https://www.pcih.fr/alip/media/consensus_prostate_2011.pdf

Magnetic Resonance Imaging for the Detection, Localisation, and Characterisation of Prostate Cancer: Recommendations from a European Consensus Meeting

Mammographic densities are hypothesized to reflect the cumulative exposure to risk factors that influence breast cancer incidence. This report analyzed percent densities over time and explored predictors of density change in relation to age. The study population consisted of 607 breast cancer cases and 667 frequency matched controls with 1,956 and 1,619 mammographic readings, respectively. Mammograms done over >20 years and before a diagnosis of breast cancer were assessed for densities using a computer-assisted method. Using multilevel modeling to allow for repeated measurements, we estimated the effect of ethnicity, case status, reproductive characteristics, hormonal therapy, body mass index, and soy intake on initial status and longitudinal change. After integrating the area under the percent density curve, cumulative percent density was compared with age-specific breast cancer rates in Hawaii. Percent densities decreased approximately 5.6% per 10 years but a nonlinear effect indicated a faster decline earlier in life. Cumulative percent densities and age-specific breast cancer rates increased at very similar rates; both standardized regression coefficients were >0.9. Japanese ancestry, overweight, estrogen/progestin treatment, and, to a lesser degree, estrogen-only therapy predicted a slower decline in densities with age. Case status and adult soy intake were related to higher densities whereas overweight and having any child were associated with lower densities at initial status. Risk factors that influence the decline in mammographic densities over time may be important for breast cancer prevention because cumulative percent densities may reflect the age-related increase in breast cancer risk.

https://cebp.aacrjournals.org/content/cebp/15/4/732.full.pdf

A longitudinal investigation of mammographic density: the multiethnic cohort.

Atypical hyperplasia (1–3) and high breast density (4–6) are two of the strongest risk factors for breast cancer. If these two risk factors are independent, then the presence of both would identify a group of women at very high risk for breast cancer. These women may benefit from more-intensive approaches to screening for breast cancer or interventions to lower their risk for breast cancer.

A small prior study found a statistically significant interaction between benign breast disease and breast density, such that women with high breast density and atypical hyperplasia were at lower than expected risk (7). This is in contrast to other recent, large studies showing that breast density in combination with other risk factors is associated with increased risk of breast cancer. For example, women with high density and postmenopausal hormone therapy use are at higher risk of breast cancer than postmenopausal non–hormone therapy users with high breast density (8). Similarly, women with a first-degree relative with breast cancer and high breast density are at higher risk of breast cancer than those without a family history of breast cancer who have high breast density (9). In contrast, women with atypical hyperplasia are at increased breast cancer risk, but presence of family history does not statistically significantly modify the risk (2). The presence or absence of a statistically significant interaction between benign breast disease and breast density has important implications for improving risk assessment models of breast cancer.

We used data from the large, prospective Breast Cancer Surveillance Consortium (BCSC) to test the hypothesis that benign breast disease and breast density are independent risk factors for breast cancer and to obtain reliable estimates for the risks associated with combinations of these two factors. This is the first large study with sufficient power to examine these two strong, prevalent risk factors in US women evaluated using modern clinical practices for mammography and breast biopsies.

/pdf/benign-breast-disease-mammographic-breast-density-and-the-44bdx2n6cl.pdf

Benign Breast Disease, Mammographic Breast Density, and the Risk of Breast Cancer

Purpose: To retrospectively determine whether a Bayesian network (BN) computer model can accurately predict the probability of breast cancer on the basis of risk factors and mammographic appearance of microcalcifications, to improve the positive predictive value (PPV) of biopsy, with pathologic examination and follow-up as reference standards. Materials and Methods: The institutional review board approved this HIPAA-compliant study; informed consent was not required. Results of 111 consecutive image-guided breast biopsies performed for microcalcifications deemed suspicious by radiologists were analyzed. Mammograms obtained before biopsy were analyzed in a blinded manner by a breast imager who recorded Breast Imaging Reporting and Data System (BI-RADS) descriptors and provided a probability of malignancy. The BN uses probabilistic relationships between breast disease and mammography findings to estimate the risk of malignancy. Probability estimates from the radiologist and the BN were used to create receiv...

