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BMC Cancer
Published in:

Open Access 01-12-2024 | Research

Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer

Authors: Junji Koyama, Masahiro Morise, Taiki Furukawa, Shintaro Oyama, Reiko Matsuzawa, Ichidai Tanaka, Keiko Wakahara, Hideo Yokota, Tomoki Kimura, Yoshimune Shiratori, Yasuhiro Kondoh, Naozumi Hashimoto, Makoto Ishii

Published in: BMC Cancer | Issue 1/2024

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Abstract

Background

Multiple first-line treatment options have been developed for advanced non-small cell lung cancer (NSCLC) in each subgroup determined by predictive biomarkers, specifically driver oncogene and programmed cell death ligand-1 (PD-L1) status. However, the methodology for optimal treatment selection in individual patients is not established. This study aimed to develop artificial intelligence (AI)-based personalized survival prediction model according to treatment selection.

Methods

The prediction model was built based on random survival forest (RSF) algorithm using patient characteristics, anticancer treatment histories, and radiomics features of the primary tumor. The predictive accuracy was validated with external test data and compared with that of cox proportional hazard (CPH) model.

Results

A total of 459 patients (training, n = 299; test, n = 160) with advanced NSCLC were enrolled. The algorithm identified following features as significant factors associated with survival: age, sex, performance status, Brinkman index, comorbidity of chronic obstructive pulmonary disease, histology, stage, driver oncogene status, tumor PD-L1 expression, administered anticancer agent, six markers of blood test (sodium, lactate dehydrogenase, etc.), and three radiomics features associated with tumor texture, volume, and shape. The C-index of RSF model for test data was 0.841, which was higher than that of CPH model (0.775, P < 0.001). Furthermore, the RSF model enabled to identify poor survivor treated with pembrolizumab because of tumor PD-L1 high expression and those treated with driver oncogene targeted therapy according to driver oncogene status.

Conclusions

The proposed AI-based algorithm accurately predicted the survival of each patient with advanced NSCLC. The AI-based methodology will contribute to personalized medicine.

