基于临床、MRI、穿刺活检指标构建预测前列腺癌患者盆腔淋巴结转移的列线图模型

于子钦 吴翰昌 刘芳

引用本文: 于子钦,吴翰昌,刘芳. 基于临床、MRI、穿刺活检指标构建预测前列腺癌患者盆腔淋巴结转移的列线图模型[J]. 海军军医大学学报,2026,47(8):1052-1058. DOI: 10.16781/j.CN31-2187/R.20250596.
Citation: Yu Z, Wu H, Liu F. Development of a nomogram model for predicting pelvic lymph node metastasis in prostate cancer patients based on clinical, MRI, and puncture biopsy parameters[J]. Acad J Naval Med Univ, 2026,47(8): 1052-1058. DOI: 10.16781/j.CN31-2187/R.20250596.

基于临床、MRI、穿刺活检指标构建预测前列腺癌患者盆腔淋巴结转移的列线图模型

doi: 10.16781/j.CN31-2187/R.20250596
基金项目: 

海军军医大学校级课题 2022QN047.

详细信息
    作者简介:

    于子钦,技师.E-mail: 80541770@qq.com;

    吴翰昌,硕士,住院医师.E-mail: wuhanc11@163.com.

    通讯作者:

    刘芳, E-mail: ahbzfanny@163.com.

  • 共同第一作者(Co-first authors).

Development of a nomogram model for predicting pelvic lymph node metastasis in prostate cancer patients based on clinical, MRI, and puncture biopsy parameters

Funds: 

Project of Naval Medical University 2022QN047.

  • 摘要:
    目的 

    基于临床、MRI及穿刺活检病理特征构建预测前列腺癌患者盆腔淋巴结转移的列线图模型, 并评估其诊断性能。

    方法 

    回顾性纳入2013年1月至2024年6月于我院诊断为前列腺癌且接受根治性前列腺切除+扩大盆腔淋巴结清扫术的患者。收集患者的临床、MRI及穿刺活检数据, 通过最小绝对收缩和选择算子(LASSO)回归和二元多因素logistic回归筛选出盆腔淋巴结转移的独立预测因子并构建列线图模型。采用ROC曲线、校准曲线和决策曲线分析评估模型性能。

    结果 

    共纳入538例前列腺癌患者, 通过LASSO回归和二元多因素logistic回归筛选出年龄、肿瘤最大径、临床T分期、前列腺特异性抗原密度、活检国际泌尿病理学会分级、欧洲泌尿生殖放射学会评分是前列腺癌患者盆腔淋巴结转移的独立预测因子, 均纳入列线图模型。该列线图模型预测前列腺癌盆腔淋巴结转移的AUC值为0.88(95%CI 0.84~0.92);平均C-index为0.877(95%CI 0.862~0.884), 表现出良好的稳定性;在阈值概率0.01~0.75及0.81~0.99范围内的净获益均优于"全部清扫"或"全部不清扫"策略。

    结论 

    基于常规临床、MRI及穿刺活检病理特征构建的列线图模型能够预测前列腺癌患者盆腔淋巴结转移风险, 可为确定前列腺癌患者淋巴结清扫及术后辅助治疗策略提供科学依据。

     

    Abstract:
    Objective 

    To develop a nomogram model for predicting pelvic lymph node metastasis in prostate cancer patients based on clinical, magnetic resonance imaging (MRI), and biopsy pathological features, and to evaluate its diagnostic performance.

    Methods 

    Patients who were diagnosed with prostate cancer and underwent radical prostatectomy plus extended pelvic lymph node dissection at our hospital from Jan. 2013 to Jun. 2024 were retrospectively enrolled. The clinical, MRI, and biopsy data were collected. The least absolute shrinkage and selection operator (LASSO) regression and binary multivariate logistic regression were used to identify independent predictors for pelvic lymph node metastasis, which were then incorporated to construct a nomogram model. The performance of the nomogram model was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis.

    Results 

    A total of 538 patients with prostate cancer were included. Age, maximum diameter of tumor, clinical T stage, prostate-specific antigen density, biopsy International Society of Urological Pathology grade, and European Society of Urogenital Radiology score were identified by LASSO regression and binary multivariate logistic regression as independent predictors for pelvic lymph node metastasis in patients with prostate cancer; these variables were incorporated into the nomogram model. The area under the ROC curve of the nomogram for predicting pelvic lymph node metastasis in prostate cancer patients was 0.88 (95% confidence interval [95%CI] 0.84-0.92). The mean C-index of the model was 0.877 (95%CI 0.862-0.884), indicating good stability. The model provided greater net benefit than either the "treat-all" or "treat-none" strategies across threshold probability ranges of 0.01-0.75 and 0.81-0.99.

