Hybrid Approach to Classifying Histological Subtypes of Non-small Cell Lung Cancer (NSCLC): Combining Radiomics and Deep Learning Features from CT Images

Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International journal of cancer. 2015;136(5):E359-E386.

Article  CAS  PubMed  Google Scholar 

Anagnostou VK, Dimou AT, Botsis T, et al. Molecular classification of nonsmall cell lung cancer using a 4‐protein quantitative assay. Cancer. 2012;118(6):1607-1618.

Article  CAS  PubMed  Google Scholar 

Gridelli C, Rossi A, Carbone DP, et al. Non-small-cell lung cancer. Nature reviews Disease primers. 2015;1(1):1-16.

Article  Google Scholar 

Zhang L, Wang L, Du B, Wang T, Tian P, Tian S. Classification of Non‐Small Cell Lung Cancer Using Significance Analysis of Microarray‐Gene Set Reduction Algorithm. BioMed Research International. 2016;2016(1):2491671.

PubMed  PubMed Central  Google Scholar 

Schuurbiers OC, Meijer TW, Kaanders JH, et al. Glucose metabolism in NSCLC is histology-specific and diverges the prognostic potential of 18FDG-PET for adenocarcinoma and squamous cell carcinoma. Journal of Thoracic Oncology. 2014;9(10):1485-1493.

Article  CAS  PubMed  Google Scholar 

Meijer TW, Schuurbiers OC, Kaanders JH, et al. Differences in metabolism between adeno-and squamous cell non-small cell lung carcinomas: spatial distribution and prognostic value of GLUT1 and MCT4. Lung cancer. 2012;76(3):316-323.

Article  PubMed  Google Scholar 

Travis WD, Brambilla E, Nicholson AG, et al. The 2015 World Health Organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. Journal of thoracic oncology. 2015;10(9):1243-1260.

Article  PubMed  Google Scholar 

Doroshow DB, Herbst RS. Treatment of advanced non–small cell lung cancer in 2018. JAMA oncology. 2018;4(4):569-570.

Article  PubMed  Google Scholar 

Stella GM, Luisetti M, Pozzi E, Comoglio PM. Oncogenes in non-small-cell lung cancer: emerging connections and novel therapeutic dynamics. The lancet Respiratory medicine. 2013;1(3):251-261.

Article  CAS  PubMed  Google Scholar 

Biancosino C, Krüger M, Vollmer E, Welker L. Intraoperative fine needle aspirations-diagnosis and typing of lung cancer in small biopsies: challenges and limitations. Diagnostic pathology. 2016;11:1-8.

Article  Google Scholar 

Kasraeian S, Allison DC, Ahlmann ER, Fedenko AN, Menendez LR. A comparison of fine-needle aspiration, core biopsy, and surgical biopsy in the diagnosis of extremity soft tissue masses. Clinical Orthopaedics and Related Research®. 2010;468:2992–3002.

Kohl SK, Lewis SE, Tunnicliffe J, et al. The College of American pathologists and national society for histotechnology workload study. Archives of Pathology & Laboratory Medicine. 2011;135(6):728-736.

Article  Google Scholar 

Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563-577.

Article  PubMed  Google Scholar 

Patil R, Mahadevaiah G, Dekker A. An approach toward automatic classification of tumor histopathology of non–small cell lung cancer based on radiomic features. Tomography. 2016;2(4):374.

Article  PubMed  PubMed Central  Google Scholar 

Wu W, Parmar C, Grossmann P, et al. Exploratory study to identify radiomics classifiers for lung cancer histology. Frontiers in oncology. 2016;6:71.

Article  PubMed  PubMed Central  Google Scholar 

Zhu X, Dong D, Chen Z, et al. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. European radiology. 2018;28:2772-2778.

Article  PubMed  Google Scholar 

Bicakci M, Ayyildiz O, Aydin Z, Basturk A, Karacavus S, Yilmaz B. Metabolic imaging based sub-classification of lung cancer. IEEE Access. 2020;8:218470-218476.

Article  Google Scholar 

Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer imaging. 2010;10(1):137.

Article  PubMed  PubMed Central  Google Scholar 

Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature communications. 2014;5(1):4006.

Article  CAS  PubMed  Google Scholar 

Pyka T, Bundschuh RA, Andratschke N, et al. Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy. Radiation oncology. 2015;10:1-9.

Article  Google Scholar 

Li H, Zhu Y, Burnside ES, et al. MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 gene assays. Radiology. 2016;281(2):382-391.

Article  PubMed  Google Scholar 

Sun Y, Li C, Jin L, et al. Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction. European radiology. 2020;30:3650-3659.

Article  PubMed  PubMed Central  Google Scholar 

Haga A, Takahashi W, Aoki S, et al. Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis. Radiological physics and technology. 2018;11:27-35.

Article  PubMed  Google Scholar 

Afshar P, Mohammadi A, Plataniotis KN, Oikonomou A, Benali H. From handcrafted to deep-learning-based cancer radiomics: challenges and opportunities. IEEE Signal Processing Magazine. 2019;36(4):132-160.

Article  Google Scholar 

Li Z, Wang Y, Yu J, Guo Y, Cao W. Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Scientific reports. 2017;7(1):5467.

Article  PubMed  PubMed Central  Google Scholar 

Hosny A, Aerts HJ, Mak RH. Handcrafted versus deep learning radiomics for prediction of cancer therapy response. The Lancet Digital Health. 2019;1(3):e106-e107.

Article  PubMed  Google Scholar 

Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Medical image analysis. 2017;42:60-88.

Article  PubMed  Google Scholar 

Paul R, Hawkins SH, Balagurunathan Y, et al. Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography. 2016;2(4):388.

Article  PubMed  PubMed Central  Google Scholar 

Zhen X, Chen J, Zhong Z, et al. Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Physics in Medicine & Biology. 2017;62(21):8246.

Article  Google Scholar 

Han W, Qin L, Bay C, et al. Deep transfer learning and radiomics feature prediction of survival of patients with high-grade gliomas. American Journal of Neuroradiology. 2020;41(1):40-48.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chmelik J, Jakubicek R, Walek P, et al. Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data. Medical image analysis. 2018;49:76-88.

Article  PubMed  Google Scholar 

Burlina P, Pacheco KD, Joshi N, Freund DE, Bressler NM. Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis. Computers in biology and medicine. 2017;82:80-86.

Article  PubMed  PubMed Central  Google Scholar 

Nie D, Zhang H, Adeli E, Liu L, Shen D. 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. Paper presented at: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 192016.

Wang S, Dong D, Li L, et al. A deep learning radiomics model to identify poor outcome in COVID-19 patients with underlying health conditions: a multicenter study. IEEE Journal of Biomedical and Health Informatics. 2021;25(7):2353-2362.

Article  PubMed  Google Scholar 

Liu W, Wang W, Zhang H, Guo M, Xu Y, Liu X. Development and Validation of Multi-Omics Thymoma Risk Classification Model Based on Transfer Learning. Journal of Digital Imaging. 2023;36(5):2015-2024.

Article  PubMed  PubMed Central  Google Scholar 

Shafiq‐ul‐Hassan M, Zhang GG, Latifi K, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Medical physics. 2017;44(3):1050-1062.

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