Survival prediction models for lung cancer patients: A systematic review and meta-analysis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LI Dan ¹ , WU Chao ¹ , LIU Chunzi ² , REN Liyuan ¹ , LI Yue ¹ ,  ZHANG Guoqing ¹

1. Department of Ambulatory Wards, Senior Department of Oncology, The Fifth Medical Center of PLA General Hospital, Beijing, 100071, P. R. China;
2. Senior Department of Oncology, The Fifth Medical Center of PLA General Hospital, Beijing, 100071, P. R. China;

Corresponding?author：

ZHANG Guoqing, Email: Zhanggq_301@sina.com

Keywords：

Lung cancer; survival; prediction model; systematic review/meta-analysis

DOI：

10.7507/1007-4848.202508061

Video：

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Objective To systematically evaluate the effectiveness of survival prediction models for lung cancer patients and analyze factors influencing model performance. Methods Relevant literature was retrieved from PubMed, EMbase, China National Knowledge Infrastructure (CNKI), and Wanfang Data up to March 2025 using computerized searches. The risk of bias and applicability assessment tool for prognostic studies was applied to evaluate methodological quality and applicability of included studies. Meta-analysis was conducted using R software (version 4.4.3). Results A total of 11 studies involving 23134 patients published between 2017 and 2025 were included. Meta-analysis demonstrated a pooled C-index of 0.72 [95%CI (0.70, 0.74)] for lung cancer survival prediction models. Subgroup analysis revealed that studies with sample size >1,000 cases showed greater stability in model performance with a C-index of 0.72 [95%CI (0.71, 0.72)]; non-small cell lung cancer (NSCLC) models exhibited a C-index of 0.71 [95%CI (0.69, 0.73)] compared to small cell lung cancer (SCLC) models at 0.70 [95%CI (0.64, 0.76)]. Studies with survival rate <50% had a C-index of 0.71 [95%CI (0.69, 0.73)] while those with survival rate ≥50% showed 0.73 [95%CI (0.70, 0.75)], with no statistically significant difference between groups (P=0.2601). Tumor staging, surgical intervention, and chemotherapy were identified as key prognostic predictors. Risk of bias assessment indicated 7 studies with high or unclear risk of bias, 3 with low risk, though overall applicability remained satisfactory (only 1 study had unclear applicability). Conclusion Lung cancer survival prediction models demonstrate good prognostic discrimination and calibration capabilities, but significant heterogeneity exists across models. Large sample size (>1000 patients) is crucial for reducing heterogeneity and improving stability. NSCLC models show slightly better predictive performance than SCLC models, with higher discriminative ability observed in populations with higher survival rates. Future research should focus on optimizing model design, minimizing bias risks, and enhancing both predictive accuracy and clinical utility.

