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Dec 4, 2013
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Dexa
AI system cuts DEXA turnaround time, improves reports
August 14, 2026
Nominations Are Open — 2026 Minnies
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Nominations Are Open — 2026 Minnies
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Radiation Therapy Suite 400
Prone positioning may benefit women undergoing PBI
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Approved Stamp 400
Lantheus wins FDA approval for tau PET imaging agent
August 14, 2026
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AI system cuts DEXA turnaround time, improves reports
By Will Morton
Median report creation and turnaround times dropped to under one minute.
August 14, 2026
Dexa
PAE effective in men over 80 years old
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Findings support PAE as an important minimally invasive treatment option for elderly patients who are nonsurgical candidates.
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Prone positioning may benefit women undergoing PBI
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Prone positioning may lead to lower heart and lung doses and lower rates of acute dermatitis.
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Lantheus wins FDA approval for tau PET imaging agent
By AuntMinnie.com staff writers
Tauklarify, previously known as MK-6240, has been approved for tau PET imaging in adults with cognitive impairment being evaluated for Alzheimer's disease.
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Approved Stamp 400
Women's Imaging MinnieCast, Episode 15: BACs and heart disease risk
By Amerigo Allegretto
Laurie Margolies, MD, shares insights on how breast arterial calcifications (BACs) on mammograms should be managed.
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FAPI-PET/CT shows promise in hepatopancreatobiliary cancers
By Will Morton
FAPI-PET/CT could potentially improve noninvasive cancer staging, and, ultimately, clinical decision making.
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Shown is a 69-year-old man with a cholangiocarcinoma in the distal common bile duct. F-18 FDG-PET/CT was negative where Ga-68 FAPI-46 PET/CT showed high focal FAPI uptake in the liver hilum and surrounding lymph nodes. a-d: F-18 FDG-PET/CT, e-h: Ga-68 FAPI-46 PET/CT. a, b, e, f: Maximum intensity projections, c, g: axial fusion images, d, h: non-contrast enhanced CT.
Longitudinal DBT could help improve long-term cancer risk prediction
By Amerigo Allegretto
A longitudinal DBT model outperformed other models based on mammography and clinical factors in predicting five-year cancer risk.
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Breast Dbt 400
SBRT for prostate cancer cuts side effects, falls short on disease control
By Will Morton
A multinational team evaluated SBRT against moderately hypofractionated intensity-modulated radiation therapy in a phase III trial.
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Prostate Imaging
DEXA reveals tea consumption supports bone health
By Will Morton
Moderate-frequency tea consumption was associated with better bone mineral status and more favorable longitudinal changes.
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AI tools may not meet breast radiologists' expectations in practice
By Amerigo Allegretto
AI tools in breast imaging may have more modest clinical impact on recall and biopsy rates.
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Stepped reminders boost CT lung cancer screening
By Will Morton
Trial findings directly address the current lack of effective lung cancer screening interventions.
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Lung Cancer
Model confidence, reader expertise affect reader-LLM collaboration
By Amerigo Allegretto
Successful human-LLM collaboration is associated with model confidence and reader expertise in chest imaging.
August 11, 2026
Example cases illustrate the double-edged role of large language model (LLM) rationale quality. (A) A 50-year-old female patient with a palpable anterior chest wall mass (correct diagnosis: solitary fibrous tumor of the pleura). The LLM incorrectly suggested synovial sarcoma but provided a persuasive rationale (quality score: 4 of 5), describing a well-defined nodule at axial contrast-enhanced CT spanning the pleura and intercostal space with heterogeneous strong enhancement. Four of five readers (80%) accepted the incorrect suggestion. FDG = fluorodeoxyglucose. (B) A 61-year-old male patient with diabetes presented with cough, sputum, and dyspnea (correct diagnosis: mucormycosis). The LLM correctly suggested mucormycosis with an excellent rationale (quality score: 5 of 5), integrating the reverse halo sign on serial axial CT images and rapidly progressive cavitary lesions. Three of five readers initially selected incorrect diagnoses; after LLM output, all five selected the correct diagnosis (100% acceptance). Blue and orange reader icons denote high- and low-expertise readers, respectively.
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