转移性骨肿瘤患者静脉血栓栓塞危险因素分析及机器学习预测模型的建立与验证

doi:10.3969/j.issn.1674-8115.2026.10.008

上海交通大学学报（医学版） ›› 2026, Vol. 46 ›› Issue (10): 1383-1391.doi: 10.3969/j.issn.1674-8115.2026.10.008

• 论著 · 临床研究 • 上一篇

转移性骨肿瘤患者静脉血栓栓塞危险因素分析及机器学习预测模型的建立与验证

阮顺毅¹, 孙宜², 王琼¹, 常玉洁¹, 顾一峰³, 徐永明⁴, 王智煜¹^,⁵(), 赵晖¹^,⁵()

^1.上海交通大学医学院附属第六人民医院肿瘤内科，上海 201306
^2.上海交通大学医学院附属第六人民医院肿瘤放疗科，上海 201306
^3.上海交通大学医学院附属第六人民医院放射介入科，上海 201306
^4.上海交通大学医学院附属第六人民医院疼痛科，上海 201306
^5.上海交通大学医学院附属第六人民医院转移性骨肿瘤临床诊治中心，上海 201306

收稿日期:2026-04-01 接受日期:2026-05-18 出版日期:2026-10-28 发布日期:2026-10-28
通讯作者: 赵晖，主任医师，博士；电子信箱：zhao-hui@sjtu.edu.cn。
王智煜，副主任医师，博士；电子信箱：areyouwangzhiyu@163.com。
作者简介:第一联系人：为共同第一作者（co-first authors）。
基金资助:
上海交通大学医学院双百人项目(20172024)

Risk factors for venous thromboembolism in patients with metastatic bone tumors: development and validation of a machine learning-based prediction model

Ruan Shunyi¹, Sun Yi², Wang Qiong¹, Chang Yujie¹, Gu Yifeng³, Xu Yongming⁴, Wang Zhiyu¹^,⁵(), Zhao Hui¹^,⁵()

^1.Department of Internal Oncology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201306, China
^2.Department of Radiotherapy for Oncology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201306, China
^3.Department of Radiology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201306, China
^4.Department of Pain Management, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201306, China
^5.Clinical Diagnosis and Treatment Center for Metastatic Bone Tumors, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201306, China

Received:2026-04-01 Accepted:2026-05-18 Online:2026-10-28 Published:2026-10-28
Contact: Zhao Hui, E-mail: zhao-hui@sjtu.edu.cn.
Wang Zhiyu, E-mail: areyouwangzhiyu@163.com.
Supported by:
“Two-hundred Talents” Program of Shanghai Jiao Tong University School of Medicine(20172024)

摘要/Abstract

摘要：

目的·探析转移性骨肿瘤患者静脉血栓栓塞（venous thromboembolism，VTE）的独立危险因素，并在转移性骨肿瘤患者中建立和验证基于机器学习的预测模型。方法·回顾性队列纳入上海交通大学医学院附属第六人民医院转移性骨肿瘤临床诊治中心2012年1月—2022年12月期间收治的439例转移性骨肿瘤患者，用于模型训练和内部测试。收集患者人口学特征（性别、转移性骨肿瘤诊断时年龄）、肿瘤原发部位、骨转移部位、Karnofsky功能状态评分、治疗情况（放射治疗、手术等）、脊髓压迫情况、入院时血常规（红细胞、白细胞、血红蛋白、血小板等）和凝血指标（D-二聚体、纤维蛋白原、凝血酶原时间等）资料。转移性骨肿瘤患者依据VTE结局分为VTE组（n=153）和非VTE组（n=286）。采用多因素Logistic回归分析识别VTE事件的危险因素。使用十折交叉验证训练CatBoost、LightGBM、XGBoost、Gaussian 过程和Logistic回归5种机器学习模型对VTE发生风险进行预测与验证，筛选出最优模型用于临床风险分层。采用受试者工作特征曲线下面积（area under the curve，AUC）、准确率、灵敏度、特异度等指标评估模型效能。前瞻性队列纳入2023年1月—2024年12月期间收治的65例转移性骨肿瘤患者，用于验证最优AUC模型的性能。夏普利加性解释（Shapley additive explanations，SHAP）用于模型特征的临床解释。结果·VTE组和非VTE组在年龄、性别分布或Karnofsky功能状态方面差异无统计学意义（均P>0.05）。转移性骨肿瘤患者中最常见的原发肿瘤为肺癌（40.3%）、乳腺癌（12.3%）和前列腺癌（7.3%）。最常见的骨转移部位为脊柱（64.7%）、骨盆（53.5%）、肋骨与锁骨（33.9%）和下肢（28.7%）。多因素分析获得3个VTE事件的独立危险因素：下肢骨转移（OR=1.89，95%CI 1.17~3.07，P=0.010）、D-二聚体升高（OR=1.06，95%CI 1.02~1.09，P<0.001）、凝血酶原时间延长（OR=1.09，95%CI 1.01~1.17，P=0.034）。5种机器学习模型中，CatBoost模型表现最优，在内部测试集中AUC为0.82（95%CI 0.73~0.90），前瞻性验证集中AUC为0.80（95%CI 0.67~0.93）。SHAP确认D-二聚体是最重要的预测因子（平均|SHAP|=1.41）。结论·下肢骨转移、D-二聚体升高和凝血酶原时间延长是转移性骨肿瘤患者VTE的独立危险因素。基于CatBoost的预测模型在前瞻性验证中表现稳健，显示出良好的临床应用价值，可用于转移性骨肿瘤患者VTE风险预测。

