RSNA 2012

Nov 26, 2012

What's cookin' at RSNA 2012? Attendees pack the halls of McCormick Place during lunchtime.

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ARRS: Breast RT doesn't impact presence of breast arterial calcifications
By Kate Madden Yee
May 2, 2025
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In her ARRS 2025 presentation, Amy Patel, MD, discussed ways that radiologists can be proactive in influencing legislation that addresses breast density. These include working with elected officials and joining medical societies, among others.
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There are a number of options for lung nodule imaging, and which to use depends on the patient.
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Most young women who are diagnosed with breast cancer had no prior imaging performed.
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Amie Lee, MD, presented at ARRS 2025 on managing 'controversial' high-risk breast lesions diagnosed on core needle biopsy. This slide outlines how to manage pleomorphic lobular carcinoma in situ (LCIS) via surgical excision.
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Images show a palpable mass in the left breast in a 55-year-old woman. (A) Mammography shows a 26-mm ill-defined mass with fine linear calcifications in the central portion of the left breast (arrows). (B) ultrasound shows a 23-mm hypoechoic mass with calcifications in the corresponding area accompanying palpability (between the arrows). (C) An axial, fat-saturated, contrast-enhanced T1-weighted MRI scan shows an 18-mm irregular heterogeneous enhancing mass in the left breast (arrows). After breast-conserving surgery, the final pathologic report confirmed a grade II estrogen receptor–negative, progesterone receptor–negative, human epidermal growth factor receptor 2–positive invasive ductal carcinoma. There was no lymphovascular invasion, and the resection margins were clear. The patient declined both adjuvant chemotherapy and radiation therapy due to psychologic distress. (D) 11 months later, surveillance ultrasound helped to detect a new 25-mm irregular hypoechoic mass at the 12-o’clock position in the left breast (arrows). A subsequent ultrasound-guided biopsy confirmed it as a recurrence of grade II invasive ductal carcinoma, maintaining the same receptor statuses. Images courtesy of the RSNA.
DL model can aid in diagnosing myocardial ischemia
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A deep-learning (DL) model can automatically measure myocardial blood flow on CT scans to help diagnose myocardial ischemia.
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Representative images from diagnostic analysis of deep learning (DL)-based CT myocardial perfusion imaging (MPI). (A-D) Representative images in a 51-year-old woman with stable angina at presentation. (A) Curved planar reformation and (B) three-dimensional maximum intensity projection images show severe stenosis of the opening left anterior descending artery (arrow). (C) Fractional flow reserve (FFR) determined with invasive coronary angiography helps confirm that there was no hemodynamic significance of the opening left anterior descending artery lesion (arrow). (D) Dynamic CT MPI provided functional information to identify hemodynamically significant stenosis with reference to invasive FFR. Myocardial blood flow (MBF) derived from DL-based CT MPI revealed the absence of decreased myocardial perfusion of the left anterior descending artery territory (the areas outlined in black represent the myocardium subtended by the left anterior descending stenosis, and the color bar is a graphical scale that maps MBF ranges). (E-H) Representative images in a 62-year-old man with exertional chest pain at presentation. (E) Curved planar reformation and (F) three-dimensional maximum intensity projection images show moderate stenosis of the middle left anterior descending artery (arrow). (G) FFR determined at invasive coronary angiography helps confirm that the middle left anterior descending lesion was hemodynamically significant (arrow). (H) Dynamic CT MPI provided functional information to identify hemodynamically significant stenosis with reference to invasive FFR. DL-based CT MPI showed a reduction in MBF within the middle and basal anterior wall of the left anterior descending artery territory (the areas outlined in black represent the myocardium with decreased perfusion, and the color bar is a graphical scale that maps MBF ranges). MBFDL = MBF derived from the DL model.
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