    Conclusion 

    The nomogram model constructed using routine clinical, MRI, and biopsy pathological features can predict the risk of pelvic lymph node metastasis in patients with prostate cancer. This model may provide a scientific basis for determining lymph node dissection strategies and postoperative adjuvant treatment decisions.

     

  • 前列腺癌是男性泌尿生殖系统最常见的恶性肿瘤之一,根治性前列腺切除术是局限性前列腺癌患者的主要根治性方案之一。扩大盆腔淋巴结清扫术(extended pelvic lymph node dissection,ePLND)有助于明确病理分期,经组织病理学证实存在盆腔淋巴结转移的患者预后较无淋巴结转移者更差,且在初始治疗后疾病复发风险更高[1-3]。准确的淋巴结分期对于评估前列腺癌患者预后以及识别可能需要额外辅助治疗或补救治疗的患者至关重要[4]。然而,高达20%的患者会因ePLND并发症而出现严重的病损[3]。因此,在进行ePLND时需权衡其诊断准确性与病损风险。目前评估前列腺癌患者发生盆腔淋巴结转移风险的常用模型有纪念斯隆-凯特琳癌症中心(Memorial Sloan Kettering Cancer Center,MSKCC)列线图[5]及Briganti列线图[6-8]。尽管这些列线图模型展现出较好的预测效果,但建模人群来自欧美国家[9],在中国人群中尚未进行充分验证,且对不同患者该如何选择也无统一推荐。因此,本研究利用患者术前临床数据、多参数磁共振成像(multiparametric magnetic resonance imaging,mpMRI)特征和活检病理参数构建了适用于中国人群的前列腺癌盆腔淋巴结转移预测模型,旨在为前列腺癌患者精准实施ePLND提供科学依据。

    回顾性选择2013年1月至2024年6月于我院接受根治性前列腺切除+ePLND的前列腺癌患者共699例。纳入标准:(1)术后病理证实为前列腺癌;(2)术前接受前列腺mpMRI检查;(3)术前接受超声引导下穿刺活检。排除标准:(1)临床资料不全(n=57);(2)mpMRI检查前已接受过前列腺癌相关治疗(n=70);(3)mpMRI图像质量不佳,影响准确评估(n=34)。通过我院住院病历系统收集所有患者的临床信息以及穿刺术前血清总前列腺特异性抗原(total prostate-specific antigen,TPSA)、游离前列腺特异性抗原(free prostate-specific antigen,FPSA),记录临床T分期[基于美国癌症联合委员会(American Joint Committee on Cancer,AJCC)2009年发布的癌症分期系统确定]。

    所有图像均通过2台3.0 T磁共振扫描仪采集,分别为美国GE Healthcare公司3.0 T核磁共振仪(Discovery GE750)和德国Siemens Healthcare公司3.0 T核磁共振仪(MagnetomSkyra)。扫描序列包括T2加权成像、多b值弥散加权成像、动态对比增强。动态对比增强序列先扫描1期作为蒙片,然后利用高压注射器经肘静脉以0.1 mmol/kg团注钆贝葡胺注射液(上海博莱科信谊药业有限公司),注射速度为2 mL/s,完成后以相同速度注射生理盐水12 mL。注射造影剂后连续完成17期增强扫描,每期扫描时间约17 s。由2名阅片经验分别为5年和15年的放射诊断科医生在对临床和病理信息完全未知的情况下分别对图像进行评估(若观点不一致时,则经共同商议达成一致意见),依据前列腺影像报告和数据系统(prostate imaging reporting and datasystem,PI-RADS)2.1版确定PI-RADS评分[10],将评分最高的靶病灶对应的评分作为多发病灶患者的PI-RADS评分。此外,在T2加权成像上测量前列腺的前后径、左右径、上下径,记录MRI上是否可见短径>cm的可疑淋巴结以及前列腺内肿瘤最大径,并根据欧洲泌尿生殖放射学会(European Society of Urogenital Radiology,ESUR)[11]标准对病灶是否突破包膜进行评分(肿瘤紧贴前列腺包膜为1分,肿瘤边缘不规则为3分,神经血管束增厚为4分,包膜隆起或中断为4分,包膜外可测量病变为5分)。根据公式计算前列腺体积、前列腺特异性抗原密度(prostate-specific antigen density,PSAD):前列腺体积=前后径×左右径×上下径×0.52,PSAD=TPSA/前列腺体积。