Trial registration

The trial design was retrospectively registered study performed in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Nagoya University Graduate School of Medicine (approval: 2020 − 0287).
Appendix
Available only for authorised users
Literature
1.
Soria JC, Ohe Y, Vansteenkiste J, et al. Osimertinib in untreated EGFR-Mutated Advanced Non-small-cell Lung Cancer. N Engl J Med. 2018;378:113–25.PubMedCrossRef
2.
Hida T, Nokihara H, Kondo M, et al. Alectinib versus Crizotinib in patients with ALK-positive non-small-cell lung cancer (J-ALEX): an open-label, randomised phase 3 trial. Lancet. 2017;390:29–39.PubMedCrossRef
3.
Peters S, Camidge DR, Shaw AT, et al. Alectinib versus Crizotinib in untreated ALK-Positive non-small-cell Lung Cancer. N Engl J Med. 2017;377:829–38.PubMedCrossRef
4.
Reck M, Rodríguez-Abreu D, Robinson AG, et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive non-small-cell Lung Cancer. N Engl J Med. 2016;375:1823–33.PubMedCrossRef
5.
Herbst RS, Giaccone G, de Marinis F, et al. Atezolizumab for First-Line treatment of PD-L1-Selected patients with NSCLC. N Engl J Med. 2020;383:1328–39.PubMedCrossRef
6.
Hellmann MD, Paz-Ares L, Bernabe Caro R, et al. Nivolumab plus Ipilimumab in Advanced Non-small-cell Lung Cancer. N Engl J Med. 2019;381:2020–31.PubMedCrossRef
7.
Gandhi L, Rodríguez-Abreu D, Gadgeel S, et al. Pembrolizumab plus Chemotherapy in Metastatic Non-small-cell Lung Cancer. N Engl J Med. 2018;378:2078–92.PubMedCrossRef
8.
Paz-Ares L, Luft A, Vicente D, et al. Pembrolizumab plus Chemotherapy for squamous non-small-cell Lung Cancer. N Engl J Med. 2018;379:2040–51.PubMedCrossRef
9.
Socinski MA, Jotte RM, Cappuzzo F, et al. Atezolizumab for First-Line treatment of metastatic nonsquamous NSCLC. N Engl J Med. 2018;378:2288–301.PubMedCrossRef
10.
West H, McCleod M, Hussein M, et al. Atezolizumab in combination with carboplatin plus nab-paclitaxel chemotherapy compared with chemotherapy alone as first-line treatment for metastatic non-squamous non-small-cell lung cancer (IMpower130): a multicentre, randomised, open-label, phase 3 trial. Lancet Oncol. 2019;20:924–37.PubMedCrossRef
11.
Nishio M, Barlesi F, West H, et al. Atezolizumab Plus Chemotherapy for First-Line treatment of Nonsquamous NSCLC: results from the Randomized phase 3 IMpower132 trial. J Thorac Oncol. 2021;16:653–64.PubMedCrossRef
12.
Paz-Ares L, Ciuleanu TE, Cobo M, et al. First-line nivolumab plus ipilimumab combined with two cycles of chemotherapy in patients with non-small-cell lung cancer (CheckMate 9LA): an international, randomised, open-label, phase 3 trial. Lancet Oncol. 2021;22:198–211.PubMedCrossRef
13.
Mok TSK, Wu YL, Kudaba I, et al. Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial. Lancet. 2019;393:1819–30.PubMedCrossRef
14.
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6.PubMedPubMedCentralCrossRef
15.
16.
Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25:954–61.PubMedCrossRef
17.
Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.PubMedCrossRef
18.
Huang Y, Liu Z, He L, et al. Radiomics signature: a potential biomarker for the prediction of Disease-Free Survival in early-stage (I or II) Non-small Cell Lung Cancer. Radiology. 2016;281:947–57.PubMedCrossRef
19.
Hosny A, Parmar C, Coroller TP, et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med. 2018;15:e1002711.PubMedPubMedCentralCrossRef
20.
Sun R, Limkin EJ, Vakalopoulou M, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol. 2018;19:1180–91.PubMedCrossRef
21.
22.
He B, Dong D, She Y et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer 2020;8.
23.
Trebeschi S, Drago SG, Birkbak NJ, et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol. 2019;30:998–1004.PubMedPubMedCentralCrossRef
24.
Tunali I, Gray JE, Qi J, et al. Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: an early report. Lung Cancer. 2019;129:75–9.PubMedCrossRef
25.
Vaidya P, Bera K, Patil PD 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 2020;8.
26.
Barabino E, Rossi G, Pamparino S et al. Exploring response to Immunotherapy in Non-small Cell Lung Cancer using Delta-Radiomics. Cancers (Basel) 2022;14.
27.
28.
Ishwaran H, Kogalur UB, Blackstone EH et al. Random survival forests. Annals Appl Stat 2008;2.
29.
Jaiyesimi IA, Leighl NB, Ismaila N, et al. Therapy for stage IV Non-small Cell Lung Cancer without driver alterations: ASCO Living Guideline, Version 2023.3. J Clin Oncol. 2024;42:e23–43.PubMedCrossRef
30.
Owen DH, Ismaila N, Freeman-Daily J, et al. Therapy for stage IV Non-small Cell Lung Cancer with driver alterations: ASCO Living Guideline, Version 2024.1. J Clin Oncol. 2024;42:e44–59.PubMedCrossRef
31.
Hendriks LE, Kerr KM, Menis J, et al. Non-oncogene-addicted metastatic non-small-cell lung cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol. 2023;34:358–76.PubMedCrossRef
32.
Hendriks LE, Kerr KM, Menis J, et al. Oncogene-addicted metastatic non-small-cell lung cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol. 2023;34:339–57.PubMedCrossRef
33.
Kang L, Chen W, Petrick NA, et al. Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach. Stat Med. 2015;34:685–703.PubMedCrossRef
34.
Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transpl. 2013;48:452–8.CrossRef
35.
Pölsterl S. scikit-survival: a Library for Time-to-event analysis built on Top of scikit-learn. J Mach Learn Res. 2020;21:1–6.
36.
Tanaka I, Furukawa T, Morise M. The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence. Cancer Cell Int. 2021;21:454.PubMedPubMedCentralCrossRef
37.
Song J, Shi J, Dong D, et al. A New Approach to predict progression-free survival in Stage IV EGFR-mutant NSCLC patients with EGFR-TKI therapy. Clin Cancer Res. 2018;24:3583–92.PubMedCrossRef
38.
Song J, Wang L, Ng NN, et al. Development and validation of a machine learning model to explore tyrosine kinase inhibitor response in patients with stage IV EGFR variant-positive Non-small Cell Lung Cancer. JAMA Netw Open. 2020;3:e2030442.PubMedPubMedCentralCrossRef
39.
40.
Tian P, He B, Mu W, et al. Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images. Theranostics. 2021;11:2098–107.PubMedPubMedCentralCrossRef
41.
Mu W, Jiang L, Shi Y et al. Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images. J Immunother Cancer 2021;9.
42.
43.
Petrelli F, Cabiddu M, Coinu A, et al. Prognostic role of lactate dehydrogenase in solid tumors: a systematic review and meta-analysis of 76 studies. Acta Oncol. 2015;54:961–70.PubMedCrossRef
44.
Gu XB, Tian T, Tian XJ, et al. Prognostic significance of neutrophil-to-lymphocyte ratio in non-small cell lung cancer: a meta-analysis. Sci Rep. 2015;5:12493.PubMedPubMedCentralCrossRef
45.
Leung EY, Scott HR, McMillan DC. Clinical utility of the pretreatment glasgow prognostic score in patients with advanced inoperable non-small cell lung cancer. J Thorac Oncol. 2012;7:655–62.PubMedCrossRef
46.
Goldstraw P, Chansky K, Crowley J, et al. 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. 2016;11:39–51.PubMedCrossRef
47.
Dercle L, Ammari S, Champiat S, et al. Rapid and objective CT scan prognostic scoring identifies metastatic patients with long-term clinical benefit on anti-PD-1/-L1 therapy. Eur J Cancer. 2016;65:33–42.PubMedCrossRef
48.
Sakata Y, Kawamura K, Ichikado K, et al. Comparisons between tumor burden and other prognostic factors that influence survival of patients with non-small cell lung cancer treated with immune checkpoint inhibitors. Thorac Cancer. 2019;10:2259–66.PubMedPubMedCentralCrossRef
49.
Isensee F, Jaeger PF, Kohl SAA, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18:203–11.PubMedCrossRef
Metadata
Title
Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer
Authors
Junji Koyama
Masahiro Morise
Taiki Furukawa
Shintaro Oyama
Reiko Matsuzawa
Ichidai Tanaka
Keiko Wakahara
Hideo Yokota
Tomoki Kimura
Yoshimune Shiratori
Yasuhiro Kondoh
Naozumi Hashimoto
Makoto Ishii
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2024
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-024-13190-w

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