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In HANSE, PLCOm2012 (6‑year risk ≥1.58%) selected more lung cancers and yielded a lower number needed to screen than NELSON criteria, supporting risk-model selection for CT screening programs, with caveats on generalisability and mortality
A large international cohort externally validated the IMPACT clinical tool, demonstrating accurate prediction of incidental meningioma progression and supporting triage to early intervention, serial monitoring, or safe discharge.
A multicentre prospective study shows the ventricular septum curvature ratio on CTPA strongly predicts 30‑day clinical deterioration in normotensive acute pulmonary embolism, outperforming conventional single metrics and offering potential
This study highlights the heterogeneity of GG1 prostate cancer and its implications for classification and management strategies.
This study identifies specific T-cell receptor clonotypes associated with aggressive subtypes and poorer survival in mycosis fungoides and Sézary syndrome, supporting TCR sequencing as a tool for refined risk stratification in cutaneous T-c
The MERLIN_001 study validates a combined clinicopathological and gene expression profile test that accurately predicts sentinel lymph node metastasis risk in patients with cutaneous melanoma, potentially guiding safer, more personalized su
The updated GRACE 3.0 scoring system improves mortality prediction and personalizes early invasive treatment decisions in non-ST-elevation acute coronary syndrome across ten countries, demonstrating excellent validation and clinical utility
AIRE-CHB, a novel AI-enhanced ECG model, significantly improves prediction of incident complete heart block, outperforming traditional bifascicular block assessment and offering promising clinical utility in risk stratification and manageme
A multicountry study across Asia demonstrates soluble TREM1’s superior accuracy over traditional clinical signs in predicting severe febrile illness in children, potentially transforming triage in resource-limited settings.
A prospective study shows that machine learning models trained on 12-lead ECG features outperform HEART score in predicting mortality among chest pain patients, improving risk-based care precision.
New meta-analysis reveals a strong dose-response relationship between fecal hemoglobin concentration and colorectal neoplasia risk, even among FIT-negative individuals, supporting personalized screening intervals.