    所有穿刺操作由我院经验丰富的泌尿外科医生在超声引导下实施。对于PI-RADS评分≥3分的患者实施10~12针的系统穿刺联合靶向穿刺,对于PI-RADS评分<3分的患者行前列腺系统穿刺。所有前列腺活检样本均由具有丰富泌尿系统病理诊断经验的病理科医生,按照国际泌尿病理学会(International Society of Urological Pathology,ISUP)2014专家共识[12]提出的前列腺癌分级分组标准进行评估,并记录每例患者活检样本总穿刺针数、阳性针数。

    所有数据采用SPSS 26.0和R 3.8.0软件进行分析。计量资料若符合正态分布,以$\bar{x} \pm s$表示,两组间比较采用独立样本t检验;若不符合正态分布,则以MQ1Q3)表示,两组间比较采用Mann-Whitney U检验。计数资料以例数和百分数表示,组间比较采用χ2检验、连续性校正χ2检验或Fisher确切概率法。等级资料以例数和百分数表示,组间比较采用Mann-Whitney U检验。采用最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归进行变量筛选,并通过10折交叉验证确定最优惩罚参数(λ)且以分类误差最小化为标准。变量筛选完成后通过二元多因素logistic回归进行模型构建,采用Bootstrap法进行内部验证(重复抽样1 000次绘制校准曲线,并计算C-index及其95%CI以评估模型的稳定性);通过决策曲线分析(decision curve analysis,DCA)评估模型的临床应用价值;采用ROC曲线评估模型的诊断准确性。检验水准(α)为0.05。

    共纳入538例接受根治性前列腺切除+ePLND的局限性前列腺癌患者,其中有盆腔淋巴结转移患者62例(11.52%)。在无盆腔淋巴结转移(pN0)和有盆腔淋巴结转移(pN1)的患者中,年龄、MRI可疑淋巴结、肿瘤最大径、临床T分期、TPSA、FPSA、PI-RADS评分、活检阳性针数、活检总针数、活检阳性针数的占比、活检ISUP分级、前列腺体积、PSAD、ESUR评分差异均有统计意义(均P<0.05)。见表 1

    表  1  接受根治性前列腺切除+ePLND的伴/不伴盆腔淋巴结转移前列腺癌患者的基线特征
    Table  1  Baseline characteristics of prostate cancer patients with or without pelvic lymph node metastasis undergoing radical prostatectomy plus ePLND
    Variable Negative (pN0) N=476 Positive (pN1) N=62 Statistic P value
    Age/year, $\bar{x} \pm s$ 68.0±7.0 65.9±8.1 t=2.21 0.027
    BMI/(kg·m-2), $\bar{x} \pm s$ 24.7±2.7 24.6±3.1 t=0.15 0.883
    Suspicious lymph node on MRI, n (%) χ2=9.51 0.002
       No 456 (95.8) 53 (85.5)
       Yes 20 (4.2) 9 (14.5)
    Maximum diameter of tumor/cm, M (Q1, Q3) 1.65 (1.20, 1.90) 2.50 (1.80, 3.28) Z=-6.6 <0.001
    Clinical T stage, n (%) Z=-8.67 <0.001
       T1 30 (6.3) 0
       T2 357 (75.0) 19 (30.6)
       T3 85 (17.9) 35 (56.5)
       T4 4 (0.8) 8 (12.9)
    TPSA/(ng·mL-1), M (Q1, Q3) 13.5 (8.1, 25.4) 27.8 (14.4, 54.5) Z=-6.18 <0.001
    FPSA/(ng·mL-1), M (Q1, Q3) 1.6 (1.0, 2.7) 4.0 (2.1, 7.7) Z=-6.84 <0.001
    FPSA/TPSA, M (Q1, Q3) 0.12 (0.08, 0.15) 0.12 (0.10, 0.17) Z=-1.51 0.130
    PI-RADS score, n (%) Z=-5.30 <0.001
       1 1 (0.2) 0
       2 17 (3.6) 0
       3 46 (9.7) 0
       4 164 (34.5) 8 (12.9)
    5 248 (52.1) 54 (87.1)
    Positive biopsy count, M (Q1, Q3) 6.0 (4.0, 9.0) 9.0 (6.0, 12.0) Z=-4.39 <0.001
    Total biopsy count, $\bar{x} \pm s$ 15.2±3.5 14.2±2.4 t=2.28 0.023
    Proportion of positive cores, M (Q1, Q3) 0.42 (0.27, 0.60) 0.60 (0.43, 0.90) Z=-5.14 <0.001
    Biopsy ISUP grade, n (%) Z=-5.40 <0.001
       1 21 (4.4) 0
       2 127 (26.7) 6 (9.7)
       3 95 (20.0) 5 (8.1)
       4 92 (19.3) 12 (19.4)
       5 141 (29.6) 39 (62.9)
    Volume of prostate/mL, M (Q1, Q3) 31.2 (24.1, 44.1) 39.7 (25.9, 56.2) Z=-2.46 0.014
    PSAD/(ng·mL-1·mL-1), M (Q1, Q3) 0.42 (0.25, 0.79) 0.86 (0.42, 1.73) Z=-4.79 <0.001
    ESUR score, n (%) Z=-5.70 <0.001
       1 96 (20.2) 3 (4.8)
       3 238 (50.0) 18 (29.0)
       4 83 (17.4) 20 (32.3)
       5 59 (12.4) 21 (33.9)
    ePLND: Extended pelvic lymph node dissection; BMI: Body mass index; MRI: Magnetic resonance imaging; TPSA: Total prostate-specific antigen; FPSA: Free prostate-specific antigen; PI-RADS: Prostate imaging reporting and data system; ISUP: International Society of Urological Pathology; PSAD: Prostate-specific antigen density; ESUR: European Society of Urogenital Radiology.

