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Article(id=1194643388420035546, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1194643387904136153, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.0390.2024.1015, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1711296000000, receivedDateStr=2024-03-25, revisedDate=null, revisedDateStr=null, acceptedDate=1715443200000, acceptedDateStr=2024-05-12, onlineDate=1762754779200, onlineDateStr=2025-11-10, pubDate=1737993600000, pubDateStr=2025-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1762754779200, onlineIssueDateStr=2025-11-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1762754779200, creator=13701087609, updateTime=1762754779200, updator=13701087609, issue=Issue{id=1194643387904136153, tenantId=1146029695717560320, journalId=1189873630562394117, year='2025', volume='50', issue='1', pageStart='1', pageEnd='120', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1762754779076, creator=13701087609, updateTime=1762756450259, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1194650397408203370, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1194643387904136153, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1194650397408203371, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1194643387904136153, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1, endPage=8, ext={EN=ArticleExt(id=1194643388654916574, articleId=1194643388420035546, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Diagnostic value of intratumoral and peritumoral MRI radiomics for bone metastasis in prostate cancer, columnId=1194643388575224795, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Special Issue on Application of Artificial Intelligence in Disease Diagnosis and Treatment, runingTitle=null, highlight=null, articleAbstract=

Objective To investigate the diagnostic value of magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics of prostate cancer (PCa) for bone metastases. Methods A total of 211 patients diagnosed with PCa by biopsy pathology at Gansu Provincial People's Hospital from January 2018 to January 2023 were retrospectively analyzed. These patients were randomly divided into a training set (n=147) and a validation set (n=64) in a 7:3 ratio. Regions of interest (ROIs) were delineated from the patients' T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient imaging (ADC) sequences to extract radiomic features. Z-score (normalization) and the LASSO algorithm were used for feature dimensionality reduction, selection, and construction. A predictive model was then built using a logistic regression (LR) machine learning classifier. The receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was calculated to assess the model's performance. Calibration curves and decision curves (DCA) were plotted to evaluate the model's fit and clinical net benefit. Results Radiomic features were extracted from the tumor and peritumoral regions in each patient's T2WI, DWI, and ADC images, with a total of 312 features from each region. The LASSO regression model ultimately identified 10 intratumoral radiomic features closely related to bone metastasis, including 2 T2 sequence features, 7 DWI features, and 1 ADC sequence feature; and 9 peritumoral radiomic features, including 4 T2 sequence features, 3 DWI features, and 2 ADC sequence features. The predictive model based on intratumoral radiomic features achieved an AUC of 0.845 (95%CI 0.747-0.943), while the predictive model based on peritumoral radiomic features had an AUC of 0.818 (95%CI 0.716-0.919). A combined nomogram model incorporating intratumoral features, peritumoral radiomic features, and clinical features (including Gleason score, total prostate specific antigen, and body mass index) yielded an AUC of 0.936 (95%CI 0.902-0.970). Calibration curves indicated that the combined model had good fit, and DCA demonstrated that the combined model provided better clinical net benefit. Conclusions Peritumoral radiomics has excellent predictive value for bone metastasis in newly diagnosed PCa. Combining with intratumoral radiomics features and clinical features, it significantly enhances the predictive capability of the model.

, correspAuthors=Feng-Hai Zhou, authorNote=null, correspAuthorsNote=
E-mail:
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目的 探讨基于磁共振成像(MRI)的前列腺癌(PCa)瘤内及瘤周影像组学对骨转移的诊断价值。方法 收集2018年1月－2023年1月在甘肃省人民医院经组织穿刺病理学检查确诊为PCa的211例患者的临床资料进行回顾性分析。将患者按照7:3的比例随机分为训练集(n=147)与验证集(n=64)；分别从患者的T2加权成像(T2WI)、扩散加权成像(DWI)及扩散系数成像(ADC)3个序列勾画感兴趣区(ROIs)，用于提取影像组学特征；使用Z-score(正则化)、LASSO算法进行影像组学特征的降维、选择、构建；然后基于逻辑回归(LR)机器学习分类器构建模型；绘制受试者操作特征(ROC)曲线并计算曲线下面积(AUC)，评估模型的效能；绘制校准曲线和决策曲线(DCA)评价模型的拟合度及临床净获益。结果 从T2WI、DWI和ADC中分别提取瘤内和瘤周影像组学特征各312个。使用LASSO回归模型最终筛选出与骨转移密切相关的瘤内影像组学特征10个(包括2个T2序列特征、7个DWI特征、1个ADC序列特征)及瘤周影像组学特征9个(包括4个T2序列特征、3个DWI特征、2个ADC序列特征)。基于瘤内影像组学特征构建的模型在验证集中AUC为0.845(95%CI 0.747~0.943)，基于瘤周影像组学特征构建的模型在验证集中AUC为0.818(95%CI 0.716~0.919)；基于瘤内、瘤周影像组学特征及临床特征(包括Gleason评分、总前列腺特异性抗原、体重指数)构建的联合模型(列线图)的AUC为0.936(95%CI 0.902~0.970)；校准曲线表明联合模型具有良好的拟合度，DCA表明联合模型具有更好的临床净获益。结论 瘤周影像组学对于初诊PCa骨转移有较高的诊断价值，且结合瘤内影像组学特征及临床特征可明显提高模型的诊断能力。

