Insights Into Imaging

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Insights Into Imaging

Insights into Imaging is a gold open access journal owned by the European Society of Radiology. Social Media Editor: Roberto Cannella

Insights into Imaging

Insights into Imaging is a gold open access journal owned by the European Society of Radiology and edited by Editor-in-Chief, Paola Clauser. It publishes educational and critical reviews, as well as radiological guidelines and statements from leading European societies. All published articles are freely accessible worldwide which allows for the widest possible dissemination.

03/02/2026

🚨 177Lu-PSMA Therapy & Imaging in Prostate Cancer

• PSMA-PET/CT guides patient selection & tracks response
• Adjuncts like 18F-FDG PET, post-therapy SPECT, & whole-body DWI-MRI catch resistant/PSMA-negative disease
• Quantitative biomarkers (SUVmean, tumor volume, heterogeneity) + AI/radiomics may personalize treatment

https://link.springer.com/article/10.1186/s13244-025-02151-x (Aditi Ranjan et al.)

03/02/2026

Meet our latest Deputy Editor!

Yosh*to Tsushima, MD, PhD, is Professor and Chair of Diagnostic Radiology and Nuclear Medicine at Gunma University Graduate School of Medicine, Japan. A board-certified radiologist and ultrasound specialist, his interests include diagnostic imaging, contrast media, radiation protection and medical safety, with a commitment to patient-centered care.

He will be taking the role of Deputy Editor for Radiation Protection. Welcome to the team!

02/02/2026

🩺 A new InsightsintoImaging study of 113 patients shows:

• FLIS correlates with fibrosis stage & clinical scores (Child–Pugh, ALBI)
• Adding parenchymal heterogeneity (FLIS-H) strengthens correlations significantly
• FLIS-H offers a fast, reliable, non-invasive liver assessment

💡 Takeaway: Incorporating heterogeneity into FLIS improves its clinical utility for fibrosis evaluation.

https://link.springer.com/article/10.1186/s13244-025-02187-z (Hande Özen Atalay et al.)

02/02/2026

🩺MRI-only B3 Lesions

A study of 59 MRI-only B3 lesions found 11.9% upgraded to malignancy after excision. Key findings:

• Lesion size >20 mm significantly increases upgrade risk (OR 8.57)
• Histology alone is less predictive; FEA

30/01/2026

🖐️ new article: FRACTURE MRI for Hand Tendons

FRACTURE, a fast field echo sequence with restricted echo-spacing, outperforms T2WI & PD-VISTA in visualizing hand tendons.

• Enables 3D volume-rendered images
• Higher contrast for muscle-tendon differentiation
• Supports surgical planning & anatomical assessment

A promising tool to enhance hand MRI evaluation and clinic

https://link.springer.com/article/10.1186/s13244-025-02182-4 (Yukari Matsuzawa et al.)

30/01/2026

🩺 RADS in Radiology: A Quick Guide for Young & General Radiologists

Reporting & Data Systems (RADS) aim to standardize imaging acquisition, interpretation, and reporting, improving communication between radiologists and clinicians.

This review covers the full RADS alphabet highlighting applications, imaging criteria, and practical tips for clinical use.

https://link.springer.com/article/10.1186/s13244-025-02154-8 (Roberto Cannella et al.)

29/01/2026

Reducing missed breast cancer diagnoses in mammography

Diagnostic errors remain a major challenge, with clinical and medico-legal consequences. Key strategies to improve accuracy include:
✅ Peer review & double reading
✅ Structured quality assurance programs
✅ Education & bias awareness
✅ AI-driven computer-aided detection

https://link.springer.com/article/10.1186/s13244-025-02168-2 (Niketa Chotai et al.)

28/01/2026

🫁AI predicts lung cancer outcomes

In 1,675 NSCLC patients, AI-quantified whole-lung emphysema and interstitial lung abnormality (ILA) severity were independent predictors of overall survival:
✅ More severe emphysema or ILA → worse survival
✅ Combined AI model outperformed TNM staging alone (C-index 0.80 vs 0.75)

💡 AI can complement standard staging and guide individualized patient management.

https://link.springer.com/article/10.1186/s13244-025-02180-6 (Guangjing Weng et al.)

28/01/2026

Patients weigh in on AI in healthcare

A survey of 198 imaging patients shows:
✅ 87% support AI in diagnostics
✅ 73% support AI in therapy
❌ Only 28% support AI for triage
💡 84% want to be informed when AI is used

Patients are generally open to AI, especially with education and personal experience. Transparency and awareness are key for adoption.

https://link.springer.com/article/10.1186/s13244-025-02159-3 (Sophie G. Baldus et al.)

27/01/2026

In early-stage HBV-related hepatocellular carcinoma, patients with two or more metabolic abnormalities face higher postoperative recurrence risk.

A new clinical-radiological model incorporating metabolic burden:
✅ Outperforms BCLC & CNLC staging
✅ Accurately predicts 2- and 5-year recurrence
✅ Supports personalized postoperative care

https://link.springer.com/article/10.1186/s13244-025-02183-3 (Beixuan Zheng et al.)

26/01/2026

A new study shows that CT acquisition settings, especially slice thickness, significantly affect radiomic feature stability.

✅ Models built on stable features maintained consistent performance across different scanners
✅ Unstable features caused variability and misclassification
✅ Highlighting the importance of feature stability analysis for generalizable radiomic-based diagnostics

https://link.springer.com/article/10.1186/s13244-025-02179-z (Qian Zhou et al.)

26/01/2026

Predicting vulnerable carotid artery plaques with CT imaging

A radiomics model of the fat surrounding the carotid artery outperformed conventional plaque scoring and fat attenuation in identifying histologically confirmed vulnerable plaques:
✅ Accuracy up to 94.5% in training, 81.7% in testing
✅ Works across different scanners
✅ Interpretable using feature importance analysis

https://link.springer.com/article/10.1186/s13244-025-02134-y (Keqiang Shu et al.)

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