Establishment of a prediction model of lymph node metastasis in patients with non-small cell lung cancer at high altitude areas based on energy spectrum CT parameters and clinicopathological factors

HU Zhipeng; YE Zhi; ZHANG Qingxin

doi:10.7619/jcmp.20220739

Journal of Clinical Medicine in Practice > 2022 > 26(18): 6-10. > DOI: 10.7619/jcmp.20220739

HU Zhipeng, YE Zhi, ZHANG Qingxin. Establishment of a prediction model of lymph node metastasis in patients with non-small cell lung cancer at high altitude areas based on energy spectrum CT parameters and clinicopathological factors[J]. Journal of Clinical Medicine in Practice, 2022, 26(18): 6-10. DOI: 10.7619/jcmp.20220739

Citation:

PDF (945 KB)

Establishment of a prediction model of lymph node metastasis in patients with non-small cell lung cancer at high altitude areas based on energy spectrum CT parameters and clinicopathological factors

Department of Thoracic Surgery, Qinghai Provincial People′s Hospital, Xining, Qinghai, 810000

More Information

Received Date: March 07, 2022
Available Online: October 23, 2022

Graphical Abstract

Abstract

Abstract

Objective
To explore the energy spectrum CT parameters, clinicopathological factors, influencing factors and the application efficiency of the prediction model of lymph node metastasis in patients with non-small cell lung cancer (NSCLC) at high altitude area.
Methods
A total of 84 NSCLC patients from January 2020 to July 2021 in Qinghai Provincial People′s Hospital were selected as research objects, of whom 50 patients were conducted with energy spectrum CT detection and their clinical materials were collected. According to the pathological examination results of lymph node metastasis, 50 patients were divided into metastasis group and non-metastasis group, the differences of clinicopathological factors and energy spectrum CT parameters between the two groups were analyzed by univariate analysis, the influencing factors of lymph node metastasis in NSCLC patients were analyzed by multivariate regression analysis, and a prediction model was established. The prediction model was used to analyze the lymph node metastasis of the other 34 NSCLC patients, and the clinical test results were used as the gold standard to evaluate the application value of the prediction model.
Results
① The incidence of lymph node metastasis in 50 patients was 30.00%. The preoperative carcinoembryonic antigen (CEA), ratio of central carina position and lymph node diameter in the metastasis group were significantly higher than those in the non-metastasis group (P < 0.05). ② Slope of energy spectrum curve of lymph nodes (λHu), normalized iodine concentration (NIC) and λHu ratio of lymph nodes to primary lesions in the metastasis group were significantly lower than those in the non-metastasis group (P < 0.05). ③ The risk factors of lymph node metastasis in NSCLC patients were the λHu ratio of lymph nodes to primary lesions, preoperative CEA≥5 ng/mL and lymph node diameter≥3 cm. ④ The accuracy, specificity and sensitivity of the prediction model for lymph node metastasis in 34 patients with NSCLC were all above 80.00%.
Conclusion
In the NSCLC patients with lymph node metastasis, energy spectrum CT parameters and related risk factors in clinicopathological factors can be used to establish a prediction model for accurate prediction, and this model can be used as the preferred tool to predict lymph node metastasis.

FullText(HTML)

References (14)