, correspAuthors=周逢海, authorNote=null, correspAuthorsNote=
周逢海，E-mail：
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张云峰，硕士研究生，主要从事人工智能技术在前列腺癌精准诊疗中应用方面的研究

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张云峰，硕士研究生，主要从事人工智能技术在前列腺癌精准诊疗中应用方面的研究

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张云峰，硕士研究生，主要从事人工智能技术在前列腺癌精准诊疗中应用方面的研究

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articleId=1194643388420035546, companyId=1194662022165009233, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3甘肃省人民医院泌尿外科干部病区，甘肃兰州 730000)])], figs=[ArticleFig(id=1194662024442516346, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1194643388420035546, language=EN, label=Fig.1, caption=Inclusion process for prostate cancer (PCa) patients, figureFileSmall=yipY24Sv9E9t/3IUs4LAgA==, figureFileBig=19RVYNqo0XuQwcIyBlt2nQ==, tableContent=null), ArticleFig(id=1194662024505430907, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1194643388420035546, language=CN, label=图1, caption=前列腺癌(PCa)患者纳入流程, figureFileSmall=yipY24Sv9E9t/3IUs4LAgA==, figureFileBig=19RVYNqo0XuQwcIyBlt2nQ==, tableContent=null), ArticleFig(id=1194662025583367036, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1194643388420035546, language=EN, label=Fig.2, caption=Region of interests (ROIs) of intratumoural and peritumour schematics of prostate cancer (PCa), figureFileSmall=IOPyax71VogDob2bWm+4ug==, figureFileBig=WpFYO1Yr7b/6XGRevQb3OQ==, tableContent=null), ArticleFig(id=1194662025684030333, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1194643388420035546, language=CN, label=图2, caption=前列腺癌(PCa)瘤内和瘤周感兴趣区(ROIs)示意图, figureFileSmall=IOPyax71VogDob2bWm+4ug==, figureFileBig=WpFYO1Yr7b/6XGRevQb3OQ==, tableContent=null), ArticleFig(id=1194662025751139198, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1194643388420035546, language=EN, label=Fig.3, caption=The feature selection process [adjustment parameter (λ) selection in the LASSO model], figureFileSmall=yJeXoYPzYMuNlIpXEkKCPA==, figureFileBig=dxjNbVMxV76lH4TqTUPKMg==, tableContent=null), ArticleFig(id=1194662025830830975, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1194643388420035546, language=CN, label=图3, caption=特征选择过程[LASSO模型中的调整参数(λ)选择], 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DWI. 扩散加权成像；ADC. 表观弥散系数；A. 瘤内影像组学特征的权重；B. 瘤周影像组学特征的权重

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ROC. 受试者工作特征；AUC. 曲线下面积

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a基于Gleason评分、tPSA、BMI指标构建临床模型；A. 基于瘤周影像组学特征、瘤内影像组学特征及临床特征构建联合模型(列线图)；B. ROC曲线分析联合模型的诊断效能

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A. 校准曲线；B. DCA

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Prostate cancer (PCa) MRI (3.0T) scan main parameters

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序列 TR(ms) TE(ms) 层厚(mm) 层距 矩阵 NEX FOV(mm)
T2WI-FS-TRA 2800 120 3 0.6 256×256 2 200×200
DWI-ADC-TRA 4000 60 3 0 128×128 2 200×200
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前列腺癌(PCa) MRI(3.0T)扫描主要参数

, figureFileSmall=null, figureFileBig=null, tableContent=
序列 TR(ms) TE(ms) 层厚(mm) 层距 矩阵 NEX FOV(mm)
T2WI-FS-TRA 2800 120 3 0.6 256×256 2 200×200
DWI-ADC-TRA 4000 60 3 0 128×128 2 200×200
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Results of multivariate logistic regression analysis of bone metastasis in prostate cancer (PCa)

