Skip to main content

Advertisement

SpringerLink
Log in

Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes

  • Chest
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Near-pure lung adenocarcinoma (ADC) subtypes demonstrate strong stratification of radiomic values, providing basic information for pathological subtyping. We sought to predict the presence of high-grade (micropapillary and solid) components in lung ADCs using quantitative image analysis with near-pure radiomic values.

Methods

Overall, 103 patients with lung ADCs of various histological subtypes were enrolled for 10-repetition, 3-fold cross-validation (cohort 1); 55 were enrolled for testing (cohort 2). Histogram and textural features on computed tomography (CT) images were assessed based on the “near-pure” pathological subtype data. Patch-wise high-grade likelihood prediction was performed for each voxel within the tumour region. The presence of high-grade components was then determined based on a volume percentage threshold of the high-grade likelihood area. To compare with quantitative approaches, consolidation/tumour (C/T) ratio was evaluated on CT images; we applied radiological invasiveness (C/T ratio > 0.5) for the prediction.

Results

In cohort 1, patch-wise prediction, combined model (C/T ratio and patch-wise prediction), whole-lesion-based prediction (using only the “near-pure”-based prediction model), and radiological invasiveness achieved a sensitivity and specificity of 88.00 ± 2.33% and 75.75 ± 2.82%, 90.00 ± 0.00%, and 77.12 ± 2.67%, 66.67% and 90.41%, and 90.00% and 45.21%, respectively. The sensitivity and specificity, respectively, for cohort 2 were 100.0% and 95.35% using patch-wise prediction, 100.0% and 95.35% using combined model, 75.00% and 95.35% using whole-lesion-based prediction, and 100.0% and 69.77% using radiological invasiveness.

Conclusion

Using near-pure radiomic features and patch-wise image analysis demonstrated high levels of sensitivity and moderate levels of specificity for high-grade ADC subtype-detecting.

Key Points

• The radiomic values extracted from lung adenocarcinoma with “near-pure” histological subtypes provide useful information for high-grade (micropapillary and solid) components detection.

• Using near-pure radiomic features and patch-wise image analysis, high-grade components of lung adenocarcinoma can be predicted with high sensitivity and moderate specificity.

• Using near-pure radiomic features and patch-wise image analysis has potential role in facilitating the prediction of the presence of high-grade components in lung adenocarcinoma prior to surgical resection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

ADC:

Adenocarcinoma

ATS:

American Thoracic Society

C/T:

Consolidation/tumour

ERS:

European Respiratory Society

GLCM:

Grey level co-occurrence matrix

GLRLM:

Grey level run length matrix

GLSZM:

Grey level size zone matrix

IASLC:

International Association for the Study of Lung Cancer

References

  1. Tsao MS, Marguet S, Le Teuff G et al (2015) Subtype classification of lung adenocarcinoma predicts benefit from adjuvant chemotherapy in patients undergoing complete resection. J Clin Oncol 33:3439–3446. https://doi.org/10.1200/JCO.2014.58.8335

    Article  CAS  PubMed  Google Scholar 

  2. Travis WD, Brambilla E, Noguchi M et al (2011) International association for the study of lung Cancer/American thoracic Society/European respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 6:244–285. https://doi.org/10.1097/JTO.0b013e318206a221

    Article  PubMed  PubMed Central  Google Scholar 

  3. Cha MJ, Lee HY, Lee KS et al (2014) Micropapillary and solid subtypes of invasive lung adenocarcinoma: clinical predictors of histopathology and outcome. J Thorac Cardiovasc Surg 147:921–928. https://doi.org/10.1016/j.jtcvs.2013.09.045

    Article  PubMed  Google Scholar 

  4. Lee HY, Lee SW, Lee KS et al (2015) Role of CT and PET imaging in predicting tumor recurrence and survival in patients with lung adenocarcinoma: a comparison with the international association for the study of lung Cancer/American thoracic Society/European respiratory society classification. J Thorac Oncol 10:1785–1794. https://doi.org/10.1097/JTO.0000000000000689

