https://www.auntminnie.com/clinical-news/ct/article/15975620/ldct-predicts-vertebral-fracture-risk-in-heavy-smokersAuntMinnieen2026-10-27T20:23:52.000ZLDCT predicts vertebral fracture risk in heavy smokershttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/ct-fracture.Igld7jUc17.jpg?auto=format%252Ccompress&q=70Images from opportunistic osteoporosis screening using automated software (SpineQ; Bonescreen) at low-dose CT lung cancer screening in a 62-year-old man. At baseline, this participant in the National Lung Screening Trial currently smoked (47 pack-years) and had a body mass index of 25.7 (calculated as weight in kilograms divided by height in meters squared), a volumetric bone mineral density of 73 mg/cm3 (averaged over T10–T12 using calibration parameters for a GE Medical Systems CT scanner and acquisition at 120 kVp), and a grade 3 (G3) vertebral fracture at T8. At 1-year follow-up, he had an incident grade 2 (G2) vertebral fracture at T6. Images from left to right are as follows: unenhanced axial low-dose CT image with 2.5-mm section thickness used as input imaging data; software-generated sagittal reformations in the full field of view and cropped to the spine, with labeled vertebrae and detected fractures; and three-dimensional renderings of the spine segmentation (regions of interest for the extraction of CT attenuation values are shown in red).https://www.auntminnie.com/clinical-news/digital-x-ray/article/15975589/chatgpt5-misses-half-of-distal-radius-fractures-on-xraysAuntMinnieen2026-10-27T19:31:26.000ZChatGPT-5 misses half of distal radius fractures on x-rayshttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/chatgpt-fractures.umykcGJONV.jpg?auto=format%252Ccompress&q=70Representative ChatGPT-5 outputs against the reference standard. <strong>A</strong> True positive: distal radius fracture with visible cortical disruption; the model answered “yes”. <strong>B</strong> True negative: normal wrist; the model answered “no”. <strong>C</strong> False positive: normal wrist; the model answered “yes”. <strong>D</strong> False negative: distal radius fracture; the model answered “no”. All four are anteroposterior projections from the analyzed sample, and each was submitted in a separate temporary chat session using the identical prompthttps://www.auntminnie.com/imaging-informatics/artificial-intelligence/article/15975544/siimcaimi-ai-triaging-for-ich-leads-to-faster-turnaround-timesAuntMinnieen2026-10-27T19:29:56.000ZSIIM-CAIMI: AI triaging for ICH leads to faster turnaround timeshttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/img-7815.SDSxU6LNkw.jpg?auto=format%252Ccompress&q=70Ai Tong, PhD, reports on her team's successful use of an AI-based ICH triaging approach in lowering turnaround times and reducing communication latency in the emergency department.https://www.auntminnie.com/clinical-news/mri/article/15975517/siimcaimi-20-of-advanced-imaging-prior-auth-denials-get-overturnedAuntMinnieen2026-10-27T15:23:35.000ZSIIM-CAIMI: 20% of advanced imaging prior auth denials get overturnedhttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/pet-ct-scan-fusion-image.lTwaJsFQ3w.jpg?auto=format%252Ccompress&q=70https://www.auntminnie.com/imaging-informatics/artificial-intelligence/article/15975432/siimcaimi-apollo-llm-framework-shows-promise-in-patient-monitoringAuntMinnieen2026-10-26T18:46:44.000ZSIIM-CAIMI: Apollo LLM framework shows promise in patient monitoringhttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/img-7736.GGvJ7s3yIH.jpg?auto=format%252Ccompress&q=70Faisal Mahmood, PhD, shares insights from his team's work in developing an LLM framework that takes data from radiology, pathology, and other specialties to predict disease risk and monitor patient health.https://www.auntminnie.com/imaging-informatics/artificial-intelligence/article/15975424/siimcaimi-llm-pipeline-tracks-critical-results-in-pediatric-patientsAuntMinnieen2026-10-26T18:45:21.000ZSIIM-CAIMI: LLM pipeline tracks critical results in pediatric patientshttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/img-7763.jp7xCq6ReO.jpg?auto=format%252Ccompress&q=70Pooja Pathak presents her team's findings at SIIM-CAIMI 2026 in Philadelphia.https://www.auntminnie.com/clinical-news/womens-imaging/breast/article/15838349/dbt-performance-improves-with-patient-ageAuntMinnieen2026-10-23T19:57:01.000ZDBT performance improves with patient agehttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/2026-10-23-radiology-dbt-age-groups.uoejh6YBrO.webp?auto=format%252Ccompress&q=70Images in an 81-year-old female individual with bilateral screen-detected invasive carcinomas. <strong>(A, B)</strong> Mediolateral oblique and <strong>(C, D)</strong> craniocaudal tomosynthesis images of the <strong>(A, C)</strong> right and <strong>(B, D)</strong> left breasts demonstrate masses in the upper outer quadrant of the right breast (arrow) and central inner left breast (arrow). <strong>(E)</strong> Targeted US image of the right breast demonstrates a corresponding irregular hypoechoic mass at 10 o’clock (arrow), yielding grade 1–2 invasive ductal carcinoma that was estrogen receptor (ER) positive, progesterone receptor (PR) negative, human epidermal growth factor receptor 2 (HER2) negative. <strong>(F)</strong> Targeted ultrasound image of the left breast demonstrates a corresponding irregular hypoechoic mass at 9 o’clock (arrow), yielding grade 1–2 invasive ductal carcinoma that was ER positive, PR negative, and HER2 negative. This case was selected as a representative example of a screen-detected cancer in an older female