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变量 OR 95%CI P
Gleason评分 1.877 1.171~3.007 0.009
tPSA 1.037 1.009~1.067 0.010
fPSA 0.952 0.887~1.021 0.168
PSAD 1.375 0.809~2.336 0.240
BMI 0.858 0.737~0.999 0.049
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前列腺癌(PCa)骨转移的多因素logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 OR 95%CI P
Gleason评分 1.877 1.171~3.007 0.009
tPSA 1.037 1.009~1.067 0.010
fPSA 0.952 0.887~1.021 0.168
PSAD 1.375 0.809~2.336 0.240
BMI 0.858 0.737~0.999 0.049
), ArticleFig(id=1194662026845852570, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1194643388420035546, language=EN, label=Tab.3, caption=

Comparison of the clinical characteristics of prostate cancer (PCa) patients in the training set and validation set [M(Q1, Q3)]

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指标 训练集(n=147) 验证集(n=64) t/Z P
年龄(岁) 73.0(66.00, 78.00) 74.0(68.00, 78.00) -0.976 0.329
Gleason评分(分) 8.00(8.00, 9.00) 8.00(8.00, 9.00) -0.010 0.992
tPSA(ng/ml) 59.35(33.13, 100.00) 63.66(21.46, 100.00) -0.004 0.997
fPSA(ng/ml) 9.25(3.21, 26.95) 12.35(3.55, 30.00) -0.551 0.581
前列腺体积(cm3) 43.50(31.30, 66.40) 43.00(32.13, 66.36) -0.085 0.933
PSAD[ng/(ml.cm3)] 1.10(0.58, 1.88) 1.41(0.40, 2.17) -0.002 0.998
BMI(kg/m2) 23.39(21.07, 25.34) 23.24(20.92, 23.35) -0.353 0.724
ALP(U/L) 85.00(69.00, 119.50) 82.50(67.50, 167.75) -0.380 0.702
FIB(g/L) 3.45(2.87, 4.36) 3.36(2.75, 4.36) -0.105 0.916
NEUT(×109/L) 3.55(2.81, 4.94) 3.60(2.74, 4.59) -0.594 0.553
Lym(×109/L) 1.38(0.95, 1.86) 1.30(0.97, 1.91) -0.179 0.858
NLR 2.57(1.90, 4.35) 2.84(2.00, 3.71) 0.050 0.964
单核细胞计数(×109/L) 0.45(0.36, 0.57) 0.44(0.36, 0.54) -0.567 0.571
血红蛋白(g/L) 144.00(129.00, 155.00) 137.50(121.00, 151.00) -1.440 0.150
PLT(×109/L) 170.00(137.00, 210.00) 183.00(155.00, 225.25) -1.542 0.123
SII 508.65(270.22, 812.62) 474.24(318.04, 769.62) -0.101 0.920
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训练集与验证集中前列腺癌(PCa)患者的临床特征比较[M(Q1, Q3)]

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指标 训练集(n=147) 验证集(n=64) t/Z P
年龄(岁) 73.0(66.00, 78.00) 74.0(68.00, 78.00) -0.976 0.329
Gleason评分(分) 8.00(8.00, 9.00) 8.00(8.00, 9.00) -0.010 0.992
tPSA(ng/ml) 59.35(33.13, 100.00) 63.66(21.46, 100.00) -0.004 0.997
fPSA(ng/ml) 9.25(3.21, 26.95) 12.35(3.55, 30.00) -0.551 0.581
前列腺体积(cm3) 43.50(31.30, 66.40) 43.00(32.13, 66.36) -0.085 0.933
PSAD[ng/(ml.cm3)] 1.10(0.58, 1.88) 1.41(0.40, 2.17) -0.002 0.998
BMI(kg/m2) 23.39(21.07, 25.34) 23.24(20.92, 23.35) -0.353 0.724
ALP(U/L) 85.00(69.00, 119.50) 82.50(67.50, 167.75) -0.380 0.702
FIB(g/L) 3.45(2.87, 4.36) 3.36(2.75, 4.36) -0.105 0.916
NEUT(×109/L) 3.55(2.81, 4.94) 3.60(2.74, 4.59) -0.594 0.553
Lym(×109/L) 1.38(0.95, 1.86) 1.30(0.97, 1.91) -0.179 0.858
NLR 2.57(1.90, 4.35) 2.84(2.00, 3.71) 0.050 0.964
单核细胞计数(×109/L) 0.45(0.36, 0.57) 0.44(0.36, 0.54) -0.567 0.571
血红蛋白(g/L) 144.00(129.00, 155.00) 137.50(121.00, 151.00) -1.440 0.150
PLT(×109/L) 170.00(137.00, 210.00) 183.00(155.00, 225.25) -1.542 0.123
SII 508.65(270.22, 812.62) 474.24(318.04, 769.62) -0.101 0.920
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前列腺癌瘤内及瘤周MRI影像组学对骨转移的诊断价值
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张云峰 1 , 杨志军 2 , 杨进 1 , 苗国良 1 , 何涵 2 , 周逢海 3, *
解放军医学杂志 | 人工智能在疾病诊疗中的应用专题 2025,50(1): 1-8
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解放军医学杂志 | 人工智能在疾病诊疗中的应用专题 2025, 50(1): 1-8
前列腺癌瘤内及瘤周MRI影像组学对骨转移的诊断价值
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张云峰1, 杨志军2, 杨进1, 苗国良1, 何涵2, 周逢海3, *
作者信息
  • 1甘肃中医药大学第一临床医学院，甘肃兰州 730000
  • 2兰州大学第一临床医学院，甘肃兰州 730000
  • 3甘肃省人民医院泌尿外科干部病区，甘肃兰州 730000

