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.

19/01/2026

A new MRI-based DLR nomogram accurately predicts vessels encapsulating tumor clusters (VETC) and recurrence-free survival (RFS) in hepatocellular carcinoma.

📌 Outperformed traditional clinical-radiologic models (AUC 0.752 vs 0.678)
📌 DLR score is an independent prognostic factor
📌 Interpretable with SHAP for clinical decision support

https://link.springer.com/article/10.1186/s13244-025-02167-3 (Jinjing Wang et al.)

19/01/2026

🧠Deep learning for Alzheimer’s MRI? Beware the “Clever Hans” effect.

This study shows 3D CNNs classify AD using skull-stripping artifacts rather than true brain tissue texture - highlighting shortcut learning.

📌 Volumetric cues, not gray-white matter texture, drove decisions.
📌 Interpretability tools are essential for robust, trustworthy AI in medical imaging.

https://link.springer.com/article/10.1186/s13244-025-02158-4 (Christian Tinauer et al.)

16/01/2026

🦵💡 Hip abductor health in GTPS
Volumetric MRI shows higher fat infiltration in gluteus minimus & medius of hips with greater trochanteric pain syndrome (GTPS) vs asymptomatic hips.

Key findings:

Females: higher fat fraction (Gmin 19%, Gmed 13%) & lower muscle volume than males

Symptomatic hips: more fatty infiltration than asymptomatic hips

📌 3D fat fraction & muscle volume could serve as imaging biomarkers for GTPS.

https://link.springer.com/article/10.1186/s13244-025-02175-3 (Georg Wilhelm Kajdi et al.)

16/01/2026

🩻 MRI in pe**le cancer: why it matters

This comprehensive review highlights MRI as the most accurate tool for staging pe**le squamous cell carcinoma—critical for assessing tumor depth, extent, and nodal disease. With optimized thin-section T2-weighted imaging and proper technique, MRI directly guides treatment planning and prognosis.

https://link.springer.com/article/10.1186/s13244-025-02089-0 (Océane Charret et al.)

15/01/2026

🩻🎯 How do you biopsy a lesion you can’t see on mammography or ultrasound?

This prospective study shows CEM-guided biopsy can accurately target enhancing-only breast lesions, with a 97% success rate and 57% malignancy yield - all with low radiation dose and short procedure time.

📌 A faster, more accessible alternative to MRI-guided biopsy.

https://link.springer.com/article/10.1186/s13244-025-02163-7 (Giulia Morano et al.)

14/01/2026

📊 Ultrafast DCE-MRI in breast imaging

Early-phase semi-quantitative analysis showed noninferior performance to full pharmacokinetic modeling for differentiating benign from malignant lesions (AUC 0.93 vs 0.92).

Both approaches had limited value for molecular subtyping and pCR prediction.

https://link.springer.com/article/10.1186/s13244-025-02166-4 (Ying Cao et al.)

14/01/2026

🧬📊 What if a CT scan could read a tumor’s DNA?

Radiomics and radiogenomics are unlocking hidden data in medical images to reveal renal cell carcinoma biology, heterogeneity, and prognosis—non-invasively. A new review shows how this tech is powering smarter, more personalized cancer care.

https://link.springer.com/article/10.1186/s13244-025-02164-6 (Zihan He & Liping Huang)

13/01/2026

🩺 New study in Biliary Atresia (BA).

A large two-center study of 1,352 infants achieved outstanding diagnostic accuracy for BA:
📈 AUC up to 0.972 across training, validation & test cohorts
📏 Optimal MR-TCT cutoff: 3.75 mm (AUC 0.828)

Crucially, 3D MRCP reduces false negatives when US is negative but clinical suspicion remains high.

https://link.springer.com/article/10.1186/s13244-025-02165-5 (Shuyi Liu et al.)

07/01/2026

Breast screening saves lives - but over-detection and false positives remain major challenges across mammography, DBT, US, and MRI, especially for BI-RADS 3 and 4A lesions.

🤖 This review shows how AI-assisted reading can help maintain cancer detection while reducing recalls, benign biopsies, and workload, supporting smarter risk stratification and follow-up decisions.

https://link.springer.com/article/10.1186/s13244-025-02160-w (Siyu Wang et al.)

06/01/2026

📢 AI in Radiology: Guidance for Safe Use

The ESR eHealth & Informatics Subcommittee provides practical recommendations for post-market surveillance of AI as medical devices (AIaMDs).

✨ Key points:

Radiologists’ awareness of EU regulations (MDR & AI Act) is limited

Vendors hold primary legal responsibility, but radiologists contribute to safety monitoring

Expert-driven guidance aims to standardize safe AI deployment in clinical imaging

https://link.springer.com/article/10.1186/s13244-025-02146-8 (Renato Cuocolo et al.)

05/01/2026

📢 RADUCATION: Digital Learning for Radiology Residents

📚 A peer-developed platform offering curriculum-based content and original board exam questions.

✨ Key highlights:

Monthly active users grew from 372 → 613; total users: 15,828

88.6% find it valuable for board exam prep; also useful for shifts and clinical routine

Users prefer video- and image-based content

Rated highly user-friendly (86%) and well-structured (88%)

https://link.springer.com/article/10.1186/s13244-025-02135-x (Isabel Molwitz et al.)

05/01/2026

📢 New study in

A study of 193 patients shows that deep learning reconstruction (DLR) for diffusion-weighted imaging (DWI) can halve acquisition time while improving image quality and lesion differentiation.

✨ Key findings:

Higher SNR and CNR, sharper liver edges

Improved lesion conspicuity and overall image quality

Better diagnostic performance for distinguishing benign vs. malignant liver lesions (AUC 0.921 vs. 0.904)

https://link.springer.com/article/10.1186/s13244-025-02150-y (Dan Zhao et al.)

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