关键词: 转移性骨肿瘤, 静脉血栓栓塞, 危险因素, 机器学习, CatBoost, 预测模型

Abstract:

Objective ·To identify independent risk factors for venous thromboembolism (VTE) in patients with metastatic bone tumors and to develop and validate a machine learning-based prediction model. Methods ·This retrospective cohort study included 439 patients with metastatic bone tumors treated at the Clinical Diagnosis and Treatment Center for Metastatic Bone Tumors, Shanghai Sixth People's Hospital, from January 2012 to December 2022 for model training and internal validation. Collected data included demographic characteristics (sex and age at diagnosis of metastatic bone tumors), primary tumor sites, Karnofsky performance status, treatment modalities (radiotherapy and surgery), spinal cord compression, admission complete blood count parameters (red blood cell count, white blood cell count, hemoglobin, and platelet count), coagulation indices (D-dimer, fibrinogen, and prothrombin time), and sites of bone metastases. Patients were divided into the VTE group (n=153) and the non-VTE group (n=286) based on VTE outcomes. Multivariate Logistic regression was used to identify risk factors. Five machine learning models—CatBoost, LightGBM, XGBoost, Gaussian process, and Logistic regression—were trained and evaluated using ten-fold cross-validation to predict VTE risk and identify the optimal model for clinical risk stratification. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A prospective cohort including 65 patients with metastatic bone tumors admitted between January 2023 and December 2024 was used to validate the performance of the model with the highest AUC. Shapley additive explanations (SHAP) was employed for clinical interpretation of model features. Results ·There were no significant differences in age, sex distribution, or Karnofsky performance status between the VTE group and the non-VTE group (all P>0.05). The most common primary tumors were lung cancer (40.3%), breast cancer (12.3%), and prostate cancer (7.3%). The most common sites of bone metastases were the spine (64.7%), pelvis (53.5%), ribs and clavicle (33.9%), and lower limbs (28.7%). Three independent risk factors were identified: lower limb bone metastases (OR=1.89, 95%CI 1.17‒3.07, P=0.010), elevated D-dimer levels (OR=1.06, 95%CI 1.02‒1.09, P<0.001), and prolonged prothrombin time (OR=1.09, 95%CI 1.01‒1.17, P=0.034). CatBoost demonstrated the best performance, with an AUC of 0.82 (95%CI 0.73‒0.90) in the internal test set and 0.80 (95%CI 0.67‒0.93) in the prospective validation set. SHAP analysis confirmed D-dimer as the most important predictor (mean |SHAP|=1.41). Conclusion ·Lower limb bone metastases, elevated D-dimer levels, and prolonged prothrombin time are independent risk factors for VTE in patients with metastatic bone tumors. The CatBoost-based prediction model demonstrated robust performance in prospective validation, showing potential for clinical application in predicting VTE risk in patients with metastatic bone tumors.

Key words: metastatic bone tumor, venous thromboembolism (VTE), risk factor, machine learning, CatBoost, prediction model

中图分类号:

R738.1

阮顺毅, 孙宜, 王琼, 常玉洁, 顾一峰, 徐永明, 王智煜, 赵晖. 转移性骨肿瘤患者静脉血栓栓塞危险因素分析及机器学习预测模型的建立与验证[J]. 上海交通大学学报（医学版）, 2026, 46(10): 1383-1391.

