Abdominal Radiology最新文献

Computed tomography (CT) is a cornerstone of abdominal imaging, playing a vital role in accurate diagnosis, appropriate treatment planning, and disease monitoring. The evolution of artificial intelligence (AI) in imaging has introduced deep learning-based reconstruction (DLR) techniques that enhance image quality, reduce radiation dose, and improve workflow efficiency. Traditional image reconstruction methods, including filtered back projection (FBP) and iterative reconstruction (IR), have limitations such as high noise levels and artificial image texture. DLR overcomes these challenges by leveraging convolutional neural networks to generate high-fidelity images while preserving anatomical details. Recent advances in vendor-specific and vendor-agnostic DLR algorithms, such as TrueFidelity, AiCE, and Precise Image, have demonstrated significant improvements in contrast-to-noise ratio, lesion detection, and diagnostic confidence across various abdominal organs, including the liver, pancreas, and kidneys. Furthermore, AI extends beyond image reconstruction to applications such as low contrast lesion detection, quantitative imaging, and workflow optimization, augmenting radiologists’ efficiency and diagnostic accuracy. However, challenges remain in clinical validation, standardization, and widespread adoption. This review explores the principles, advancements, and future directions of AI-driven CT image reconstruction and its expanding role in abdominal imaging.

Expanding imaging indications and increasing patient complexity have created an increasing burden on radiologists for MRI safety related concerns and queries, especially related to implantable devices and foreign bodies. We present a single institution experience identifying deficiencies in radiologist MRI safety education and the subsequent implementation of a module-based training system. Using a pre- and post- intervention analysis, we demonstrated that 39% of institutional radiologists did not feel comfortable making MRI safety related decisions. Following a structure modular educational intervention, 95% of participants reported increased confidence in making MRI safety related decisions. We hope our institutional experience can highlight the need for MRI safety education and serve as a framework for future implementations preventing exam delays, inappropriate cancelations, or adverse safety events.

Intraductal papillary mucinous neoplasm (IPMN) is the most common cystic neoplasm of the pancreas, encompassing a spectrum from benign to malignant lesions. Recently, the international guidelines for IPMN management were revised as the Kyoto guidelines, emphasizing the critical role of imaging in diagnosis, risk assessment, and surveillance. This article provides a comprehensive review of IPMN based on the updated guidelines, focusing on imaging-related aspects while elucidating the underlying pathological background. We present the three interrelated classification systems for IPMN: anatomical location (branch-duct, main-duct, or mixed type), histological subtype (gastric, intestinal, or pancreatobiliary), and degree of dysplasia (low-grade, high-grade, or associated invasive carcinoma). Understanding these classifications and their correlations is fundamental for imaging-based risk assessment and clinical decision-making. We discuss the two distinct carcinogenesis patterns in IPMN—sequential pattern resulting in high-grade dysplasia or invasive carcinoma associated with IPMN, and concomitant pattern leading to pancreatic ductal adenocarcinoma in IPMN-harboring pancreas. The article reviews high-risk stigmata and worrisome features that guide risk stratification, providing illustrative examples and highlighting potential diagnostic pitfalls. We also examine differential diagnoses including serous cystic neoplasm, mucinous cystic neoplasm, pancreatic intraepithelial neoplasia, pseudocysts, and large duct type pancreatic ductal adenocarcinoma. Finally, we review the current management algorithm and surveillance methods recommended by the Kyoto guidelines. This review aims to enhance radiologists' and clinicians' understanding of IPMN by integrating pathological knowledge with imaging findings, emphasizing that while high-risk stigmata are strong predictors of high-grade dysplasia or invasive carcinoma, surgical decisions should be individualized considering multiple factors including patient preferences, comorbidities, and life expectancy.

Graphical Abstract

Objective(s)

This study aims to assess the diagnostic accuracy of the revised Morphologic Uterus Sonographic Assessment (MUSA) criteria for adenomyosis.

Methods

Retrospective review on 96 patients who underwent hysterectomy following ultrasound assessment between 1/3/2020–11/30/2023 for clinical reasons. Two blinded physician reviewers independently evaluated ultrasound images using the modified MUSA criteria. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and interrater reliability of the modified MUSA criteria were determined using hysterectomy specimens as the reference gold standard.

Results

Reviewer 1 found the modified MUSA criteria were found to have a sensitivity of 63.2%, specificity of 65.5%, PPV of 54.4%, and NPV of 73.1%. For reviewer 2, sensitivity was 42.1%, specificity 62.1%, PPV 42.1%, and NPV 62.1%. Interrater agreement using Cohen’s kappa was 72.9%.