References

[1]	CARBONE D P, RECK M, PAZ-ARES L, et al. First-line nivolumab in stage IV or recurrent non-small-cell lung cancer[J]. N Engl J Med, 2017, 376(25): 2415-2426. doi: 10.1056/NEJMoa1613493
[2]	ETTINGER D S, WOOD D E, AISNER D L, et al. Non-small cell lung cancer, version 5. 2017, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2017, 15(4): 504-535. doi: 10.6004/jnccn.2017.0050
[3]	张娇, 赖远阳, 孙盈, 等. 非小细胞肺癌淋巴结转移与临床病理特征及预后的关系[J]. 现代肿瘤医学, 2019, 27(18): 3238-3241. doi: 10.3969/j.issn.1672-4992.2019.18.017
[4]	SORIA J C, TAN D S W, CHIARI R, et al. First-line ceritinib versus platinum-based chemotherapy in advanced ALK-rearranged non-small-cell lung cancer (ASCEND-4): a randomised, open-label, phase 3 study[J]. Lancet, 2017, 389(10072): 917-929. doi: 10.1016/S0140-6736(17)30123-X
[5]	POSTMUS P E, KERR K M, OUDKERK M, et al. Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up[J]. Ann Oncol, 2017, 28(suppl_4): iv1-iv21.
[6]	FENNELL D A, SUMMERS Y, CADRANEL J, et al. Cisplatin in the modern era: the backbone of first-line chemotherapy for non-small cell lung cancer[J]. Cancer Treat Rev, 2016, 44: 42-50. doi: 10.1016/j.ctrv.2016.01.003
[7]	KATAKAMI N, UCHINO J, YOKOYAMA T, et al. Anamorelin (ONO-7643) for the treatment of patients with non-small cell lung cancer and Cachexia: results from a randomized, double-blind, placebo-controlled, multicenter study of Japanese patients (ONO-7643-04)[J]. Cancer, 2018, 124(3): 606-616. doi: 10.1002/cncr.31128
[8]	LI A, WEI Z J, DING H, et al. Docetaxel versus docetaxel plus cisplatin for non-small-cell lung cancer: a meta-analysis of randomized clinical trials[J]. Oncotarget, 2017, 8(34): 57365-57378. doi: 10.18632/oncotarget.17071
[9]	陈盈, 姚琼瑛, 郑昊, 等. 能谱CT在肺癌淋巴结转移诊断中的临床应用研究[J]. 肿瘤学杂志, 2016, 22(8): 632-638. https://www.cnki.com.cn/Article/CJFDTOTAL-XHON201608005.htm
[10]	李伟婷, 李永文, 张洪兵, 等. 基于TCGA数据库的中央型与周围型肺鳞癌基因表达差异性研究[J]. 中国肺癌杂志, 2019, 22(5): 280-288. https://www.cnki.com.cn/Article/CJFDTOTAL-FAIZ201905005.htm
[11]	申磊磊, 云天洋, 郭俊唐, 等. 左侧非小细胞肺癌4L组淋巴结转移的临床病理学特征及危险因素分析[J]. 南方医科大学学报, 2020, 40(12): 1793-1798. doi: 10.12122/j.issn.1673-4254.2020.12.14
[12]	李季, 沈艳, 姜研. CT仿真结肠镜联合肿瘤标志物检测在结直肠癌中的诊断价值[J]. 医学综述, 2021, 27(5): 1032-1036. doi: 10.3969/j.issn.1006-2084.2021.05.037
[13]	钱香, 王宏, 任真, 等. NLR与FIB和CEA及CA19-9在结直肠癌中的应用价值[J]. 中华预防医学杂志, 2021, 55(4): 499-505. doi: 10.3760/cma.j.cn112150-20200805-01094
[14]	龚麒麟, 边聪, 刘辉. 早期舌鳞状细胞癌颈隐匿性淋巴结转移的回顾性分析[J]. 中华耳鼻咽喉头颈外科杂志, 2016, 51(10): 773-775. doi: 10.3760/cma.j.issn.1673-0860.2016.10.012