    本研究采用LASSO回归进行变量筛选,以解决因淋巴结转移阳性事件数有限可能导致的过拟合问题。考虑到PSAD为TPSA与前列腺体积的衍生变量,存在多重共线性的潜在风险,因此本研究系统比较了3种变量组合策略:策略1仅纳入年龄、肿瘤最大径、临床T分期、TPSA、ESUR评分、活检ISUP分级和前列腺体积;策略2以PSAD替代其组成变量TPSA和前列腺体积;策略3同时纳入所有变量。通过10折交叉验证确定3种策略的λ值分别为0.009 2、0.014 6和0.008 3。最终基于模型区分度和变量间相关性分析选择最优策略,结果显示策略2(C-index=0.882 3)略优于策略1(C-index=0.877 2)和策略3(C-index=0.881 6),且策略3中TPSA与PSAD存在高度相关性(r=0.802)。基于模型简洁性、临床可解释性及避免多重共线性原则,最终选择策略2构建预测模型,筛选出的变量包括年龄、肿瘤最大径、临床T分期、ESUR评分、活检ISUP分级和PSAD。策略2的LASSO回归分析及结果见图 1

    图  1  LASSO回归分析结果
    Fig.  1  Results of LASSO regression analysis
    A: LASSO regression coefficient path plot. Each curve showed the coefficient shrinkage of candidate variables with the increase of λ.The red dashed line indicated the optimal λ for minimum cross-validation error (λmin), and the orange dotted line represented the λ selected by the 1-standard error criterion (λ1se). B: Ten-fold cross-validation deviance plot. The blue band indicated the 95% confidence interval of cross-validation binomial deviance. The gray dashed line represented the λmin, whereas the orange dotted line indicated the λ1se.LASSO: Least absolute shrinkage and selection operator.
    下载: 全尺寸图片

    将LASSO回归筛选出的变量进一步纳入二元多因素logistic回归分析,结果显示,年龄、肿瘤最大径、临床T分期、活检ISUP分级、PSAD及ESUR评分均为前列腺癌患者盆腔淋巴结转移的独立影响因素,其中年龄与盆腔淋巴结转移风险呈负相关,肿瘤最大径、临床T分期、活检ISUP分级、PSAD及ESUR评分升高均与盆腔淋巴结转移风险增加相关(表 2)。基于上述变量构建列线图模型,用于估算患者发生盆腔淋巴结转移的个体化风险(图 2)。

