Abdominal Radiology最新文献

Purpose

To evaluate imaging findings of patients with locally advanced pancreatic cancer (LAPC) following treatment with combination stereotactic body radiotherapy (SBRT) and chemotherapy versus stand-alone chemotherapy.

Methods

This retrospective study included patients with LAPC who received combination SBRT/chemotherapy versus those that received stand-alone chemotherapy from 2005 to 2018. Comparisons were made pre-treatment and at four standardized post-treatment intervals (1 month, 3–6 months, 7–12 months, greater than 12 months) in patients without disease progression. Imaging variables included degree of vascular involvement graded on a standardized scale and peripancreatic fat stranding. A p-value < 0.05 was considered significant.

Results

A total of 96 patients were included, 64 patients (37 men; mean age, 68 ± 11 years) treated with SBRT/chemotherapy and 32 patients (17 men; mean age, 69 ± 10 years) treated with stand-alone chemotherapy. Increased vascular involvement over time in the absence of disease progression was significantly higher in the SBRT/chemotherapy group (17%) versus the stand-alone chemotherapy group (9%), p = 0.004 (95% CI 2–12%). Peripancreatic fat stranding increased over time in the SBRT/chemotherapy group being present in 29/64 (45%) patients on the pre-treatment computed tomography (CT) versus 55/64 (86%) patients on the last recorded CT, p < 0.001 (McNemar OR = 14, 95% CI 3.3–58.8). No significant change in peripancreatic fat stranding over time was noted in the stand-alone chemotherapy group being present in 9/32 (28%) patients on the pre-treatment CT and 7/32 (22%) patients on the last imaging recorded, p = 0.45 (McNemar OR = 2, 95% CI 0.4–10.9).

Conclusions

Increased vascular involvement and peripancreatic fat stranding over time in LAPC patients treated with combination SBRT/chemotherapy should be a potential anticipated treatment-related effect, not necessarily indicating disease progression.

Abdominal manifestations of tropical infectious diseases are commonly encountered in clinical practice, especially in endemic regions. These conditions, caused by a wide range of infectious agents - including helminths, protozoa, bacteria, fungi, and viruses - can exhibit distinctive features on ultrasound, CT, and MRI. This pictorial review highlights the most relevant imaging findings of selected tropical diseases affecting the abdomen, such as schistosomiasis, echinococcosis, leishmaniasis, Chagas disease, tuberculosis, paracoccidioidomycosis, histoplasmosis, and dengue. Through integration of imaging, clinical, and epidemiological aspects, the article aims to enhance diagnostic accuracy and awareness among radiologists.

Liver cancer remains a significant global health concern, ranking as the sixth most common malignancy and the third leading cause of cancer-related deaths worldwide. Medical imaging plays a vital role in managing liver tumors, particularly hepatocellular carcinoma (HCC) and metastatic lesions. However, the large volume and complexity of imaging data can make accurate and efficient interpretation challenging. Artificial intelligence (AI) is recognized as a promising tool to address these challenges. Therefore, this review aims to explore the recent advances in AI applications in liver tumor imaging, focusing on key areas such as image reconstruction, image quality enhancement, lesion detection, tumor characterization, segmentation, and radiomics. Among these, AI-based image reconstruction has already been widely integrated into clinical workflows, helping to enhance image quality while reducing radiation exposure. While the adoption of AI-assisted diagnostic tools in liver imaging has lagged behind other fields, such as chest imaging, recent developments are driving their increasing integration into clinical practice. In the future, AI is expected to play a central role in various aspects of liver cancer care, including comprehensive image analysis, treatment planning, response evaluation, and prognosis prediction. This review offers a comprehensive overview of the status and prospects of AI applications in liver tumor imaging.

Purpose

To develop and validate a scoring system that combines clinical and radiological variables to predict the likelihood of clinically significant prostate cancer (csPCa, which is defined as Gleason Grade group ≥ 2) before biopsy and stratify patients by predicted risk.

Methods

This retrospective study enrolled 788 patients. Data were stratified into a derivation cohort and two validation cohorts by institutions. Imaging evaluation included: Prostate Imaging Reporting and Data System v2.1 (PI-RADS v2.1), Simplified PI-RADS [S-PI-RADS, incorporating qualitative and quantitative assessment of diffusion restriction degree and lesion volume (LV) on DWI/ADC sequences], prostate volume (PV), LV, and the longest to shortest diameter ratio (LD/SD). %fPSA was the ratio of free prostate-specific antigen (PSA) to total PSA (tPSA). The adjusted PSA (aPSA) was derived as: aPSA = tPSA×(LV/PV). Independent csPCa predictors were determined through multivariate regression analysis and transformed into the scoring system. Diagnostic performance of PI-RADS, S-PI-RADS, and the scoring system were compared using ROC analysis. The scoring system was stratified into four risk categories based on total scores.