Conclusion(s)

The modified MUSA criteria demonstrate moderate sensitivity and specificity in diagnosing adenomyosis. Inter-rater agreement was moderate with 72.9% concordance between ultrasound examiners. While useful in clinical assessment, this study suggests that the modified MUSA criteria lack high specificity and sensitivity, limiting their standalone diagnostic reliability.

PSMA-RADS version 1, introduced by Rowe et al. in 2017, provides a framework for classifying PSMA-targeted PET scans and individual findings based on their likelihood of representing prostate cancer. The system was optimized for findings outside the prostate and was structured as a five-point scale (Rowe et al., Eur Urol 73:485–487, 2018. https://doi.org/10.1016/j.eururo.2017.10.027. In 2022, an updated PSMA-RADS version was proposed to refine category definitions, address limitations of the initial version, and enhance its role in guiding clinical decisions. The framework includes both lesion-level and patient-level classifications, offering confidence and probability scores in support of clinical decision-making (Leung et al., EJNMMI Res. https://doi.org/10.1186/S13550-022-00948-1). This article aims to explore the changes introduced in the updated scale and evaluate their impact on clinical management. It is intended to inform abdominal and general radiologists about recent developments in PSMA-targeted imaging to support multidisciplinary collaboration and patient care.

Objective

Accurate segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding anatomical structures is critical for diagnosis, treatment planning, and outcome assessment. This study proposes a deep learning-based framework to automate multi-class segmentation in CT images, comparing the performance of four state-of-the-art architectures.

Materials and methods

This retrospective multi-center study included 3265 patients from six institutions. Four deep learning models—UNet, nnU-Net, UNETR, and Swin-UNet—were trained using five-fold cross-validation on data from five centers and tested independently on a sixth center (n = 569). Preprocessing included intensity normalization, voxel resampling, and standardized annotation for six structures: PDAC lesion, pancreas, veins, arteries, pancreatic duct, and common bile duct. Evaluation metrics included Dice Similarity Coefficient (DSC), Intersection over Union (IoU), directed Hausdorff Distance (dHD), Average Symmetric Surface Distance (ASSD), and Volume Overlap Error (VOE). Statistical comparisons were made using Wilcoxon signed-rank tests with Bonferroni correction.

Results

Swin-UNet outperformed all models with a mean validation DSC of 92.4% and test DSC of 90.8%, showing minimal overfitting. It also achieved the lowest dHD (4.3 mm), ASSD (1.2 mm), and VOE (6.0%) in cross-validation. Per-class DSCs for Swin-UNet were consistently higher across all anatomical targets, including challenging structures like the pancreatic duct (91.0%) and bile duct (91.8%). Statistical analysis confirmed the superiority of Swin-UNet (p < 0.001). All models showed generalization capability, but Swin-UNet provided the most accurate and robust segmentation across datasets.

Conclusions

Transformer-based architectures, particularly Swin-UNet, enable precise and generalizable multi-class segmentation of PDAC and surrounding anatomy. This framework has potential for clinical integration in PDAC diagnosis, staging, and therapy planning.

Purpose

This study aims to highlight the major modifications introduced in the FIGO 2023 staging system for endometrial cancer (EC) and their implications for MRI interpretation.

Methods

This pictorial essay was based on a retrospective review of 27 histologically confirmed cases of endometrial cancer (EC) imaged between 2009 and 2023 at our institution. Cases were selected to represent a broad spectrum of FIGO 2009 and FIGO 2023 stages, emphasizing features with updated staging implications. Inclusion criteria were availability of preoperative pelvic MRI and complete histopathological data, including molecular classification when available. Exclusion criteria included suboptimal image quality or incomplete clinical records. MRI assessments were performed by two radiologists with 25 years of experience in gynecological imaging, respectively. In illustrative examples where inter-reader differences arose, consensus was reached after joint review; however, no formal inter-reader agreement statistics (e.g., kappa values) were calculated, given the descriptive nature of the study.

Results

The revised FIGO 2023 staging incorporates molecular subtypes, refines classification criteria, and improves the prognostic significance of MRI findings in EC staging.

Conclusion

The integration of histopathology, molecular markers, and MRI features enhances diagnostic accuracy and treatment planning.

