Parathyroid four-dimensional (4D) computed tomography (CT) is an imaging technique for preoperative localization of parathyroid adenomas that involves multidetector CT image acquisition during two or more contrast enhancement phases. Four-dimensional CT offers an alternative or additional tool in the evaluation of primary hyperparathyroidism. The purpose of this article is to describe the 4D CT technique and provide a practical guide to the radiologist for imaging interpretation. The article will discuss the rationale for imaging, approach to interpretation, imaging findings, and pitfalls.

ammographic breast density can mask cancers at mammography and is an independent risk factor for breast cancer (1-3). Legislation mandating patients be notified of mammographic breast density has passed in more than 30 states, and a federal bill is under consideration. Details of state legislation vary, but most states require direct reporting to the patient that breast density can mask cancers at mammography and that the patient may benefit from additional testing. Qualitative assessment of mammographic breast density is subjective and varies widely between radiologists (4-10). In a study of 83 radiologists who assessed breast density, Sprague et al (4) found extreme variation in qualitative density assessment per the Breast Imaging Reporting and Data System (BI-RADS), with 6%-85% of mammograms assessed as either heterogeneously or extremely dense depending on radiologist interpretation. In a study of 34 radiologists, the intraradiologist agreement of density assessments among women who underwent two examinations varied from 62% to 87% (6). Commercially available methods for automated assessment of breast density do exist; however, they yield mixed results in agreement with expert qualitative density assessments, with k scores of 0.32-0.61 (11,12). These methods tend to result in over-or underreporting of breast density when compared with qualitative assessment by radiologists (11,13). A recent study found significant differences in density assessments in the same 4170 women with two software programs (Volpara, Volpara Solutions, Wellington, New Zealand; Quantra, Hologic, Bedford, Mass), with the software programs showing 37% and 51%, respectively, of women had dense breast tissue. In the same set of mammograms, radiologists determined 43% of the women had dense breast tissue (13). Deep learning (DL) has been gaining traction in radiology (12,14-17). Specifically, there has been preliminary work with DL methods to assess breast density (12,18); however, none of these techniques have been implemented in clinical practice, raising questions about clinical acceptance by practicing radiologists and the effect on patient care. In contrast, our purpose was to develop a DL algorithm we could use to reliably assess breast density and to measure the acceptance of its predictions in real-time clinical practice. We hypothesize that DL models can be applied to assess breast density at the same level as experienced breast imagers and that they can be accepted into routine clinical practice.

Purpose To develop a machine learning model that allows high-risk breast lesions (HRLs) diagnosed with image-guided needle biopsy that require surgical excision to be distinguished from HRLs that are at low risk for upgrade to cancer at surgery and thus could be surveilled. Materials and Methods Consecutive patients with biopsy-proven HRLs who underwent surgery or at least 2 years of imaging follow-up from June 2006 to April 2015 were identified. A random forest machine learning model was developed to identify HRLs at low risk for upgrade to cancer. Traditional features such as age and HRL histologic results were used in the model, as were text features from the biopsy pathologic report. Results One thousand six HRLs were identified, with a cancer upgrade rate of 11.4% (115 of 1006). A machine learning random forest model was developed with 671 HRLs and tested with an independent set of 335 HRLs. Among the most important traditional features were age and HRL histologic results (eg, atypical ductal hyperplasia). An important text feature from the pathologic reports was "severely atypical." Instead of surgical excision of all HRLs, if those categorized with the model to be at low risk for upgrade were surveilled and the remainder were excised, then 97.4% (37 of 38) of malignancies would have been diagnosed at surgery, and 30.6% (91 of 297) of surgeries of benign lesions could have been avoided. Conclusion This study provides proof of concept that a machine learning model can be applied to predict the risk of upgrade of HRLs to cancer. Use of this model could decrease unnecessary surgery by nearly one-third and could help guide clinical decision making with regard to surveillance versus surgical excision of HRLs. RSNA, 2017.

For females presenting with pathologic nipple discharge, ultrasound is a useful diagnostic tool and may be worth including in the routine evaluation.

Parathyroid adenomas and hyperplasia can be grouped into three relative enhancement patterns based on a protocol with a non-contrast-enhanced and two contrast-enhanced phases. The type B pattern is most common and could be diagnosed with two contrast-enhanced phases. However, almost one quarter of lesions have the type C pattern and thus could be missed without the non-contrast-enhanced phase.

The PPV of architectural distortion for malignancy is 74.5%. Architectural distortion is less likely to represent malignancy if detected on screening mammography than on diagnostic mammography or if there is no sonographic correlate.

Architectural distortion is more commonly detected on DBT than DM and is less likely to represent malignancy on DBT. Architectural distortion on DBT is less likely to represent malignancy if there is no sonographic correlate; however, biopsy is warranted even in the absence of a sonographic correlate, given the nearly 30% risk of malignancy in this setting.

The TNM staging system for cancer was developed by Pierre Denoix in France in the 1940s and 1950s. The North American effort to standardize the TNM system for cancer staging was first organized in 1959 as the American Joint Committee for Cancer Staging and End-Results Reporting, which is now the American Joint Committee on Cancer (AJCC). The most recent edition of the AJCC Cancer Staging Manual, the eighth edition, was globally adopted on January 1, 2018. Previous editions of the manual have relied on anatomic methods of staging alone, which used population-based survival data to predict clinical outcomes. In the era of precision medicine, the major change in the eighth edition is the incorporation of prognostic biomarkers to more accurately predict clinical outcomes and treatment response on an individual basis, without relying solely on the anatomic extent of disease. Factors such as tumor grade, hormone receptor and oncogene expression, and multigene panel recurrence scores are now integrated with anatomic information to yield a final prognostic stage group, which will provide better stratification of patient prognosis. The purpose of this article is to review the major changes in the AJCC eighth edition for breast cancer staging, review anatomic TNM staging, familiarize the radiologist with prognostic biomarkers and prognostic staging, and identify key sites of disease that may alter clinical management. ©