Results

The scoring system—integrating %fPSA, S-PI-RADS, aPSA, and LD/SD—demonstrated robust predictive performance for csPCa across all derivation and validation cohorts (AUC: 0.891, 0.875, and 0.897, respectively), comparable to PI-RADS (AUC: 0.861, 0.880, and 0.865; all P > 0.05). It achieved the highest PPV among three systems in all cohorts (0.780, 0.832, and 0.722). Median predicted probabilities for the low (0–2 points), intermediate-low (3–7), intermediate-high (8–12), and high-risk groups (13–17) were 3.8%, 14.4%, 67.8%, and 90.3%, respectively, aligning with observed risks. While inter-reader agreement for PI-RADS was suboptimal between trained and untrained radiologists (κ = 0.643), the scoring system showed stronger consensus (κ = 0.808).

Conclusion

This scoring system demonstrating comparable diagnostic performance to PI-RADS and improving PPV, highlighting its potential clinical practicability.

Female pelvic pathologies—including infectious or inflammatory conditions as well as benign or malignant tumors—are frequently encountered in clinical practice and may require tissue sampling, aspiration or drainage to guide management and therapeutic decisions. Due to anatomical constraints, transabdominal or transgluteal percutaneous access to target lesions in the pelvis may be technically challenging or unsafe. In such cases, transvaginal ultrasound (TVUS)-guided procedures offer a valuable alternative, providing direct, safe, and effective access, making it a highly useful technique in both diagnostic and therapeutic contexts. Despite its advantages, broader implementation of TVUS-guided interventions is often limited by variability in procedural familiarity and operator confidence. This review aims to outline the procedural technique, indications, and practical considerations associated with TVUS-guided procedures, with the goal of promoting wider adoption and proficiency among practitioners.

Purpose

To identify independent risk factors for hemorrhagic complications after ultrasound-guided percutaneous native renal biopsy (UG-PRB) from clinical and ultrasonographic parameters, and to develop prediction models.

Methods

The single-center retrospective study included 2822 patients who underwent a UG-PRB between May 2022 and July 2024. Patients with post-procedural hemorrhagic complications were included as the case group. Under a nest case-control design, one procedure date-matched control without complication was randomly selected for each case. Independent predictors for overall hemorrhagic complication and severe hemorrhagic complication were investigated by multivariate logistic regression.

Results

A total of 204 patients (36.0 years ± 16.1[SD]; 104 [51%] female) developed post-biopsy hemorrhagic complications in this study. The incidence of overall and severe bleeding complications was 8.8% (204/2328) and 1.03% (24/2328), respectively. Right renal biopsy (OR 0.40, p = 0.02), number of needle passes (OR 0.40, p = 0.002), and hypertension (OR 1.29, p = 0.02) were independent risk factors for overall bleeding complications. Hyperlipidemia (OR 0.10, p = 0.04), D-dimer (OR 1.27, p = 0.03), and eGFR (OR 0.90, p = 0.002) were independent risk factors for severe hemorrhagic complications. The prediction models for overall and severe complications had AUCs of 0.64 and 0.91, and a five-cross validation in the cohort showed a mean AUC of 0.65 and 0.85 in each model. In predicting severe complications, the sensitivity and specificity were 87.5% and 75.3%, respectively.

Conclusion

The developed prediction models provide effective tools for risk stratification, early prevention, monitoring, and management for patients undergoing UG-PRB.

Purpose

Effectiveness of programmed cell death 1 blockade (anti-PD1) treatment plus neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer and proficient mismatch repair (pMMR-LARC) has been recently proven. However, the role of MRI in tumor restaging following anti-PD1 plus NCRT is less established. This study aims to evaluate the diagnostic performance and challenges of MRI for restaging pMMR-LARC patients after anti-PD1 plus NCRT treatment.

Methods

This prospective multicenter study involved pMMR-LARC patients exhibiting following anti-PD1 plus NCRT treatment from 2021 to 2022. The evaluation of preoperative treatment response included assessments of complete response and near complete response (CR/nCR), N category, extramural venous invasion (EMVI) and mesorectal fascia (MRF), all of which were analyzed using MRI. The diagnostic accuracy was assessed using pathology as the reference standard.