[1]	SHI Haomin, YAN Su, QIAO Mengmeng, YANG Huilian. Research on gastric cancer lymph node metastasis prediction model based on machine learning algorithms[J]. Journal of Clinical Medicine in Practice, 2024, 28(1): 41-47, 61. DOI: 10.7619/jcmp.20233076
[2]	LIU Shuang, GAO Rui, CHEN Yue, QI Aiying. Risk factors of cervical metastatic lymph nodes in multifocal papillary thyroid carcinoma and its prediction model construction[J]. Journal of Clinical Medicine in Practice, 2023, 27(19): 123-127, 133. DOI: 10.7619/jcmp.20232498
[3]	DENG Longlian, SUN Haibin, Enri-Letu, WANG Tengqi. Research progresses of nomogram in predicting lymph node metastasis of colorectal cancer[J]. Journal of Clinical Medicine in Practice, 2023, 27(5): 143-148. DOI: 10.7619/jcmp.20223328
[4]	LI Tingting, XUE Jiping, SU Lili. Prediction of axillary lymph node metastasis by shear wave elastography and conventional ultrasound features of breast cancer and model establishment[J]. Journal of Clinical Medicine in Practice, 2023, 27(5): 11-15. DOI: 10.7619/jcmp.20221769
[5]	WANG Hui, ZHA Hailing, MA Pei, YU Feihong. Predictive value of ultrasound elastography in axillary lymph node metastasis in early small breast cancer[J]. Journal of Clinical Medicine in Practice, 2023, 27(5): 6-10, 15. DOI: 10.7619/jcmp.20223474
[6]	GAO Xiong, JIANG Meng, GAO Ying. Application value of glucocorticoid combined with temporary stent placement in preventing esophageal stenosis after circumferential or near circumferential endoscopic submucosal dissection[J]. Journal of Clinical Medicine in Practice, 2022, 26(18): 16-20. DOI: 10.7619/jcmp.20221954
[7]	XIONG Chaoyue, ZHOU Min, HE Xiaofang, HUA Xiaofan. Value of prediction model established based on ultrasound features combined with clinical data in evaluating axillary lymph node metastasis in patients with early breast cancer[J]. Journal of Clinical Medicine in Practice, 2022, 26(12): 14-18, 22. DOI: 10.7619/jcmp.20220195
[8]	CHAI Jixin, CHEN Yong, QI Yantao, WANG Jingjing, LI Bo, AO Yazhou. A nomogram model for early lymph node metastasis of differentiated thyroid carcinoma on basis of thyroglobulin and antibody quantification[J]. Journal of Clinical Medicine in Practice, 2021, 25(21): 7-11. DOI: 10.7619/jcmp.20213894
[9]	LIU Yan, WU Xiaoli, LI Keji, LAI Ping, KOU Sen, QIU Guoyuan. Value of two indicators in predicting lateral cervical lymph node metastasis in patients with papillary thyroid carcinoma[J]. Journal of Clinical Medicine in Practice, 2021, 25(17): 105-108, 113. DOI: 10.7619/jcmp.20211873
[10]	WANG Lu, MA Zheng, XU Xiaoyang, LIU Yali. Research report on the status of interdisciplinary combination between medicine and engineering in China and its countermeasures(2019)[J]. Journal of Clinical Medicine in Practice, 2019, (5): 1-6. DOI: 10.7619/jcmp.201905001

Cited By

Cited by

Periodical cited type(5)

1.	李雅倩，刘义，刘百娜. 能谱CT定量参数联合MSCT三维重建在肺癌患者术前淋巴结转移评估中的应用价值. 实用癌症杂志. 2025(03): 479-483 .
2.	曾海燕，杨小林. CT能谱参数对结直肠癌病理类型鉴别的价值. 中外医学研究. 2024(09): 64-67 .
3.	王海波，段宏伟，左自军，丁琦峰，张鹏辉. 术前能谱CT多参数成像对晚期肺癌患者淋巴结转移的预测价值. 河南医学研究. 2024(06): 1071-1074 .
4.	刘贲，张楠，邓治平. miR-126及Th1相关细胞因子在非小细胞肺癌患者血清中的表达水平及临床意义. 热带医学杂志. 2024(08): 1154-1158 .
5.	周宇元. 增强CT联合能谱CT预测肺癌患者淋巴结转移的临床分析. 江西医药. 2024(07): 676-679 .

Other cited types(0)

Get Citation

PDF

XML

Article views (152) PDF downloads (6) Cited by(5)

Turn off MathJax

Article Contents

Abstract

References

Establishment of a prediction model of lymph node metastasis in patients with non-small cell lung cancer at high altitude areas based on energy spectrum CT parameters and clinicopathological factors