To determine whether presence of endometriomas on routine pelvic ultrasound (US) can predict presence of deep infiltrating endometriosis (DE) on magnetic resonance imaging (MRI) and correlate endometrioma size with the presence of DE on MRI.
In this IRB approved, HIPAA compliant study, radiology data base was queried for MRI exams dedicated for endometriosis evaluation in patients with surgically proven endometriosis over a three-year period (2016–2019). Imaging reports were reviewed for the presence of endometriomas and DE on MRI. For the patients with endometriomas, records were reviewed for concurrent routine pelvic US imaging, which were not tailored to DE protocols. US images were reviewed for the presence of endometriomas, their total number, laterality and largest dimension. Descriptive statistics and receiver operating curve (ROC) analysis were performed. Pathology and surgical notes were used as reference standard.
253 patients with surgically confirmed endometriosis underwent MRI for DIE over a 3-year duration. 47 patients had a concurrent US exam. 33/47 patients (70.2%) had endometriomas seen on US (size range 0.9–7.0 cm). 12 had bilateral endometriomas and 13 had multiple (more than 1) endometriomas. 27/33 (82%) of these patients had DE on MRI. 6/33 (18%) patients had additional sites of DE on surgery, which was not reported preoperatively on MRI. When endometriomas more than 3 cm as were evaluated, 19/21 (90%) of patients had DE on MRI. AUC was 0.7106. Using ROC analysis, a threshold of 3.8 cm, provided a sensitivity of 71% and specificity of 78% for detection of DE on MRI.
Presence of endometriomas on routine pelvic US is associated with a high frequency of DE detected on MRI performed for endometriosis. The frequency of DE on MRI was higher in patients with endometrioma size > 3 cm compared to endometriomas of all size range.
Renal cell carcinoma (RCC) represents the most prevalent malignant neoplasm of the kidney, with a rising global incidence. Tumor nuclear grade is a crucial prognostic factor, guiding treatment decisions, but current histopathological grading via biopsy is invasive and prone to sampling errors. This study aims to assess the diagnostic performance and quality of CT-based radiomics for preoperatively predicting RCC nuclear grade.
A comprehensive search was conducted across PubMed, Scopus, Embase, and Web of Science to identify relevant studies up until 19 April 2025. Quality was assessed using the QUADAS-2 and METRICS tools. A bivariate random-effects meta-analysis was performed to evaluate model performance, including sensitivity, specificity, and Area Under the Curve (AUC). Results from separate validation cohorts were pooled, and clinical and combined models were analyzed separately in distinct analyses.
A total of 26 studies comprising 1993 individuals in 10 external and 16 internal validation cohorts were included. Meta-analysis of radiomics models showed pooled AUC of 0.88, sensitivity of 0.78, and specificity of 0.82. Clinical and combined (clinical-radiomics) models showed AUCs of 0.73 and 0.86, respectively. QUADAS-2 revealed significant risk of bias in the Index Test and Flow and Timing domains. METRICS scores ranged from 49.7 to 88.4%, with an average of 66.65%, indicating overall good quality, though gaps in some aspects of study methodologies were identified.
This study suggests that radiomics models show great potential and diagnostic accuracy for non-invasive preoperative nuclear grading of RCC. However, challenges related to generalizability and clinical applicability remain, as further research with standardized methodologies, external validation, and larger cohorts is needed to enhance their reliability and integration into routine clinical practice.
Appropriate categorization based on magnetic resonance imaging (MRI) findings is important for managing intraductal papillary mucinous neoplasms (IPMNs). In this study, a large language model (LLM) that classifies IPMNs based on MRI findings was developed, and its performance was compared with that of less experienced human readers.
The medical image management and processing systems of our hospital were searched to identify MRI reports of branch-duct IPMNs (BD-IPMNs). They were assigned to the training, validation, and testing datasets in chronological order. The model was trained on the training dataset, and the best-performing model on the validation dataset was evaluated on the test dataset. Furthermore, two radiology residents (Readers 1 and 2) and an intern (Reader 3) manually sorted the reports in the test dataset. The accuracy, sensitivity, and time required for categorizing were compared between the model and readers.
