ECR 2013

Mar 7, 2013

ECR 2013 is reprising the popular button-collection contest first introduced at last year's meeting.

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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.
Model confidence, reader expertise affect reader-LLM collaboration
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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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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.
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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.
PET/CT predicts response to neoadjuvant therapy in breast cancer
By Will Morton
Monitoring neoadjuvant therapy response in HER2-positive breast cancer is challenging with existing tools.
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Ga-68 HER2-PET/CT images before neoadjuvant therapy, at end of second cycle, and before surgery. (A) Baseline scan demonstrated soft-tissue lesion measuring approximately 2.3 × 2.2 cm in upper outer quadrant of left breast, with marked radiotracer uptake and SUVmax of 10.1. (B) At end of second cycle, imaging revealed residual lesion of approximately 2.2 × 1.0 cm in same region, without abnormal radiotracer accumulation. (C) Preoperative imaging demonstrated further reduction in lesion size to approximately 2.0 × 0.6 cm, with no abnormal tracer uptake detected.
5 factors that led to California workplace MRI accident
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What happened at a Redwood City hospital in California in 2023.
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NME breast lesions have high malignancy rate on MRI-guided biopsy
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Biopsies from women with a known ipsilateral malignancy have a higher frequency of breast cancer.
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Many women interested in AI-based risk assessment for breast cancer
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Survey findings also show that most women at higher risk are interested in additional screening tests.
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CT 5-star quality rating system supports optimization
By Will Morton
The rating framework demonstrated robust reproducibility, with three-reader concordance reaching up to 93% in highly standardized centers.
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READ-DVT model performs well in finding DVT from ultrasound reports
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The AI model accurately identified the presence or absence of deep vein thrombosis on unstructured ultrasound reports.
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Bone scan testing has fallen 35% among Medicare beneficiaries
By Will Morton
Declines in testing were greatest among certain underserved groups.
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MRI-based models may predict atypical brain development in children
By Amerigo Allegretto
The models could provide a framework for identifying which adolescents are at higher risk of developing psychiatric conditions.
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