Abdominal Radiology最新文献

Purpose

Dimensions of the bony birth canal are hypothesized to be associated with pelvic organ prolapse (POP). However, the pelvic midplane, which represents the narrowest obstetrical plane, has not been thoroughly investigated. This study aims to compare the bony dimensions at the level of the pelvic midplane in childbearing women with and without POP and to evaluate their association with POP.

Methods

Data from 131 patients with POP were prospectively collected between 2021 and 2023. A total of 85 cases were matched with 85 controls who had comparable demographic characteristics. A case-control study was conducted to compare bony dimensions at the pelvic midplane level between women with and without POP using 3D MRI models. Additionally, analysis of covariance was performed to examine the relationship between these dimensions and the maximum prolapse.

Results

The comparison of dimensions (in centimeters) between 85 cases and 85 controls revealed the following significant differences: anterior–posterior diameter (11.3 ± 0.7 vs. 10.7 ± 0.7, p < 0.001); pubic symphysis to ischial spine—left (9.4 ± 0.4 vs. 9.1 ± 0.4, p < 0.001),—right (9.5 ± 0.4 vs. 9.1 ± 0.4, p < 0.001); Ischial spine to sacrococcygeal junction—left (6.7 ± 0.5 vs. 6.5 ± 0.5, p = 0.002),—right (6.7 ± 0.5 vs. 6.5 ± 0.5, p = 0.002); interspinous diameter (11.1 ± 0.7 vs. 10.8 ± 0.7, p = 0.004); and the area of the pelvic midplane (63.0 ± 0.6 vs. 58.5 ± 0.6 cm², p < 0.001). A larger pelvic midplane was significantly associated with an increased risk of prolapse, with an odds ratio of 2.534 (95% CI 1.647–3.901, p < 0.001) for every 5 cm² increase. Classification based on the presence or absence of prolapse showed that receiver operating characteristic analysis yielded an area under curve of 70.3% (p < 0.001). Furthermore, among POP patients, those with a larger pelvic midplane area exhibited more severe prolapse (trend p = 0.017).

Conclusion

Women with POP have significantly larger bony dimensions at the level of the pelvic midplane compared to women without POP. Additionally, advanced prolapse is associated with larger mid-pelvic dimensions.

Objective

Image-derived machine learning (ML) is a robust and growing field in diagnostic imaging systems for both clinicians and radiologists. Accurate preoperative radiological evaluation of the invasive ability of endometrial cancer (EC) can increase the degree of clinical benefit. The present study aimed to investigate the diagnostic performance of magnetic resonance imaging (MRI)-derived artificial intelligence for accurate preoperative assessment of the invasive risk.

Methods

The PubMed, Embase, Cochrane Library and Web of Science databases were searched, and pertinent English-language papers were collected. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and positive and negative likelihood ratios (PLR and NLR, respectively) of all the papers were calculated using Stata software. The results were plotted on a summary receiver operating characteristic (SROC) curve, publication bias and threshold effects were evaluated, and meta-regression and subgroup analyses were conducted to explore the possible causes of intratumoral heterogeneity.

Results

MRI-based radiomics revealed pooled sensitivity (SEN) and specificity (SPE) values of 0.85 and 0.82 for the prediction of high-grade EC; 0.80 and 0.85 for deep myometrial invasion (DMI); 0.85 and 0.73 for lymphovascular space invasion (LVSI); 0.79 and 0.85 for microsatellite instability (MSI); and 0.90 and 0.72 for lymph node metastasis (LNM), respectively. For LVSI prediction and high-grade histological analysis, meta-regression revealed that the image segmentation and MRI-based radiomics modeling contributed to heterogeneity (p = 0.003 and 0.04).

Conclusion

Through a systematic review and meta-analysis of the reported literature, preoperative MRI-derived ML could help clinicians accurately evaluate EC risk factors, potentially guiding individual treatment thereafter.

Objectives

To evaluate the potential of Photon counting CT (PCCT) -derived iodine densities and Virtual-Non-Contrast (VNC) reconstructions for the differentiation between adrenal adenomas and adrenal metastases.