    • 张云峰，硕士研究生，主要从事人工智能技术在前列腺癌精准诊疗中应用方面的研究

通讯作者:

周逢海，E-mail：
Diagnostic value of intratumoral and peritumoral MRI radiomics for bone metastasis in prostate cancer
Yun-Feng Zhang1, Zhi-Jun Yang2, Jin Yang1, Guo-Liang Miao1, Han He2, Feng-Hai Zhou3, *
Affiliations
  • 1First Clinical Medical College of Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu 730000, China
  • 2First Clinical Medical College of Lanzhou University, Lanzhou, Gansu 730000, China
  • 3Department of Urology, Gansu Provincial People's Hospital, Lanzhou, Gansu 730000, China
出版时间: 2025-01-28 doi: 10.11855/j.issn.0577-7402.0390.2024.1015
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摘要
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目的 探讨基于磁共振成像(MRI)的前列腺癌(PCa)瘤内及瘤周影像组学对骨转移的诊断价值。方法 收集2018年1月－2023年1月在甘肃省人民医院经组织穿刺病理学检查确诊为PCa的211例患者的临床资料进行回顾性分析。将患者按照7:3的比例随机分为训练集(n=147)与验证集(n=64)；分别从患者的T2加权成像(T2WI)、扩散加权成像(DWI)及扩散系数成像(ADC)3个序列勾画感兴趣区(ROIs)，用于提取影像组学特征；使用Z-score(正则化)、LASSO算法进行影像组学特征的降维、选择、构建；然后基于逻辑回归(LR)机器学习分类器构建模型；绘制受试者操作特征(ROC)曲线并计算曲线下面积(AUC)，评估模型的效能；绘制校准曲线和决策曲线(DCA)评价模型的拟合度及临床净获益。结果 从T2WI、DWI和ADC中分别提取瘤内和瘤周影像组学特征各312个。使用LASSO回归模型最终筛选出与骨转移密切相关的瘤内影像组学特征10个(包括2个T2序列特征、7个DWI特征、1个ADC序列特征)及瘤周影像组学特征9个(包括4个T2序列特征、3个DWI特征、2个ADC序列特征)。基于瘤内影像组学特征构建的模型在验证集中AUC为0.845(95%CI 0.747~0.943)，基于瘤周影像组学特征构建的模型在验证集中AUC为0.818(95%CI 0.716~0.919)；基于瘤内、瘤周影像组学特征及临床特征(包括Gleason评分、总前列腺特异性抗原、体重指数)构建的联合模型(列线图)的AUC为0.936(95%CI 0.902~0.970)；校准曲线表明联合模型具有良好的拟合度，DCA表明联合模型具有更好的临床净获益。结论 瘤周影像组学对于初诊PCa骨转移有较高的诊断价值，且结合瘤内影像组学特征及临床特征可明显提高模型的诊断能力。

