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European Radiology

22-09-2023 | NSCLC | Chest

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

Authors: Xiaoyu Han, Mingliang Wang, Yuting Zheng, Na Wang, Ying Wu, Chengyu Ding, Xi Jia, Ran Yang, Mingfei Geng, Zhen Chen, Songlin Zhang, Kailu Zhang, Yumin Li, Jia Liu, Jin Gu, Yongde Liao, Jun Fan, Heshui Shi

Published in: European Radiology

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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.
Appendix
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Literature
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 CrossRefPubMed
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 CrossRefPubMed
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 CrossRefPubMedPubMedCentral
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 CrossRefPubMed
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 CrossRefPubMedPubMedCentral
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 CrossRefPubMed
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 CrossRefPubMedPubMedCentral
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 CrossRefPubMedPubMedCentral
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 CrossRefPubMedPubMedCentral
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 CrossRefPubMed
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 CrossRefPubMed
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 CrossRefPubMedPubMedCentral
13.
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577 CrossRefPubMed
14.
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 CrossRefPubMed
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 CrossRefPubMedPubMedCentral
18.
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 CrossRef
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.
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 CrossRefPubMedPubMedCentral
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 CrossRefPubMed
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 CrossRefPubMed
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 CrossRefPubMedPubMedCentral
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 CrossRef
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 CrossRefPubMedPubMedCentral
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 CrossRefPubMed
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 CrossRefPubMedPubMedCentral
Metadata
Title
Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer
Authors
Xiaoyu Han
Mingliang Wang
Yuting Zheng
Na Wang
Ying Wu
Chengyu Ding
Xi Jia
Ran Yang
Mingfei Geng
Zhen Chen
Songlin Zhang
Kailu Zhang
Yumin Li
Jia Liu
Jin Gu
Yongde Liao
Jun Fan
Heshui Shi
Publication date
22-09-2023
Publisher
Springer Berlin Heidelberg
Published in
European Radiology
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-023-10241-x