
采用SPSS 26.0和R 4.2.2软件进行统计分析。计数资料以例(%)表示,组间比较采用χ2检验。符合正态分布的计量资料以$\bar{x}±s$表示,组间比较采用独立样本t检验;非正态分布的计量资料以M(Q1Q3)表示,组间比较采用秩和检验。语义特征的选择采用χ2检验,筛选P<0.05的特征建立模型。影像组学特征的选择采用t检验或Mann-Whitney U检验,筛选P<0.1的特征建立模型。通过LASSO回归10折交叉验证进行降维。采用DeLong检验比较不同模型的AUC差异。以双侧检验,P<0.05为差异有统计学意义。
共收集PAP患者24例,男18例(75.0%),女6例(25.0%)。患者确诊PAP的年龄为14~80(44.6±15.2)岁。有吸烟史11例(45.8%);有基础疾病8例(33.3%),均与PAP无关;有特殊接触史7例(29.2%),包括粉尘、棉絮或油料等长期接触史。患者均经过病理确诊,其中8例经支气管镜肺泡灌洗确诊,肺泡灌洗液涂片光镜下可见嗜伊红物质,PAS染色阳性;其余16例经肺活检确诊,包括CT引导下经皮肺穿刺活检11例、经支气管镜肺活检3例、支气管镜冷冻肺活检1例、手术活检1例,活检肺组织光镜下可见肺泡腔内嗜伊红均质无结构物质沉积,且PAS染色阳性。
PAP组主要临床症状为气促、咳嗽、咳痰、胸闷等,无明显呼吸道症状4例(16.7%);胸部体征无异常19例(79.2%),有湿啰音4例(16.7%),有干啰音1例(4.2%)。接受支气管镜检查22例,镜下可见支气管黏膜充血3例,无明显异常19例。接受支气管肺泡灌洗21例,其中灌洗液外观为乳白色17例,淡红色4例。接受肺功能检查19例,其中轻度限制性通气功能障碍7例(36.8%),轻度阻塞性通气功能障碍1例(5.3%);弥散功能重度下降2例(10.5%),中度下降3例(15.8%),轻度下降12例(63.2%)。进行动脉血气分析检测21例,其中低氧血症(PaO2<80 mmHg)13例(61.9%)。
对照组包括肺孢子菌肺炎19例、COVID-19肺炎15例及间质性肺疾病19例(12例非特异性间质性肺炎、2例特发性肺间质纤维化、5例结缔组织病相关性间质性肺疾病),其中28例接受了肺功能检查,40例进行了动脉血气分析检测。PAP组咳嗽、发热、湿啰音发生率明显低于对照组(P<0.05),其他项目差异无统计学意义(P>0.05,表1)。
胸部CT征象分析显示,PAP患者较多见的胸部CT征象为磨玻璃影(100.0%)和小叶间隔增厚(91.7%)。PAP组胸腔积液发生率明显低于对照组(P<0.05),而磨玻璃影、小叶间隔增厚、结节影、实变影、条索影、囊状影、蜂窝影、支气管充气征及淋巴结肿大等CT征象的发生率差异无统计学意义(P>0.05,表2)。
“磨玻璃影、小叶间隔增厚、结节影、实变影、条索影、囊状影、蜂窝状改变、支气管充气征、淋巴结肿大、胸腔积液”这10个语义特征中,仅“胸腔积液”在训练组PAP与非PAP患者间存在差异(P<0.05)。
在训练组中经单因素分析筛选出6个影像组学特征,经LASSO降维后筛选出3个影像组学特征构建模型方程,分别为Original_glcm_Idn (X1)、 Original_glcm_ClusterProminence (X2)、 Original_gldm_LargeDependenceHighGrayLevelEmphasis (X3) (表3)。
以“胸腔积液”为特征建立CT语义特征模型,其方程为:ln[P/(1-P)]=-0.624-1.573×(胸腔积液=有)。该模型在训练组、验证组诊断PAP的AUC分别为0.590(95%CI 0.497~0.683)、0.594(95%CI 0.495~0.693);以最佳阈值点0.224进行分类,其在验证组诊断PAP的准确度、敏感度、特异度分别为0.188、1.000、0.188。
影像组学模型建立的4种模型包括LR模型、SVM模型、AB模型、DT模型。这4种模型在训练组和验证组诊断PAP的效能见表4,ROC曲线见图1。选择LR为最终建模方法,模型方程为:RadscoreRad=81.957-81.524×X1-2.084×X2-0.838×X3。该模型在训练组、验证组诊断PAP的AUC分别为0.845(95%CI 0.737~0.953)、0.867(95%CI 0.708~1.000);以最佳阈值点0.981进行分类,其在验证组诊断PAP的准确度、敏感度、特异度分别为0.641、0.938、0.703。