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. CA Cancer J Clin, 2024, 74(3): 229-263.
2.	Thandra KC, Barsouk A, Saginala K, et al. Epidemiology of lung cancer. Contemp Oncol (Pozn), 2021, 25(1): 45-52.
3.	Fernandez-Felix BM, Lopez-Alcalde J, Roque M, et al. CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models. BMC Med Res Methodol, 2023, 23(1): 44.
4.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
5.	陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 2020, 20(6): 737-744.Chen XP, Zhang Y, Zhuang YY, et al. PROBAST: a tool for assessing risk of bias in the study of diagnostic or prognostic multi-factorial predictive models. Chin J Evid-Based Med, 2020, 20(6): 737-744.
6.	PAGE M J, MCKENZIE J E, BOSSUYT P M, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 2021, 372: n71.
7.	左丙麗, 余以珊, 孫雅文. 局限期小細胞肺癌生存期預測模型的建立. 中華腫瘤防治雜志, 2023, 30(6): 360-367.Zuo BL, Yu YS, Sun YW. Establishment of survival prediction model for limited stage small cell lung cancer. Chin J Cancer Prev Treat, 2023, 30(6): 360-367.
8.	潘輝, 張亞雷, 肖大凱, 等. 基于SEER數據庫構建小細胞肺癌術后患者生存預測模型. 腫瘤預防與治療, 2019, 32(6): 516-523.Pan H, Zhang YL, Xiao DK, et al. Nomogram for Prediction of Survival of Postoperative Small Cell Lung Cancer Patients: An Analysis Based on SEER. J Cancer Control Treat, 2019, 32(6): 516-523.
9.	尹玢璨, 辛世超, 張晗, 等. 基于SEER數據庫應用貝葉斯網絡構建亞洲腫瘤患者預后模型——以非小細胞肺癌為例. 數據分析與知識發現, 2017, 1(2): 41-46.Yin BC, Xin SC, Zhang H, et al. Building Asian tumor-patients prognostic model with Bayesian network and SEER database: case study of non-small cell lung cancer. Data Anal Knowl Discov, 2017, 1(2): 41-46.
10.	Ang C, Zhang M, Jia H, et al. The systemic inflammation response index(SIRI) predicts survival in advanced non-small cell lung cancer patients undergoing immunotherapy and the construction of a nomogram model. Front Immunol, 2024, 15: 1516737.
11.	Zheng K, Zhang J, Xu T, et al. Establishment and validation of a survival prediction model for stage Ⅳ non-small cell lung cancer: a real-world study. Front Immunol, 2025, 16: 1508721.
12.	Ji Y, Zhao Q, Liu Y, et al. Establishment of a survival predictive model for patients with two synchronous multiple primary lung cancers: a multicenter cohort analysis. Transl Lung Cancer Res, 2024, 13(9): 2254-2268.
13.	Wang QA, Tsai IL, Lin CY, et al. Multivariable model for predicting 5-year survival in patients with EGFR-mutated non-small cell lung cancer treated with EGFR tyrosine kinase inhibitors: a retrospective study. Ther Adv Med Oncol, 2025, 17: 17588359251321901.
14.	Li Y, Cao L, Ding Y, et al. Survival prognostic nomogram for young metastatic non-small cell lung cancer: a study of the US SEER database and a Chinese cohort. Front Oncol, 2025, 15: 1502253.
15.	Guo H, Nie G, Zhao X, et al. A nomogram for cancer-specific survival of lung adenocarcinoma patients: a SEER based analysis. Surg Open Sci, 2024, 22: 13-23.
16.	Xia Y, Qu J, Wang Y, et al. Prognostic nomogram for predicting survival in Asian patients with small-cell lung cancer: a comprehensive population-based study and external verification. Clin Respir J, 2024, 18(11): e70021.
17.	Fu R, Jing C, Zhang W. Risk stratification model for foreseeing overall survival in Chinese patients with initially metastatic small-cell lung cancer. Medicine (Baltimore), 2024, 103(42): e40145.
18.	He Y, Zhao F, Han Q, et al. Prognostic nomogram for predicting long-term cancer-specific survival in patients with lung carcinoid tumors. BMC Cancer, 2021, 21(1): 141.
19.	Zeng Q, Li J, Tan F, et al. Development and validation of a nomogram prognostic model for resected limited-stage small cell lung cancer patients. Ann Surg Oncol, 2021, 28(9): 4893-4904.
20.	Guo Y, Li L, Zheng K, et al. Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression. Front Immunol, 2024, 15: 1431150.
21.	Zuo ZC, Wang LD, Peng K, et al. Development and Validation of a Nomogram for Predicting the 1-, 3-, and 5-year Survival in Patients with Acinar-predominant Lung Adenocarcinoma. Curr Med Sci, 2022, 42(6): 1178-1185.
22.	Chen S, Gao C, Du Q, et al. A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study. J Transl Med, 2020, 18(1): 436.
23.	Liang M, Chen M, Singh S, et al. Prognostic Nomogram for Overall Survival in Small Cell Lung Cancer Patients Treated with Chemotherapy: A SEER-Based Retrospective Cohort Study. Adv Ther, 2022, 39(1): 346-359.