前列腺癌  /  骨转移  /  瘤周影像组学  /  机器学习

Objective To investigate the diagnostic value of magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics of prostate cancer (PCa) for bone metastases. Methods A total of 211 patients diagnosed with PCa by biopsy pathology at Gansu Provincial People's Hospital from January 2018 to January 2023 were retrospectively analyzed. These patients were randomly divided into a training set (n=147) and a validation set (n=64) in a 7:3 ratio. Regions of interest (ROIs) were delineated from the patients' T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient imaging (ADC) sequences to extract radiomic features. Z-score (normalization) and the LASSO algorithm were used for feature dimensionality reduction, selection, and construction. A predictive model was then built using a logistic regression (LR) machine learning classifier. The receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was calculated to assess the model's performance. Calibration curves and decision curves (DCA) were plotted to evaluate the model's fit and clinical net benefit. Results Radiomic features were extracted from the tumor and peritumoral regions in each patient's T2WI, DWI, and ADC images, with a total of 312 features from each region. The LASSO regression model ultimately identified 10 intratumoral radiomic features closely related to bone metastasis, including 2 T2 sequence features, 7 DWI features, and 1 ADC sequence feature; and 9 peritumoral radiomic features, including 4 T2 sequence features, 3 DWI features, and 2 ADC sequence features. The predictive model based on intratumoral radiomic features achieved an AUC of 0.845 (95%CI 0.747-0.943), while the predictive model based on peritumoral radiomic features had an AUC of 0.818 (95%CI 0.716-0.919). A combined nomogram model incorporating intratumoral features, peritumoral radiomic features, and clinical features (including Gleason score, total prostate specific antigen, and body mass index) yielded an AUC of 0.936 (95%CI 0.902-0.970). Calibration curves indicated that the combined model had good fit, and DCA demonstrated that the combined model provided better clinical net benefit. Conclusions Peritumoral radiomics has excellent predictive value for bone metastasis in newly diagnosed PCa. Combining with intratumoral radiomics features and clinical features, it significantly enhances the predictive capability of the model.

