eRAD brings new Conn. client online

AuntMinnie.com staff writers

Nov 22, 2016

RIS/PACS vendor eRAD has signed Open MRI of Connecticut as a new customer.

The vendor installed its RIS, PACS, and speech recognition offerings at the outpatient imaging practice.

DeepHealth secures FDA clearance, CE marking for neuro, prostate AI

May 20, 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.

MRI-based ML model shows promise in colorectal cancer subtyping

May 19, 2026

The Invisible Force Podcast Album Cover Auntminnie Jan 2026 Thumbnail sra O0 D Ij Z6

Podcast: Expert witness hats come out to begin closing investigation

May 19, 2026

Cochrane updates 2013 prostate cancer screening review

May 18, 2026

DeepHealth secures FDA clearance, CE marking for neuro, prostate AI

By AuntMinnie.com staff writers

The company has won multiple regulatory clearances for neuro, prostate, and lumbar MRI AI.

May 20, 2026

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.

May 19, 2026

The Invisible Force Podcast Album Cover Auntminnie Jan 2026 Thumbnail sra O0 D Ij Z6

Cochrane updates 2013 prostate cancer screening review

By Kate Madden Yee

Standard PSA screening has a well-known weakness: it catches many slow-growing cancers that would never have caused harm, Cochrane said.

May 18, 2026

SimonMed launches discounted whole-body MRI for veterans

By AuntMinnie.com staff writers

SimonMed is launching an initiative offering military personnel, veterans, and first responders 20% off whole-body MRI screening.

May 18, 2026

ISMRM: fMRI flags brain network failures in fibromyalgia, long COVID

By Kate Madden Yee

While both conditions demonstrate reduced brain network flexibility, the underlying mechanisms differ.

May 18, 2026

Muscle Trigger Points Sized

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

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

May 15, 2026

Fibrotic Buckets Disease Inclusion Uk Biobank Population Ismrm2026

State investigating death following MRI scan

By AuntMinnie.com staff writers

Investigation focused on whether a pacemaker and implantable cardiac defibrillator were properly set to MRI safe mode.

May 15, 2026

Yellow Caution Tape Social

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

By Kate Madden Yee

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

ISMRM: fMRI reveals brain differences tied to psychological resilience

By Kate Madden Yee

Resilience is the ability to adapt to stress, and "low resilience is a risk factor for psychiatric disorders."

May 15, 2026

Ismrm Thumbnail

ISMRM: Brain activity changes, birth trauma linked to postpartum PTSD

By Kate Madden Yee

The findings could help clinicians develop new tools for early psychological intervention for the condition.

May 14, 2026

The PPD group showed significant increased VMHC in the dorsolateral prefrontal cortex (DLPFC), precentral gyrus (PreCG) and parietal lobule (PL) (FDR correction q-value < 0.005).

ISMRM: Quantitative intracranial vessel wall MR clarifies vascular pathology

Researchers applied two different types of software.

May 14, 2026

Software analysis shows distribution, contours, and contrast enhancement. (a)Vasculitis lesion showing diffuse concentric enhancement, (b) atherosclerotic plaque showing eccentric wall thickening and less enhancing volume, (c) RCVS showing lower percent wall volume.

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