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28-07-2022 | Biomarkers | Nuclear Medicine

Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma

Authors: Meixin Zhao, Kilian Kluge, Laszlo Papp, Marko Grahovac, Shaomin Yang, Chunting Jiang, Denis Krajnc, Clemens P. Spielvogel, Boglarka Ecsedi, Alexander Haug, Shiwei Wang, Marcus Hacker, Weifang Zhang, Xiang Li

Published in: European Radiology | Issue 10/2022

Abstract

Objectives

This study investigates the ability of machine learning (ML) models trained on clinical data and 2-deoxy-2-[18F]fluoro-D-glucose(FDG) positron emission tomography/computed tomography (PET/CT) radiomics to predict overall survival (OS), tumor grade (TG), and histologic growth pattern risk (GPR) in lung adenocarcinoma (LUAD) patients.

Methods:

A total of 421 treatment-naive patients with histologically-proven LUAD and available FDG PET/CT imaging were retrospectively included. Four cohorts were assessed for predicting 4-year OS (n = 276), 3-year OS (n = 280), TG (n = 298), and GPR (n = 265). FDG-avid lesions were delineated, and 2082 radiomics features were extracted and combined with endpoint-specific clinical parameters. ML models were built for the prediction of 4-year OS (M4OS), 3-year OS (M3OS), tumor grading (MTG), and histologic growth pattern risk (MGPR). A 100-fold Monte Carlo cross-validation with 80:20 training to validation split was employed as a performance evaluation for all models. The association between the M4OS and M3OS predictions with OS was assessed by the Kaplan-Meier survival analysis.

Results

The area under the receiver operator characteristics curve (AUC) was the highest for M4OS (AUC 0.88, 95% confidence interval (CI) 86.7–88.7), followed by M3OS (AUC 0.84, CI 82.9–84.9), while MTG and MGPR performed equally well (AUC 0.76, CI 74.4–77.9, CI 74.6–78, respectively). Predictions of M4OS (hazard ratio (HR) −2.4, CI −2.47 to −1.64, p < 0.05) and M3OS (HR −2.36, CI −2.79 to −1.93, p < 0.05) were independently associated with OS.

Conclusion

ML models are able to predict long-term survival outcomes in LUAD patients with high accuracy. Furthermore, histologic grade and predominant growth pattern risk can be predicted with satisfactory accuracy.

Key Points

• Machine learning models trained on pre-therapeutic PET/CT radiomics enable highly accurate long-term survival prediction of patients with lung adenocarcinoma.

• Highly accurate survival predictions are achieved in lung adenocarcinoma patients despite heterogenous histologies and treatment regimens.

• Radiomic machine learning models are able to predict lung adenocarcinoma tumor grade and histologic growth pattern risk with satisfactory accuracy.

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Herbst RS, Morgensztern D, Boshoff C (2018) The biology and management of non-small cell lung cancer. Nature 553:446–454CrossRef

Miller KD, Nogueira L, Mariotto AB et al (2019) Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin 69:363–385CrossRef

Curado MP, Edwards B, Shin HR (2007) Cancer Incidence in Five Continents

Yoshizawa A, Motoi N, Riely GJ et al (2011) Impact of proposed IASLC/ATS/ERS classification of lung adenocarcinoma: prognostic subgroups and implications for further revision of staging based on analysis of 514 stage i cases. Mod Pathol 24:653–664CrossRef

Warth A, Muley T, Meister M et al (2012) The novel histologic International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification system of lung adenocarcinoma is a stage-independent predictor of survival. J Clin Oncol 30:1438–1446CrossRef

Strand TE, Rostad H, Strøm EH, Hasleton P (2015) The percentage of lepidic growth is an independent prognostic factor in invasive adenocarcinoma of the lung. Diagn Pathol 10:1–7CrossRef

Li H, Cao W (2020) Pulmonary enteric adenocarcinoma: a literature review. J Thorac Dis 12:3217–3226CrossRef

Travis WD, Brambilla E, Nicholson AG et al (2015) The 2015 World Health Organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol 10:1243–1260CrossRef

Loo PS, Thomas SC, Nicolson MC et al (2010) Subtyping of undifferentiated non-small cell carcinomas in bronchial biopsy specimens. J Thorac Oncol 5:442–447CrossRef

10.

Soler Cataluña JJ, Perpiñá M, Greses JV et al (1996) Cell type accuracy of bronchial biopsy specimens in primary lung cancer. Chest 109:1199–1203CrossRef

11.

Huang K-Y, Ko P-Z, Yao C-W et al (2017) Inaccuracy of lung adenocarcinoma subtyping using preoperative biopsy specimens. J Thorac Cardiovasc Surg 154. https://doi.org/10.1016/j.jtcvs.2017.02.059

12.

Ettinger DS, Wood DE, Aggarwal C et al (2019) NCCN Guidelines insights: non-small cell lung cancer, Version 1.2020. J Natl Compr Canc Netw 17:1464–1472CrossRef

13.

Planchard D, Popat S, Kerr K et al (2018) Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 29:iv192–iv237CrossRef

14.

