Special Feature - Artificial Intelligence

Dec 10, 2019
2019 12 09 17 23 6730 Artificial Intelligence Ai Suit 400
Potential for racial bias raises concerns for radiology AI
Potential for racial bias raises concerns for radiology AI

A preprint study that raised troublesome questions last year about potential bias in radiology artificial intelligence (AI) algorithms has now been formally published in the Lancet Digital Health with the same conclusions. Learn more

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ARRS: Breast RT doesn't impact presence of breast arterial calcifications
By Kate Madden Yee
May 2, 2025
Thumbnail 71 Year Old Patient With History Of Left Breast Invasive Lobular Carcinoma Mindul Thumbnail
IMV: PET/CT service contracts are a key aspect of system ownership
By Davin Korstjens
May 2, 2025
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ARRS: Get involved with breast health advocacy
By Amerigo Allegretto
At ARRS 2025, Amy Patel, MD, discussed the need for advocacy to drive legislation on insurance coverage for women with dense breasts.
May 2, 2025
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.
ARRS: The state of lung imaging
By Kate Madden Yee
There are a number of options for lung nodule imaging, and which to use depends on the patient.
May 1, 2025
Lung Illustration Blue
ARRS: Most women under 40 have no imaging before breast cancer diagnosis
By Amerigo Allegretto
Most young women who are diagnosed with breast cancer had no prior imaging performed.
May 1, 2025
At ARRS 2025, Dana Kong from Case Western Reserve University presented on breast cancer trends among women aged 40 years and younger. Among her team's findings, young women who had imaging prior to their cancer diagnosis had higher receptor status and underwent surgery more frequently than those with no prior imaging.
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By Will Morton
Radiology societies and patient advocates are at odds over a bill in the U.S. Congress.
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ARRS: Mobile mammo boosts breast screening, but no-show rate still high
By Amerigo Allegretto
Free mobile screening mammography improves breast screening access for uninsured women, but doesn't lead to follow-up adherence.
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ARRS: How to manage 'controversial' high-risk breast lesions
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Managing high-risk breast lesions with no consensus can be tricky, but breast radiologists can navigate this area.
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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.
Preop MRI fails to deliver results for breast cancer subtype
Preoperative MRI didn't improve outcomes in women with HER2-positive and hormone receptor-negative breast cancer in a new study.
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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
By Will Morton
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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A conceptual framework for neurotheranostics.
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