    Article  CAS  PubMed  Google Scholar 

  5. Lee HJ, Lee CH, Jeong YJ et al (2012) IASLC/ATS/ERS international multidisciplinary classification of lung adenocarcinoma novel concepts and radiologic implications. J Thorac Imaging 27:340–353. https://doi.org/10.1097/RTI.0b013e3182688d62

    Article  PubMed  Google Scholar 

  6. Lederlin M, Puderbach M, Muley T et al (2013) Correlation of radio-and histomorphological pattern of pulmonary adenocarcinoma. Eur Respir J 41:943–951. https://doi.org/10.1183/09031936.00056612

  7. Lee HJ, Kim YT, Kang CH et al (2013) Epidermal growth factor receptor mutation in lung adenocarcinomas: relationship with CT characteristics and histologic subtypes. Radiology 268:254–264. https://doi.org/10.1148/radiol.13112553

    Article  PubMed  Google Scholar 

  8. Song SH, Park H, Lee G et al (2017) Imaging phenotyping using radiomics to predict micropapillary pattern within lung adenocarcinoma. J Thorac Oncol 12:624–632. https://doi.org/10.1016/j.jtho.2016.11.2230

  9. O’Connor JP, Rose CJ, Waterton JC, Carano RA, Parker GJ, Jackson A (2015) Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res 21:249–257

    Article  Google Scholar 

  10. Motoi N, Szoke J, Riely GJ et al (2008) Lung adenocarcinoma: modification of the 2004 WHO mixed subtype to include the major histologic subtype suggests correlations between papillary and micropapillary adenocarcinoma subtypes, EGFR mutations and gene expression analysis. Am J Surg Pathol 32:810–827

    Article  Google Scholar 

  11. Choi ER, Lee HY, Jeong JY et al (2016) Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma. Oncotarget 7:67302–67306

    Article  Google Scholar 

  12. Yang SM, Chen LW, Wang HJ et al (2018) Extraction of radiomic values from lung adenocarcinoma with near-pure subtypes in the International Association for the Study of Lung Cancer/the American Thoracic Society/the European Respiratory Society (IASLC/ATS/ERS) classification. Lung Cancer 119:56–63. https://doi.org/10.1016/j.lungcan.2018.03.004

    Article  PubMed  Google Scholar 

  13. Katsumata S, Aokage K, Nakasone S et al (2019) Radiologic criteria in predicting pathologic less invasive lung cancer according to TNM. 8th Edition. Clin Lung Cancer 20(2):e163–e170. https://doi.org/10.1016/j.cllc.2018.11.001

    Article  PubMed  Google Scholar 

  14. Kouwenhoven E, Giezen M, Struikmans H (2009) Measuring the similarity of target volume delineations independent of the number of observers. Phys Med Biol 54(9):2863. https://doi.org/10.1088/0031-9155/54/9/018

    Article  PubMed  Google Scholar 

  15. Goldstraw P, Chansky K, Crowley J et al (2016) The IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol 11:39–51. https://doi.org/10.1016/j.jtho.2015.09.009

    Article  PubMed  Google Scholar 

  16. Ito H, Nakayama H, Murakami S et al (2017) Does the histologic predominance of pathological stage IA lung adenocarcinoma influence the extent of resection? Gen Thorac Cardiovasc Surg 65:512–518. https://doi.org/10.1007/s11748-017-0790-0

    Article  PubMed  Google Scholar 

  17. Nitadori JI, Bograd AJ, Kadota K et al (2013) Impact of micropapillary histologic subtype in selecting limited resection vs lobectomy for lung adenocarcinoma of 2 cm or smaller. J Natl Cancer Inst 105:1212–1220. https://doi.org/10.1093/jnci/djt166

    Article  PubMed  Google Scholar 

  18. Yutaka Y, Sonobe M, Kawaguchi A et al (2018) Prognostic impact of preoperative comorbidities in geriatric patients with early-stage lung cancer: Significance of sublobar resection as a compromise procedure. Lung Cancer 125:192–197. https://doi.org/10.1016/j.lungcan.2018.09.023

    Article  PubMed  Google Scholar 

  19. Okami J (2019) Treatment strategy and decision-making for elderly surgical candidates with early lung cancer. J Thorac Dis 11:S987–S997. https://doi.org/10.21037/jtd.2019.04.01

