A meta-learning approach to improving radiation response prediction in cancers

Zhang, Yuening; Li, Qiu; Ren, Yongyong; Cheng, Zhiwei; Li, Leijie; Yao, Siqiong; Zhang, Chengdong; Luo, Zhiguo; Liu, Hui

doi:10.1016/j.compbiomed.2022.106163

Cited by 10 publications

(2 citation statements)

References 49 publications

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“…These can be further combined with radiomics to identify populations that could be radiosensitive [186][187][188]. For example, Zhang et al combined gene expression, DNA methylation, and clinical data to identify eight radiosensitivityrelated genes (AR, WBP1, AKR1E2, FANCG, NR2C2AP, CXCR4, SYNE4, and WFDC2) [189], while Liu et al identified 12 genes (BEST2, TMPRSS15, FGF19, ALP1, KCNB2, CLDN6, IL17REL, RORB, DDX25, TDRD9, CELF3, and FABP7) that can aid in identifying population responses in head and neck cancer patients [190].…”

Section: Challenges and Opportunities For Cancer Radiosensitivity Bio...mentioning

confidence: 99%

Molecular Biomarkers for Predicting Cancer Patient Radiosensitivity and Radiotoxicity in Clinical Practice

Gkikoudi,

Kalospyros,

Triantopoulou

et al. 2023

Applied Sciences

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Radiotherapy (RT) is a major part of cancer treatment. The reported variability in patient response to this modality can interfere with the continuation of best-possible care, promote side effects, and lead to long-term morbidity. Tools to predict a patient’s response to radiation could be highly useful in improving therapeutic outcomes while minimizing unnecessary and toxic exposure to radiation. This study investigates the potential of using molecular biomarkers as predictors of radiosensitivity in clinical practice. We review relative studies researching the positive correlation between various molecular biomarkers and patient radiosensitivity, including DNA damage response and repair proteins, inflammation and apoptosis markers, cell cycle regulators, and other biological markers. The clinical perspectives and applicability of these biomarkers in the prediction of radiosensitivity are also critically discussed. Conclusively, we underline the dynamics of molecular biomarkers to improve the efficacy and safety of radiotherapy in clinical practice and highlight the need for further research in this field. Identification of the most prominent markers is crucial for the personalization of therapies entailing ionizing radiation.

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Section: Challenges and Opportunities For Cancer Radiosensitivity Bio...mentioning

confidence: 99%

Molecular Biomarkers for Predicting Cancer Patient Radiosensitivity and Radiotoxicity in Clinical Practice

Gkikoudi,

Kalospyros,

Triantopoulou

et al. 2023

Applied Sciences

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show abstract

“…In a recent study on cancer research, meta-learning was successfully applied to extract biological knowledge from an omics dataset for survival analysis or treatment response. This approach allows for investigating biological systems in higher resolution and at lower costs [165][166][167]. Moreover, it can play an important role in high-throughput bioinformatics research.…”

Section: Intra-species Transfer Learning Using Meta-learningmentioning

confidence: 99%

Machine Learning-based Approaches to Integrate Heterogeneous Data for Biological Knowledge Transfer

Park

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Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter

Siddiqui

Chaurasia

Shandilya

2023

J Cancer Res Clin Oncol

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A meta-learning approach to improving radiation response prediction in cancers

Cited by 10 publications

References 49 publications

Molecular Biomarkers for Predicting Cancer Patient Radiosensitivity and Radiotoxicity in Clinical Practice

Molecular Biomarkers for Predicting Cancer Patient Radiosensitivity and Radiotoxicity in Clinical Practice

Machine Learning-based Approaches to Integrate Heterogeneous Data for Biological Knowledge Transfer

Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter

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