24.	Zeng Q, Li J, Tan F, et al. Development and validation of a nomogram prognostic model for resected limited-stage small cell lung cancer patients. Ann Surg Oncol, 2021, 28(9): 4893-4904.
25.	Liu XY, Zhang X, Zhang Q, et al. The value of CRP-albumin-lymphocyte index (CALLY index) as a prognostic biomarker in patients with non-small cell lung cancer. Support Care Cancer, 2023, 31(9): 533.
26.	Miyawaki T, Naito T, Doshita K, et al. Predicting the efficacy of first-line immunotherapy by combining cancer cachexia and tumor burden in advanced non-small cell lung cancer. Thorac Cancer, 2022, 13(14): 2064-2074.
27.	Liang J, Guan X, Bao G, et al. Molecular subtyping of small cell lung cancer. Semin Cancer Biol, 2022, 86(Pt 2): 450-462.
28.	Gao H, Dang Y, Qi T, et al. Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model. Medicine (Baltimore), 2020, 99(33): e21798.
29.	曹焯, 姚銳, 胡佳培, 等. 非小細胞肺癌早期死亡預測模型的構建和驗證. 醫學信息, 2025, 38(6): 15-21,28.Cao Z, Yao R, Hu JP, et al. Construction and validation of early death prediction model for non-small cell lung cancer. J Med Inf, 2025, 38(6): 15-21,28.
30.	Taylor M, Martin GP, Abah U, et al; RESECT-90 collaborators. Multicentre validation of the RESECT-90 prediction model for 90-day mortality after lung resection. Clin Lung Cancer, 2025, 26(1): e73-e80.
31.	Cai JS, Dou XM, Li JB, et al. Nomogram to predict cancer specific survival in patients with pathological stage ⅠA non-small cell lung cancer. Semin Thorac Cardiovasc Surg, 2022, 34(3): 1040-1048.
32.	Tran QNN, Le MK, Kondo T, et al. A machine learning-based model to predict in-hospital mortality of lung cancer patients: a population-based study of 523 959 cases. Adv Respir Med, 2023, 91(4): 310-323.
33.	Defraene G, Dankers FJWM, Price G, et al. Multifactorial risk factors for mortality after chemotherapy and radiotherapy for non-small cell lung cancer. Radiother Oncol, 2020, 152: 117-125.
34.	Ding T, Ge X, Hao S, et al. Nomogram prediction model for overall survival of late-stage lung cancer patients undergoing iodine-125 particle implantation brachytherapy. J Contemp Brachytherapy, 2024, 16(6): 410-427.
35.	趙祺旸, 趙旭, 張穎, 等. 基于改進DeepSurv模型的肺癌生存分析及其影響因素. 中國醫學物理學雜志, 2025, 42(6): 832-840.Zhao QY, Zhao X, Zhang Y, et al. Improved DeepSurv model for survival analysis in lung cancer and exploration of influencing factors. Chin J Med Phys, 2025, 42(6): 832-840.
36.	王毅誠, 寧泰, 劉心宇, 等. 基于位置感知的多模態肺癌生存預測方法. 計算機科學, 2025, 52(S1): 80-87.Wang YC, Ning T, Liu XY, et al. Position-aware based multi-modality lung cancer survival prediction method. Comput Sci, 2025, 52(S1): 80-87.
37.	楊蒙, 周銀平. 肺癌生存者自我管理行為現狀及相關影響因素分析. 罕少疾病雜志, 2025, 32(7): 70-73.Yang M, Zhou YP. Status quo of self-management behavior in lung cancer survivors and analysis of related influencing factors. J Rare Uncommon Dis, 2025, 32(7): 70-73.
38.	侯麗華, 王冬冬, 閆小龍. 階段性認知行為療法對肺癌化療患者心境、癌因性疲乏及生存質量的影響. 國際精神病學雜志, 2025, 52(2): 638-641.Hou LH, Wang DD, Yan XL. Effects of phased cognitive behavioral therapy on mood, cancer-related fatigue and quality of life in patients with lung cancer undergoing chemotherapy. J Int Psychiatry, 2025, 52(2): 638-641.
39.	李艷芬. 探討康復訓練對肺癌術后患者肺功能及生存質量的臨床價值. 中國現代藥物應用, 2024, 18(22): 159-161.Li YF. Exploring the clinical value of rehabilitation training on lung function and quality of life in postoperative lung cancer patients. Chin J Mod Drug Appl, 2024, 18(22): 159-161.
40.	付浩, 楊永波, 梁震, 等. 亞肺葉切除與肺葉切除在T≤3 cm N0M0合并胸膜侵犯的早期非小細胞肺癌患者中的遠期生存比較. 科學技術與工程, 2025, 25(7): 2732-2740.Fu H, Yang YB, Liang Z, et al. Long-term survival comparison of sublobar resection versus lobectomy in early- stage non-small cell lung cancer patients(T≤3 cm N0M0) with visceral pleural invasion. Sci Technol Eng, 2025, 25(7): 2732-2740.
41.	任磊. 化療同期三維放療治療Ⅳ期非小細胞肺癌 (NSCLC) 的效果及患者生存狀態影響因素分析. 航空航天醫學雜志, 2024, 35(10): 1176-1178.Ren L. Analysis of the efficacy of chemotherapy concurrent with three-dimensional radiotherapy for stage Ⅳnon-small cell lung cancer (NSCLC) and influencing factors of patient survival status. J Aerosp Med, 2024, 35(10): 1176-1178.
42.	郭文靜, 丁廣成, 時昌立, 等. SRS治療時機對EGFR突變型肺腺癌腦轉移患者的生存影響. 醫學研究雜志, 2024, 53(6): 59-64.Guo WJ, Ding GC, Shi CL, et al. Effect of SRS Treatment timing on survival of EGFR-mutated lung adenocarcinoma with brain metastasis. J Med Res, 2024, 53(6): 59-64.

