To evaluate the feasibility of use of dark borosilicate oral contrast media (DBOCM) in abdominopelvic CT in comparison with conventional oral contrast media (COCM) in a multi-institutional setting.

Posterior rectus sheath hernia (PRSH) is a rare interparietal hernia that is often undetected or misdiagnosed on imaging. We conducted a retrospective review of a case series to define the specific imaging findings and clinical features associated with these hernias.

To investigate the value of habitat imaging employing baseline dual-layer spectral CT (DLCT) for preoperative prediction of recurrence in pancreatic ductal adenocarcinoma (PDAC) after radical resection, and explore the relationship with pathological tumor-stroma ratio (TSR).

To evaluate whether ultrasound-radiomics (US-radiomics) features extracted from ultrasound, integrated with genomic data of single nucleotide polymorphisms (SNPs) associated with cervical cancer (CC) susceptibility and clinical features, could improve the prediction of lymph node metastasis (LNM) in patients with CC.

To identify the imaging features, clinicopathological characteristics and imaging-pathology correlation, of eosinophilic vacuolar tumor of the kidney (EVT).

To evaluate the value of the whole-tumor histogram model based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in diagnosing tumor deposits (TDs) in rectal cancer (RC) patients.

Hereditary renal cell carcinoma (RCC) accounts for approximately 5-8% of all renal cancers. This review provides a comprehensive overview of the seven hereditary RCC syndromes recognized by the National Comprehensive Cancer Network: Tuberous Sclerosis Complex (TSC), Von Hippel-Lindau (VHL), Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC), Hereditary Papillary Renal Carcinoma (HPRC), Birt-Hogg-Dubé syndrome (BHDS), Succinate dehydrogenase (SDH)-deficient RCC/Hereditary Paraganglioma/Pheochromocytoma (PGL/PCC) syndrome, and BAP1 tumor predisposition syndrome (BAP1-TPDS). For each syndrome, the underlying inheritance, epidemiology, and clinicopathologic features of their associated RCCs are detailed. Furthermore, radiographic challenges faced in the diagnosis of RCC in some of these syndromes are discussed, including differentiating RCC from the more common lipid-poor AML in TSC, distinguishing papillary RCC from hemorrhagic cysts in HPRC, and discerning chromophobe RCC from oncocytomas in BHDS. Radiologists are integral to the multidisciplinary management of hereditary RCC, as they are often the first to identify clinicopathologic features suggestive of an underlying genetic syndrome. Recognizing these patterns is crucial for prompting timely genetic evaluation, guiding decisions for nephron-sparing interventions, and establishing appropriate surveillance for patients and their at-risk family members. Advances in radiogenomics and artificial intelligence hold promise for further refining non-invasive diagnosis and personalizing patient care.

Ustekinumab is a biological treatment for Crohn's disease, but some patients do not respond. This study aimed to assess the role of radiomic techniques in predicting the treatment response by quantifying transmural inflammation in Crohn's disease.

To develop and validate a radiomics-based machine learning nomogram using multiparametric MRI for preoperative prediction of aggressive histology in endometrial cancer (EC) patients.

This systematic review critically appraises the current landscape of physics-aware artificial intelligence (AI) in medical imaging for quantitative biomarker mapping in Metabolic dysfunction-associated steatotic liver disease (MASLD) and its progressive form, MASH. It focuses on deep learning and radiomics applications across ultrasound, CT, and MRI.

To develop and validate a biparametric MRI (bpMRI)-based nomogram for distinguishing malignant or potentially malignant uterine smooth muscle tumors from atypical benign leiomyomas.

Precise and accurate staging of rectal cancer is critical for formulating an appropriate treatment plan, including determining the necessity for neoadjuvant therapy and establishing an adequate surgical approach. MRI is the gold standard for locoregional tumor staging, also playing a central role for patients selected for watchful waiting surveillance after total neoadjuvant therapy. Nevertheless, challenges exist from acquisition to interpretation of rectal MRI examinations and, despite societal guidelines, practices vary across institutions. In this paper, we review the optimal technique for acquisition of diagnostic quality rectal MRI and address its existing challenges and unmet needs, encompassing both acquisition and image interpretation.

This study proposes a framework to address the problem of multimodal MRI-to-CT image registration by incorporating feature-based registration approach and segmentation, focusing especially on liver-specific clinical applications.

Birt-Hogg-Dubé (BHD) syndrome is a rare genetic condition characterized by pathogenic variation in the folliculin (FLCN) gene on chromosome 17p11.2. Individuals affected with BHD are at risk to develop renal cysts and masses (13-34% of cases); renal masses are mostly hybrid oncocytic/chromophobe tumors, chromophobe renal cell carcinoma, or oncocytomas. This study aims to investigate the computed tomography (CT) manifestations of renal masses associated with BHD syndrome.

Adrenal incidentalomas (AI) are increasingly detected with the widespread use of abdominal computed tomography (CT). Although a relationship between AI, metabolic syndrome, and metabolic dysfunction-associated steatotic liver disease (MASLD) has been suggested, the evidence remains limited.

This study aimed to develop and validate deep learning models for non-invasive assessment of hepatic steatosis and fibrosis using conventional B-mode ultrasound images, with Fibroscan-derived measurement as reference standard.

Accurate interpretation of CT scans after pancreatic resection is crucial for detecting abnormalities, including postoperative complications and cancer recurrence. This study investigates the feasibility and clinical utility of a novel MKNet-family deep learning architecture for auto-segmentation of the residual pancreas on postoperative CT imaging, in comparison to previous approaches.

Radiologic errors arise from the interaction of human fallibility and systemic weakness. Using a fatigue-related missed renal mass, this paper proposes a model that joins two complementary duties: ethical transparency through disclosure and apology, and system redesign grounded in Just Culture and human factors engineering. Together, these principles create a sustainable path toward safety. We outline common malpractice sources, offer practical guidance for disclosure and apology, and emphasize institutional strategies that transform individual error into system learning and patient-centered improvement.

Appendicoliths are associated with failed nonoperative management in acute appendicitis and are used to exclude patients from this treatment. This study evaluated whether portovenous phase (PVP) and virtual noncontrast (VNC) images from rapid-kVP-switching dual-energy CT (rsDECT), alone or combined, can reliably detect appendicoliths using true noncontrast (TNC) images as the reference. Additional aims included identifying CT features of overlooked appendicoliths and those linked to complicated appendicitis.

The aim of this study was to investigate the utility of segmental hepatic extracellular volume fractions (fECV) calculated using a photon-counting detector CT (PCD-CT) for predicting liver function decline, as reflected by the modified albumin-bilirubin (mALBI) grade in chronic liver disease (CLD).

Fumarate hydratase (FH)-deficient renal cell carcinoma (RCC) is a rare and aggressive RCC subtype defined in the 2022 WHO classification. This study aimed to describe its imaging and clinicopathological features.