Analysis of diagnostic valne of stage Ⅰ poorly differentiated lung adenocarcinoma using deep learning model

CHEN Jie; GAO Zeqiang; GAO Jie; TANG Siyuan; DAI Ping; XIANG Gang

doi:10.16766/j.cnki.issn.1674-4152.004304

Volume 23 Issue 12

Dec. 2025

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of General Practice > 2025 > 23(12): 2114-2117

CHEN Jie, GAO Zeqiang, GAO Jie, TANG Siyuan, DAI Ping, XIANG Gang. Analysis of diagnostic valne of stage Ⅰ poorly differentiated lung adenocarcinoma using deep learning model[J]. Chinese Journal of General Practice, 2025, 23(12): 2114-2117. doi: 10.16766/j.cnki.issn.1674-4152.004304

Citation:

CHEN Jie, GAO Zeqiang, GAO Jie, TANG Siyuan, DAI Ping, XIANG Gang. Analysis of diagnostic valne of stage Ⅰ poorly differentiated lung adenocarcinoma using deep learning model[J]. Chinese Journal of General Practice, 2025, 23(12): 2114-2117. doi: 10.16766/j.cnki.issn.1674-4152.004304

Citation:

CHEN Jie, GAO Zeqiang, GAO Jie, TANG Siyuan, DAI Ping, XIANG Gang. Analysis of diagnostic valne of stage Ⅰ poorly differentiated lung adenocarcinoma using deep learning model[J]. Chinese Journal of General Practice, 2025, 23(12): 2114-2117. doi: 10.16766/j.cnki.issn.1674-4152.004304

PDF( 704 KB)

Analysis of diagnostic valne of stage Ⅰ poorly differentiated lung adenocarcinoma using deep learning model

doi: 10.16766/j.cnki.issn.1674-4152.004304

CHEN Jie¹
, GAO Zeqiang²
, GAO Jie³
, TANG Siyuan⁴
, DAI Ping⁵
, XIANG Gang^{5
,
,}

1.
Department of Radiology, Luzhou Psychiatric Hospital, Luzhou, Sichuan 646000, China

Funds:

2024LHMS06006

S202410130004

Received Date: 2024-10-28
Available Online: 2026-03-13

Abstract

Abstract

Objective Pulmonary adenocarcinoma high-grade components include micropapillary type and solid type, and high-grade components ≥20% are defined as poorly differentiated and are independent predictors of poor prognosis. Lobectomy is recommended. In this study, four kinds of deep learning models were used to predict poorly differentiated adenocarcinoma, and the diagnostic efficiency of each model was compared to find the best model to improve the prediction accuracy of poorly differentiated adenocarcinoma. Methods Retrospective analysis of 253 lung adenocarcinoma lesions confirmed by pathology at the Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University from October 2021 to March 2024. The CT images were preprocessed and abnormal data screened, then the training, validation, and EfficientNet test sets were divided in the ratio of 8∶1∶1 and fed to the four models of ResNet, MobileNet, DenseNet, and EfficientNet for high-level component prediction. Results The AUC values of the four models, ResNet, MobileNet, DenseNet, and EfficientNet, are 0.757, 0.872, 0.877, and 0.812, respectively. DenseNet showed excellent performance in this task. Accuracy, Precision, Recall, and F1-Score were 84.97%, 84.26%, 83.28%, and 84.67%. Conclusion Four kinds of deep learning models have a good predictive effect on high-grade components of lung adenocarcinoma, and the DenseNet model has higher predictive accuracy.
- Lung adenocarcinoma,
- Poorly differentiated,
- Deep learning,
- Model

FullText(HTML)

References(22)

