Breast Cancer Screening for Average-Risk Women: Recommendations From the ACR Commission on Breast Imaging

Monticciolo, Debra L.; Newell, Mary S.; Hendrick, R. Edward; Helvie, Mark A.; Moy, Linda; Monsees, Barbara; Kopans, Daniel B.; Eby, Peter R.; Sickles, Edward A.

doi:10.1016/j.jacr.2017.06.001

Cited by 226 publications

(171 citation statements)

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“…There are relatively minor differences in the breast cancer screening guidelines of major medical and public health organizations . The main difference concerns the age at which mammography screening should start without the need for shared decision making and the recommended age‐specific screening intervals, which, in turn, are influenced by different approaches to assessing the burden of disease, evaluating the evidence for the benefit of screening, the age to stop screening, and different judgements about the balance of benefits and harms.…”

Section: Breast Cancermentioning

confidence: 99%

“…43 There are relatively minor differences in the breast cancer screening guidelines of major medical and public health organizations. [43][44][45][46] The main difference concerns the age at which mammography screening should start without the need for shared decision making and the recommended age-specific screening intervals, which, in turn, are influenced by different approaches to assessing the burden of disease, evaluating the evidence for the benefit of screening, the age to stop screening, and different judgements about the balance of benefits and harms. All major guidelines recommend that women have the opportunity to choose to start screening at age 40 years; some recommend using a shared decision-making process for women in their early 40s, and some recommend using a shared decision-making process throughout the age range from 40 to 49 years.…”

Section: Screening Guidelinesmentioning

confidence: 99%

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A blueprint for cancer screening and early detection: Advancing screening’s contribution to cancer control

Wender

Brawley

Fedewa

et al. 2018

CA A Cancer J Clinicians

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From the mid‐20th century, accumulating evidence has supported the introduction of screening for cancers of the cervix, breast, colon and rectum, prostate (via shared decisions), and lung. The opportunity to detect and treat precursor lesions and invasive disease at a more favorable stage has contributed substantially to reduced incidence, morbidity, and mortality. However, as new discoveries portend advancements in technology and risk‐based screening, we fail to fulfill the greatest potential of the existing technology, in terms of both full access among the target population and the delivery of state‐of‐the art care at each crucial step in the cascade of events that characterize successful cancer screening. There also is insufficient commitment to invest in the development of new technologies, incentivize the development of new ideas, and rapidly evaluate promising new technology. In this report, the authors summarize the status of cancer screening and propose a blueprint for the nation to further advance the contribution of screening to cancer control.

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Section: Breast Cancermentioning

confidence: 99%

Section: Screening Guidelinesmentioning

confidence: 99%

A blueprint for cancer screening and early detection: Advancing screening’s contribution to cancer control

Wender

Brawley

Fedewa

et al. 2018

CA A Cancer J Clinicians

View full text Add to dashboard Cite

show abstract

“…57 The American College of Radiology Commission on Breast Imaging now recommends that mammography and DBT are "usually appropriate" for screening of average-risk women, noting that DBT addresses some limitations of standard digital mammography. 58 In Hong Kong, DBT has been increasingly adopted to replace or serve as an adjunct to 2D mammography in opportunistic screening. We anticipate that the shift to DBT screening will become a global trend.…”

Section: Breast-screening Programmes In Hong Kong: Room For Developmentmentioning

confidence: 99%

Understanding breast cancer screening—past, present, and future

Sitt¹,

Lui²,

Sinn³

et al. 2018

Hong Kong Med J

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“…Some feature interpretations are very well known, and consensually classified and correlated to a known risk. For example in Breast Cancer medical imaging, American College of Radiologist (ACR) defines the Bi-Rads classification (Breast Imaging Reporting and Data System), (Monticciolo et al 2017) , which aims to classify pattern of radiologic features in 6 classes (from 0 to 5) corresponding to 6 differents probabilities of malignancy. So, The Bi-Rads provides an evidence based decision tool for the physician, indexed on a probabilistic risk of malignancy (practice a follow up exam, make a biopsy…) .…”

Section: Introductionmentioning

confidence: 99%

Brain age prediction of healthy subjects on anatomic MRI with deep learning: going beyond with an "explainable AI" mindset

Hérent¹,

Jégou²,

Wainrib³

et al. 2018

Preprint

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Objectives: Define a clinically usable preprocessing pipeline for MRI data. Predict brain age using various machine learning and deep learning algorithms. Define Caveat against common machine learning traps. Data and Methods: We used 1597 open-access T1 weighted MRI from 24 hospitals. Preprocessing consisted in applying: N4 bias field correction, registration to MNI152 space, white and grey stripe intensity normalization, skull stripping and brain tissue segmentation. Prediction of brain age was done with growing complexity of data input (histograms, grey matter from segmented MRI, raw data) and models for training (linear models, non linear model such as gradient boosting over decision trees, and 2D and 3D convolutional neural networks). Work on interpretability consisted in (i) proceeding on basic data visualization like correlations maps between age and voxels value, and generating (ii) weights maps of simpler models, (iii) heatmap from CNNs model with occlusion method. Results: Processing time seemed feasible in a radiological workflow: 5 min for one 3D T1 MRI. We found a significant correlation between age and gray matter volume with a correlation r = -0.74. Our best model obtained a mean absolute error of 3.60 years, with fine tuned convolution neural network (CNN) pretrained on ImageNet. We carefully analyzed and interpreted the center effect. Our work on interpretability on simpler models permitted to observe heterogeneity of prediction depending on brain regions known for being involved in ageing (grey matter, ventricles). Occlusion method of CNN showed the importance of Insula and deep grey matter (thalami, caudate nuclei) in predictions. Conclusions: Predicting the brain age using deep learning could be a standardized metric usable in daily neuroradiological reports. An explainable algorithm gives more confidence and acceptability for its use in practice. More clinical studies using this new quantitative biomarker in neurological diseases will show how to use it at its best.

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Breast Cancer Screening for Average-Risk Women: Recommendations From the ACR Commission on Breast Imaging

Cited by 226 publications

References 0 publications

A blueprint for cancer screening and early detection: Advancing screening’s contribution to cancer control

A blueprint for cancer screening and early detection: Advancing screening’s contribution to cancer control

Understanding breast cancer screening—past, present, and future

Brain age prediction of healthy subjects on anatomic MRI with deep learning: going beyond with an "explainable AI" mindset

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