Cuaron J, Dunphy M, Rimner A (2013) Role of FDG-PET scans in staging, response assessment, and follow-up care for non-small cell lung cancer. Front Oncol 2:208. https://doi.org/10.3389/fonc.2012.00208CrossRefPubMedPubMedCentral

15.

Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446CrossRef

16.

Papp L, Spielvogel CP, Rausch I et al (2018) Personalizing medicine through hybrid imaging and medical big data analysis. Front Phys 6:1–19

17.

Sha X, Gong G, Qiu Q et al (2019) Identifying pathological subtypes of non-small-cell lung cancer by using the radiomic features of 18F-fluorodeoxyglucose positron emission computed tomography. Transl Cancer Res 8:1741–1749CrossRef

18.

Shao X, Niu R, Shao X et al (2020) Value of 18F-FDG PET/CT-based radiomics model to distinguish the growth patterns of early invasive lung adenocarcinoma manifesting as ground-glass opacity nodules. EJNMMI Res 10. https://doi.org/10.1186/s13550-020-00668-4

19.

Chang C, Sun X, Wang G et al (2021) A machine learning model based on PET/CT radiomics and clinical characteristics predicts ALK rearrangement status in lung adenocarcinoma. Front Oncol 11:1–12

20.

Kirienko M, Cozzi L, Antunovic L et al (2018) Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging 45:207–217CrossRef

21.

Valentinuzzi D, Vrankar M, Boc N et al (2020) [18F]FDG PET immunotherapy radiomics signature (iRADIOMICS) predicts response of non-small-cell lung cancer patients treated with pembrolizumab. Radiol Oncol 54:285–294CrossRef

22.

Kirienko M, Sollini M, Corbetta M, et al (2021) Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer. Eur J Nucl Med Mol Imaging 48:3643–3655

23.

Edge SB, Compton CC (2010) The american joint committee on cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 17:1471–1474CrossRef

24.

Travis WD, Brambilla E, Noguchi M et al (2011) International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society International Multidisciplinary Classification of Lung Adenocarcinoma. J Thorac Oncol 6:244–285CrossRef

25.

Joo Hyun O, Lodge MA, Wahl RL (2016) Practical percist: a simplified guide to PET response criteria in solid tumors 1.0. Radiology 280:576–584CrossRef

26.

Stytz MR, Parrott RW (1993) Using kriging for 3d medical imaging. Comput Med Imaging Graph 17:421–442CrossRef

27.

Zwanenburg A, Leger S, Vallières M, Löck S (2016) Image biomarker standardisation initiative. Radology 295:328–338 https://doi.org/10.1148/radiol.2020191145

28.

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

29.

Amin A, Anwar S, Adnan A, et al (2016) Comparing oversampling techniques to handle the class imbalance problem: a customer churn prediction case study. IEEE Access https://doi.org/10.1109/ACCESS.2016.2619719

30.

Huang Y, Liu Z, He L et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non—small cell lung cancer. Radiology 281:947–957CrossRef

31.

Mu W, Jiang L, Zhang JY et al (2020) Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat Commun 11. https://doi.org/10.1038/s41467-020-19116-x

32.

Yang B, Ji H, Zhong J et al (2020) Value of 18F-FDG PET/CT-based radiomics nomogram to predict survival outcomes and guide personalized targeted therapy in lung adenocarcinoma with EGFR mutations. Front Oncol 10. https://doi.org/10.3389/fonc.2020.567160

33.

Choe J, Lee SM, Do KH et al (2020) Outcome prediction in resectable lung adenocarcinoma patients: value of CT radiomics. Eur Radiol 30:4952–4963CrossRef

34.

Krajnc D, Papp L, Nakuz TS et al (2021) Breast tumor characterization using [18F]FDG-PET/CT imaging combined with data preprocessing and radiomics. Cancers 13. https://doi.org/10.3390/cancers13061249

35.

Naimi AI, Balzer LB (2018) Stacked generalization: an introduction to super learning. Eur J Epidemiol 33. https://doi.org/10.1007/s10654-018-0390-z

36.

Carvalho S, Leijenaar RTH, Troost EGC et al (2018) 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) – A prospective externally validated study. PLoS One 13:e0192859. https://doi.org/10.1007/s00259-020-04839-2CrossRefPubMedPubMedCentral

37.

Bae JM, Jeong JY, Lee HY et al (2017) Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images. Oncotarget 8:523–535CrossRef

38.

Yang SM, Chen LW, Wang HJ et al (2018) Extraction of radiomic values from lung adenocarcinoma with near-pure subtypes in the International Association for the Study of Lung Cancer/the American Thoracic Society/the European Respiratory Society (IASLC/ATS/ERS) classification. Lung Cancer 119:56–63CrossRef

Title: Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma
Authors: Meixin Zhao
Kilian Kluge
Laszlo Papp
Marko Grahovac
Shaomin Yang
Chunting Jiang
Denis Krajnc
Clemens P. Spielvogel
Boglarka Ecsedi
Alexander Haug
Shiwei Wang
Marcus Hacker
Weifang Zhang
Xiang Li
Publication date: 28-07-2022
Publisher: Springer Berlin Heidelberg
Keyword: Biomarkers
Published in: European Radiology / Issue 10/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08999-7

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma

Abstract

Objectives

Methods:

Results

Conclusion

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods:

Results

Conclusion

Key Points

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