References

[1]	NICHOLSON A G, TSAO M S, BEASLEY M B, et al. The 2021 WHO classification of lung tumors: impact of advances since 2015[J]. J Thorac Oncol, 2022, 17(3): 362-387. doi: 10.1016/j.jtho.2021.11.003
[2]	WENG C F, HUANG C J, HUANG S H, et al. New International Association for the Study of Lung Cancer (IASLC) pathology committee grading system for the prognostic outcome of advanced lung adenocarcinoma[J]. Cancers, 2020, 12(11): 1-14.
[3]	E H R, WU J Q, REN Y J, et al. The IASLC grading system for invasive pulmonary adenocarcinoma: a potential prognosticator for patients receiving neoadjuvant therapy[J]. Ther Advmed Oncol, 2023, 15: 1-13. DOI: 10.1177/17588359221148028.
[4]	谢忠海, 李鸿伟, 臧金, 等. 非小细胞肺癌根治术患者术后临床特征调查及预后影响因素分析[J]. 中华全科医学, 2022, 20(11): 1860-1862. doi: 10.16766/j.cnki.issn.1674-4152.002720 XIE Z H, LI H W, ZANG J, et al. Investigation of clinical characteristics and prognostic factors in patientswith non-small cell lung cancer after radical resection[J]. Chin J Gen Pract, 2022, 20(11): 1860-1862. doi: 10.16766/j.cnki.issn.1674-4152.002720
[5]	BOKYUNG AHN S Y, DEOKHOON K I M. Clinicopathologic and genomic features of high-grade pattern and their subclasses in lung adenocarcinoma[J]. Lung cancer, 2022, 170: 176-184. doi: 10.1016/j.lungcan.2022.07.003
[6]	YANG Z B, DONG H, FU C L, et al. A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study[J]. Front Oncol, 2024, 14: 1289555. DOI: 10.3389/fonc.2024.1289555.
[7]	陈璐, 杨虹. 联合CT纹理分析和高分辨率CT图像特征预测肺腺癌组织学分化程度的研究[J]. 临床放射学杂志, 2021, 40(4): 707-711. CHEN L, YANG H. Prediction of histological differentiation degree of lung adenocarcinoma based on high resolution chest CT texture analysis and lmaging findings[J]. J Clin Radiology, 2021, 40(4): 707-711.
[8]	CHEN M, COPLEY S, VIOLA P, et al. Radiomics and artificial intelligence for precision medicine in lung cancer treatment[J]. Semin cancer biol, 2023, 93: 97-113. doi: 10.1016/j.semcancer.2023.05.004
[9]	CHOI Y, AUM J, LEE S, et al. Deep learning analysis of CT images reveals high-grade pathological features to predict survival in lung adenocarcinoma[J]. Cancers, 2021, 13(16): 1-17.
[10]	黎超, 陈优美, 段亚妮, 等. 生成式人工智能在生成影像学报告方面的表现评估[J]. 新医学, 2024, 55(11): 853-860. LI C, CHEN Y M, DUAN Y N, et al. Evaluation of the performance of generative artificial intelligence in generating imaging reports[J]. Journal of New Medicine, 2024, 55(11): 853-860.
[11]	国家卫生健康委办公厅. 原发性肺癌诊疗指南(2022年版)[J]. 协和医学杂志, 2022, 13(4): 549-570. General Office of the National Health Commission. Diagnosis and treatment guidelines for primary lung cancer (2022 Edition)[J]. Med J Peking Union Med Coll Hosp, 2022, 13(4): 549-570.
[12]	MARAPPAN S, MUJIB M D, SIDDIQUI A A, et al. Lightweight deep learning classification model for identifying low-resolution CT images of lung cancer[J]. Comput Intel Neurosc, 2022: 1-10. DOI: 10.1155/2022/3836539.
[13]	黄碧云, 丁佳, 李仕广, 等. 深度学习重建辅助压缩感知对乳腺T2W脂肪抑制序列图像质量的影响[J]. 贵州医科大学学报, 2024, 49(8): 1191-1197. HUANG B Y, DING J, LI S G, et al. Effect of deep learning reconstruction-assisted compressed sensing on theimage quality of breast T2W fat-sat sequences[J]. J Guizhou Med Univ, 2024, 49(8): 1191-1197.
[14]	刘德真, 李圆媛. 基于深度学习和多组学数据的肺腺癌分期预测研究[J]. 武汉工程大学学报, 2024, 46(2): 190-196. LIU D Z, LI Y Y. Stage prediction of lung adenocarcinoma based on deep learning andmultiomics data[J]. J Wuhan Inst Technol, 2024, 46(2): 190-196.
[15]	张俊杰, 郝李刚, 许茜, 等. 基于临床及CT特征构建预测肺浸润性黏液腺癌的机器学习模型[J]. 中华全科医学, 2023, 21(1): 6-9, 49. doi: 10.16766/j.cnki.issn.1674-4152.002799 ZHANG J J, HAO L G, XV Q, et al. CT-derived model for the diagnosis of pulmonary invasive mucinous adenocarcinoma by machine learning[J]. Chin J Gen Pract, 2023, 21(1): 6-9. doi: 10.16766/j.cnki.issn.1674-4152.002799
[16]	UDDIN J. Attention-based densenet for lung cancer classification using CT scan and histopathological images[J]. Designs, 2024, 8(2): 27. doi: 10.3390/designs8020027
[17]	ZHOU T, HUO B Q, LU H L, et al. NSCR-based denseNet for lung tumor recognition using chest CT image[J]. Biomed Res Int, 2020: 6636321. DOI: 10.1155/2020/6636321.
[18]	黄超, 王涛, 邱志新, 等. 不同病理类型肺腺癌临床和影像特征及预后分析[J]. 现代肿瘤医学, 2022, 30(14): 2548-2553. HUANG C, WANG T, QIU Z X, et al. Clinical and imaging characteristics of lung adenocarcinoma of differentpathological types and analysis for prognosis[J]. J Modern Oncol, 2022, 30(14): 2548-2553.
[19]	MUTHULAKSHMI M, VENKATESAN K, HARIGARAN R, et al. Comparative study of efficientNet and mobileNet models for lung cancer classification using chest CT scan images: 2024 second international conference on emerging trends in information technology and engineering (ICETITE)[C]. 2024. DOI: 10.1109/ic-ETITE58242.2024.10493412.
[20]	RAZA R, ZULFIQAR F, KHAN M O, et al. Lung-effNet: lung cancer classification using eficientNet from CT-scan images[J]. Eng Appl Artif Intel, 2023, 126: 106902. DOI: 10.1016/j.engappai.2023.106902.
[21]	ZHANG C, AAMIR M, GUAN Y, et al. Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN[J]. Cloud Comput, 2024, 13(1): 1-10.
[22]	KARIMULLAH S, KHAN M, SHAIK F, et al. An integrated method for detecting lung cancer via CT scanning via optimization, deep learning, and IoT data transmission[J]. Front Oncol, 2024, 14: 1435041. DOI: 10.3389/fonc.2024.1435041.

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(3) / Tables(1)

Get Citation

PDF

XML

Article Metrics

Article views (14) PDF downloads(0)

Analysis of diagnostic valne of stage Ⅰ poorly differentiated lung adenocarcinoma using deep learning model

doi: 10.16766/j.cnki.issn.1674-4152.004304

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Analysis of diagnostic valne of stage Ⅰ poorly differentiated lung adenocarcinoma using deep learning model

doi: 10.16766/j.cnki.issn.1674-4152.004304

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content