Results

A cohort of 44 patients (mean age, 60.5 years ± 10.4 [SD]; 15 females) was evaluated. The pathologic CR/nCR rate was 75.0%. MRI demonstrated an accuracy of 86.4% for identifying pathologic CR/nCR (pathologic tumor regression grade 0–1). The accuracy for involvement of ypN, circumferential resection margin (CRM) and ypEMVI were 86.4%, 100% and 90.9%, respectively. Minimal fibrosis (p = 0.022) and irregular fibrosis (p = 0.030) rather than oedema or mucin, contributed to misinterpretations in tumor treatment response assessment and ypEMVI using MRI, respectively.

Conclusion

Standard MRI restaging criteria was suitable for tumor restaging in pMMR-LARC patients following anti-PD1 plus NCRT therapy. Fibrotic-pattern-based interpretation methodology are needed in improving diagnostic accuracy of MRI.

Graphical abstract

Purpose

This study aimed to develop a nomogram model based on computed tomography (CT) assessed body composition parameters to predict recurrence-free survival (RFS) and stratify the risk of recurrence in advanced gastric cancer (GC) patients.

Methods

This retrospective study included 111 patients with locally advanced GC. Preoperative CT-assessed body composition and parenchymal fat parameters of all patients were collected. Univariate and multivariate Cox analyses were performed to determine independent predictors for RFS. A nomogram model was subsequently established on the basis of the independent risk factors. The performance of the nomogram was evaluated utilizing the concordance index (C-index), calibration curve, and receiver operating characteristic curve analysis.

Results

The nomogram model integrating four independent predictors, including the skeletal muscle index, visceral adipose tissue radiation attenuation, the body of pancreatic density (PD), and PD (tail), was established for predicting RFS in advanced GCs and achieved a C-index of 0.743 (95% confidence interval: 0.678–0.808). The calibration curves showed good concordances. In addition, compared to the pathological tumor-node-metastasis classification, the nomogram model showed comparable performance for predicting 1-year RFS and better efficacy for predicting 3- and 5-year RFS. The Kaplan-Meier curves demonstrated the ability of the nomogram to stratify patients according to risk (p < 0.001).

Conclusion

The nomogram model exhibited favorable predictive performance and could stratify patients according to the risk of postoperative recurrence for advanced GCs, which might help enhance individualized surveillance in clinical practice.

Graphical Abstract

Purpose

This study was designed to investigate the preoperative prediction of the mid-term efficacy of high-intensity focused ultrasound (HIFU) in the treatment of uterine fibroids by apparent diffusion coefficient (ADC) histogram analysis.

Methods

Eighty-six patients with a total of 101 uterine fibroids were retrospectively collected consecutively. The region of interest (ROI) was outlined layer by layer on diffusion-weighted imaging (DWI) images with b = 1000 s/mm² to obtain ADC histogram parameters, including the mean, maximum, minimum, 10th, 25th, 50th, 75th and 90th percentiles as well as skewness, kurtosis, and entropy. The volume reduction rate (VRR) of fibroids at 6 months postoperatively was calculated as an indicator of the mid-term effect, and according to the VRR, the cohort was divided into a markedly effective group (VRR ≥ 50%) and a nonmarkedly effective group (VRR < 50%). Differences in ADC histogram parameters between the markedly effective and nonmarkedly effective groups were analyzed by applying univariate and multivariate binary logistic regression to screen out statistically significant indicators, and the predictive efficacy of the relevant ADC histogram parameters was assessed by receiver operating characteristic (ROC) curves.

Results

The markedly effective group included 42 fibroids, and the noneffective group included 59 fibroids. The results of the univariate analysis showed that ADCmean, ADCmax, ADC10th, ADC25th, skewness, kurtosis, and entropy were significantly different between the two groups. The results of the multivariate analysis showed that ADC10th and skewness were statistically significant indicators, the area under the ROC curve (AUC) was 0.678 and 0.739, respectively, and the combined AUC of the two was 0.790. Major study limitations include: this is a single-center retrospective study, the investigator manually segmented the lesions, and no further analysis of postoperative pathology regarding the findings of the study.

Conclusion

ADC histogram analysis helps to preoperatively predict the mid-term efficacy of HIFU in the treatment of uterine fibroids. ADC10th and skewness are independent predictors of the mid-term efficacy of HIFU, and the combined efficacy of the two is superior to that of a single indicator.