    表  2  前列腺癌患者盆腔淋巴结转移影响因素的二元多因素logistic回归分析
    Table  2  Binary multivariate logistic regression analysis of influencing factors for pelvic lymph node metastasis in prostate cancer patients
    Variable b SE Wald χ2 OR (95%CI) P value
    Intercept -5.988 1.713 1 12.218
    Age -0.063 0.023 1 7.413 0.939 (0.897, 0.982) 0.007
    Maximum diameter of tumor 0.631 0.181 3 12.106 1.879 (1.319, 2.693) <0.001
    Clinical T stage 0.992 0.306 5 10.474 2.697 (1.489, 4.980) 0.001
    Biopsy ISUP grade 0.514 0.161 6 10.131 1.673 (1.234, 2.334) 0.002
    PSAD 0.475 0.141 2 11.287 1.607 (1.233, 2.192) <0.001
    ESUR score 0.556 0.157 8 12.407 1.743 (1.292, 2.403) <0.001
    ISUP: International Society of Urological Pathology; PSAD: Prostate specific-antigen density; ESUR: European Society of Urogenital Radiology; b: Regression coefficient; SE: Standard error; OR: Odds ratio; 95%CI: 95% confidence interval.
    图  2  用于预测前列腺癌患者盆腔淋巴结转移的列线图
    Fig.  2  Nomogram for predicting pelvic lymph node metastasis in prostate cancer patients
    ISUP: International Society of Urological Pathology; PSAD: Prostate-specific antigen density; ESUR: European Society of Urogenital Radiology.
    下载: 全尺寸图片

    列线图模型预测前列腺癌患者存在盆腔淋巴结转移的AUC值为0.88(95%CI 0.84~0.92,图 3A)。通过1 000次Bootstrap自抽样验证绘制校准曲线,该模型的平均C-index为0.877(95%CI 0.862~0.884),表现出良好的稳定性。DCA显示,列线图模型在风险概率阈值0.01~0.75及0.81~0.99范围内决策的净收益优于“全部清扫”或“全部不清扫”策略(图 3B)。

    图  3  列线图预测前列腺癌患者盆腔淋巴结转移的ROC曲线(A)及决策曲线分析(B)
    Fig.  3  ROC curve (A) and decision curve analysis (B) of nomogram for predicting pelvic lymph node metastasis in prostate cancer patients
    ROC: Receiver operating characteristic; AUC: Area under curve; 95%CI: 95% confidence interval.
    下载: 全尺寸图片

    本研究成功开发并验证了一个基于常规临床、MRI影像及穿刺活检病理指标、能够预测中国前列腺癌患者盆腔淋巴结转移的列线图模型,该模型展现出卓越的区分度(AUC值为0.88),且具有良好的校准度和临床实用性。这为解决ePLND决策中“过度清扫”与“清扫不足”临床困境提供了一个针对中国人群的高精度个体化方案。

    该模型良好的性能可能源于本研究对关键预测因子的精心筛选与整合。与既往主要依赖TPSA、活检病理分级等指标的模型不同,本研究引入了ESUR评分和肿瘤最大径这2个MRI影像学特征。ESUR评分可直接量化肿瘤突破包膜的风险,而包膜侵犯是淋巴结转移的重要前哨步骤;肿瘤最大径则直观反映了肿瘤负荷。这2项指标均为前列腺癌侵袭性生物学行为的直接影像学表现[10-11],它们的纳入使得模型能够从临床参数和肿瘤原位侵袭特性2个维度综合评估淋巴结转移风险。此外,本研究采用LASSO回归进行变量筛选,有效避免了过拟合,进一步保证了模型的稳健性。

    有研究以700例接受机器人辅助根治性前列腺切除+ePLND的前列腺癌患者作为模型构建队列,通过多因素logistic回归筛选出初始TPSA水平、MRI T分期、最高活检病理分级、活检方式、系统活检中临床显著性前列腺癌(最高活检病理分级≥2级)阳性穿刺针数占比、前列腺特异性膜抗原正电子发射断层显像淋巴结状态等6个变量并纳入模型,结果显示,该模型在建模队列中预测淋巴结转移的AUC值为0.81(95%CI 0.78~0.85),但正电子发射断层显像高昂的成本和有限的普及度限制了该模型的临床应用[13]。Liu等[14]开发并验证了基于表观弥散系数图、手动校正或自动分割前列腺/癌灶的影像组学模型对前列腺癌盆腔淋巴结转移的预测效果,基于不同分割方式构建的模型预测性能存在差异,虽然有的模型在内部队列AUC值达到0.94,但在外部测试集中预测性能不足(AUC值<0.73),另外该方法需要进行前列腺及病灶的分割,导致可重复性差及泛化能力不足。本研究构建的列线图模型所用的变量均为临床、影像及病理常规指标,容易获取,在临床上易于推广普及。

    从临床决策的角度看,DCA结果直观证实了本列线图模型的临床应用价值。在较大的阈值概率范围内,使用本模型的净获益均高于“全部清扫”或“全部不清扫”的极端策略。这意味着,对于本模型预测的低风险患者,临床医生可以更有信心地规避ePLND,减少相关的近远期并发症(如淋巴囊肿、下肢水肿等),从而改善患者术后生活质量并节约医疗资源;反之,对于高风险患者,本模型预测结果则能为实施ePLND提供强有力的决策支持,确保精准分期,从而为后续是否需要辅助治疗提供关键依据。因此,本列线图模型有望成为一个实用的临床决策辅助工具,推动前列腺癌手术实现更精准的个体化治疗。

