Abdominal Radiology最新文献

Cholangiocarcinoma (CCA) is a unifying title granted to epithelial adenocarcinomas specific to the bile ducts making up 10-25% of all hepatobiliary malignancies. CCA is more appropriately classified based on anatomic site of origin within the biliary tract into intrahepatic cholangiocarcinoma (iCCA), peri-hilar (pCCA) cholangiocarcinoma, and distal cholangiocarcinoma (dCCA). Intrahepatic cholangiocarcinoma makes up 10-20% of CCA and originates within and/or proximal to the second order bile ducts. The incidence of iCCA has been rising overtime with up to 1.26 per 100,000 persons, per year in the United States and up to 3.3 per 100, 000 persons, per year affected globally. Risk factors include chronic hepatic inflammation secondary to viral hepatitis, alcohol/NASH cirrhosis, biliary cystic lesions, and endemic causes, among other less common genetic drivers. Given its rarity, the recognition and diagnosis of cholangiocarcinoma, iCCA specifically, remains challenging resulting in delays in treatment initiation or any treatment at all. Median overall survival (mOS) for iCCA remains low. Early diagnosis, and stage-based treatment approaches have evolved and are associated with improved survival. To this goal, a multi-disciplinary treatment approach has been demonstrated to improve patient outcomes by providing expert evaluation as it pertains to an accurate imaging and histologic diagnosis, staging, radiologic and surgical review for resectability, operative expertise, post operative care, as well as comprehensive knowledge and implementation of systemic/targeted or liver directed therapies. Here, we discuss the central role of imaging in the diagnosis of intrahepatic cholangiocarcinoma to implement a comprehensive treatment plan that frequently involves multiple disciplines to achieve the best outcome for each patient.

Adenomyosis is a common, estrogen-dependent condition where endometrial tissue grows within the myometrium, often accompanied by smooth muscle hypertrophy. Initially thought to represent a condition primarily seen in multiparas with menorrhagia, and dysmenorrhea, adenomyosis is now increasingly recognized in younger patients and those with infertility and subfertility. As a result, conservative treatments aimed at preserving the uterus and improving reproductive outcomes have gained attention to treat adenomyosis. While research has largely focused on managing abnormal uterine bleeding and dysmenorrhea, there is limited evidence on the treatment of infertility associated with adenomyosis, particularly in terms of imaging follow-up. This paper reviews the emerging literature, highlighting key imaging findings before and after uterus-preserving treatments for adenomyosis, to better inform management and decision-making.

Purpose: Radiologic examinations frequently identify cystic liver lesions, which encompass various entities from simple benign cysts to malignant neoplasms. This work analyses the available data to compare diagnostic features of biliary cystic neoplasms and hepatic simple cysts.

Methods: A systematic search of PubMed, Scopus, Embase, and Web of Science up to October 2024 was conducted. The characteristics were categorized into hepatic simple cysts (HSC) and mucinous cystic neoplasms (MCN), including biliary cystadenoma (BCA) and cystadenocarcinoma (BCAC) detected by imaging modalities including ultrasound, CT scans with IV contrast, or MRI. We analyzed biliary cystic neoplasms and hepatic simple cysts across multiple studies using Review Manager Ver. 5, calculating summary measures for each feature.

Results: The study analyzed 577 lesions in 577 patients and 49 studies. Hepatic simple cysts were the most common finding, with 349 identified, mainly in the right hepatic lobe, presented with abdominal pain or incidentally. Intracystic septation was found in 50.1% of HSC lesions, with thick septation in 10.52% of lesions. 228 (49.9%) patients were diagnosed with MCN, with abdominal swelling and pain as the most common presentation. Septation was the most common radiological feature of MCNs, with thick septa in 50.61%. MCNs had internal septa, solid mural nodule, upstream bile duct dilation, presence in the left hepatic lobe, septal thickening, cystic wall enhancement, calcifications, and internal debris. The presence of a cyst in the left lobe was more related to MCNs.

Conclusion: Characterizing cystic liver lesions necessitates a comprehensive evaluation of the lesions' location, size, and complexity. Imaging and clinical findings are essential for a final diagnosis.

Purpose: To evaluate the utility of deep learning-based automated attenuation measurements on contrast-enhanced CT (CECT) for diagnosing moderate-to-severe hepatic steatosis (HS), using histology as reference standard.

