Skip to main content

Optimizing the Size of Peritumoral Region for Assessing Non-Small Cell Lung Cancer Heterogeneity Using Radiomics

  • Conference paper
  • First Online:
Health Information Science (HIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14305))

Included in the following conference series:

  • 432 Accesses

Abstract

Objectives: Radiomics has a novel value in accurately and noninvasively characterizing non-small cell lung cancer (NSCLC), but the role of peritumoral features has not been discussed in depth. This work aims to systematically assess the additional value of peritumoral features by exploring the impact of peritumoral region size. Materials and methods: A total of 370 NSCLC patients who underwent preoperative contrast-enhanced CT scans between October 2017 and September 2021 were retrospectively analyzed. The study was carefully designed with a radiomics pipeline to predict lymphovascular invasion, pleural invasion, and T4 staging. To assess the impact of peritumoral features, tumor regions of interest (ROIs) annotated by two medical experts were automatically expanded to produce peritumoral ROIs of different regional sizes, with edge thicknesses of 1 mm, 3 mm, 5 mm, and 7 mm. In a custom pipeline, prediction models were constructed using peritumoral features with different margin thicknesses and intratumoral features of the primary tumor. Results: Radiomics features combining intratumoral and peritumoral regions were created based on the best features of each ROI. Models incorporating peritumoral features yielded varying degrees of improvement in AUCs compared to models using only intratumoral features. The choice of peritumoral size may impact the degree of improvement in radiomics analysis. Conclusions: The integration of peritumoral features has shown potential for improving the predictive value of radiomics. However, selecting an appropriate peritumoral region size is constrained by various factors such as clinical issues, imaging modalities, and ROI annotations. Therefore, future radiomics studies should consider these factors and optimize peritumoral features to cater to specific applications.

X. Zhang, G. Zhang, X. Qiu, J. Yin, W. Tan, X. Yin, H. Yang, K. Wang, Y. Zhang—All authors contribute equally to this work.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alvi, A.M., Siuly, S., Wang, H.: A long short-term memory based framework for early detection of mild cognitive impairment from EEG signals. IEEE Trans. Emerg. Top. Comput. Intell. 7, 375–388 (2023). https://api.semanticscholar.org/CorpusID:250397486

  2. Braman, N., et al.: Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for HER2 (ERBB2)-positive breast cancer. JAMA Netw. Open 2(4), e192561–e192561 (2019)

    Article  Google Scholar 

  3. Chen, Q.L., et al.: Intratumoral and peritumoral radiomics nomograms for the preoperative prediction of lymphovascular invasion and overall survival in non-small cell lung cancer. Eur. Radiol. 33(2), 947–958 (2023)

    Article  Google Scholar 

  4. Chetan, M.R., Gleeson, F.V.: Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur. Radiol. 31(2), 1049–1058 (2021)

    Article  Google Scholar 

  5. Chong, H.H., et al.: Multi-scale and multi-parametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma \(<\)= 5 cm. Eur. Radiol. 31(7), 4824–4838 (2021)

    Article  Google Scholar 

  6. Cong, M.D., et al.: Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer. Lung Cancer 139, 73–79 (2020)

    Article  Google Scholar 

  7. Cortiula, F., et al.: Immunotherapy in unresectable stage iii non-small-cell lung cancer: state of the art and novel therapeutic approaches. Ann. Oncol. 33(9), 893–908 (2022)

    Article  Google Scholar 

  8. Deniz, E., Sobahi, N., Omar, N., Şengur, A., Acharya, U.R.: Automated robust human emotion classification system using hybrid EEG features with ICBrainDB dataset. Health Inf. Sci. Syst. 10, 1–14 (2022). https://api.semanticscholar.org/CorpusID:253422345

  9. Dercle, L., et al.: Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics. Clin. Cancer Res. 26(9), 2151–2162 (2020)

    Article  Google Scholar 

  10. Du, J., Michalska, S., Subramani, S., Wang, H., Zhang, Y.: Neural attention with character embeddings for hay fever detection from Twitter. Health Inf. Sci. Syst. 7, 1–7 (2019). https://api.semanticscholar.org/CorpusID:204456482

  11. Fan, Y., et al.: Preoperative MRI-based radiomics of brain metastasis to assess T790M resistance mutation after EGFR-TKI treatment in NSCLC. J. Magn. Reson. Imaging 57, 1778–1787 (2022)

    Article  Google Scholar 

  12. Forouzannezhad, P., et al.: Multitask learning radiomics on longitudinal imaging to predict survival outcomes following risk-adaptive chemoradiation for non-small cell lung cancer. Cancers 14(5), 1228 (2022)