Materials and methods

This retrospective study enrolled 53 PCCT scans of patients with adrenal lesions (29 adenomas, 24 metastases) including early-arterial (ea) and portal-venous (pv) contrast phase. Iodine maps and VNC reconstructions were calculated from the acquired sequences. Using the measured values, several parameters such as relative enhancement (RE), normalized iodine density (NID), and iodine to VNC ratio (IVR) were calculated. In the acquired image series, iodine maps and VNC reconstructions density values were evaluated (adrenal lesion, ipsilateral adrenal tissue, contralateral adrenal tissue, portal vein, descending aorta, inferior vena cava, subcutaneous adipose tissue, in the first lumbar vertebral body, surrounding air, and in the psoas muscle) and compared.

Results

In total, the measured structures showed significant variation in density values due to different contrast phases and reconstructions. VNC reconstructions of portal-venous contrast phase showed significant differences in measured density mean values between adrenal adenomas and adrenal metastases (21.64 HU vs. 28.26 HU, p: 0.027, AUC: 0.68). No significant differences between metastases and adenomas were observed in iodine concentration, RE of ea, NID of ea and pv, as IVR of ea. Significant alterations were observed in RE of pv (p: 0.023, AUC: 0.7) and IVR of pv (pv: 0.029, AUC: 0.69).

Conclusion

The recent study shows that PCCT derived VNC reconstructions of portal venous contrast phase can be used for reliable differentiation of adrenal adenomas and adrenal metastases and underlines the value of PCCT in oncological imaging.

Purpose

Small bowel obstruction (SBO) is a common indication for diagnostic imaging, hospital admission, and surgical consultation. At our institution, patients with SBO identified on an initial CT scan without enteric contrast often underwent a second CT with enteric contrast. A new protocol was implemented in a collaboration between the department of surgery to eliminate this second CT, instead utilizing enteric water-soluble contrast (WSC) and serial abdominal radiographs for further assessment of SBO in clinically stable patients. This study aims to assess the impact of this protocol on radiation exposure and resource utilization.

Methods

A retrospective cohort study was conducted on patients with SBO diagnosed on initial abdominopelvic CT for whom the general surgery service was consulted. The control group included patients prior to protocol implementation who underwent two abdominopelvic CT scans within 24 h–one with and one without enteric contrast. The experimental group included patients managed under the new protocol. Ionizing radiation exposure, contrast media utilization, and CT technologist time were recorded for both groups.

Results

Eighteen patients were included in the experimental group and 38 patients were included in the control group. Total effective dose (mSv) and CT technologist time were significantly less with the new protocol (p = 0.02 and p < 0.001, respectively). Although the use of intravenous contrast was lower in the experimental group, this did not reach statistical significance (p = 0.06).

Conclusion

The implementation of a collaborative SBO imaging and care algorithm between general surgery and radiology resulted in reduced radiation exposure to patients and decreased CT technologist time. This highlights the value of multidisciplinary approaches in improving the efficiency of imaging strategies for SBO.

Graphical abstract

Young-onset colorectal cancer (YOCRC) refers to colorectal cancer diagnosed in individuals under the age of 50. Whilst the overall incidence of colorectal cancer is decreasing, YOCRC cases are increasing and now accounts for up to 10% of all colorectal cancers. YOCRC more frequently presents with acute symptoms, where radiologists play an important role in identifying malignancy and distinguishing it from benign colonic pathologies. Risk factors associated with YOCRC, such as inflammatory bowel disease and hereditary syndromes, may exhibit specific imaging manifestations. In addition, YOCRC is frequently associated with a mucinous histopathological subtype which may be identifiable based on the presence of specific imaging features. Given their younger age, these patients are more likely to undergo aggressive treatment and complex surgical interventions. Specific considerations such as fertility preserving surgical techniques must be factored in when managing these patients. As the incidence of YOCRC increases, guidance for colonoscopy screening protocols may need revision. This includes evaluating the role of ionising imaging techniques in both diagnosing and follow-up to balance early detection and minimising radiation exposure in this younger patient population.

Graphical abstract

Purpose

To development of a preoperative CT-based radiomics model for predicting muscle invasion in patients with upper tract urothelial carcinoma below T3 stage.