    本研究存在以下局限性:首先,回顾性研究设计可能引入患者选择偏倚;其次,放射诊断科医生对MRI的相关评估以及病理科医生对前列腺活检结果的判定可能存在差异;最后,本研究为单中心数据,缺乏外部数据集验证。未来研究拟进一步增加样本量并在外部数据中进行验证,以增加结果的普适性与可信度。

    综上所述,本研究通过整合患者年龄、肿瘤最大径、临床T分期、PSAD、ESUR评分、活检ISUP分级成功构建出适用于中国前列腺癌患者盆腔淋巴结转移的列线图预测模型,能够指导临床进行淋巴结清扫及术后辅助治疗决策。

  • 图  1   LASSO回归分析结果

    Fig.  1   Results of LASSO regression analysis

    A: LASSO regression coefficient path plot. Each curve showed the coefficient shrinkage of candidate variables with the increase of λ.The red dashed line indicated the optimal λ for minimum cross-validation error (λmin), and the orange dotted line represented the λ selected by the 1-standard error criterion (λ1se). B: Ten-fold cross-validation deviance plot. The blue band indicated the 95% confidence interval of cross-validation binomial deviance. The gray dashed line represented the λmin, whereas the orange dotted line indicated the λ1se.LASSO: Least absolute shrinkage and selection operator.

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    图  2   用于预测前列腺癌患者盆腔淋巴结转移的列线图

    Fig.  2   Nomogram for predicting pelvic lymph node metastasis in prostate cancer patients

    ISUP: International Society of Urological Pathology; PSAD: Prostate-specific antigen density; ESUR: European Society of Urogenital Radiology.

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    图  3   列线图预测前列腺癌患者盆腔淋巴结转移的ROC曲线(A)及决策曲线分析(B)

    Fig.  3   ROC curve (A) and decision curve analysis (B) of nomogram for predicting pelvic lymph node metastasis in prostate cancer patients

    ROC: Receiver operating characteristic; AUC: Area under curve; 95%CI: 95% confidence interval.

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    表  1   接受根治性前列腺切除+ePLND的伴/不伴盆腔淋巴结转移前列腺癌患者的基线特征

    Table  1   Baseline characteristics of prostate cancer patients with or without pelvic lymph node metastasis undergoing radical prostatectomy plus ePLND

    Variable Negative (pN0) N=476 Positive (pN1) N=62 Statistic P value
    Age/year, $\bar{x} \pm s$ 68.0±7.0 65.9±8.1 t=2.21 0.027
    BMI/(kg·m-2), $\bar{x} \pm s$ 24.7±2.7 24.6±3.1 t=0.15 0.883
    Suspicious lymph node on MRI, n (%) χ2=9.51 0.002
       No 456 (95.8) 53 (85.5)
       Yes 20 (4.2) 9 (14.5)
    Maximum diameter of tumor/cm, M (Q1, Q3) 1.65 (1.20, 1.90) 2.50 (1.80, 3.28) Z=-6.6 <0.001
    Clinical T stage, n (%) Z=-8.67 <0.001
       T1 30 (6.3) 0
       T2 357 (75.0) 19 (30.6)
       T3 85 (17.9) 35 (56.5)
       T4 4 (0.8) 8 (12.9)
    TPSA/(ng·mL-1), M (Q1, Q3) 13.5 (8.1, 25.4) 27.8 (14.4, 54.5) Z=-6.18 <0.001
    FPSA/(ng·mL-1), M (Q1, Q3) 1.6 (1.0, 2.7) 4.0 (2.1, 7.7) Z=-6.84 <0.001
    FPSA/TPSA, M (Q1, Q3) 0.12 (0.08, 0.15) 0.12 (0.10, 0.17) Z=-1.51 0.130
    PI-RADS score, n (%) Z=-5.30 <0.001
       1 1 (0.2) 0
       2 17 (3.6) 0
       3 46 (9.7) 0
       4 164 (34.5) 8 (12.9)
    5 248 (52.1) 54 (87.1)
    Positive biopsy count, M (Q1, Q3) 6.0 (4.0, 9.0) 9.0 (6.0, 12.0) Z=-4.39 <0.001
    Total biopsy count, $\bar{x} \pm s$ 15.2±3.5 14.2±2.4 t=2.28 0.023
    Proportion of positive cores, M (Q1, Q3) 0.42 (0.27, 0.60) 0.60 (0.43, 0.90) Z=-5.14 <0.001
    Biopsy ISUP grade, n (%) Z=-5.40 <0.001
       1 21 (4.4) 0
       2 127 (26.7) 6 (9.7)
       3 95 (20.0) 5 (8.1)
       4 92 (19.3) 12 (19.4)
       5 141 (29.6) 39 (62.9)
    Volume of prostate/mL, M (Q1, Q3) 31.2 (24.1, 44.1) 39.7 (25.9, 56.2) Z=-2.46 0.014
    PSAD/(ng·mL-1·mL-1), M (Q1, Q3) 0.42 (0.25, 0.79) 0.86 (0.42, 1.73) Z=-4.79 <0.001
    ESUR score, n (%) Z=-5.70 <0.001
       1 96 (20.2) 3 (4.8)
       3 238 (50.0) 18 (29.0)
       4 83 (17.4) 20 (32.3)
       5 59 (12.4) 21 (33.9)
    ePLND: Extended pelvic lymph node dissection; BMI: Body mass index; MRI: Magnetic resonance imaging; TPSA: Total prostate-specific antigen; FPSA: Free prostate-specific antigen; PI-RADS: Prostate imaging reporting and data system; ISUP: International Society of Urological Pathology; PSAD: Prostate-specific antigen density; ESUR: European Society of Urogenital Radiology.