prostate cancer  /  bone metastases  /  peritumoral radiomics  /  machine learning
张云峰, 杨志军, 杨进, 苗国良, 何涵, 周逢海. 前列腺癌瘤内及瘤周MRI影像组学对骨转移的诊断价值. 解放军医学杂志, 2025 , 50 (1) : 1 -8 . DOI: 10.11855/j.issn.0577-7402.0390.2024.1015
Yun-Feng Zhang, Zhi-Jun Yang, Jin Yang, Guo-Liang Miao, Han He, Feng-Hai Zhou. Diagnostic value of intratumoral and peritumoral MRI radiomics for bone metastasis in prostate cancer[J]. Medical Journal of Chinese People’s Liberation Army, 2025 , 50 (1) : 1 -8 . DOI: 10.11855/j.issn.0577-7402.0390.2024.1015
正文
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前列腺癌(prostate cancer，PCa)是全球男性中第二大常见癌症和第六大癌症死亡原因[1]。虽然我国PCa的发病率低于欧美国家，但近年来呈现明显上升趋势，已成为泌尿系统肿瘤的常见类型[2]。PCa发病往往隐匿，多数患者在初诊时已处于晚期，且常伴有骨转移，PCa骨转移不仅会导致骨骼相关症状，如骨痛、活动受限和骨折风险增加等，还会使患者失去手术治疗的机会[3]，且免疫治疗的疗效更差，明显降低患者的生存率[4]。因此早期诊断PCa骨转移对于改善患者预后具有重要意义。影像组学可从医学影像中提取大量的特征[5]，包括形态学、纹理、灰度直方图等，然后将这些特征与疾病的诊断、治疗反应和预后之间的关系进行关联和分析[6-9]，已广泛应用于PCa的早期诊断及风险分层[10-11]。但目前肿瘤影像组学研究主要集中于肿瘤内部区域，忽视了肿瘤周围相关影像信息的价值。有研究发现，针对瘤内及瘤周的成像数据进行定量分析，能够提供更全面的肿瘤全景图[12-13]。肿瘤微环境中的相关功能细胞可分泌大量细胞因子，诱导缺氧和促进血管生成，在肿瘤的发生、发展和转移中发挥了重要作用[14]。通过综合分析肿瘤及其周围环境的信息，能够更全面地描绘肿瘤的侵袭和转移特性。因此，提取并融合肿瘤和瘤周影像特征有望为影像组学模型的构建提供一种全新的思路，进一步提高模型的诊断或预测效能。本研究基于瘤内、瘤周影像组学特征构建诊断PCa骨转移模型，并分析其在临床工作中的应用价值，旨在充分发挥影像组学较强的诊断能力，并为PCa患者骨转移的早期筛查提供精确指导。
1 资料与方法
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1.1 一般资料
本研究遵守《赫尔辛基宣言》，经甘肃省人民医院伦理委员会批准(2023-355)，免除受试者知情同意。回顾性收集2018年1月－2023年1月在甘肃省人民医院确诊为PCa的患者临床资料，包括年龄、体重指数(body mass index，BMI)、前列腺特异性抗原(prostate specific antigen，PSA)、前列腺体积、Gleason评分、纤维蛋白原、碱性磷酸酶、血肌酐、血红蛋白、血细胞计数等，同时收集其磁共振成像(magnetic resonance imaging，MRI)图像资料。纳入标准：(1)经组织穿刺病理学检查初次确诊为PCa；(2)MRI检查在病理确诊前2周内完成且先于全身骨扫描检查，排除混杂因素的影响；(3)无原发性骨疾病(骨肉瘤、骨囊肿、血液系统疾病)；(4)临床及影像资料完整。排除标准：(1)接受过内分泌治疗或放化疗；(2)部分PCa影像显示不清；(3)全身骨扫描信息缺失。共收集454例PCa患者，根据纳入、排除标准排除243例，最终纳入211例。按照7:3的比例将患者随机分为训练集(n=147)与验证集(n=64)，然后根据全身骨扫描的结果确定患者是否存在骨转移，并将其分为骨转移组与非骨转移组。患者纳入流程如图1所示。
1.2 图像获取及病灶分割
收集PCa患者的MRI图像，参数见表1。医学数字成像和通信(DICOM)标准文件格式(通常用于管理医学成像信息和相关数据)被归一化为重采样格式，分辨率为1 mm×1 mm×1 mm。为了保证MRI图像质量的一致性，在勾画前对所有要标注的图像进行N4偏置场校验。
图像预处理完成后，分别从T2加权成像(T2 weighted image，T2WI)、扩散加权成像(diffusion-weighted imaging，DWI)以及扩散系数成像(apparent diffusion coefficient，ADC)3个序列勾画感兴趣区(region of interest，ROI)。ROI由两位资深影像科医师使用ITK-SNAP软件(version 4.0.0)进行标注，标注时沿着肿瘤最大边缘对PCa进行逐层分割，避开尿道、射精管、精阜及精囊根部，当遇到前列腺多灶性肿瘤时，只对最大的病灶进行标注，勾画完成后由软件自动生成全PCa三维感兴趣容积(volum of interest，VOI)。1个月后，随机抽取30例患者，由两位医师重新划定ROI，然后计算组内相关系数(intraclass correlation coefficient，ICC)，得到ICC>0.8，表明肿瘤划分具有较好的一致性。勾画结束后，使用Python工具在原有ROI基础上沿着最大边缘向外扩充2个体素，得到瘤周ROI(图2)。
1.3 特征提取
