Insights Into Imaging

02/09/2026

🫁 Can artificial intelligence improve preoperative assessment of small lung adenocarcinomas?

📊 A hybrid radiomics–deep learning model combining solid, ground-glass, and peritumoral computed tomography features achieved high sensitivity for predicting visceral pleural invasion

🎯 Multi-component imaging better captured tumor heterogeneity, supporting personalized treatment planning

https://link.springer.com/article/10.1186/s13244-026-02351-z (Yuanxin Sun et al.)

02/09/2026

🤖 How can artificial intelligence be safely integrated into cancer imaging?

📋 A multidisciplinary expert panel reached consensus on 30 recommendations spanning screening, diagnosis, treatment planning, workflow integration, and post-deployment monitoring

🌍 The consensus highlights multicenter validation, interpretability, data governance, and clinical implementation as priorities for future adoption

https://link.springer.com/article/10.1186/s13244-026-02359-5 (Hong Wu et al.)

Abstract Artificial intelligence (AI) demonstrates potential throughout the cancer care continuum, with evidence supporting its application in medical imaging for detection, staging, treatment planning, and prognostic evaluation. However, clinical translation is hindered by challenges, data curation...

01/09/2026

🫁 Not all pure-solid stage I lung adenocarcinomas behave the same.

📊 Mucinous and non-mucinous subtypes showed distinct imaging features, recurrence patterns, and survival outcomes

🎯 Mucinous adenocarcinoma was associated with better 5-year recurrence-free and overall survival

https://link.springer.com/article/10.1186/s13244-026-02353-x (Wei Li et al.)

31/08/2026

Breast cancer risk is not static.

📊 Over 10 years, more than 40% of women with elevated baseline risk were reclassified into a different risk category using the International Breast Cancer Intervention Study risk model

🔄 Periodic risk reassessment may help optimize personalized screening and surveillance strategies

https://link.springer.com/article/10.1186/s13244-026-02355-9 (Machteld Keupers et al.)

31/08/2026

Can perivascular adipose tissue improve identification of high-risk carotid plaques?

📊 Spectral computed tomography parameters differed between symptomatic and asymptomatic plaques and correlated with plaque composition

🎯 Adding perivascular adipose tissue metrics improved identification of symptomatic plaques beyond stenosis severity alone

https://link.springer.com/article/10.1186/s13244-026-02354-w (Xiaohan Zheng et al.)

28/08/2026

In suspected acute appendicitis, how much does report wording matter?

📊 Radiologist-adjudicated computed tomography impressions reduced indeterminate reports and achieved the highest agreement with the final diagnosis

✍️ Careful communication of diagnostic certainty may improve clinical decision-making beyond structured lexicon-based approaches

https://link.springer.com/article/10.1186/s13244-026-02352-y (Piyachai Siriphiphatcharoen et al.)

28/08/2026

How reproducible is Node-RADS for lymph node assessment in colon cancer?

📊 Node-RADS showed substantial agreement across readers, with higher consistency and diagnostic performance among senior radiologists

🔍 High specificity supports its clinical reliability, while limited sensitivity highlights the need for further refinement

https://link.springer.com/article/10.1186/s13244-026-02349-7 (Hui Wang et al.)

27/08/2026

🧠 An interpretable deep learning framework based on magnetic resonance imaging enables noninvasive preoperative grading of intrahepatic mass-forming cholangiocarcinoma.

📊 The combined model achieved strong external validation performance and outperformed radiomics and clinical-laboratory-imaging models

🎯 Deep learning features contributed most to prediction and supported exploratory prognostic stratification

https://link.springer.com/article/10.1186/s13244-026-02350-0 (Liyong Zhuo et al.)

26/08/2026

🚨 Could artificial intelligence accelerate pneumoperitoneum detection in emergency care?

📊 High accuracy for detecting clinically relevant free air on abdominal computed tomography, with excellent agreement for volume quantification

⚡ Sensitivity reached 96% after excluding trace-volume pneumoperitoneum, supporting emergency workflow prioritization

https://link.springer.com/article/10.1186/s13244-026-02348-8 (Yuwan Hu et al.)

26/08/2026

🎗️ Can contrast-enhanced mammography reshape response assessment during neoadjuvant therapy?

📊 Evidence suggests it is comparable to magnetic resonance imaging for treatment response evaluation and preoperative restaging

⚕️ It may also predict chemosensitivity while improving patient workflow and imaging access

https://link.springer.com/article/10.1186/s13244-026-02294-5 (Lígia Pires-Gonçalves et al.)