The accuracy of the fine-tuned LLM for the test dataset was 0.966, which was comparable to that of Readers 1 and 2 (0.931–0.972) and significantly better than that of Reader 3 (0.907). The fine-tuned LLM had an area under the receiver operating characteristic curve of 0.982 for the classification of cyst diameter ≥ 10 mm, which was significantly superior to that of Reader 3 (0.944). Furthermore, the fine-tuned LLM (25 s) completed the test dataset faster than the readers (1,887–2,646 s).
The fine-tuned LLM classified BD-IPMNs based on MRI findings with comparable performance to that of radiology residents and significantly reduced the time required.
This study aims to evaluate the efficacy of a three-dimensional visualization operative planning system (3DVOPS) in ultrasound-guided percutaneous microwave ablation (US-PMWA) for the treatment of large uterine fibroids.
From October 2020 to December 2023, a total of 30 patients with symptomatic uterine fibroids (≥ 7 cm) who underwent US-PMWA with the assistance of a 3D visualization operative planning system were included in this retrospective study. A control group of 60 patients who underwent US-PMWA using conventional 2D image operative planning methods was also studied. Assessment endpoints included technical efficacy and complications.
The ablation time and energy consumption in the 3D group were significantly lower than those in the 2D group (45.2 ± 7.5 min vs. 56.6 ± 8.9 min and 77.5 ± 19.3 kJ vs. 100.9 ± 36.7 kJ, respectively; P < 0.05). There was no significant difference in the ablation rate between the two groups. The incidence of vaginal discharge after ablation was lower in the 3D group compared to the 2D group (6.6% vs. 13.3%, P < 0.05). No severe complications were reported during the follow-up period.
The 3DVOPS can reduce ablation time and microwave energy requirements for the treatment of large uterine fibroids via US-PMWA, while also enhancing the accuracy of ablation.
Accurate liver-volume measurements from CT scans are essential for treatment planning, particularly in liver resection cases, to avoid postoperative liver failure. However, manual segmentation is time-consuming and prone to variability. Advancements in artificial intelligence (AI), specifically convolutional neural networks, have enhanced liver segmentation accuracy. We aimed to identify optimal CT phases for AI-based liver volume estimation and apply the model to track liver volume changes over time. We also evaluated temporal changes in liver volume in participants without liver disease.
In this retrospective, single-center study, we assessed the performance of an open-source AI-based liver segmentation model previously reported, using non-contrast and dynamic CT phases. The accuracy of the model was compared with that of expert radiologists. The Dice similarity coefficient (DSC) was calculated across various CT phases, including arterial, portal venous, and non-contrast, to validate the model. The model was then applied to a longitudinal study involving 39 patients without liver disease (527 CT scans) to examine age-related liver volume changes over 5 to 20 years.
The model demonstrated high accuracy across all phases compared to manual segmentation. Among the CT phases, the highest DSC of 0.988 ± 0.010 was in the arterial phase. The intraclass correlation coefficients for liver volume were also high, exceeding 0.9 for contrast-enhanced phases and 0.8 for non-contrast CT. In the longitudinal study, the model indicated an annual decrease of 0.95%.
This model provides high accuracy in liver segmentation across various CT phases and offers insights into age-related liver volume reduction. Measuring changes in liver volume may help with the early detection of diseases and the understanding of pathophysiology.
Various mutations in hepatocellular carcinoma (HCC) carry prognostic implications. The objective of this study is to assess CT and MRI imaging features associated with Catenin Beta-1 (CTNNB1) mutation in HCC.
This retrospective, IRB- approved multi-reader, single-center study included treatment-naive, pathologic-proven HCC that underwent contrast-enhanced CT, MRI or both, with subsequent targeted tumor sequencing test. Preoperative CT and MRI were reviewed for the Liver Imaging Reporting and Data System (LI-RADS, LR) features and prognostic imaging features. Fisher’s exact test and multiple testing adjustment were used to assess the association of imaging features and CTNNB1 mutation status.
