"Ours is the first population-based study to capture data on every CT scan to an individual during childhood or young adulthood and then measure the subsequent cancer risk." Despite the elevation in cancer risk, these two malignancies are relatively rare and the actual number of additional cases...
Twenty of the studies were estimated cancer incidence and not case-control or cohort studies, the quality scores ranged from 2 to 6 (Table S3B). Cancer risk from CT scans The cancer risks for adults following CT scans were inordinately increased (LAR adults, OR, 10.00 [95% CI, 5.87 to ...
https://www.nature.com/articles/s41591-023-02620-0 https://www.health.harvard.edu/cancer/radiation-risk-from-medical-imaging 订阅关注防失联 订阅备用号 请订阅youtube:Y博的科普园 https://www.youtube.com/@Doctor_YZ/featured 原创不易 赞赏随缘...
in the subsequent 12 years. Our results strengthen the body of evidence of increased cancer risk at low radiation doses and highlight the need for continued justification of pediatric CT examinations and optimization of doses. ...
韩国论文 Association of Exposure to Diagnostic Low-Dose Ionizing Radiation With Risk of Cancer Among...
and precursor conditions using data from Haematological Malignancy Research Network (HMRN). Cancer ...
[5]. Mikhael PG, Wohlwend J, Yala A, et al. Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography [published online ahead of print, 2023 Jan 12]. J Clin Oncol. 2023;JCO2201345. doi:10.1200/JCO.22.01345 ...
作者认为，具有高质量方法的文章明显占优势，发现低剂量辐射不会增加癌症风险。证据表明，暴露于多次CT扫描和低剂量辐射的其他来源，累积剂量高达100 mSv（大约10次扫描），并且可能高达200mSv（约20次扫描），不会增加癌症风险。Schultz CH, Fairley R, Murphy LS, et al. The Risk of Cancer from CT Scans and...
[5]. Mikhael PG, Wohlwend J, Yala A, et al. Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography [published online ahead of print, 2023 Jan 12]. J Clin Oncol. 2023;JCO2201345. doi:10.1200/JCO.22.01345 ...
这种方法是泛加拿大预测模型和计算因子(www. brocku.ca/lung-cancer-risk-calculator),其能在不需要模型来分辨非实质性和部分实质性结节的情况下简化临床决策的制定。既往的肺部结节预测模型都是通过回顾性研究来确定的,是基于医院或诊所,基于肺癌高危人群所确定的。