    表  2   前列腺癌患者盆腔淋巴结转移影响因素的二元多因素logistic回归分析

    Table  2   Binary multivariate logistic regression analysis of influencing factors for pelvic lymph node metastasis in prostate cancer patients

    Variable b SE Wald χ2 OR (95%CI) P value
    Intercept -5.988 1.713 1 12.218
    Age -0.063 0.023 1 7.413 0.939 (0.897, 0.982) 0.007
    Maximum diameter of tumor 0.631 0.181 3 12.106 1.879 (1.319, 2.693) <0.001
    Clinical T stage 0.992 0.306 5 10.474 2.697 (1.489, 4.980) 0.001
    Biopsy ISUP grade 0.514 0.161 6 10.131 1.673 (1.234, 2.334) 0.002
    PSAD 0.475 0.141 2 11.287 1.607 (1.233, 2.192) <0.001
    ESUR score 0.556 0.157 8 12.407 1.743 (1.292, 2.403) <0.001
    ISUP: International Society of Urological Pathology; PSAD: Prostate specific-antigen density; ESUR: European Society of Urogenital Radiology; b: Regression coefficient; SE: Standard error; OR: Odds ratio; 95%CI: 95% confidence interval.
  • [1] Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3):229-263. DOI: 10.3322/caac.21834.
    [2] Giuliano A E, Ballman K V, McCall L, et al. Effect of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis: the ACOSOG Z0011(Alliance) randomized clinical trial[J]. JAMA, 2017, 318(10): 918-926. DOI: 10.1001/jama.2017.11470.
    [3] Zhao D, Xu M, Yang S, et al. Specific diagnosis of lymph node micrometastasis in breast cancer by targeting activatable near-infrared fluorescence imaging[J]. Biomaterials, 2022, 282: 121388. DOI: 10.1016/j.biomaterials.2022.121388.
    [4] Zhang D, Zhou W, Lu W W, et al. Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study[J]. Oncologist, 2025, 30(5): oyaf090. DOI: 10.1093/oncolo/oyaf090.
    [5] Jiang M, Li C L, Luo X M, et al. Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer[J]. Eur Radiol, 2022, 32(4): 2313-2325. DOI: 10.1007/s00330-021-08330-w.
    [6] Liu H, Zou L, Xu N, et al. Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer[J]. NPJ Breast Cancer, 2024, 10: 22. DOI: 10.1038/s41523-024-00628-4.
    [7] Yang Z Q, Zhang Y, Lu F, et al. Integrating multimodal ultrasound imaging for improved radiomics sentinel lymph node assessment in breast cancer[J]. Gland Surg, 2025, 14(7): 1348-1365. DOI: 10.21037/gs-2025-223.
    [8] Zhang W, Wang S, Wang Y, et al. Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in early-stage breast cancer[J]. Radiol Med, 2024, 129(2): 211-221. DOI: 10.1007/s11547-024-01768-0.
    [9] Hu Z, Tian Z, Wei X, et al. Machine learning model for predicting axillary lymph node metastasis in clinically node positive breast cancer based on peritumoral ultrasound radiomics and SHAP feature analysis[J]. J Ultrasound Med, 2024, 43(10): 2007-2008. DOI: 10.1002/jum.16520.
    [10] Chen Y, Li J, Zhang J, et al. Radiomic nomogram for predicting axillary lymph node metastasis in patients with breast cancer[J]. Acad Radiol, 2024, 31(3): 788-799. DOI: 10.1016/j.acra.2023.10.026.
    [11] Qiu X, Jiang Y, Zhao Q, et al. Could ultrasound-based radiomics noninvasively predict axillary lymph node metastasis in breast cancer?[J]. J Ultrasound Med, 2020, 39(10): 1897-1905. DOI: 10.1002/jum.15294.
    [12] Guo Q, Dong Z, Zhang L, et al. Ultrasound features of breast cancer for predicting axillary lymph node metastasis[J]. J Ultrasound Med, 2018, 37(6): 1345-1353. DOI: 10.1002/jum.14469.