The epididymis, a crucial component of the male reproductive system, plays a pivotal role in sperm maturation, transport, and storage. It can be affected by a diverse spectrum of pathologies, including congenital anomalies, infectious and inflammatory diseases, cystic and neoplastic lesions, and traumatic injuries. These conditions can present with overlapping clinical symptoms such as scrotal pain, swelling or infertility, making imaging essential for accurate diagnosis and appropriate management. Ultrasound is the primary imaging modality for evaluating epididymal abnormalities due to its high resolution, real-time capabilities, and lack of ionizing radiation. Doppler ultrasound enhances diagnostic precision by assessing vascularity, particularly in inflammatory and neoplastic conditions. Magnetic resonance imaging provides superior soft tissue contrast and is useful for characterizing complex or indeterminate lesions, while computed tomography is primarily reserved for staging malignancies or evaluating severe trauma. This pictorial review aims to provide an overview of epididymal pathologies by correlating clinical presentation with multimodal imaging findings, predominantly with the use of ultrasound. Emphasis is placed on characteristic imaging features that facilitate differentiation between benign and malignant conditions, guiding radiologists in forming accurate differential diagnoses. By illustrating key pathologic entities, this review seeks to enhance diagnostic confidence and improve clinical decision-making in the assessment of epididymal disorders.

Purpose

The purpose of this single center retrospective study is to evaluate the safety and effectiveness of cryoablation for T1 renal cell carcinoma(RCC) in patients who have undergone organ or tissue transplant of any kind.

Materials and methods

All patients who underwent cryoablation for T1 renal cell carcinoma at a single academic institution were retrospectively reviewed. The patients complications, local recurrence free survival (LRFS), overall recurrence free survival (RFS), and overall survival (OS) were reviewed. Patients were separated into transplant and non-transplant cohorts and compared. Given baseline differences in the cohorts a propensity score matching analysis was performed.

Results

In total 148 patients with 160 lesions were included, including 19 lesions in transplant and 141 lesions in non-transplant patients. When comparing the transplant and non-transplant cohorts there was no difference in rate of local recurrence (1(1/19, 5.3%) vs. 12(12/141, 8.5%)(p = 1)) or overall recurrence (1(1/19, 5.3%) vs. 15(15/141, 10.6%)(p = 0. 696)). Kaplan-Meier curves did not show any difference in the LRFS (p = 0.452, Hazard ratio (HR):0.56(95% confidence interval (CI):0.12–2.56) or OS (p = 0.430, HR(95%CI):0.7(0.29–1.7), respectively) between transplant and non-transplant patients. However, the RFS was significantly better in transplant as compared to non-transplant patients (HR:0.35(95%CI: 0.14–0.83), p = 0.018). When a propensity score matching analysis was performed there was no significant difference in LRFS (33.3(interquartile range (IQR):12.9–72.4) vs. 35.1(IQR:17.8–84.4) months, p = 0.881), RFS (33.3(IQR:12.9–72.4) vs. 35.1(IQR:17.8–84.4) months, p = 0.881), or OS (33.3(IQR:12.9–72.4) vs. 38.8(IQR:17.8–84.4) months, p = 1).

Conclusions

Cryoablation of T1 RCC may be as safe and effective in transplant patients as non-transplant patients.

Graphical Abstract

Purpose

Organizing pneumonia is an inflammatory disorder that may co-exist with malignancy in the lung or elsewhere in the body. We aimed to assess patients with a lung biopsy diagnosis of organizing pneumonia for subsequent pathology confirmation of co-existing malignancy.

Methods

In this retrospective IRB-approved, HIPAA–compliant study, 1314 consecutive patients who underwent CT-guided lung biopsy for suspected lung cancer or metastatic disease from 02/2014 to 04/2022 at a single tertiary referral hospital were identified. In 98/1314 (7.5%) patients, biopsies showed organizing pneumonia, which represented our study cohort. Clinical outcomes, including follow-up imaging and repeat tissue sampling if performed, were evaluated through chart review. Descriptive statistics were calculated.

Results

There were 43/98 (44%) females, mean age was 66 ± 14 years, mean lesion size 2.9 ± 2.1 cm, and 11/98 (11.2%) had prior history of malignancy. Of 98 patients initially diagnosed with organizing pneumonia on lung biopsy, 11 (11.2%) were subsequently found to have malignancy. Among these, 6 (54.5%) had pulmonary metastases and 5 (45.5%) had primary lung cancer. Malignancies were confirmed through percutaneous re-biopsy in 3/11 (27%) and bronchoscopic, endoscopic, or surgical procedures in 8/11 (73%).

Conclusion

Malignancy can co-exist with organizing pneumonia in a substantial percentage of initial lung biopsies. Therefore, repeat tissue sampling should be considered when there is high clinical suspicion of malignancy despite an initial histopathological diagnosis of organizing pneumonia. This is especially relevant in lesions that demonstrate FDG avidity on PET/CT or an increase in size on interval imaging, or in instances where the biopsy core sizes are small or where the biopsies have intraprocedural complications.