Abdominal radiology (New York)
Audio Summaries

Autonomous AI in prostate cancer: the road ahead towards clinical implementation

Aug 8, 2026

The authors investigate the barriers to the clinical implementation of autonomous AI for detecting clinically significant prostate cancer (csPCa) on MRI, despite its promising diagnostic performance. They identify key challenges in evidence, safety, and acceptance among patients and radiologists, emphasizing the need for large-scale trials, improved transparency, and education to facilitate the responsible deployment of this technology in clinical practice.

Time-dependent diffusion MRI for assessing tumor microstructure and prognostic risk factors in cervical cancer

Aug 8, 2026

The authors aimed to assess the utility of time-dependent diffusion MRI (td-dMRI) in noninvasively characterizing tumor microstructure and predicting prognostic risk factors in cervical cancer. Their findings indicate that specific td-dMRI parameters, particularly the intracellular water exchange time and certain diffusion coefficients, may serve as potential imaging biomarkers for differentiating tumor types and assessing histological grade and lymphovascular space invasion. However, the authors emphasize the need for further validation in larger, multicenter studies.

Autoimmune diseases involving the gastrointestinal tract: clinical patterns, imaging spectrum, and complications

Aug 8, 2026

The authors aim to elucidate the clinical patterns, imaging characteristics, and complications associated with autoimmune diseases that affect the gastrointestinal tract. They categorize these diseases into three groups based on their primary or secondary involvement of the GI system and emphasize the importance of integrating imaging findings with clinical and serologic data to improve diagnosis and management. This review highlights how advances in imaging can enhance understanding and recognition of these complex conditions.

A clinical-radiomics model based on MRI sub-regions of gluteus maximus for recurrence prediction in high-grade serous ovarian cancer

Aug 8, 2026

The authors aimed to evaluate the predictive value of MRI sub-regional radiomics of the gluteus maximus for recurrence in high-grade serous ovarian cancer (HGSOC) and to compare its performance with conventional radiomics and deep learning models. Their findings indicate that the sub-regional radiomics model outperformed traditional methods, achieving higher predictive accuracy when combined with clinical factors, thus offering a valuable tool for assessing recurrence risk in HGSOC patients post-treatment.

Local recurrence in rectal cancer: from detection to structured reporting

Aug 7, 2026

The authors investigate the challenges and advancements in detecting local recurrence of rectal cancer following total mesorectal excision and neoadjuvant therapy, which affects a significant percentage of patients. They emphasize the importance of early recognition through multimodal imaging techniques, particularly MRI, to differentiate between viable tumors and postoperative changes, and discuss the role of structured reporting and emerging technologies in improving patient management. The review aims to provide a comprehensive overview of recurrence patterns and optimal imaging strategies to enhance surgical planning and outcomes.

Application of transrectal three-dimensional ultrasound for sub-staging of locally advanced rectal cancer at stage T3

Aug 6, 2026

The authors aimed to evaluate the effectiveness of transrectal three-dimensional ultrasound (3D-TRUS) in the T3 sub-staging of locally advanced rectal cancer. Their study found that 3D-TRUS achieved a high lesion detection rate and demonstrated substantial agreement with standard MRI, indicating it is a reliable and practical tool for clinical management in this context.

Rethinking imaging-based IPMN subtype classification: is mixed-type a necessary radiologic category?

Aug 6, 2026

The authors aimed to evaluate the effectiveness of imaging-based classification of intraductal papillary mucinous neoplasms (IPMNs) in predicting malignancy risk, particularly questioning the necessity of the mixed-type category. Their findings revealed that radiologic subtype classification showed only moderate concordance with pathology and did not enhance malignancy risk stratification beyond the measurement of main pancreatic duct (MPD) diameter, which proved to be a more reliable predictor. Consequently, the study suggests that the mixed-type IPMN subtype may not be essential for malignancy assessment, advocating for a focus on objective MPD measurements instead.

Time-dependent diffusion MRI for differentiating cervical cancer subtypes: impact of ROI delineation strategies on diagnostic performance

Aug 6, 2026

The authors aimed to evaluate how different region-of-interest (ROI) delineation strategies affect the diagnostic performance of time-dependent diffusion MRI (TDD-MRI) in distinguishing between adenocarcinoma and squamous cell carcinoma in cervical cancer patients. Their findings indicate that the single-slice ROI strategy provided the best differentiation between the cancer subtypes, achieving an area under the receiver operating characteristic curve (AUC) of 0.917, suggesting its potential as an effective imaging biomarker for this purpose.

Deep learning-based prostate cancer diagnosis on MRI with hip prostheses: artifact and sequence effects

Aug 4, 2026

The authors aimed to assess how hip prosthesis-induced artifacts affect the diagnostic performance of deep learning (DL) models for prostate cancer detection on MRI and to identify the optimal MRI sequence combinations for analysis. Their findings revealed that DL models showed decreased accuracy in the presence of moderate-to-severe artifacts compared to examinations without prostheses, while traditional radiologist assessments maintained better performance. This study underscores the limitations of DL approaches in this context and emphasizes the importance of expert radiologist interpretation.

Deep learning image reconstruction improves visualization of arterial phase hyperenhancement and washout appearance on dual-energy CT for hepatocellular carcinoma: a non-inferiority study

Aug 4, 2026

The authors aimed to assess the effectiveness of deep learning image reconstruction (DLIR) in enhancing image quality for dual-energy CT (DECT) in the detection of hepatocellular carcinoma (HCC) features, specifically arterial phase hyperenhancement and washout appearance. Their findings indicate that the DLIR-H/50-keV protocol significantly improves image quality and is non-inferior to MRI for identifying key HCC characteristics, suggesting it as a valuable alternative in settings with limited MRI availability.