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhao ZR, Lau RW, Long H et al (2018) Novel method for rapid identification of micropapillary or solid components in early-stage lung adenocarcinoma. J Thorac Cardiovasc Surg 156:2310–2318. https://doi.org/10.1038/modpathol.2015.71

    Article  PubMed  Google Scholar 

  21. Zhou QJ, Zheng ZC, Zhu YQ et al (2017) Tumor invasiveness defined by IASLC/ATS/ERS classification of ground-glass nodules can be predicted by quantitative CT parameters. J Thorac Dis 9:1190–1200. https://doi.org/10.21037/jtd.2017.03.170

    Article  PubMed  PubMed Central  Google Scholar 

  22. Liu Y, Sun H, Zhou F et al (2017) Imaging features of TSCT predict the classification of pulmonary preinvasive lesion, minimally and invasive adenocarcinoma presented as ground glass nodules. Lung Cancer 108:192–197. https://doi.org/10.1016/j.lungcan.2017.03.011

    Article  PubMed  Google Scholar 

  23. Ko JP, Suh J, Ibidapo O et al (2016) Lung adenocarcinoma: correlation of quantitative CT findings with pathologic findings. Radiology 280:931–939. https://doi.org/10.1148/radiol.2016142975

    Article  PubMed  Google Scholar 

  24. Bae JM, Jeong JY, Lee HY et al (2017) Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images. Oncotarget 8(1):523–535. https://doi.org/10.18632/oncotarget.13476

    Article  PubMed  Google Scholar 

  25. Hu SY, Hsieh MS, Hsu HH et al (2018) Correlation of tumor spread through air spaces and clinicopathological characteristics in surgically resected lung adenocarcinomas. Lung Cancer 126:189–193. https://doi.org/10.1016/j.lungcan.2018.11.003

    Article  PubMed  Google Scholar 

Download references

Funding

This work was supported by the National Taiwan University Hospital, Hsin-Chu Branch, Taiwan (grant number 108-HCH061), and the Ministry of Science and Technology, Taiwan (grant number 107-2221-E-002-074-MY3, 107-2221-E-002-080-MY3).

Author information

Author notes

  1. Li-Wei Chen and Shun-Mao Yang contributed equally to this work.

Authors and Affiliations

  1. Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan

    Li-Wei Chen, Shun-Mao Yang, Hao-Jen Wang, Yi-Chang Chen & Chung-Ming Chen

  2. Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, No. 2, Sec.1, Shengyi Rd., Zhubei City, Hsinchu County, 302, Taiwan

    Shun-Mao Yang

  3. Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei, 100, Taiwan

    Yi-Chang Chen

  4. Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei, 100, Taiwan

    Mong-Wei Lin

  5. Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei, 100, Taiwan

    Min-Shu Hsieh

  6. Department of Pathology, National Taiwan University Hospital, Hsin-Chu Branch, No. 25, Lane 442, Sec.1, Jingguo Rd., Hsinchu, 300, Taiwan

    Hsiang-Lin Song

  7. Department of Surgery, National Taiwan University Hospital, Hsin-Chu Branch, No. 25, Lane 442, Sec.1, Jingguo Rd., Hsinchu, 300, Taiwan

    Huan-Jang Ko

  8. Department of Medical Imaging, National Taiwan University Hospital, 7 Chung-Shan South Road, Taipei, Taiwan

    Yeun-Chung Chang

Authors
  1. Li-Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shun-Mao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao-Jen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi-Chang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mong-Wei Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Min-Shu Hsieh
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hsiang-Lin Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Huan-Jang Ko
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Chung-Ming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yeun-Chung Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Chung-Ming Chen or Yeun-Chung Chang.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Chung-Ming Chen.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

The “near-pure” lung adenocarcinoma subtypes data has been previously reported in LUNG CANCER 119 (2018) 56-63 [9].

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

ESM 1

(DOCX 1111 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, LW., Yang, SM., Wang, HJ. et al. Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes. Eur Radiol 31, 5127–5138 (2021). https://doi.org/10.1007/s00330-020-07570-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-07570-6

Keywords

Search

Navigation