以CT语义特征和影像组学特征建立联合模型,其方程为:RadscoreCombined=93.976-130.482×RadscoreRad-1.670×(胸腔积液=有);该模型在训练组、验证组诊断PAP的AUC分别为0.850(95%CI 0.747~0.952)、0.883(95%CI 0.736~1.000);以最佳阈值点0.484进行分类,其在训练组诊断PAP的准确度、敏感度、特异度分别为0.688、0.750、0.938。
通过DeLong检验各模型诊断PAP的AUC差异显著性,结果显示在训练组或验证组中,联合模型(P<0.001)或影像组学模型(P=0.001)均与语义模型存在明显差异,但联合模型与影像组学模型差异无统计学意义(训练组:P=0.429;验证组:P=0.369;图2)。
临床决策曲线分析结果显示,在阈值0~1范围内,使用联合模型或影像组学模型预测患者是否为PAP的价值均较高(图3)。影像组学评分瀑布图也显示,依据Radscore评分可较准确地区分PAP与非PAP患者(图4)。
PAP是以肺泡表面活性物质在肺泡内异常沉积为主要特征的罕见肺部疾病,国外报道其发病率约为(0.36~1.65)/百万[20-22]。PAP的主要特征为肺泡表面活性物质产生异常或清除障碍,在肺泡内异常沉积,导致气体交换受损及进行性呼吸功能障碍。PAP患者的临床表现缺乏特异性,轻者无明显症状或仅有轻微咳嗽,重者可出现呼吸衰竭,甚至危及生命;其确诊依据为肺泡灌洗液或肺活检组织PAS染色阳性。但部分重症PAP患者因氧分压过低、呼吸困难或其他禁忌证,通过有创的侵入性手段获取组织标本有一定风险,较难获得明确的病理诊断,而诊断及治疗不及时可能导致呼吸衰竭甚至死亡。CT表现“铺路石征”对诊断PAP有一定的提示意义,但此征象也可见于其他肺部疾病,引入新的图像分析技术有可能提高其诊断效能。已有多项研究探索了CT影像组学技术在不同疾病的诊断、分型、疗效评估和预后预测中的价值[7-14],而其对PAP诊断的临床价值有待评估。本研究构建了一个基于CT的影像组学特征模型,用于PAP的临床诊断;结果显示,该模型能较好地区分PAP患者与CT表现为磨玻璃影、小叶间隔增厚的非PAP患者,其诊断效率显著优于常规CT。
本研究共纳入经病理确诊的PAP患者24例,平均年龄44.6岁,与既往国内外其他研究中PAP患者的年龄相近(37.8~50.8岁)[23-27]。本组PAP患者的男女比例为3:1。国外一项纳入410例PAP患者的系统评价结果显示男女比例为2.65:1[23];中国一项纳入241例PAP患者的研究结果显示男女比例为2.21:1[28];日本一项纳入248例PAP患者的全国性队列研究中男女比例为2.1:1[21]。本组PAP患者的男女性别比略高于上述研究,可能与本研究例数相对较少有关。
PAP可分为先天性、自身免疫性及继发性共3种类型[1]。其中自身免疫性PAP更为常见,占90%以上,患者体内产生的粒细胞-巨噬细胞集落刺激因子(granulocyte-macrophage colony stimulating factor,GM-CSF)抗体中和了GM-CSF,削弱了其对巨噬细胞成熟的刺激作用,导致肺泡表面活性物质清除障碍。先天性PAP主要发病机制包括肺泡表面活性蛋白或ATP结合盒转运蛋白A3(ATP binding cassette transporter A3,ABCA3)编码基因突变导致肺泡表面活性物质异常产生,或GM-CSF受体α或β链(CSF2RA、CSF2RB)编码基因突变导致巨噬细胞成熟障碍。而继发性PAP由巨噬细胞数量减少或功能异常导致,与血液系统疾病、恶性肿瘤、免疫缺陷综合征、慢性感染或粉尘接触等有关。本研究中PAP患者的最小发病年龄为14岁,可基本排除先天性PAP,所有患者均无PAP相关基础疾病,但未进行GM-CSF抗体检测,因此无法明确具体分型。本组PAP患者临床表现无特异性,常见症状为气促(70.8%)、咳嗽(54.2%)、咳痰(41.7%)及胸闷(41.7%),无明显呼吸道症状者占16.7%,无异常胸部体征者占79.2%,与既往其他研究相符[21,28]。所有患者均存在肺功能异常,以弥散功能障碍为主(89.5%,17/19),且多为轻度(63.2%,12/19);有轻度阻塞性或限制性通气功能障碍者占42.1%(8/19);动脉血气分析显示低氧血症者占61.9%(13/21)。