Methods

163 consecutive patients who underwent radical nephroureterectomy for stage pT1–2 UTUC were retrospectively enrolled two medical centers (116 patients in training data and 47 patients in external validation data). Lesion segmentation, extraction and selection of radiomic features on pre-surgical CT urography, development and validation of predictive models were performed. Risk stratification of UTUC was evaluated. The diagnostic performance of the radiomics model and risk stratification was analyzed. Reference standard was histopathological analysis.

Results

Among 163 patients (mean age, 52 years ± 9 [standard deviation], 97 men), 61.5% had pT2 grade tumors. 1165 features with intraclass coefficients > 0.75 were retained for least absolute shrinkage and selection operator (LASSO) regression. Nine radiomic features with non-zero coefficients on LASSO regression were selected from the training dataset and used for constructing the radiomics model. Good discrimination capability of the predictive model was observed, as AUCs were 0.859 (95% CI, 0.782–0.917) in the training dataset and 0.821 (95% CI, 0.682–0.918) in the validation dataset, respectively. Based on judgement by the model, When the tumor length diameter > 3 cm, combining ureteroscopy biopsy would improve sensitivity and NPV to 0.86 (95% CI, 0.776–0.922), 0.81 (95% CI, 0.714–0.903).

Conclusion

The preoperative radiomics model showed promising diagnostic performance in predicting UTUC muscle invasion. This could help patients receive more accurate risk classification, especially help patients avoiding radical nephroureterectomy.

Graphical abstract

To development of a preoperative CT-based radiomics model for predicting muscle invasion in patients with upper tract urothelial carcinoma below T3 stage, 163 consecutive patients who underwent radical nephroureterectomy were retrospectively enrolled two medical centers. Nine radiomic features with non-zero coefficients on LASSO regression were selected. Good discrimination capability of the predictive model was observed, as AUCs were 0.859 (95% CI, 0.782-0.917) in the training dataset and 0.821 (95% CI, 0.682-0.918) in the validation dataset, respectively.

Purpose

High-grade clear cell renal cell carcinoma (ccRCC) is linked to lower survival rates and more aggressive disease progression. This study aims to assess the diagnostic performance of MRI-derived radiomics as a non-invasive approach for pre-operative differentiation of high-grade from low-grade ccRCC.

Methods

A systematic search was conducted across PubMed, Scopus, and Embase. Quality assessment was performed using QUADAS-2 and METRICS. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were estimated using a bivariate model. Separate meta-analyses were conducted for radiomics models and combined models, where the latter integrated clinical and radiological features with radiomics. Subgroup analysis was performed to identify potential sources of heterogeneity. Sensitivity analysis was conducted to identify potential outliers.

Results

A total of 15 studies comprising 2,265 patients were included, with seven and six studies contributing to the meta-analysis of radiomics and combined models, respectively. The pooled estimates of the radiomics model were as follows: sensitivity, 0.78; specificity, 0.84; PLR, 4.17; NLR, 0.28; DOR, 17.34; and AUC, 0.84. For the combined model, the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.87, 0.81, 3.78, 0.21, 28.57, and 0.90, respectively. Radiomics models trained on smaller cohorts exhibited a significantly higher pooled specificity and PLR than those trained on larger cohorts. Also, radiomics models based on single-user segmentation demonstrated a significantly higher pooled specificity compared to multi-user segmentation.

Conclusion

Radiomics has demonstrated potential as a non-invasive tool for grading ccRCC, with combined models achieving superior performance.

Graphical Abstract

Purpose

The aim of this study was to develop and validate CT venous phase image-based radiomics to predict BRAF gene mutation status in preoperative colorectal cancer patients.

Methods

In this study, 301 patients with pathologically confirmed colorectal cancer were retrospectively enrolled, comprising 225 from Centre I (73 mutant and 152 wild-type) and 76 from Centre II (36 mutant and 40 wild-type). The Centre I cohort was randomly divided into a training set (n = 158) and an internal validation set (n = 67) in a 7:3 ratio, while Centre II served as an independent external validation set (n = 76). The whole tumor region of interest was segmented, and radiomics characteristics were extracted. To explore whether tumor expansion could improve the performance of the study objectives, the tumor contour was extended by 3 mm in this study. Finally, a t-test, Pearson correlation, and LASSO regression were used to screen out features strongly associated with BRAF mutations. Based on these features, six classifiers—Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost)—were constructed. The model performance and clinical utility were evaluated using receiver operating characteristic (ROC) curves, decision curve analysis, accuracy, sensitivity, and specificity.