使用PyRadiomics软件包(http://www.radiomics.io/pyradiomics.html)分别从3个序列进行影像组学特征的提取，共提取到瘤内和瘤周影像组学特征各312个。特征种类包括一阶直方图特征(first order statistics)、形态学特征(shape-based)、灰度共生矩阵(gray level cooccurrence matrix，GLCM)、灰度游程矩阵(gray level run length matrix，GLRLM)、灰度大小区域矩阵(gray level size zone matrix，GLSZM)、灰度依赖矩阵(gray level dependence matrix，GLDM)、相邻灰度差矩阵(neighbourhood gray-tone difference matrix，NGTDM)。
1.4 特征选择及模型构建
采用Z-score(正则化)来标准化数据集，方法为column=(column－mean)/std，采用Spearman相关系数(ρ)评估特征提取在观察者之间的一致性，ρ>0.9的特征被认为是可靠的，将可靠的特征建立一个特征集，用于后续分析。使用基于LASSO算法的逐步搜索来找到基于精度的特征的最佳组合，并执行多次迭代来评估每个特征的重要性。按照7:3的比例将患者随机分为训练集(n=147)与验证集(n=64)，基于逻辑回归(logistic regression，LR)机器学习分类器构建模型。此外，针对临床指标及其组合指标进行多因素logistic回归分析，寻找PCa患者发生骨转移的临床独立危险因素，然后基于这些指标构建logistic回归临床模型。
1.5 模型的评估
使用训练集数据进行模型构建，使用验证集数据对模型进行评估。绘制受试者操作特征(receiver operating characteristic，ROC)曲线并计算曲线下面积(area under curve，AUC)以评估模型的诊断效能，绘制决策曲线(decision curve analysis，DCA)和校准曲线评估模型的拟合度及临床净获益。
1.6 统计学处理
采用SPSS 23.0软件和R软件3.6.1版(https://www.r-project.org/)进行统计分析。使用Kolmogorov-Smirnov检验对计量资料进行正态性检验，符合正态分布且方差齐的计量资料以$\bar{x}±s$表示，两组间比较采用独立样本t检验；不符合正态分布且方差不齐的计量资料以M(Q1，Q3)表示，组间比较采用Mann-Whitney U检验。P<0.05为差异有统计学意义。
2 结果
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2.1 临床资料
多因素logistic回归分析结果显示，Gleason评分、总前列腺特异性抗原(tPSA)、体重指数(BMI)是PCa患者发生骨转移的独立危险因素(表2)。训练集与验证集患者的年龄、Gleason评分、tPSA、游离前列腺特异性抗原(fPSA)、前列腺体积等临床特征比较差异均无统计学意义(P>0.05，表3)。
2.2 特征选择
从T2WI、DWI和ADC中分别提取瘤内和瘤周影像组学特征各312个。为了确定超参数，如模型的特征数量，对训练集进行5倍的交叉验证。使用LASSO回归模型(图3)筛选出与骨转移密切相关的瘤内影像组学特征10个，包括2个T2序列特征、7个DWI特征、1个ADC序列特征(图4A)，瘤周影像组学特征9个，包括4个T2序列特征、3个DWI特征、2个ADC序列特征(图4B)。相较于瘤内影像组学模型，T2序列特征在瘤周影像组学模型中权重更高，其他两个序列特征权重则降低。基于这些被选中的特征，采用LR分类器建立模型。
2.3 模型评估
基于临床特征构建的模型AUC为0.894(95%CI 0.850~0.938)(图5A)；基于瘤内影像组学特征构建的模型在训练集中的AUC为0.853(95%CI 0.793~0.912)，验证集中的AUC为0.845(95%CI 0.747~0.943)(图5B)；基于瘤周影像组学特征构建的模型在训练集中的AUC为0.840(95%CI 0.778~0.902)，验证集中的AUC为0.818(95%CI 0.716~0.919)(图5C)。基于瘤内、瘤周影像组学特征及临床特征(包括Gleason评分、tPSA、BMI)构建联合模型(列线图)，结果显示，瘤内影像组学模型优于瘤周影像组学模型，但瘤周影像组学模型也具有较好的诊断性能(图6A)，且联合模型的诊断效能最佳，AUC为0.936(95%CI 0.902~0.970)(图6B)。校准曲线和DCA分析显示，联合模型具有良好的拟合度和临床净获益(图7)。
3 讨论
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本回顾性队列研究分析了瘤内、瘤周影像组学特征对于初诊PCa骨转移的诊断价值。结果显示，瘤周影像组学模型能够很好地诊断PCa的骨转移状态，其在训练集中的AUC为0.840，验证集中为0.818，尽管该结果略差于瘤内影像组学模型(训练集中AUC=0.853，验证集中AUC=0.845)，但依然可作为诊断PCa骨转移的较好的辅助方法，其与瘤内影像组学特征及临床特征的联合模型具有更佳的诊断效能(AUC=0.936)。
PCa极易发生骨转移，骨转移后患者的生存质量明显下降，生存期明显缩短。目前，全身骨扫描是早期发现骨转移灶的主要影像学检查办法，相较于X线检查可提前3~6个月探测到病灶，已广泛应用于PCa骨转移的筛查[15]。然而对于是否进行骨扫描，当前尚无统一标准。PSA被认为是发生PCa骨转移的独立影响因素，可根据症状和PSA水平来评估是否存在骨转移病灶，但其敏感度和特异度均不理想，因为PSA具有前列腺器官特异性，却不具备PCa特异性，许多药物、疾病、临床干预都会致使其水平升高[16-19]。而影像组学基于高通量的算法可直接从病灶提取特征，并将这些信息降维为二维数据，使用这些数据可很好地分析肿瘤个体本身，获得许多肉眼无法获取的信息，具有较好的特异性，能够为肿瘤的综合评估提供帮助[20-21]。
