Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.

Article  PubMed  Google Scholar 

Guckenberger M, Andratschke N, Dieckmann K, et al. ESTRO ACROP consensus guideline on implementation and practice of stereotactic body radiotherapy for peripherally located early stage non-small cell lung cancer. Radiother Oncol. 2017;124(1):11–7.

Article  PubMed  Google Scholar 

Videtic GMM, Donington J, Giuliani M, et al. Stereotactic body radiation therapy for early-stage non-small cell lung cancer: executive summary of an ASTRO evidence-based guideline. Pract Radiat Oncol. 2017;7(5):295–301.

Article  PubMed  Google Scholar 

Timmerman R, Paulus R, Galvin J, et al. Stereotactic body radiation therapy for inoperable early stage lung cancer. JAMA. 2010;303(11):1070–6.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Fakiris AJ, McGarry RC, Yiannoutsos CT, et al. Stereotactic body radiation therapy for early-stage non-small-cell lung carcinoma: four-year results of a prospective phase II study. Int J Radiat Oncol Biol Phys. 2009;75(3):677–82.

Article  PubMed  Google Scholar 

National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines in Oncology: Non-Small Cell Lung Cancer. Version 5. 2023. Available from: https://www.nccn.org.

Global Initiative for Chronic Obstructive Lung Disease 2021 REPORT, https://goldcopd.org/gold-reports/gold-report-2021-v1-0-11nov20_wmv/ Accessed 11 July 2022.

Torre-Bouscoulet L, Munoz-Montano WR, Martinez-Briseno D, et al. Abnormal pulmonary function tests predict the development of radiation-induced pneumonitis in advanced non-small cell lung cancer. Respir Res. 2018;19(1):72.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Bellia V, Pistelli R, Catalano F, et al. Quality control of spirometry in the elderly: the SA. RA study. Am J Respir Crit Care Med. 2000;161(4):1094–100.

Article  PubMed  CAS  Google Scholar 

Melo SMD, Oliveira LA, Wanderley JLF, et al. Evaluating the extremely elderly at a pulmonary function clinic for the diagnosis of respiratory disease: frequency and technical quality of spirometry. J Bras Pneumol. 2019;45(4):e20180232.

Article  PubMed  PubMed Central  Google Scholar 

Abe K, Kadoya N, Ito K, et al. Evaluation of the MVCT-based radiomic features as prognostic factor in patients with head and neck squamous cell carcinoma. BMC Med Imaging. 2023. https://doi.org/10.1186/s12880-023-01055-w.

Article  PubMed  PubMed Central  Google Scholar 

Ishizawa M, Tanaka S, Takagi H, et al. Development of a prediction model for head and neck volume reduction by clinical factors, dose-volume histogram parameters and radiomics in head and neck cancerdagger. J Radiat Res. 2023;64:788.

Article  Google Scholar 

Tanaka S, Kadoya N, Sugai Y, et al. A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy. Sci Rep. 2022. https://doi.org/10.1038/s41598-022-12170-z.

Article  PubMed  PubMed Central  Google Scholar 

Sugai Y, Kadoya N, Tanaka S, et al. Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients. Radiat Oncol. 2021;16(1):1–12.

Article  Google Scholar 

Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5(1):1–9.

Google Scholar 

Jiang W, Song Y, Sun Z, et al. Dosimetric factors and radiomics features within different regions of interest in planning CT images for improving the prediction of radiation pneumonitis. Int J Radiat Oncol Biol Phys. 2021;110:1161.

Article  PubMed  Google Scholar 

Ieko Y, Kadoya N, Sugai Y, et al. Assessment of a computed tomography-based radiomics approach for assessing lung function in lung cancer patients. Physica Med. 2022;101:28–35.

Article  Google Scholar 

Liang B, Yan H, Tian Y, et al. Dosiomics: extracting 3D spatial features from dose distribution to predict incidence of radiation pneumonitis. Front Oncol. 2019;9:269.

Article  PubMed  PubMed Central  Google Scholar 

Rossi L, Bijman R, Schillemans W, et al. Texture analysis of 3D dose distributions for predictive modelling of toxicity rates in radiotherapy. Radiother Oncol. 2018;129(3):548–53.

Article  PubMed  Google Scholar 

Gabrys HS, Buettner F, Sterzing F, et al. Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia. Front Oncol. 2018;8:35.

Article  PubMed  PubMed Central  Google Scholar 

Sheen H, Cho W, Kim C, et al. Radiomics-based hybrid model for predicting radiation pneumonitis: a systematic review and meta-analysis. Phys Med. 2024;123: 103414.

Article  PubMed  Google Scholar 

Lafata KJ, Zhou Z, Liu J-G, et al. An Exploratory radiomics approach to quantifying pulmonary function in CT images. Sci Rep. 2019. https://doi.org/10.1038/s41598-019-48023-5.

Article  PubMed  PubMed Central  Google Scholar 

Guerrero T, Sanders K, Castillo E, et al. Dynamic ventilation imaging from four-dimensional computed tomography. Phys Med Biol. 2006;51(4):777–91.

Article  PubMed  Google Scholar 

Ieko Y, Kadoya N, Kanai T, et al. The impact of 4DCT-ventilation imaging-guided proton therapy on stereotactic body radiotherapy for lung cancer. Radiol Phys Technol. 2020;13:230.

Article  PubMed  Google Scholar 

Zwanenburg A, Vallieres M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328–38.

Article  PubMed  Google Scholar 

Berenguer R, Pastor-Juan MDR, Canales-Vazquez J, et al. Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology. 2018;288(2):407–15.

Article  PubMed  Google Scholar 

Zhao B, Tan Y, Tsai W-Y, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016;6(1):1–7.

Google Scholar 

Orlhac F, Frouin F, Nioche C, et al. Validation of a method to compensate multicenter effects affecting CT radiomics. Radiology. 2019;291(1):53–9.

Article  PubMed  Google Scholar 

Papadimitroulas P, Brocki L, Christopher Chung N, et al. Artificial intelligence: deep learning in oncological radiomics and challenges of interpretability and data harmonization. Phys Med. 2021;83:108–21.

Article  PubMed  Google Scholar 

Lv W, Yuan Q, Wang Q, et al. Radiomics analysis of PET and CT components of PET/CT imaging integrated with clinical parameters: application to prognosis for nasopharyngeal carcinoma. Mol Imag Biol. 2019;21(5):954–64.

Article  CAS  Google Scholar 

Li L, Hu X, Tao X, et al. Radiomic features of plaques derived from coronary CT angiography to identify hemodynamically significant coronary stenosis, using invasive FFR as the reference standard. Eur J Radiol. 2021;140: 109769.

Article  PubMed  Google Scholar 

Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1.

Article  PubMed  PubMed Central  Google Scholar 

Kadoya N, Tanaka S, Kajikawa T, et al. Homology-based radiomic features for prediction of the prognosis of lung cancer based on CT-based radiomics. Med Phys. 2020;47:2197.

Article  PubMed  Google Scholar 

Soufi M, Arimura H, Nagami N. Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based radiomic features. Med Phys. 2018;45(11):5116–28.

Article  PubMed  Google Scholar 

Castiglioni I, Rundo L, Codari M, et al. AI applications to medical images: From machine learning to deep learning. Phys Med. 2021;83:9–24.

Article  PubMed  Google Scholar 

Berglund E, Birath G, Bjure J, et al. Spirometric studies in normal subjects. I. Forced expirograms in subjects between 7 and 70 years of age. Acta Med Scand. 1963;173:185–92.

Article  PubMed  CAS