Residents Online CME Special Feature

Oct 19, 2010

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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.

MRI-based ML model shows promise in colorectal cancer subtyping

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Mri Scanner Kid

Almost half of families face financial hardship from kids' scans

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A graphical abstract of the study.

Combined fluoride and lead exposure reduces bone health in youth

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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

The episode the hosts have eagerly anticipated.

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Almost half of families face financial hardship from kids' scans

By Kate Madden Yee

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

Co-exposure to fluoride and lead exacerbates changes in BMD more than single exposure, suggesting synergistic effects on bone impairment.

May 18, 2026

A graphical abstract of the study.

Medical societies applaud proposed CEUS training recommendations

By Amerigo Allegretto

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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ISMRM: PET/MRI highly effective in patients with atherosclerosis

PET/MRI distinguished symptomatic from asymptomatic carotid plaques among 145 patients.

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Multimodality imaging (MRI, PET, and fused PET/MRI) of carotid artery plaques in asymptomatic and symptomatic subjects, illustrating distinct features of surface ulceration, plaque enhancement, metabolic uptake, and SUVmax.

ISMRM: Researchers eye fibrosis patterns in the body's organs

Quantitative measures obtained from MRIs enable fibrotic scoring system, according to group.

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Fibrotic Buckets Disease Inclusion Uk Biobank Population Ismrm2026

ISMRM: PET/MRI-guided treatment reduces low back pain

This study is the first to acquire follow-up FDG-PET/MRI scans after treatment to assess the correlation between clinical outcome and FDG uptake.

May 15, 2026

A 70-year-old female patient with chronic low back pain. History of radicular syndrome which led to targeted nerve block with positive effect. However, lower back pain persisted. FDG-PET/MRI showed increased FDG uptake in a disc herniation at L5-S1 (SUVmax 1.6) convincing the treating physician to proceed to discectomy. After discectomy, the patient is now mostly pain free.

ISMRM: MRI-based AI predicts organ aging before disease signs emerge

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A DL model combining MRI scans with blood markers, imaging biomarkers, and lifestyle data can predict organ-specific biological age.

May 15, 2026

Ismrm Thumbnail

Ultrasound MinnieCast, Episode 8: Ultrasound's role in sustainability

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Researchers from VUMC share results and strategies from their study on ultrasound's role in environmental sustainability.

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