PAP的CT表现通常被认为是非特异性的,其典型CT表现为双肺弥漫分布的磨玻璃影合并小叶间隔增厚,即“铺路石征”[29]。然而,“铺路石征”并非特异性表现,也可见于肺出血、心源性肺水肿、细支气管肺泡癌、细菌性肺炎、肺结核、肺孢子菌肺炎、非特异性间质性肺炎、急性呼吸窘迫综合征等多种肺部疾病[28]。本研究中,磨玻璃影和小叶间隔增厚存在于90%以上的PAP和非PAP患者,提示PAP的典型“铺路石征”不能用于临床诊断。而胸腔积液征是两组患者间唯一存在明显差异的CT特征,在非PAP患者中发生率明显高于PAP患者;但该征象同样为非特异性特征[30],以此建立的诊断模型准确度低(0.188),不具备临床应用价值。其他肺部语义特征,如结节影、实变影、条索影、囊状影、蜂窝影、支气管充气征、淋巴结肿大等在两组间差异均无统计学意义。因此,CT语义特征因诊断效能较低而不适合用于PAP的诊断。
影像组学是过去10年中出现的一种新的图像处理方法。通过将图像转换为海量数据,可以提取和分析大量肉眼看不见的特征,用于疾病的诊断、严重程度评估和预后[4]。影像组学技术已被广泛应用于肺结节的诊断、分期和预后研究;近年来,其应用已逐渐从肿瘤学扩展到其他研究领域[31-32]。然而,笔者文献调研未见PAP影像组学特征的相关研究报道。本研究报告的PAP特异性影像组学特征均由原始图像中获得,与临床和语义CT模型相比,影像组学模型显示出更好的诊断性能,提示借助影像组学可获得更多的图像细节信息,优于传统的人工判读;同时,决策曲线分析显示影像组学模型在诊断PAP方面具有较高的临床价值,表明影像组学可作为一种工具,协助临床医师诊断PAP。
既往的PAP影像学研究多利用半定量CT分析等传统手段进行,主要用于疾病严重程度及疗效评估[33-36]。Tokura等[33]计算32例PAP患者CT磨玻璃影密度及受累区域大小,并将之转换为严重程度评分,结果显示该CT评分与肺功能指标(PaO2、DLCO%)有较强的相关性,可用于PAP疗效评估。Sui等[33]报道,低剂量CT可有效显示PAP病变区域,依据此类CT获得的磨玻璃影(GGO)人工视觉评分与肺功能明显相关。本研究展示了影像组学技术在PAP诊断中的可行性。同时,可以预见的是,未来借助影像组学技术,有可能发现更多的PAP严重程度、疗效相关的影像组学特征,在PAP病情评估、疗效预测等方面发挥重要作用。
本研究存在一定局限性。首先,由于PAP属于较罕见病种,本研究纳入了本医疗机构近15年所有确诊PAP、有胸部高分辨率CT影像资料的病例,但样本量仍较小。其次,本研究为单中心回顾性研究,未进行外部验证,可能导致模型性能偏倚。因此,需要大型的多中心前瞻性研究来进一步验证影像组学在PAP诊断中的应用价值。
综上所述,与传统CT特征比较,影像组学对PAP患者表现出良好的诊断效能和临床价值。应用CT影像组学辅助PAP的诊断,有可能减少对支气管灌洗等侵入性检查方式的需求,对临床诊断有一定的参考价值。
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2024年第49卷第7期
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doi: 10.11855/j.issn.0577-7402.0355.2023.1031
  • 接收时间:2023-03-08
  • 首发时间:2025-11-21
  • 出版时间:2024-07-28
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  • 收稿日期:2023-03-08
  • 录用日期:2023-05-24
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    1南开大学医学院,天津 300071
    2解放军总医院第一医学中心呼吸与危重症医学科,北京 100853
    3解放军医学院,北京 100853

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2种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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