    Article  Google Scholar 

  13. Hu, H., Li, J., Wang, H., Daggard, G., Shi, M.: A maximally diversified multiple decision tree algorithm for microarray data classification (2006). https://api.semanticscholar.org/CorpusID:12168114

  14. Hu, Y.H., et al.: Assessment of intratumoral and peritumoral computed tomography radiomics for predicting pathological complete response to neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma. JAMA Netw. Open 3(9), e2015927–e2015927 (2020)

    Article  MathSciNet  Google Scholar 

  15. Huang, Y., et al.: Preoperative prediction of mediastinal lymph node metastasis in non-small cell lung cancer based on 18F-FDG PET/CT radiomics. Clin. Radiol. 78(1), 8–17 (2023)

    Article  MathSciNet  Google Scholar 

  16. Jiang, H., Zhou, R., Zhang, L., Wang, H., Zhang, Y.: Sentence level topic models for associated topics extraction. World Wide Web 22, 1–16 (2018). https://api.semanticscholar.org/CorpusID:53085050

  17. Jiang, L., et al.: Radiogenomic analysis reveals tumor heterogeneity of triple-negative breast cancer. Cell Rep. Med. 3(7) (2022)

    Google Scholar 

  18. Lee, J., Park, J.S., Wang, K., Feng, B., Tennant, M., Kruger, E.: The use of telehealth during the coronavirus (COVID-19) pandemic in oral and maxillofacial surgery - a qualitative analysis. EAI Endorsed Trans. Scalable Inf. Syst. 9, 2 (2021). https://api.semanticscholar.org/CorpusID:244846117

  19. Li, J.Q., et al.: ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features. Eur. Radiol. 33(2), 893–903 (2023)

    Article  Google Scholar 

  20. Liu, D., et al.: Radiogenomics to characterize the immune-related prognostic signature associated with biological functions in glioblastoma. Eur. Radiol. 33(1), 209–220 (2023)

    Article  Google Scholar 

  21. Mao, N., et al.: Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on contrast-enhanced spectral mammography. Eur. Radiol. 32(5), 3207–3219 (2022)

    Article  MathSciNet  Google Scholar 

  22. Ninatti, G., Kirienko, M., Neri, E., Sollini, M., Chiti, A.: Imaging-based prediction of molecular therapy targets in NSCLC by radiogenomics and AI approaches: a systematic review. Diagnostics 10(6), 359 (2020)

    Article  Google Scholar 

  23. Pandey, D., Wang, H., Yin, X., Wang, K.N., Zhang, Y., Shen, J.: Automatic breast lesion segmentation in phase preserved DCE-MRIS. Health Inf. Sci. Syst. 10, 9 (2022). https://api.semanticscholar.org/CorpusID:248924735

  24. Pang, X., Ge, Y.F., Wang, K.N., Traina, A.J.M., Wang, H.: Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm. Health Inf. Sci. Syst. 11, 30 (2023). https://api.semanticscholar.org/CorpusID:259277247

  25. Rehman, O.M.H., Al-Busaidi, A.M., Ahmed, S., Ahsan, K.: Ubiquitous healthcare system: architecture, prototype design and experimental evaluations. EAI Endorsed Trans. Scalable Inf. Syst. 9, 6 (2018). https://api.semanticscholar.org/CorpusID:245777204

  26. Sarki, R., Ahmed, K., Wang, H., Zhang, Y., Wang, K.N.: Convolutional neural network for multi-class classification of diabetic eye disease. EAI Endorsed Trans. Scalable Inf. Syst. 9, 5 (2018). https://api.semanticscholar.org/CorpusID:245295045

  27. Siddiqui, S.A., Fatima, N., Ahmad, A.: Chest X-ray and CT scan classification using ensemble learning through transfer learning. EAI Endorsed Trans. Scalable Inf. Syst. 9, e8 (2022). https://api.semanticscholar.org/CorpusID:249557133

  28. Singh, R., et al.: Antisocial behavior identification from twitter feeds using traditional machine learning algorithms and deep learning. ICST Trans. Scalable Inf. Syst. (2023). https://api.semanticscholar.org/CorpusID:258671645

  29. Song, F., et al.: Radiomics feature analysis and model research for predicting histopathological subtypes of non-small cell lung cancer on CT images: a multi-dataset study. Med. Phys. 50, 4351–4365 (2023)

