RSNA 2012

Nov 26, 2012

Early birds arrive at McCormick Place for RSNA 2012.

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Pacsman Pontification
Podcast: The PACSMan Pontificates, Episode 2 -- RFPs
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Radiology is Under Pressure
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Case example of a false-negative AI result. An 85-year-old female presented to the emergency department with dyspnea. Axial 0.625-mm from CT pulmonary angiography examination with contrast (CTPA) images demonstrate a thin, linear right-sided filling defect extending from the right interlobar pulmonary artery (A and B, red arrows) into the right middle lobar artery (B, yellow arrow), consistent with acute pulmonary embolism (PE). The finding was described in the radiology report; however, the AI result was negative for PE. Adjudication was consistent with acute PE.
Human-AI collaboration beats AI alone for identifying PE
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AI model predicts 10-year breast cancer risk
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Podcast: The PACSMan Pontificates, Episode 2 -- RFPs
By Michael J. Cannavo
Are RFPs still needed for buying a PACS? Michael J. Cannavo, aka the PACSMan, tackles this question in episode 2 of The PACSMan Pontificates podcast series.
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TAE reduces pain in patients with chronic wrist pain
By Will Morton
The finding suggests that the emerging interventional radiology procedure could be a new option for patients who have exhausted conservative management.
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A 44-year-old woman with TFCC injury treated by transcatheter arterial embolization (TAE). a) Right brachial arteriography via common femoral artery access with a 5-Fr catheter demonstrates hyperstaining on the ulnar side of the wrist joint (white circle). b) An ulnar artery branch is superselected with a 1.9-Fr microcatheter, showing hypervascular staining corresponding to the pain site (white arrow). c) Delayed-phase angiography reveals early venous drainage (white arrow) adjacent to the hyperstaining, a finding often observed in TAE and considered an additional marker for embolization. d) Final angiography after injection of 0.5 mL quick-soluble gelatin sponge particles (QS-GSPs) demonstrates resolution of the hyperstaining (white circle). The VAS pain score improved from 7 at baseline to 1 at six months.
Human-AI collaboration beats AI alone for identifying PE
By Kate Madden Yee
Study findings highlight the continued central role of radiologists for this indication.
May 20, 2026
Case example of a false-negative AI result. An 85-year-old female presented to the emergency department with dyspnea. Axial 0.625-mm from CT pulmonary angiography examination with contrast (CTPA) images demonstrate a thin, linear right-sided filling defect extending from the right interlobar pulmonary artery (A and B, red arrows) into the right middle lobar artery (B, yellow arrow), consistent with acute pulmonary embolism (PE). The finding was described in the radiology report; however, the AI result was negative for PE. Adjudication was consistent with acute PE.
AI model predicts 10-year breast cancer risk
By Amerigo Allegretto
A mammography-based AI model showed strong performance in predicting 10-year breast cancer risk over clinical and other AI models.
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AI devices vary widely in lung cancer detection
By Will Morton
Three devices helped detect more cancerous tumors, whereas the other four devices helped detect fewer tumors.
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Cropped secondary capture examples. These are illustrative and not intended to imply superiority or inferiority of any device. (A) Posteroanterior radiograph in a 46-year-old female patient. The device correctly identified a right lower lobe nodule projected below the right hemidiaphragm and hilar lymphadenopathy. (B) Posteroanterior radiograph in an 86-year-old female patient with a classic Golden S sign highly suggestive of cancer. Three devices did not identify any findings. (C) The output from one device for the same radiograph as in B. The device placed a contour around the area of abnormality but mislabeled it as segmental collapse, and there are no other elements in the output to raise suspicion of cancer. (D) Posteroanterior radiograph in a 60-year-old male patient -- a case of confirmed lung cancer that was not deemed visible in retrospect. The device identified multiple false-positive abnormalities. (E) Posteroanterior radiograph in a 77-year-old female patient with two right lower lobe nodules. The device mislabeled the abnormality as infection -- a diagnostic term that could incorrectly influence clinical management. (F) Posteroanterior radiograph in a 77-year-old female patient with a right hilar tumor. Most of the lungs have been labeled by the device, with excessive overlap of the abnormality that pragmatically represents an incorrect result. All annotations shown were produced by the devices. LL = lung lesion, LO = lung opacity, PO = pleural other, TBC = tuberculosis.
MRI-based ML model shows promise in colorectal cancer subtyping
By Amerigo Allegretto
A machine-learning model based on MRI radiomics achieved high performance in predicting colorectal cancer subtypes.
May 19, 2026
Overview of the study design, including data collection, radiomics model construction, and biologic interpretation. (A) The primary cohort for model development and evaluation in the training and internal test sets includes 168 patients with colorectal cancer (CRC) from center 1 (Fudan University Shanghai Cancer Center), whereas external testing was performed using data from 85 patients from centers 2 (Huashan Hospital, Fudan University) and 3 (Zhongshan Hospital, Fudan University). For model interpretation, RNA sequencing (RNA-seq) data from 18 patients with CRC in center 1 and single-cell data from 35 patients from the Gene Expression Omnibus (GEO) database are incorporated. Mismatch repair (MMR) protein expression, obtained from pathologic reports in electronic medical records (EMRs), was used to identify patients with consensus molecular subtype 1 (CMS1) CRC. Tissue microarrays (TMAs) from surgically obtained CRC tissues were analyzed for immunohistochemical staining of caudal-type homeobox 2 (CDX2), 5-hydroxytryptamine receptor 2B (HTR2B), FERM domain containing 6 (FRMD6), and zinc finger E-box binding homeobox 1 (ZEB1), and the results were subsequently entered into an online classification tool that assigned patients to CMS2, 3, or 4 subtypes. (B) Radiomics workflow for CMS4 prediction. Lesions were manually delineated on T2-weighted imaging (T2WI) and contrast-enhanced (CE) T1-weighted imaging (T1WI), followed by radiomics feature extraction. Feature selection and model development were performed in the training set using machine-learning algorithms with fivefold cross-validation for model selection, and the final model was evaluated in the internal and external test sets using receiver operating characteristic (ROC) curve analysis. (C) Transcriptomic interpretation workflow. Using the radiomics-predicted CMS4 classification, bulk RNA sequencing (RNA-seq) data were used to perform differential expression analysis between predicted CMS4 and non-CMS4 groups, followed by pathway enrichment analyses to assess biologic relevance. Public single-cell RNA sequencing data from the Gene Expression Omnibus database were then used for cell-type–level interrogation to support interpretation of the associated pathways.
Podcast: Expert witness hats come out to begin closing investigation
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The episode the hosts have eagerly anticipated.
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Almost half of families face financial hardship from kids' scans
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These families are more than three times as likely to delay or skip recommended scans because of cost.
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Combined fluoride and lead exposure reduces bone health in youth
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Co-exposure to fluoride and lead exacerbates changes in BMD more than single exposure, suggesting synergistic effects on bone impairment.
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A graphical abstract of the study.
Medical societies applaud proposed CEUS training recommendations
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Ultrasound societies are voicing support for new proposed training standards by CAAHEP for CEUS education.
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Cochrane updates 2013 prostate cancer screening review
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Standard PSA screening has a well-known weakness: it catches many slow-growing cancers that would never have caused harm, Cochrane said.
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ISMRM: fMRI flags brain network failures in fibromyalgia, long COVID
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While both conditions demonstrate reduced brain network flexibility, the underlying mechanisms differ.
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