Ruan Shunyi, Sun Yi, Wang Qiong, Chang Yujie, Gu Yifeng, Xu Yongming, Wang Zhiyu, Zhao Hui. Risk factors for venous thromboembolism in patients with metastatic bone tumors: development and validation of a machine learning-based prediction model[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026,(10): 1383-1391.

图/表 6

参考文献 26

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Variable	Total (n=439)	Without VTE (n=286)	With VTE (n=153)	P value
Gender/n(%)				0.117
Female	176 (40.1)	107 (37.4)	69 (45.1)
Male	263 (59.9)	179 (62.6)	84 (54.9)
Age/year	61.9±11.4	61.3±12.1	63.0±10.1	0.155
Karnofsky performance status/score	73.5±8.2	73.7±8.0	73.1±8.5	0.465

Variable	Total (n=439)	Without VTE (n=286)	With VTE (n=153)	P value
Gender/n(%)				0.117
Female	176 (40.1)	107 (37.4)	69 (45.1)
Male	263 (59.9)	179 (62.6)	84 (54.9)
Age/year	61.9±11.4	61.3±12.1	63.0±10.1	0.155
Karnofsky performance status/score	73.5±8.2	73.7±8.0	73.1±8.5	0.465

Variable	Total (n=439)	Without VTE (n=286)	With VTE (n=153)	P value
Primary tumor site/n(%)				0.062
Breast	54 (12.3)	35 (12.2)	19 (12.4)	0.956
Lung	177 (40.3)	99 (34.6)	78 (51.0)	<0.001
Kidney	21 (4.8)	17 (5.9)	4 (2.6)	0.119
Esophagus and stomach	21 (4.8)	14 (4.9)	7 (4.6)	0.881
Colon and rectum	25 (5.7)	17 (5.9)	8 (5.2)	0.758
Other abdominal	26 (5.9)	22 (7.7)	4 (2.6)	0.032
Head and neck	21 (4.8)	18 (6.3)	3 (2.0)	0.043
Prostate	32 (7.3)	21 (7.3)	11 (7.2)	0.953
Ovary and cervix	17 (3.9)	12 (4.2)	5 (3.3)	0.631
Nasopharynx	10 (2.3)	9 (3.1)	1 (0.7)	0.176
Pancreas	6 (1.4)	4 (1.4)	2 (1.3)	0.992
Sarcoma	6 (1.4)	5 (1.7)	1 (0.7)	0.669
Bladder	5 (1.1)	3 (1.0)	2 (1.3)	0.997
Skin	8 (1.8)	4 (1.4)	4 (2.6)	0.458
Thymus	3 (0.7)	1 (0.3)	2 (1.3)	0.279
Other	7 (1.6)	5 (1.7)	2 (1.3)	0.996
Bone metastasis site/n(%)
Skull	15 (3.4)	9 (3.1)	6 (3.9)	0.670
Spine	284 (64.7)	190 (66.4)	94 (61.4)	0.297
Ribs and clavicle	149 (33.9)	103 (36.0)	46 (30.1)	0.210
Upper limbs	39 (8.9)	30 (10.5)	9 (5.9)	0.106
Pelvis	235 (53.5)	137 (47.9)	98 (64.1)	0.195
Lower limbs	126 (28.7)	65 (22.7)	61 (39.9)	<0.001
SREs/n(%)
Radiotherapy	102 (23.2)	62 (21.7)	40 (26.1)	0.291
Operation	167 (38.0)	98 (34.3)	69 (45.1)	0.026
Bone fractures	121 (27.6)	73 (25.5)	48 (31.4)	0.191
Spinal cord compression	41 (9.3)	30 (10.5)	11 (7.2)	0.258
Laboratory finding
White blood cell/(×10⁹·L^-1)	7.6 ± 3.5	7.3 ± 3.3	8.2 ± 3.6	0.008
Red blood cell/(×10¹²·L^-1)	3.9 ± 0.7	4.0 ± 0.7	3.8 ± 0.6	0.014
Hemoglobin/(g·L^-1)	116.9 ± 29.5	119.3 ± 33.2	112.2 ± 20.1	0.015
Platelet/(×10⁹·L^-1)	228.7 ± 107.1	230.9 ± 110.7	224.6 ± 100.0	0.555
Fibrinogen/(g·L^-1)	3.8 ± 1.4	3.8 ± 1.4	3.8 ± 1.3	0.844
Prothrombin time/s	12.4 ± 3.4	12.0 ± 2.0	13.1 ± 4.9	<0.001
D-dimer/(μg·mL^-1)	1.9 (0.5, 7.1)	0.9 (0.2, 3.1)	6.4 (2.4, 11.2)	<0.001

