iRadiology最新文献

Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) is an established tool in clinical molecular imaging for evaluating metabolic activity. Advanced PET systems enable high-resolution three-dimensional analysis of FDG distribution, offering insightful information for lesion characterization. Furthermore, dynamic whole-body imaging facilitates assessment of temporal FDG uptake following its administration. This development introduces the concept of four-dimensional (4D) FDG PET/CT, enhancing the precision of lesion characterization. A critical advantage of applying 4D FDG PET/CT is the ability to distinguish pathological from physiological FDG uptake by analyzing motion-related uptake. Correcting motion and integrating serial imaging data enable accurate interpretation. Additionally, FDG uptake quantification is possible using Patlak analysis. This review discusses innovative clinical applications and quantitative techniques for analyzing FDG distribution through 4D FDG PET/CT.

Artificial intelligence (AI) is changing how cancer is diagnosed, predicted, and treated, opening up new approaches to make cancer care more individualized. Rather than offering a broad but superficial overview, this review focuses on four cancers—lung, breast, brain (gliomas), and colorectal—for which AI was shown to be useful in the clinic. AI algorithms, specifically those using convolutional neural networks (CNNs), can enhance early diagnosis while realizing molecular profiling and treatment response assessment through quantitative imaging evaluations. Radiomics together with radiogenomics improves treatment accuracy through the assessment of imaging characteristics that help identify targeted genomic therapies. AI technologies can enhance tumor segmentation precision, stage determination, and target outlining capabilities, which enable adaptive radiation therapy. Initiatives that merge AI with images, clinical results, and genetic science information can deliver thorough personalized assessments that enhance treatment planning decisions. However, AI technology needs to overcome data quality issues, interpretability limitations, and generalizability challenges and needs to meet regulatory compliance requirements before achieving safe and fair implementation. The next phase of development will focus on federated learning to safeguard privacy while institutions collaborate, explainable AI to build transparent systems, and the fusion of diverse data types for comprehensive patient identification and real-time medical decision support through establishing digital twins for individualized treatment assessments. Precision oncology will be transformed by maturing innovations in predictive imaging that allow better timing of diagnosis while providing customized treatments to achieve improved medical results.

A 45-year-old male presented with upper abdominal pain that began 1 week ago, described as intermittent and dull. Physical examination revealed tenderness in the upper abdomen. The liver was palpable 10 cm below the right midclavicular line at the costal margin. Laboratory tests showed no significant abnormalities. The computed tomography image is shown in Figure 1a. The patient underwent right hemihepatectomy with caudate lobe resection. Histopathological findings are illustrated in Figure 1b. The diagnosis was a malignant tumor with necrosis, consistent with an undifferentiated sarcoma of the liver (UESL). During a 6-month follow-up, tumor metastasis was noted in the gastrointestinal space, along with multiple masses in the anterior left lobe of the liver and right renal space, indicating tumor recurrence. Dynamic axial contrast-enhanced CT scans showing mild heterogeneous enhancement of these lesions.

UESL, is an exceedingly rare malignant liver tumor. UESL ranks third in children, with adult occurrences being particularly uncommon.

Due to the rarity of UESL, imaging features lack specificity. CT scans revealed solid components often reside at the tumor margins with irregular septations and hemorrhage. Contrast-enhanced scans may demonstrate fast in fast out enhancement or delayed enhancement patterns. This case lacks typical imaging manifestations of enhancement, showing mild heterogeneous enhancement, which may be related to extensive hemorrhage and necrosis.

Lingqing Tang: writing – original draft (lead), resources (equal). Bin Yang: resources (equal), writing – review and editing (lead).

The authors have nothing to report.

The patient has provided written informed consent prior to taking part in this study.

The authors declare no conflicts of interest.

Advances in fetal surgery techniques have enabled the treatment of certain congenital defects before birth. A critical area of focus is the role of perinatal imaging in optimizing prenatal interventions within the precision medicine framework. Magnetic resonance imaging (MRI) is emerging as an indispensable tool for guiding these intricate procedures with the potential to significantly enhance the standard of care and outcomes for affected fetuses. This review begins with an overview of the classification and indications for fetal surgical interventions. It then explores the detailed applications of prenatal MRI scanning and diagnostic techniques across various categories of fetal surgery. A key focus is how fetal MRI provides critical insights into specific lesion characteristics and tissue involvement, thereby aiding healthcare professionals in selecting the optimal surgical strategies for prenatal and postnatal interventions. Fetal MRI offers detailed visualizations that complement traditional ultrasound findings, enhancing the precision of radiological planning for fetal surgery. Finally, the review highlights how integration of fetal MRI into the decision-making process enables healthcare providers to make well-informed choices, ultimately improving the prognosis and outcomes for both the mother and fetus.

Fetal intracranial tumors are rare, accounting for approximately 0.5%–1.9% of all pediatric tumors, though the true incidence may be underestimated. These tumors often present with distinct histopathological features, imaging characteristics, and clinical behavior compared to their postnatal counterparts. This review summarizes the current understanding of the prenatal diagnosis and characterization of fetal brain tumors, with a particular focus on the role of fetal magnetic resonance imaging (MRI). We discuss the advantages of advanced MR sequences in enhancing lesion detection and anatomical delineation following suspicious findings on obstetric ultrasound. Common tumor types encountered in utero—including teratomas, astrocytomas, medulloblastomas, choroid plexus papillomas, and craniopharyngiomas—are reviewed in terms of imaging features, differential diagnosis, and clinical implications. Furthermore, the review addresses the diagnostic challenges, prognostic considerations, and the potential role of fetal MRI in guiding perinatal management and parental counseling.