    Article  Google Scholar 

  30. Sun, Y., Li, J., Xu, Z., Liu, Y., Hou, L., Huang, Z.Z.: Exploring relationship between emotion and probiotics with knowledge graphs. Health Inf. Sci. Syst. 10, 1–11 (2022). https://api.semanticscholar.org/CorpusID:252182735

  31. Tomaszewski, M.R., Gillies, R.J.: The biological meaning of radiomic features. Radiology 298(3), 505–516 (2021)

    Article  Google Scholar 

  32. Vaidya, P., et al.: Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade. J. Immunother. Cancer 8(2), 11 (2020)

    Article  Google Scholar 

  33. Vicini, S., et al.: A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers. Radiol. Med. 127(8), 819–836 (2022)

    Article  Google Scholar 

  34. Vimalachandran, P., Liu, H., Lin, Y., Ji, K., Wang, H., Zhang, Y.: Improving accessibility of the Australian my health records while preserving privacy and security of the system. Health Inf. Sci. Syst. 8, 1–9 (2020). https://api.semanticscholar.org/CorpusID:222233812

  35. Wang, M.N., Herbst, R.S., Boshoff, C.: Toward personalized treatment approaches for non-small-cell lung cancer. Nat. Med. 27(8), 1345–1356 (2021)

    Article  Google Scholar 

  36. Wang, T.T., et al.: Radiomics for survival risk stratification of clinical and pathologic stage IA pure-solid non-small cell lung cancer. Radiology 302(2), 425–434 (2022)

    Article  Google Scholar 

  37. Wu, L.Y., Lou, X.J., Kong, N., Xu, M.S., Gao, C.: Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review. Eur. Radiol. 33(3), 2105–2117 (2023)

    Article  Google Scholar 

  38. Wu, Y.J., Wu, F.Z., Yang, S.C., Tang, E.K., Liang, C.H.: Radiomics in early lung cancer diagnosis: from diagnosis to clinical decision support and education. Diagnostics 12(5), 1064 (2022)

    Article  Google Scholar 

  39. Xie, N., et al.: Peritumoral and intratumoral texture features based on multiparametric MRI and multiple machine learning methods to preoperatively evaluate the pathological outcomes of pancreatic cancer. J. Magn. Reson. Imaging 58(2), 379–391 (2022)

    Article  MathSciNet  Google Scholar 

  40. Xu, H., et al.: Intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI for preoperative prediction of intraductal component in invasive breast cancer. Eur. Radiol. 32(7), 4845–4856 (2022)

    Article  MathSciNet  Google Scholar 

  41. Yin, J., Tang, M., Cao, J., Wang, H., You, M., Lin, Y.: Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web 25, 401–423 (2021). https://api.semanticscholar.org/CorpusID:237746297

  42. You, M., Yin, J., Wang, H., Cao, J., Miao, Y.: A minority class boosted framework for adaptive access control decision-making. In: WISE (2021). https://api.semanticscholar.org/CorpusID:244852711

  43. You, M., et al.: A knowledge graph empowered online learning framework for access control decision-making. World Wide Web 26, 827–848 (2022). https://api.semanticscholar.org/CorpusID:250007362

  44. Yu, Y.X., et al.: GD-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma. Eur. Radiol. 32(2), 959–970 (2022)

    Article  MathSciNet  Google Scholar 

  45. Zhang, X.B., et al.: Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature. Eur. Radiol. 33(2), 825–835 (2023)

    Article  Google Scholar 

  46. Zhang, X.P., et al.: Deep learning with radiomics for disease diagnosis and treatment: challenges and potential. Front. Oncol. 12, 773840 (2022)

    Article  Google Scholar 

  47. Zhang, X.P., et al.: Prospective clinical research of radiomics and deep learning in oncology: a translational review. Crit. Rev. Oncol. Hematol. 179, 103823 (2022)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Overseas Joint Training Program and the Innovative Research Grant Program (Grant No. 2022GDJC-D20) for Postgraduates of Guangzhou University, as well as by the National Natural Science Foundation of China (Grant No. 61971118) and the Natural Science Foundation of Guangdong (Grant No. 2022A1515010102).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Hong Yang or Yanchun Zhang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, X. et al. (2023). Optimizing the Size of Peritumoral Region for Assessing Non-Small Cell Lung Cancer Heterogeneity Using Radiomics. In: Li, Y., Huang, Z., Sharma, M., Chen, L., Zhou, R. (eds) Health Information Science. HIS 2023. Lecture Notes in Computer Science, vol 14305. Springer, Singapore. https://doi.org/10.1007/978-981-99-7108-4_26

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-7108-4_26

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7107-7

  • Online ISBN: 978-981-99-7108-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics