Skip to main content

Advertisement

SpringerLink
Log in

Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer

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

Abstract

Objectives

To investigate if delta-radiomics features have the potential to predict the major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) patients.

Methods

Two hundred six stage IIA-IIIB NSCLC patients from three institutions (Database1 = 164; Database2 = 21; Database3 = 21) who received neoadjuvant chemoimmunotherapy and surgery were included. Patients in Database1 were randomly assigned to the training dataset and test dataset, with a ratio of 0.7:0.3. Patients in Database2 and Database3 were used as two independent external validation datasets. Contrast-enhanced CT scans were obtained at baseline and before surgery. The delta-radiomics features were defined as the relative net change of radiomics features between baseline and preoperative. The delta-radiomics model and pre-treatment radiomics model were established. The performance of Immune-Related Response Evaluation Criteria in Solid Tumors (iRECIST) for predicting MPR was also evaluated.

Results

Half of the patients (106/206, 51.5%) showed MPR after neoadjuvant chemoimmunotherapy. For predicting MPR, the delta-radiomics model achieved a satisfying area under the curves (AUCs) values of 0.768, 0.732, 0.833, and 0.716 in the training, test, and two external validation databases, respectively, which showed a superior predictive performance than the pre-treatment radiomics model (0.644, 0.616, 0.475, and 0.608). Compared with iRECIST criteria (0.624, 0.572, 0.650, and 0.466), a mixed model that combines delta-radiomics features and iRECIST had higher AUC values for MPR prediction of 0.777, 0.761, 0.850, and 0.670 in four sets.

Conclusion

The delta-radiomics model demonstrated superior diagnostic performance compared to pre-treatment radiomics model and iRECIST criteria in predicting MPR preoperatively in neoadjuvant chemoimmunotherapy for stage II-III NSCLC.

Clinical relevance statement

Delta-radiomics features based on the relative net change of radiomics features between baseline and preoperative CT scans serve a vital support tool in accurately identifying responses to neoadjuvant chemoimmunotherapy, which can help physicians make more appropriate treatment decisions.

Key Points

• The performances of pre-treatment radiomics model and iRECIST model in predicting major pathological response of neoadjuvant chemoimmunotherapy were unsatisfactory.

• The delta-radiomics features based on relative net change of radiomics features between baseline and preoperative CT scans may be used as a noninvasive biomarker for predicting major pathological response of neoadjuvant chemoimmunotherapy.

• Combining delta-radiomics features and iRECIST can further improve the predictive performance of responses to neoadjuvant chemoimmunotherapy.

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

AUC:

Area under the curve

CR:

Complete response

CT:

Computed tomography

IASLC:

International Association for the Study of Lung Cancer

ICC:

Intraclass correlation coefficient

ICIs:

Immune checkpoint inhibitors

irAEs:

Immune-related adverse events

iRECIST:

Immune-Related Response Evaluation Criteria in Solid Tumors

LASSO:

Least absolute shrinkage and selection operator

LR:

Logistic regression

LUAD:

Lung adenocarcinoma

MPR:

Major pathologic response

NSCLC:

Non-small cell lung carcinoma

PD:

Progressive disease

PR:

Partial response

ROC:

Receiver operating characteristic

ROIs:

Regions of interest

SD:

Stable disease

TNM:

Tumor node metastasis

References

  1. Shu CA, Gainor JF, Awad MM et al (2020) Neoadjuvant atezolizumab and chemotherapy in patients with resectable non-small-cell lung cancer: an open-label, multicentre, single-arm, phase 2 trial. Lancet Oncol 21:786–795

    Article  CAS  PubMed  Google Scholar 

  2. Wu J, Hou L, Haoran E et al (2022) Real-world clinical outcomes of neoadjuvant immunotherapy combined with chemotherapy in resectable non-small cell lung cancer. Lung Cancer 165:115–123

    Article  CAS  PubMed  Google Scholar 

  3. Chen Y, Yan B, Xu F et al (2021) Neoadjuvant chemoimmunotherapy in resectable stage IIIA/IIIB non-small cell lung cancer. Transl Lung Cancer Res 10:2193–2204

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Chen T, Ning J, Campisi A et al (2022) Neoadjuvant PD-1 inhibitors and chemotherapy for locally advanced NSCLC: a retrospective study. Ann Thorac Surg 113:993–999

    Article  PubMed  Google Scholar 

  5. Hellmann MD, Chaft JE, William WN Jr et al (2014) Pathological response after neoadjuvant chemotherapy in resectable non-small-cell lung cancers: proposal for the use of major pathological response as a surrogate endpoint. Lancet Oncol 15:e42-50

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Esfahani K, Elkrief A, Calabrese C et al (2020) Moving towards personalized treatments of immune-related adverse events. Nat Rev Clin Oncol 17:504–515

    Article  CAS  PubMed  Google Scholar 

  7. Zhao H, Ning J, Gu Y et al (2021) Consecutive severe immune-related adverse events after PD-1 inhibitor induction and surgery in locally advanced non-small cell lung cancer: a case report. Transl Lung Cancer Res 10:3682–3688

    Article  PubMed  PubMed Central  Google Scholar 

  8. Mulkey F, Theoret MR, Keegan P, Pazdur R, Sridhara R (2020) Comparison of iRECIST versus RECIST V.1.1 in patients treated with an anti-PD-1 or PD-L1 antibody: pooled FDA analysis. J Immunother Cancer 8:e000146

    Article  PubMed  PubMed Central  Google Scholar 

  9. Seymour L, Bogaerts J, Perrone A et al (2017) iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol 18:e143–e152

    Article  PubMed  PubMed Central  Google Scholar 

  10. Win T, Miles KA, Janes SM et al (2013) Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer. Clin Cancer Res 19:3591–3599

    Article  CAS  PubMed  Google Scholar 

  11. Khorrami M, Prasanna P, Gupta A et al (2020) Changes in CT radiomic features associated with lymphocyte distribution predict overall survival and response to immunotherapy in non-small cell lung cancer. Cancer Immunol Res 8:108–119

    Article  CAS  PubMed  Google Scholar 

  12. William WN Jr, Pataer A, Kalhor N et al (2013) Computed tomography RECIST assessment of histopathologic response and prediction of survival in patients with resectable non-small-cell lung cancer after neoadjuvant chemotherapy. J Thorac Oncol 8:222–228

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  PubMed  Google Scholar 

  14. Fave X, Zhang L, Yang J et al (2017) Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep 7:588

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  15. Tankyevych O, Trousset F, Latappy C et al (2022) Development of radiomic-based model to predict clinical outcomes in non-small cell lung cancer patients treated with immunotherapy. Cancers (Basel) 14:5931

    Article  CAS  PubMed  Google Scholar 

  16. 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

  17. Travis WD, Dacic S, Wistuba I et al (2020) IASLC multidisciplinary recommendations for pathologic assessment of lung cancer resection specimens after neoadjuvant therapy. J Thorac Oncol 15:709–740

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    Article  PubMed  PubMed Central  Google Scholar 

  19. Trebeschi S, Kurilova I, Călin AM et al (2017) Radiomic biomarkers for the prediction of immunotherapy outcome in patients with metastatic non-small cell lung cancer. J Clin Oncol 35:e14520–e14520

    Article  Google Scholar 

  20. Grove O, Berglund AE, Schabath MB et al (2015) Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS One 10:e0118261

  21. Grossmann P, Stringfield O, El-Hachem N et al (2017) Defining the biological basis of radiomic phenotypes in lung cancer. Elife 6:e23421

    Article  PubMed  PubMed Central  Google Scholar 

  22. Gerlinger M, Rowan AJ, Horswell S et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhang J, Wu Z, Zhao J et al (2021) Intrahepatic cholangiocarcinoma: MRI texture signature as predictive biomarkers of immunophenotyping and survival. Eur Radiol 31:3661–3672

    Article  CAS  PubMed  Google Scholar 

  24. Raskov H, Orhan A, Christensen JP, Gögenur I (2021) Cytotoxic CD8(+) T cells in cancer and cancer immunotherapy. Br J Cancer 124:359–367

    Article  CAS  PubMed  Google Scholar 

  25. Nasief H, Zheng C, Schott D et al (2019) A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer. NPJ Precis Oncol 3:25

    Article  PubMed  PubMed Central  Google Scholar 

  26. Fave X, Zhang L, Yang J et al (2016) Delta-radiomics: the prognostic value of therapy-induced changes in radiomics features for stage III non-small cell lung cancer patients. Med Phys 43:3750–3750

    Article  Google Scholar 

  27. Jeon SH, Song C, Chie EK et al (2019) Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer. Radiat Oncol 14:43

    Article  PubMed  PubMed Central  Google Scholar 

  28. Beaumont H, Iannessi A, Cucchi JM, Bertrand AS, Lucidarme O (2022) Intra-scan inter-tissue variability can help harmonize radiomics features in CT. Eur Radiol 32:783–792

    Article  PubMed  Google Scholar 

  29. Mali SA, Ibrahim A, Woodruff HC et al (2021) Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods. J Pers Med 11:842

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank all colleagues for helping us during the current study.

We thank the patients and their families, and the participating study teams for making this study possible.

HSS, JF, and YDL did the conception and design of the study and the acquisition of data. XYH, YW, CYD, JL, XJ, KLZ, YML, JL, RY, ZC, and JG did analysis and interpretation of the data. XYH and MLW draft the article. All authors did final approval of the version to be submitted.

Funding

This study was supported by the National Natural Science Foundation of China (grant numbers: 82071921).

Author information

Author notes

  1. Xiaoyu Han and Mingliang Wang have contributed equally to this work as first authors.

  2. Yongde Liao, Jun Fan, and Heshui Shi have contributed equally to this work as last authors.

Authors and Affiliations

  1. Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People’s Republic of China

    Xiaoyu Han, Yuting Zheng, Xi Jia, Kailu Zhang, Yumin Li, Jia Liu, Jin Gu & Heshui Shi

  2. Hubei Province Key Laboratory of Molecular Imaging, Wuhan, The People’s Republic of China

    Xiaoyu Han, Yuting Zheng, Xi Jia, Kailu Zhang, Yumin Li, Jia Liu, Jin Gu & Heshui Shi

  3. Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Mingliang Wang & Yongde Liao

  4. Department of Thoracic Surgery, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China

    Mingliang Wang

  5. Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People’s Republic of China

    Na Wang & Ying Wu

  6. ShuKun (BeiJing) Technology Co., Ltd., Beijing, The People’s Republic of China

    Chengyu Ding

  7. Department of Thoracic Surgery, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang, China

    Ran Yang & Mingfei Geng

  8. Department of Cardiothoracic Surgery, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China

    Zhen Chen, Songlin Zhang & Jun Fan

  9. Department of Cardiothoracic Surgery, Yichang Central People’s Hospital, Yichang, China

    Zhen Chen & Songlin Zhang

Authors
  1. Xiaoyu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingliang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuting Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Na Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ying Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chengyu Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xi Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ran Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mingfei Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Zhen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Songlin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Kailu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yumin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jia Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jin Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Yongde Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jun Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Heshui Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yongde Liao, Jun Fan or Heshui Shi.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Heshui Shi.

Conflict of interest

Chengyu Ding is an employee of ShuKun (BeiJing) Technology Co., Ltd.. The rest of 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

This retrospective study was approved by the Ethics Committee of Wuhan Union Hospital (0648–01).

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported in other places.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Multicentre study

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 285 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, X., Wang, M., Zheng, Y. et al. Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer. Eur Radiol 34, 2716–2726 (2024). https://doi.org/10.1007/s00330-023-10241-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-10241-x

Keywords

Search

Navigation