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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
May 19, 2026
Radiology is Under Pressure
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Radiology is Under Pressure
May 2, 2026
Mri Scanner Kid
Almost half of families face financial hardship from kids' scans
May 19, 2026
A graphical abstract of the study.
Combined fluoride and lead exposure reduces bone health in youth
May 18, 2026
More in Home
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
By Liz Carey
The episode the hosts have eagerly anticipated.
May 19, 2026
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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.
May 19, 2026
Mri Scanner Kid
Combined fluoride and lead exposure reduces bone health in youth
By Will Morton
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.
May 18, 2026
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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
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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
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ISMRM: PET/MRI highly effective in patients with atherosclerosis
By Will Morton
PET/MRI distinguished symptomatic from asymptomatic carotid plaques among 145 patients.
May 15, 2026
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
By Liz Carey
Quantitative measures obtained from MRIs enable fibrotic scoring system, according to group.
May 15, 2026
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ISMRM: PET/MRI-guided treatment reduces low back pain
By Will Morton
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
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
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Ultrasound MinnieCast, Episode 8: Ultrasound's role in sustainability
By Amerigo Allegretto
Researchers from VUMC share results and strategies from their study on ultrasound's role in environmental sustainability.
May 15, 2026
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