Artificial Intelligence-based Personalized Survival Prediction Using Clinical and Radiomics Features in Patients with Advanced Non-small Cell Lung Cancer

Overview

Journal BMC Cancer

Publisher Biomed Central

Specialty Oncology

Date 2024 Nov 19

PMID 39558311

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

Affiliations

Soon will be listed here.

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).

References

Paz-Ares L, Ciuleanu T, Cobo M, Schenker M, Zurawski B, Menezes J . 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(2):198-211. DOI: 10.1016/S1470-2045(20)30641-0. View

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(1):454. PMC: 8393743. DOI: 10.1186/s12935-021-02165-7. View

Ardila D, Kiraly A, Bharadwaj S, Choi B, Reicher J, Peng L . End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019; 25(6):954-961. DOI: 10.1038/s41591-019-0447-x. View

Sakata Y, Kawamura K, Ichikado K, Shingu N, Yasuda Y, Eguchi Y . 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(12):2259-2266. PMC: 6885438. DOI: 10.1111/1759-7714.13214. View

Hosny A, Parmar C, Coroller T, Grossmann P, Zeleznik R, Kumar A . Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med. 2018; 15(11):e1002711. PMC: 6269088. DOI: 10.1371/journal.pmed.1002711. View

Owen D, Ismaila N, Freeman-Daily J, Roof L, Singh N, Velazquez A . Therapy for Stage IV Non-Small Cell Lung Cancer With Driver Alterations: ASCO Living Guideline, Version 2024.1. J Clin Oncol. 2024; 42(20):e44-e59. DOI: 10.1200/JCO.24.00762. View

Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt W . 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(1):39-51. DOI: 10.1016/j.jtho.2015.09.009. View

Kang L, Chen W, Petrick N, Gallas B . Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach. Stat Med. 2014; 34(4):685-703. PMC: 4314453. DOI: 10.1002/sim.6370. View

Socinski M, Jotte R, Cappuzzo F, Orlandi F, Stroyakovskiy D, Nogami N . Atezolizumab for First-Line Treatment of Metastatic Nonsquamous NSCLC. N Engl J Med. 2018; 378(24):2288-2301. DOI: 10.1056/NEJMoa1716948. View

10.

Huang Y, Liu Z, He L, Chen X, Pan D, Ma Z . 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(3):947-957. DOI: 10.1148/radiol.2016152234. View

11.

Vaidya P, Bera K, Patil P, Gupta A, Jain P, Alilou M . 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(2). PMC: 7555103. DOI: 10.1136/jitc-2020-001343. View

12.

Paz-Ares L, Luft A, Vicente D, Tafreshi A, Gumus M, Mazieres J . Pembrolizumab plus Chemotherapy for Squamous Non-Small-Cell Lung Cancer. N Engl J Med. 2018; 379(21):2040-2051. DOI: 10.1056/NEJMoa1810865. View

13.

Tang C, Hobbs B, Amer A, Li X, Behrens C, Rodriguez Canales J . Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer. Sci Rep. 2018; 8(1):1922. PMC: 5792427. DOI: 10.1038/s41598-018-20471-5. View

14.

Soria J, Ohe Y, Vansteenkiste J, Reungwetwattana T, Chewaskulyong B, Lee K . Osimertinib in Untreated EGFR-Mutated Advanced Non-Small-Cell Lung Cancer. N Engl J Med. 2017; 378(2):113-125. DOI: 10.1056/NEJMoa1713137. View

15.

Tian P, He B, Mu W, Liu K, Liu L, Zeng H . 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(5):2098-2107. PMC: 7797686. DOI: 10.7150/thno.48027. View

16.

Herbst R, Giaccone G, de Marinis F, Reinmuth N, Vergnenegre A, Barrios C . Atezolizumab for First-Line Treatment of PD-L1-Selected Patients with NSCLC. N Engl J Med. 2020; 383(14):1328-1339. DOI: 10.1056/NEJMoa1917346. View

17.

Barabino E, Rossi G, Pamparino S, Fiannacca M, Caprioli S, Fedeli A . Exploring Response to Immunotherapy in Non-Small Cell Lung Cancer Using Delta-Radiomics. Cancers (Basel). 2022; 14(2). PMC: 8773717. DOI: 10.3390/cancers14020350. View

18.

Reck M, Rodriguez-Abreu D, Robinson A, Hui R, Csoszi T, Fulop A . Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med. 2016; 375(19):1823-1833. DOI: 10.1056/NEJMoa1606774. View

19.

Leung E, Scott H, McMillan D . Clinical utility of the pretreatment glasgow prognostic score in patients with advanced inoperable non-small cell lung cancer. J Thorac Oncol. 2012; 7(4):655-62. DOI: 10.1097/JTO.0b013e318244ffe1. View

20.

Mu W, Jiang L, Shi Y, Tunali I, Gray J, Katsoulakis E . Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images. J Immunother Cancer. 2021; 9(6). PMC: 8211060. DOI: 10.1136/jitc-2020-002118. View