Bayesian Network to Predict Breast Cancer Risk of Mammographic Microcalcifications and Reduce Number of Benign Biopsy Results: Initial Experience

DIFFUSION-WEIGHTED (DW) magnetic resonance imaging (MRI) and measurements of the apparent diffusion coefficient (ADC) have proven useful in the detection and characterization of cancer (1). The ADC is sensitive to tissue cellularity and is usually lower in malignant tumors, in which water diffusion is more restricted because of the increased cell density and reduced extracellular space compared to the normal tissue. DW images may also reflect perfusion effects, as the microscopic blood flow in a randomly oriented capillary network creates a pseudodiffusion contribution to the DW signal. This effect, known as the intravoxel incoherent motion (IVIM) (2), has been observed in a variety of well-vascularized tissues, including brain, liver, pancreas, kidney, muscle, and placenta (3–8). The IVIM measurements have been reported in liver lesions (9), prostate cancer (10), and head and neck cancer (11,12) and used to differentiate pancreatic tumors (5) and salivary gland tumors (12). The IVIM component may reduce the accuracy of cancer differentiation using ADC by introducing a positive bias proportional to the perfusion fraction (13) into the ADC values and increasing their variability and dependence on the choice of the diffusion-weighting factors, or b-values (14,15). The b-value scheme also strongly affects the IVIM parameters and the separation between their values in cancer and normal tissue when the number of b-values is small (10). Therefore, the reliability of the IVIM measurements achievable in clinical practice and their usefulness in cancer diagnosis need to be further evaluated.

In the diagnosis of breast lesions, DW imaging may be used as an adjunct technique to the contrast-enhanced MRI, which provides high sensitivity, but limited specificity (16). Differentiation between malignant and benign breast lesions based on ADC has been shown to achieve a sensitivity of 85–95% and a specificity of 50–90% (15,17–21). Perfusion effects are negligible in the DW signal of normal breast parenchyma, as shown by Baron et al (22) in healthy volunteers, but can be appreciable in breast cancer (15,23). In patients with locally advanced breast cancer, Sigmund et al (24) found the average perfusion fraction in tumors to be about 10% (range, 3–22%) and demonstrated that the true diffusion coefficient provided a better separation between cancer and fibroglandular tissue (FGT) than the ADC. In agreement with Baron et al (22), Sigmund et al (24) found that the IVIM contribution in normal parenchyma was small; however, neither study reported the perfusion fraction values in FGT. Such estimates may be useful as a measure of robustness of the IVIM measurements.

The purpose of this study was to use DW MRI at 3.0 T and 1) measure the IVIM parameters in breast parenchyma in high-risk normal subjects and determine a range of b-values that provide reliable IVIM parameter estimates; 2) determine the IVIM parameters in malignant and benign breast lesions and normal parenchyma in patients with breast lesions; and 3) assess the ability of the IVIM parameters and ADC to differentiate malignant lesions from benign lesions and FGT.

/pdf/intravoxel-incoherent-motion-diffusion-weighted-mri-at-3-0-t-19m20bd3hs.pdf

Intravoxel Incoherent Motion Diffusion-Weighted MRI at 3.0 T Differentiates Malignant Breast Lesions From Benign Lesions and Breast Parenchyma

Purpose : The purpose of this study was to assess the mammographic features and pathologic outcome of category 4 lesions using the Breast Imaging Reporting and Data System(BI-RADS), and to evaluate the significance of final assessment categories. Materials and Methods : Using BI-RADS, the interpretations of 8,134 mammograms acquired between January 1997 and May 1998 were categorized. From among 161 lesions categorized as “4 ”(“suspicious abnormality ”) and pathologically confirmed by surgery or biopsy, we analysed 113, found in 66 patients. Results : The pathologic outcome of these 113 lesions was as follows: infiltrating ductal carcinoma, 17.7%(20/113); DCIS(ductal carcinoma in sitv), 8.0 %(9/113); ADH(atypical ductal hyperplasia), 5.3 % (6/113); DEH(ductal epithelial hyperplasia), 1.8 %(2/113); ductectasia, 0.9 %(1/113), FCD(fibrocystic change), 27 .4 %(31/113); fibroadenoma, 7.1 %(8/113); stromal fibrosis, 9.7%(11/113); normal parenchyma, 7.1 % (8/113); other pathology, 15.0 %(17/113). The most frequent mammographic features of BI-RADS category 4 lesions were irregular mass shape(41.2 %), spiculated mass margin(52.3%), amorphous calcification(47.3%) and clustered calcification distribution(37.1% ). Conclusion : Because category 4 lesions account for about 25.7 % of all breast malignancies, mammographic lesions in this category (“suspicious abnormality ”)should be considered for supplementary study and breast biopsy rather than short-term follow-up. Initial pathologic findings can thus be confirmed.

https://pc.jksronline.org/Synapse/Data/PDFData/1016JKRS/jkrs-41-1219.pdf

Mammographic Evaluation of Suspicious Malignant Lesions Based on ACR(American College of Radiology) Breast Imaging Reporting and Data System( BI-RADS )

So Yong Chang

Papers

Mammographic Evaluation of Suspicious Malignant Lesions Based on ACR(American College of Radiology) Breast Imaging Reporting and Data System( BI-RADS )

Comparison of Survival Rates among Different Treatment Methods of Transcatheter Hepatic Arterial Chemoembolization for hepatocellular Carcinoma