部分研究已经表明，影像组学对于PCa骨转移有很好的诊断能力[22-23]。如Zhang等[24]发现12个影像组学特征与骨质状况明显相关，他们基于多参数MRI影像组学与临床危险因素建立的骨转移预测模型具有很好的预测能力，可为初诊PCa患者的骨转移提供个体化评估。遗憾的是以上研究均忽视了肿瘤周围图像包含的大量信息，仅分析了肿瘤内部区域影像组学特征与骨转移的联系，存在一定的局限性。近年来，随着肿瘤影像组学研究的深入，瘤周影像组学进入了学者们的视野。在肿瘤进展过程中，肿瘤在细胞水平侵袭到外周正常组织可能表现为组织形态的改变[25]，因此肿瘤所处的环境也包含着大量信息，瘤周影像组学特征在一定程度上能够反映肿瘤的状态，这为其用于分析肿瘤特性提供了可能。大量研究已经揭示了瘤周影像组学与肿瘤特征的相关性，其在肿瘤鉴别诊断、转移预测、疗效评估、复发及预后预测等方面表现出较高的价值[26-28]。如Liu等[29]基于CT影像构建了肺癌的瘤周影像组学模型(AUC=0.70)，且瘤周影像组学特征结合瘤内影像组学特征及临床特征的联合模型对肺癌患者预后的预测效能明显提高(AUC=0.85)，可能为患者的术前干预及预后评估提供有效指导；徐海敏等[30]的研究也表明，瘤周及瘤内影像组学特征联合临床特征可有效预测乳腺癌的新辅助化疗效果(AUC=0.88)；林苗苗等[31]发现，瘤内及瘤周影像组学特征联合临床特征和CT特征可实现对浸润性肺腺癌周围脉管侵犯的精准预测(AUC=0.87)。
本研究开发的基于PCa MRI的瘤内影像组学模型可对初诊患者的骨转移状态进行有效评估，与Zhang等[24]和Wang等[32]的研究结果基本一致。不同的是，本研究还分析了瘤周MRI影像组学特征与骨转移的关系，并对两类模型进行了比较，结果显示，瘤内和瘤周与骨转移相关的组学特征存在明显差异(图4)，这与既往瘤内、瘤周的研究表现一致[29-31]，分析原因可能是由于不同序列图像质量的不同和瘤内与瘤周的组织学差异所致。尽管瘤周影像组学模型的诊断能力弱于瘤内影像组学模型，但本研究在一定程度上证实了瘤周影像组学对于PCa骨转移也有较高的诊断价值，补充了既往MRI影像组学诊断PCa骨转移未分析瘤周区域的不足，为PCa影像组学研究的扩展提供了可能。此外，本研究并未对瘤周区域进行手工标注，而是使用算法在原有ROI的基础上进行自动扩充，既保证了瘤周区域的一致性，也节省了开展研究所需要的时间和精力。最后本研究使用列线图整合了不同来源的数据，构建了联合模型，结果显示其显著提高了对PCa骨转移的诊断能力。
传统影像组学研究依赖于提取医学影像图像的物理特征，如形状、灰度、纹理等，然后利用机器学习算法构建模型用于疾病的诊断及预后分析。随着人工智能在医学领域的快速发展，深度学习技术能够模拟人脑神经网络，对医学图像进行更深入的分析，既可以以图像为单位直接进行模型训练，也可以提取更高维度的特征用于肿瘤的诊断及预后分析。其次，当前ROI的分割依赖于影像科医师的手工标注，研究过程耗时费力，且可重复性受到限制，基于深度学习技术的自动和半自动勾画技术可解决这一困境；此外，受到不同影像设备差异的影响，组学模型的外部验证存在一定困难，而深度学习图像处理技术有助于推进不同数据的规范化处理。因此，随着人工智能算法的更新迭代，肿瘤影像组学将在疾病的研究中发挥更大作用。
综上所述，本研究开发的基于MRI的瘤周影像组学模型可很好地诊断PCa患者的骨转移状态，且联合瘤内影像组学特征及临床特征可明显提高诊断效能，有利于临床医师的决策，同时可给患者带来更大的获益。但本研究仍存在一定局限性：为回顾性研究，可能存在选择偏倚，所得结果仍需通过前瞻性研究加以验证；研究样本来自单中心，模型的拓展性可能较差，后续应加入多中心数据，以期获得高水平的临床应用证据；不同序列(如T2WI、DWI)的体素大小往往不一致，外扩2个体素得到的瘤周ROI大小存在差异，可能会对研究结果产生影响，未来有待进一步研究验证。
基金
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  • 甘肃省重点研发计划(21YF5FA016)
  • 甘肃省人民医院院内基金项目(23GSSYD-12)
  • 甘肃省人民医院院内基金项目(22GSSYD-15)
文献
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doi: 10.11855/j.issn.0577-7402.0390.2024.1015
  • 接收时间：2024-03-25
  • 首发时间：2025-11-10
  • 出版时间：2025-01-28
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  • 收稿日期：2024-03-25
  • 录用日期：2024-05-12
基金
Gansu Provincial Key Research and Development Programme(21YF5FA016)
甘肃省重点研发计划(21YF5FA016)
Intramural Fund Project of Gansu Provincial Hospital(23GSSYD-12)
甘肃省人民医院院内基金项目(23GSSYD-12)
Intramural Fund Project of Gansu Provincial Hospital(22GSSYD-15)
甘肃省人民医院院内基金项目(22GSSYD-15)
作者信息
    1甘肃中医药大学第一临床医学院，甘肃兰州 730000
    2兰州大学第一临床医学院，甘肃兰州 730000
    3甘肃省人民医院泌尿外科干部病区，甘肃兰州 730000

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周逢海，E-mail：
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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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