Variable	Total (n=439)	Without VTE (n=286)	With VTE (n=153)	P value
Primary tumor site/n(%)				0.062
Breast	54 (12.3)	35 (12.2)	19 (12.4)	0.956
Lung	177 (40.3)	99 (34.6)	78 (51.0)	<0.001
Kidney	21 (4.8)	17 (5.9)	4 (2.6)	0.119
Esophagus and stomach	21 (4.8)	14 (4.9)	7 (4.6)	0.881
Colon and rectum	25 (5.7)	17 (5.9)	8 (5.2)	0.758
Other abdominal	26 (5.9)	22 (7.7)	4 (2.6)	0.032
Head and neck	21 (4.8)	18 (6.3)	3 (2.0)	0.043
Prostate	32 (7.3)	21 (7.3)	11 (7.2)	0.953
Ovary and cervix	17 (3.9)	12 (4.2)	5 (3.3)	0.631
Nasopharynx	10 (2.3)	9 (3.1)	1 (0.7)	0.176
Pancreas	6 (1.4)	4 (1.4)	2 (1.3)	0.992
Sarcoma	6 (1.4)	5 (1.7)	1 (0.7)	0.669
Bladder	5 (1.1)	3 (1.0)	2 (1.3)	0.997
Skin	8 (1.8)	4 (1.4)	4 (2.6)	0.458
Thymus	3 (0.7)	1 (0.3)	2 (1.3)	0.279
Other	7 (1.6)	5 (1.7)	2 (1.3)	0.996
Bone metastasis site/n(%)
Skull	15 (3.4)	9 (3.1)	6 (3.9)	0.670
Spine	284 (64.7)	190 (66.4)	94 (61.4)	0.297
Ribs and clavicle	149 (33.9)	103 (36.0)	46 (30.1)	0.210
Upper limbs	39 (8.9)	30 (10.5)	9 (5.9)	0.106
Pelvis	235 (53.5)	137 (47.9)	98 (64.1)	0.195
Lower limbs	126 (28.7)	65 (22.7)	61 (39.9)	<0.001
SREs/n(%)
Radiotherapy	102 (23.2)	62 (21.7)	40 (26.1)	0.291
Operation	167 (38.0)	98 (34.3)	69 (45.1)	0.026
Bone fractures	121 (27.6)	73 (25.5)	48 (31.4)	0.191
Spinal cord compression	41 (9.3)	30 (10.5)	11 (7.2)	0.258
Laboratory finding
White blood cell/(×10⁹·L^-1)	7.6 ± 3.5	7.3 ± 3.3	8.2 ± 3.6	0.008
Red blood cell/(×10¹²·L^-1)	3.9 ± 0.7	4.0 ± 0.7	3.8 ± 0.6	0.014
Hemoglobin/(g·L^-1)	116.9 ± 29.5	119.3 ± 33.2	112.2 ± 20.1	0.015
Platelet/(×10⁹·L^-1)	228.7 ± 107.1	230.9 ± 110.7	224.6 ± 100.0	0.555
Fibrinogen/(g·L^-1)	3.8 ± 1.4	3.8 ± 1.4	3.8 ± 1.3	0.844
Prothrombin time/s	12.4 ± 3.4	12.0 ± 2.0	13.1 ± 4.9	<0.001
D-dimer/(μg·mL^-1)	1.9 (0.5, 7.1)	0.9 (0.2, 3.1)	6.4 (2.4, 11.2)	<0.001

Variable	OR (95%CI)	P value
Lower limb metastasis	1.89 (1.17‒3.07)	0.010
White blood cell	1.05 (0.99‒1.11)	0.137
Red blood cell	0.80 (0.59‒1.09)	0.163
Prothrombin time	1.09 (1.01‒1.17)	0.034
D-dimer	1.06 (1.02‒1.09)	<0.001
Operation	1.49 (0.87‒2.56)	0.149

转移性骨肿瘤患者静脉血栓栓塞危险因素分析及机器学习预测模型的建立与验证

Risk factors for venous thromboembolism in patients with metastatic bone tumors: development and validation of a machine learning-based prediction model

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