Results

Gender was an independent predictor of BRAF mutations. The unexpanded RF model, constructed using 11 imaging histologic features, demonstrated the best predictive performance. For the training cohort, it achieved an AUC of 0.814 (95% CI 0.732–0.895), an accuracy of 0.810, and a sensitivity of 0.620. For the internal validation cohort, it achieved an AUC of 0.798 (95% CI 0.690–0.907), an accuracy of 0.761, and a sensitivity of 0.609. For the external validation cohort, it achieved an AUC of 0.737 (95% CI 0.616–0.847), an accuracy of 0.658, and a sensitivity of 0.667.

Conclusions

A machine learning model based on CT radiomics can effectively predict BRAF mutations in patients with colorectal cancer. The unexpanded RF model demonstrated optimal predictive performance.

Objectives

Adequate bowel preparation and tagging are critical in optimizing CTC performance. Iohexol has a higher safety profile than other available tagging agents. This study aims to determine if iohexol serves as an adequate fluid and stool tagging agent in conjunction with minimally cathartic bowel preparation.

Methods

In this prospective observational study, 50 participants ingested 50 mL of oral iohexol for tagging and 10 oz magnesium citrate for bowel preparation prior to CTC. Written informed consent was obtained. CTC was performed in all participants in at least two of the standard four positions (supine, prone, right decubitus, and left decubitus). Two board-certified abdominal radiologists independently scored the 6 colonic segments of participants who underwent successful CTC. The amount of residual fluid and solid stool, attenuation of tagged fluid, and efficacy of fluid and stool tagging were recorded in each segment. Statistical analyses were performed with R-4.4.0.

Results

47 participants (mean age 66.39 ± 8.65 years; 39 female) underwent successful CTC. Of 1252 total colonic segments, 14.8% had no residual fluid and 59.5% had < 25% residual fluid. 73.6% of segments with residual fluid demonstrated good tagging. The mean fluid tagging efficacy ratio for all segments was 0.737 (95% CI: 0.700–0.775) with mean attenuation of 467 HU. Fluid tagging efficacy decreased from the cecum (0.934) to rectum (0.493). 92.8% of segments had no residual solid stool. Of the 7.2% of segments containing solid stool, 4.7% of segments had submerged stool ≤ 5 mm, 0.8% had 1–3 pieces of retained stool between 6 and 9 mm, and 1.8% had > 3 pieces 6–9 mm or single pieces > 1 cm.

Conclusion

Low-volume (50 mL) iohexol is an effective fluid and fecal tagging agent for CTC with a minimally cathartic bowel preparation. This provides an easy option to label residual material and cleanse the bowel for patients undergoing CTC.

Graphical abstract

Bilateral adnexal lesions involve structures such as the ovaries, fallopian tubes, and surrounding tissues, arising from diverse etiologies, including inflammatory, infectious, neoplastic, and functional causes. Their variable presentation poses a diagnostic challenge in clinical practice, necessitating a multidisciplinary approach for accurate assessment and management. The American College of Radiology (ACR) introduced the Ovarian-Adnexal Reporting and Data System (O-RADS) as a standardized lexicon and risk stratification tool for evaluating adnexal lesions via ultrasound (US) and magnetic resonance imaging (MRI). While MRI is the most accurate modality for assessing indeterminate adnexal masses, bilateral lesions frequently present diagnostic dilemmas, particularly when they exhibit divergent O-RADS classifications or arise from different etiologies. The O-RADS system does not provide specific guidelines for bilateral lesions, requiring independent classification of each lesion, with management dictated by the highest assigned category. Certain pathologies demonstrate a propensity for bilateral involvement, underscoring the importance of recognizing their imaging characteristics and differential diagnoses. Integrating this knowledge into diagnostic reports enhances clinical decision-making and optimizes patient outcomes.

Graphical Abstract