individual.https://www.auntminnie.com/editors-note/article/15838357/week-in-review-eanm-2026-fapipet-for-hcc-womens-imaging-minniecastAuntMinnieen2026-10-23T19:50:50.000ZWeek in Review: EANM 2026 | FAPI-PET for HCC | Women’s Imaging MinnieCasthttps://www.auntminnie.com/clinical-news/digital-x-ray/article/15838345/ai-fails-to-compensate-for-nightshift-fatigue-among-residentsAuntMinnieen2026-10-23T18:56:51.000ZAI fails to compensate for night-shift fatigue among residentshttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/ai-fatigue.c48mc7dd9z.jpg?auto=format%252Ccompress&q=70Technical setup and customized DICOM viewer. a) Radiology resident reads chest x-rays on a single-screen workstation equipped with screen-based eye tracking (bar unit below the screen). b) The x-ray (right) was displayed alongside the structured reporting template (left). c) Without AI assistance, readers reported the presence and severity of findings using drop-down menus. d) With AI assistance, the template was pre-filled with the model’s suggestions, which could be accepted or modified.https://www.auntminnie.com/practice-management/article/15838323/traineeled-informed-consent-talks-with-patients-often-insufficientAuntMinnieen2026-10-23T17:44:03.000ZTrainee-led informed consent talks with patients often insufficienthttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/patient-consulting-doctor-400.gjiLWOM2mc.jpg?auto=format%252Ccompress&q=70https://www.auntminnie.com/clinical-news/ct/article/15838280/high-cac-scores-nearly-doubles-30year-mortality-in-young-adultsAuntMinnieen2026-10-23T15:01:09.000ZHigh CAC scores nearly doubles 30-year mortality in young adultshttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/heart-1.2stjDfmzAq.jpg?auto=format%252Ccompress&q=70https://www.auntminnie.com/clinical-news/digital-x-ray/article/15838265/rale-score-accurately-identifies-ards-on-chest-xraysAuntMinnieen2026-10-22T20:09:45.000ZRALE score accurately identifies ARDS on chest x-rayshttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/lung2.J5WWT5Pf0I.jpg?auto=format%252Ccompress&q=70https://www.auntminnie.com/clinical-news/ct/article/15838238/pilot-project-triples-ldct-ordering-among-hispanic-patientsAuntMinnieen2026-10-22T19:39:35.000ZPilot project triples LDCT ordering among Hispanic patientshttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/lung-cancer.7pKtveVx2Z.jpg?auto=format%252Ccompress&q=70https://www.auntminnie.com/clinical-news/mri/article/15838212/fmri-reveals-how-music-enhances-food-tasteAuntMinnieen2026-10-22T16:53:54.000ZfMRI reveals how music enhances food tastehttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/figur-4.jYbPEtXEeQ.png?auto=format%252Ccompress&q=70The figure shows what happens in the brain when taste is combined with music. On the left (A), sweet taste is paired with music associated with sweetness and compared with sweet taste alone. On the right (B), the same comparison is shown for sour taste. The yellow and orange areas indicate regions where the brain responds more strongly when music and taste are experienced together. These include areas involved in processing taste and sensory signals from the mouth. The findings show that what we hear can influence how the brain processes what we taste.https://www.auntminnie.com/clinical-news/molecular-imaging/nuclear-medicine/article/15838154/eanm-is-research-fragmentation-holding-back-nuclear-medicineAuntMinnieen2026-10-21T20:28:55.000ZEANM: Is ‘research fragmentation’ holding back nuclear medicine?https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/roslyn-francis.cC1Wj81psC.jpg?auto=format%252Ccompress&q=70Roslyn Francis, PhD, delivered a talk on the importance of overcoming research fragmentation October 20 at EANM in Vienna.https://www.auntminnie.com/clinical-news/ct/article/15838105/pcct-improves-image-quality-when-scanning-patients-with-obesityAuntMinnieen2026-10-21T18:03:04.000ZPCCT improves image quality when scanning patients with obesityhttps://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/10/images-radiol260573fig5.pJ5HrI5l9x.jpg?auto=format%252Ccompress&q=70Representative abdominal CT images in three patients with obesity and increasing body mass index (BMI; calculated as patient weight in kilograms divided by patient height in meters squared). Contrast-enhanced axial abdominal CT images in the venous phase are shown for three patients undergoing clinically indicated routine examinations with <strong>(A–C)</strong> energy-integrating detector (EID) CT (top row) and <strong>(D–F)</strong> photon-counting CT (PCCT) (bottom row); each column represents one patient, and BMI increases from left to right. Volume CT dose index (CTDI<sub>vol</sub>) is displayed on each image to document the radiation dose of the individual examination. <strong>(A, D)</strong> Images in a 46-year-old male patient; CTDI<sub>vol</sub> is 12.43 mGy at EID CT and 10.32 mGy at PCCT, and BMI is 34.79. <strong>(B, E)</strong> Images in a 69-year-old female patient; CTDI<sub>vol</sub> is 24.24 mGy at EID CT and 16.39 mGy at PCCT, and BMI is 42.52. <strong>(C, F)</strong> Images in a 54-year-old female patient; CTDI<sub>vol</sub> is 29.44 mGy at EID CT and 18.98 mGy at PCCT, and BMI is 49.21. Window center is 50 HU, and window width is 340 HU. In each of the three patients, the PCCT images were acquired at a lower CTDI<sub>vol</sub> than the corresponding EID CT images (<i>P</i> < .001).https://www.auntminnie.com/clinical-news/womens-imaging/article/15838029/auntminniecom-womens-imaging-insiderAuntMinnieen2026-10-20T20:28:05.000ZAuntMinnie.com Women's Imaging Insider