Machine learning and radiomics applications in esophageal cancers using non-invasive imaging methods—a critical review of literature.
AffiliationDepartment of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
MetadataShow full item record
AbstractEsophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.
CitationXie C-Y, Pang C-L, Chan B, Wong EY-Y, Dou Q, Vardhanabhuti V. Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature. Cancers. 2021 May 19;13(10):2469.
- Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection.
- Authors: Baessler B, Nestler T, Pinto Dos Santos D, Paffenholz P, Zeuch V, Pfister D, Maintz D, Heidenreich A
- Issue date: 2020 Apr
- Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers.
- Authors: Giraud P, Giraud P, Gasnier A, El Ayachy R, Kreps S, Foy JP, Durdux C, Huguet F, Burgun A, Bibault JE
- Issue date: 2019
- Machine learning-based analysis of [(18)F]DCFPyL PET radiomics for risk stratification in primary prostate cancer.
- Authors: Cysouw MCF, Jansen BHE, van de Brug T, Oprea-Lager DE, Pfaehler E, de Vries BM, van Moorselaar RJA, Hoekstra OS, Vis AN, Boellaard R
- Issue date: 2021 Feb
- Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.
- Authors: Jang K, Russo C, Di Ieva A
- Issue date: 2020 Jul
- Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer.
- Authors: Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ
- Issue date: 2015