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. CA Cancer J Clin, 2024, 74(3): 229-263.
2. Thandra KC, Barsouk A, Saginala K, et al. Epidemiology of lung cancer. Contemp Oncol (Pozn), 2021, 25(1): 45-52.
3. Fernandez-Felix BM, Lopez-Alcalde J, Roque M, et al. CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models. BMC Med Res Methodol, 2023, 23(1): 44.
4. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
5. 陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 2020, 20(6): 737-744.Chen XP, Zhang Y, Zhuang YY, et al. PROBAST: a tool for assessing risk of bias in the study of diagnostic or prognostic multi-factorial predictive models. Chin J Evid-Based Med, 2020, 20(6): 737-744.
6. PAGE M J, MCKENZIE J E, BOSSUYT P M, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 2021, 372: n71.
7. 左丙麗, 余以珊, 孫雅文. 局限期小細胞肺癌生存期預測模型的建立. 中華腫瘤防治雜志, 2023, 30(6): 360-367.Zuo BL, Yu YS, Sun YW. Establishment of survival prediction model for limited stage small cell lung cancer. Chin J Cancer Prev Treat, 2023, 30(6): 360-367.
8. 潘輝, 張亞雷, 肖大凱, 等. 基于SEER數據庫構建小細胞肺癌術后患者生存預測模型. 腫瘤預防與治療, 2019, 32(6): 516-523.Pan H, Zhang YL, Xiao DK, et al. Nomogram for Prediction of Survival of Postoperative Small Cell Lung Cancer Patients: An Analysis Based on SEER. J Cancer Control Treat, 2019, 32(6): 516-523.
9. 尹玢璨, 辛世超, 張晗, 等. 基于SEER數據庫應用貝葉斯網絡構建亞洲腫瘤患者預后模型——以非小細胞肺癌為例. 數據分析與知識發現, 2017, 1(2): 41-46.Yin BC, Xin SC, Zhang H, et al. Building Asian tumor-patients prognostic model with Bayesian network and SEER database: case study of non-small cell lung cancer. Data Anal Knowl Discov, 2017, 1(2): 41-46.
10. Ang C, Zhang M, Jia H, et al. The systemic inflammation response index(SIRI) predicts survival in advanced non-small cell lung cancer patients undergoing immunotherapy and the construction of a nomogram model. Front Immunol, 2024, 15: 1516737.
11. Zheng K, Zhang J, Xu T, et al. Establishment and validation of a survival prediction model for stage Ⅳ non-small cell lung cancer: a real-world study. Front Immunol, 2025, 16: 1508721.
12. Ji Y, Zhao Q, Liu Y, et al. Establishment of a survival predictive model for patients with two synchronous multiple primary lung cancers: a multicenter cohort analysis. Transl Lung Cancer Res, 2024, 13(9): 2254-2268.
13. Wang QA, Tsai IL, Lin CY, et al. Multivariable model for predicting 5-year survival in patients with EGFR-mutated non-small cell lung cancer treated with EGFR tyrosine kinase inhibitors: a retrospective study. Ther Adv Med Oncol, 2025, 17: 17588359251321901.
14. Li Y, Cao L, Ding Y, et al. Survival prognostic nomogram for young metastatic non-small cell lung cancer: a study of the US SEER database and a Chinese cohort. Front Oncol, 2025, 15: 1502253.
15. Guo H, Nie G, Zhao X, et al. A nomogram for cancer-specific survival of lung adenocarcinoma patients: a SEER based analysis. Surg Open Sci, 2024, 22: 13-23.