Of the 160 HCCs included (median age 69 [IQR: 62, 75], 125 men), 58 (36%) had CTNNB1 mutation. Compared to wildtype, CTNNB1-mutated HCCs were more likely to be present as solitary lesion (CT: 26/43[60%] vs. 31/80 [40%], p = 0.024), have mosaic appearance (MRI: 9/34[26%] vs. 3/68[4.4%], p = 0.002), blood products in mass (CT: 7/43[16%] vs. 2/80[2.5%], p = 0.009; MRI: 12/34[35%] vs. 8/68[12%], p = 0.008), necrosis (CT: 16/43[37%] vs. 14/80[18]%, p = 0.026), intralesional arteries (CT: 26/43[60%] vs. 32/80[40%], p = 0.038). A subgroup of 98 high risk patients (hepatitis B, morphologic cirrhosis) were assigned LI-RADS categorization; majority of patients were assigned LR-5 (CT: 15/25[60%] vs. 21/52[40%]; MRI: 10/18[56%] vs. 19/44[43%]). No feature was significantly associated with CTNNB1 mutation status after multiple testing adjustment.
Compared to wildtype, CTNNB1-mutated HCCs are more likely to appear as solitary masses with mosaic, heterogeneous appearance containing blood products, necrosis and intralesional arteries. Majority of CTNNB1-mutated tumors were categorized as LR-5 in a subgroup of high risk patients. No imaging feature independently predicted CTNNB1-mutated HCCs.
The objective was to offer imaging-based evidence to analyze the functional changes in the bladder and urethra associated with pelvic floor reconstruction.
The study included patients with grade II or higher pelvic organ prolapse (POP). The primary analysis involved comparing changes in the function parameters before and after pelvic reconstruction. Secondly, patients were divided into two groups: those who had pelvic floor reconstruction combined with a mid-urethral sling (MUS) and those without MUS. The impact of combining MUS on changes in bladder and urethral functions was then compared.
Forty-three patients were included in the study. 5 of the 43 enrolled patients refused postoperative MRD and were excluded from paired analysis. Compared with the preoperative measurements, the urethral length (2.28 cm ± 0.82 cm vs. 1.95 cm ± 0.54 cm) and urethral angle (103.60° ± 65.02° vs. 52.75° ± 27.40°) decreased significantly after surgery. The bladder-urethral angle (97.20° ± 35.10° vs. 134.80° ± 31.27°) and the incidence of bladder funneling (21.05% vs. 44.74%) increased postoperatively. Secondly, 20 patients who had undergone isolated POP repair compared with 18 patients who had undergone POP repair combined with MUS. A statistically significant increase in the incidence of bladder funneling was observed in the isolated POP repair group (20.00% vs. 50.00%, p = 0.047).
Pelvic floor reconstruction with or without a MUS, consistently resulted in significant improvements in the anatomical positions of the pelvic organs within 3–6 months of the surgery. The incidence of bladder funneling increased after surgery indicates that the weakness at the urethro-vesical junction becomes more pronounced.
The uterine isthmus, the narrowest portion of the uterus, plays an essential role in female reproductive health and can be the site of several gynecological pathologies. Due to its strategic location, lesions in this region have unique characteristics and can lead to a wide variety of symptoms and diagnostic challenges. Diagnosis is usually made through imaging tests, such as ultrasound (US) or magnetic resonance imaging (MRI), with appropriate treatment depending on the precise identification of the type of lesion and evaluation of factors such as size, location, and risk of malignancy. This pictorial essay provides a comprehensive, image-rich review of multiple uterine isthmic lesions—including isthmoceles (cesarean scar defects) and their complications, fibroids in the isthmus, cesarean scar ectopic pregnancies (CSEP), isthmic endometriosis, adenomyosis/adenomyomas, post-curettage vascular malformations, niche-related intrauterine device (IUD) malposition, trophoblastic tissue implants, and endometrial polyps—highlighting their MRI appearances, epidemiological and clinical correlations, and management options.
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