AIMC Topic: Neoplasms, Radiation-Induced

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Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations: a real-world clinical analysis.

European radiology Nov 29, 2024
PURPOSE: The purpose of this study is to estimate the extent to which the implementation of deep learning reconstruction (DLR) may reduce the risk of radiation-induced cancer from CT examinations, utilizing real-world clinical data.
Humans Retrospective Studies Tomography, X-Ray Computed Female Risk Factors Aged Adult Male Middle Aged Deep Learning Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Propensity Score Neoplasms, Radiation-Induced
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A competing risks machine learning study of neutron dose, fractionation, age, and sex effects on mortality in 21,000 mice.

Scientific reports Aug 2, 2024
This study explores the impact of densely-ionizing radiation on non-cancer and cancer diseases, focusing on dose, fractionation, age, and sex effects. Using historical mortality data from approximately 21,000 mice exposed to fission neutrons, we empl...
Machine Learning Female Animals Male Mice Age Factors Sex Factors Dose Fractionation, Radiation Dose-Response Relationship, Radiation Neutrons Neoplasms, Radiation-Induced
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Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study.

PloS one Feb 27, 2020
The purpose of this study was to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process. Fifty-two w...
Humans Machine Learning Female Risk Factors Young Adult Adult Breast Neoplasms Case-Control Studies Genome-Wide Association Study Cohort Studies Polymorphism, Single Nucleotide Genotype Cancer Survivors Germ Cells Neoplasms, Radiation-Induced
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Utilizing patient data: A tutorial on predicting second cancer with machine learning models.

Cancer medicine Sep 1, 2024
BACKGROUND: The article explores the potential risk of secondary cancer (SC) due to radiation therapy (RT) and highlights the necessity for new modeling techniques to mitigate this risk.
Humans Machine Learning Precision Medicine Risk Factors Risk Assessment Decision Trees Neoplasms, Second Primary Neoplasms, Radiation-Induced
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