16. Xia Y, Qu J, Wang Y, et al. Prognostic nomogram for predicting survival in Asian patients with small-cell lung cancer: a comprehensive population-based study and external verification. Clin Respir J, 2024, 18(11): e70021.
17. Fu R, Jing C, Zhang W. Risk stratification model for foreseeing overall survival in Chinese patients with initially metastatic small-cell lung cancer. Medicine (Baltimore), 2024, 103(42): e40145.
18. He Y, Zhao F, Han Q, et al. Prognostic nomogram for predicting long-term cancer-specific survival in patients with lung carcinoid tumors. BMC Cancer, 2021, 21(1): 141.
19. Zeng Q, Li J, Tan F, et al. Development and validation of a nomogram prognostic model for resected limited-stage small cell lung cancer patients. Ann Surg Oncol, 2021, 28(9): 4893-4904.
20. Guo Y, Li L, Zheng K, et al. Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression. Front Immunol, 2024, 15: 1431150.
21. Zuo ZC, Wang LD, Peng K, et al. Development and Validation of a Nomogram for Predicting the 1-, 3-, and 5-year Survival in Patients with Acinar-predominant Lung Adenocarcinoma. Curr Med Sci, 2022, 42(6): 1178-1185.
22. Chen S, Gao C, Du Q, et al. A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study. J Transl Med, 2020, 18(1): 436.
23. Liang M, Chen M, Singh S, et al. Prognostic Nomogram for Overall Survival in Small Cell Lung Cancer Patients Treated with Chemotherapy: A SEER-Based Retrospective Cohort Study. Adv Ther, 2022, 39(1): 346-359.
24. Zeng Q, Li J, Tan F, et al. Development and validation of a nomogram prognostic model for resected limited-stage small cell lung cancer patients. Ann Surg Oncol, 2021, 28(9): 4893-4904.
25. Liu XY, Zhang X, Zhang Q, et al. The value of CRP-albumin-lymphocyte index (CALLY index) as a prognostic biomarker in patients with non-small cell lung cancer. Support Care Cancer, 2023, 31(9): 533.
26. Miyawaki T, Naito T, Doshita K, et al. Predicting the efficacy of first-line immunotherapy by combining cancer cachexia and tumor burden in advanced non-small cell lung cancer. Thorac Cancer, 2022, 13(14): 2064-2074.
27. Liang J, Guan X, Bao G, et al. Molecular subtyping of small cell lung cancer. Semin Cancer Biol, 2022, 86(Pt 2): 450-462.
28. Gao H, Dang Y, Qi T, et al. Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model. Medicine (Baltimore), 2020, 99(33): e21798.
29. 曹焯, 姚銳, 胡佳培, 等. 非小細胞肺癌早期死亡預測模型的構建和驗證. 醫學信息, 2025, 38(6): 15-21,28.Cao Z, Yao R, Hu JP, et al. Construction and validation of early death prediction model for non-small cell lung cancer. J Med Inf, 2025, 38(6): 15-21,28.
30. Taylor M, Martin GP, Abah U, et al; RESECT-90 collaborators. Multicentre validation of the RESECT-90 prediction model for 90-day mortality after lung resection. Clin Lung Cancer, 2025, 26(1): e73-e80.
31. Cai JS, Dou XM, Li JB, et al. Nomogram to predict cancer specific survival in patients with pathological stage ⅠA non-small cell lung cancer. Semin Thorac Cardiovasc Surg, 2022, 34(3): 1040-1048.