    [13] Dong Y, Feng Q, Yang W, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI[J]. Eur Radiol, 2018, 28(2): 582-591. DOI: 10.1007/s00330-017-5005-7.
    [14] Han L, Zhu Y, Liu Z, et al. Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer[J]. Eur Radiol, 2019, 29(7): 3820-3829. DOI: 10.1007/s00330-018-5981-2.
    [15] Yu F H, Wang J X, Ye X H, et al. Ultrasound-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer[J]. Eur J Radiol, 2019, 119: 108658. DOI: 10.1016/j.ejrad.2019.108658.
    [16] Li X, Yang L, Jiao X. Comparison of traditional radiomics, deep learning radiomics and fusion methods for axillary lymph node metastasis prediction in breast cancer[J]. Acad Radiol, 2023, 30(7): 1281-1287. DOI: 10.1016/j.acra.2022.10.015.
    [17] Leong S P, Naxerova K, Keller L, et al. Molecular mechanisms of cancer metastasis via the lymphatic versus the blood vessels[J]. Clin Exp Metastasis, 2022, 39(1): 159-179. DOI: 10.1007/s10585-021-10120-z.
    [18] Ferroni G, Sabeti S, Abdus-Shakur T, et al. Noninvasive prediction of axillary lymph node breast cancer metastasis using morphometric analysis of nodal tumor microvessels in a contrast-free ultrasound approach[J]. Breast Cancer Res, 2023, 25(1): 65. DOI: 10.1186/s13058-023-01670-z.
    [19] Gao Y, Luo Y, Zhao C, et al. Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients[J]. Eur Radiol, 2021, 31(2): 928-937. DOI: 10.1007/s00330-020-07181-1.
    [20] Wang L, Li J, Qiao J, et al. Establishment of a model for predicting sentinel lymph node metastasis in early breast cancer based on contrast-enhanced ultrasound and clinicopathological features[J]. Gland Surg, 2021, 10(5): 1701-1712. DOI: 10.21037/gs-21-245.
    [21] Yajima R, Fujii T, Yanagita Y, et al. Prognostic value of extracapsular invasion of axillary lymph nodes combined with peritumoral vascular invasion in patients with breast cancer[J]. Ann Surg Oncol, 2015, 22(1): 52-58. DOI: 10.1245/s10434-014-3941-x.
    [22] Sun R, Limkin E J, Vakalopoulou M, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study[J]. Lancet Oncol, 2018, 19(9): 1180-1191. DOI: 10.1016/S1470-2045(18)30413-3.
    [23] Yu Y, He Z, Ouyang J, et al. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: a machine learning, multicenter study[J]. EBioMedicine, 2021, 69: 103460. DOI: 10.1016/j.ebiom.2021.103460.
    [24] Saha A, Harowicz M R, Grimm L J, et al. A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features[J]. Br J Cancer, 2018, 119(4): 508-516. DOI: 10.1038/s41416-018-0185-8.
    [25] Zha H L, Zong M, Liu X P, et al. Preoperative ultrasound-based radiomics score can improve the accuracy of the Memorial Sloan Kettering Cancer Center nomogram for predicting sentinel lymph node metastasis in breast cancer[J]. Eur J Radiol, 2021, 135: 109512. DOI: 10.1016/j.ejrad.2020.109512.
    [26] Gu J, Tong T, Xu D, et al. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: a multicenter study[J]. Cancer, 2023, 129(3): 356-366. DOI: 10.1002/cncr.34540.
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  • 收稿日期:  2025-09-03
  • 接受日期:  2026-02-28

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