32. Tran QNN, Le MK, Kondo T, et al. A machine learning-based model to predict in-hospital mortality of lung cancer patients: a population-based study of 523 959 cases. Adv Respir Med, 2023, 91(4): 310-323.
33. Defraene G, Dankers FJWM, Price G, et al. Multifactorial risk factors for mortality after chemotherapy and radiotherapy for non-small cell lung cancer. Radiother Oncol, 2020, 152: 117-125.
34. Ding T, Ge X, Hao S, et al. Nomogram prediction model for overall survival of late-stage lung cancer patients undergoing iodine-125 particle implantation brachytherapy. J Contemp Brachytherapy, 2024, 16(6): 410-427.
35. 趙祺旸, 趙旭, 張穎, 等. 基于改進DeepSurv模型的肺癌生存分析及其影響因素. 中國醫學物理學雜志, 2025, 42(6): 832-840.Zhao QY, Zhao X, Zhang Y, et al. Improved DeepSurv model for survival analysis in lung cancer and exploration of influencing factors. Chin J Med Phys, 2025, 42(6): 832-840.
36. 王毅誠, 寧泰, 劉心宇, 等. 基于位置感知的多模態肺癌生存預測方法. 計算機科學, 2025, 52(S1): 80-87.Wang YC, Ning T, Liu XY, et al. Position-aware based multi-modality lung cancer survival prediction method. Comput Sci, 2025, 52(S1): 80-87.
37. 楊蒙, 周銀平. 肺癌生存者自我管理行為現狀及相關影響因素分析. 罕少疾病雜志, 2025, 32(7): 70-73.Yang M, Zhou YP. Status quo of self-management behavior in lung cancer survivors and analysis of related influencing factors. J Rare Uncommon Dis, 2025, 32(7): 70-73.
38. 侯麗華, 王冬冬, 閆小龍. 階段性認知行為療法對肺癌化療患者心境、癌因性疲乏及生存質量的影響. 國際精神病學雜志, 2025, 52(2): 638-641.Hou LH, Wang DD, Yan XL. Effects of phased cognitive behavioral therapy on mood, cancer-related fatigue and quality of life in patients with lung cancer undergoing chemotherapy. J Int Psychiatry, 2025, 52(2): 638-641.
39. 李艷芬. 探討康復訓練對肺癌術后患者肺功能及生存質量的臨床價值. 中國現代藥物應用, 2024, 18(22): 159-161.Li YF. Exploring the clinical value of rehabilitation training on lung function and quality of life in postoperative lung cancer patients. Chin J Mod Drug Appl, 2024, 18(22): 159-161.
40. 付浩, 楊永波, 梁震, 等. 亞肺葉切除與肺葉切除在T≤3 cm N0M0合并胸膜侵犯的早期非小細胞肺癌患者中的遠期生存比較. 科學技術與工程, 2025, 25(7): 2732-2740.Fu H, Yang YB, Liang Z, et al. Long-term survival comparison of sublobar resection versus lobectomy in early- stage non-small cell lung cancer patients(T≤3 cm N0M0) with visceral pleural invasion. Sci Technol Eng, 2025, 25(7): 2732-2740.
41. 任磊. 化療同期三維放療治療Ⅳ期非小細胞肺癌 (NSCLC) 的效果及患者生存狀態影響因素分析. 航空航天醫學雜志, 2024, 35(10): 1176-1178.Ren L. Analysis of the efficacy of chemotherapy concurrent with three-dimensional radiotherapy for stage Ⅳnon-small cell lung cancer (NSCLC) and influencing factors of patient survival status. J Aerosp Med, 2024, 35(10): 1176-1178.
42. 郭文靜, 丁廣成, 時昌立, 等. SRS治療時機對EGFR突變型肺腺癌腦轉移患者的生存影響. 醫學研究雜志, 2024, 53(6): 59-64.Guo WJ, Ding GC, Shi CL, et al. Effect of SRS Treatment timing on survival of EGFR-mutated lung adenocarcinoma with brain metastasis. J Med Res, 2024, 53(6): 59-64.

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Latest ArticlesSurvival prediction models for lung cancer patients: A systematic review and meta-analysis

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