Methods: This retrospective study included 3,620 liver donors (2,393 men and 1,227 women; mean age, 31.7 ± 9.4 years), divided into the development (n = 2,714) and test (n = 906) cohorts. Attenuation values of the liver and spleen on CECT were measured both manually and using a deep learning algorithm (before and after radiologists' correction of segmentation errors). Performance of: (1) liver attenuation and (2) liver-spleen attenuation difference for diagnosing moderate-to-severe HS (> 33%) was assessed using the area under the receiver operating characteristic curve (AUC). Three different criteria targeting 95% sensitivity, 95% specificity, and the maximum Youden's index, respectively, for diagnosing moderate-to-severe HS, were developed and validated.

Results: The performance of deep learning-based measurements did not differ significantly, with or without radiologists' corrections (p = 0.13). Liver-spleen attenuation difference outperformed liver attenuation alone in diagnosing moderate-to-severe HS in both deep learning-based (AUC, 0.868 vs. 0.821; p = 0.001) and manual (AUC, 0.871 vs. 0.823; p = 0.001) measurements. In the test cohort, the criterion targeting 95% sensitivity for diagnosing moderate-to-severe HS (liver-spleen attenuation difference ≤ 2.8 HU) yielded 92.0% (69/75) sensitivity and 48.5% (403/831) specificity. The criterion targeting 95% specificity (liver-spleen attenuation difference ≤ -18.8 HU) yielded 53.3% (40/75) sensitivity and 95.7% (795/831) specificity. The criterion targeting the maximum Youden's index (liver-spleen attenuation difference ≤ -8.2 HU) yielded 82.7% (62/75) sensitivity and 80.7% (671/831) specificity.

Conclusion: Deep learning-based automated measurements of liver and spleen attenuation on CECT can be used reliably to detect moderate-to-severe HS.

Vascular malformations of the gastrointestinal (GI) tract are a rare yet significant cause of GI bleeding, that can present at any age and require a high index of suspicion for timely diagnosis. While the majority of lesions may be asymptomatic and detected incidentally on imaging, they may also present with anemia if there is occult blood loss, as acute GI bleeding, or as lead points for intussusception. The presence of multiple vascular malformations may be associated with underlying syndromes, such as Hereditary Hemorrhagic Telangiectasia syndrome and Klippel-Trénaunay syndrome. While luminal endoscopy is the first-line diagnostic test to evaluate overt and occult GI bleeding, imaging plays a very significant role in detecting these vascular malformations and planning the best treatment approach. In this review, we describe the various imaging findings of GI tract vascular malformations and available treatment options, focusing on endovascular interventional treatments where possible.

Objective: To evaluate the value of ¹⁸F-FDG-positron emission tomography (PET)/magnetic resonance imaging (MRI) functional imaging in the radiotherapy of locally recurrent rectal cancer by comparing the target volume delineation based on PET/MRI and MRI.

Materials and methods: Twenty-six patients who were diagnosed with locally recurrent rectal cancer were included in this study. Patients underwent PET/MRI, and the target volume was delineated independently by three radiation oncologists. The degree of overlap, spatial consistency, and difference in the target volume delineated based on the two methods were compared. The efficacy of PET/MRI and MRI in detecting metastatic lymph nodes was analyzed.

Results: In radiotherapy for patients with recurrent rectal cancer, the gross tumor volume (GTV), clinical target area (CTV), and nodal gross tumor volume (GTVn) delineated based on MRI and PET/MRI were correlated (P < 0.001, P < 0.001, and P < 0.001, respectively). Differences in CTV were statistically significant (P < 0.001), and the CTV greatly overlapped spatially. There is spatial heterogeneity in GTV and GTVn based on the two imaging modalities. Metastatic lymph node analysis revealed that the detection efficiency of the two modalities was the same at the population level. There was no significant difference in the number of metastatic lymph nodes detected (P = 0.521).

Conclusion: PET/MRI can improve the accuracy of target volume delineation and has similar advantages to MRI in assessing the number of metastatic lymph nodes in patients with recurrent rectal cancer.

Rationale and objectives: To develop and evaluate the performance of a noninvasive radiomics combined model based on preoperative bi-parametric MRI to assess biochemical recurrence (BCR) risk factors and to predict biochemical recurrence free survival in PCa patients.

Materials and methods: Pretreatment bp-MRI and clinicopathology data of 666 (discovery cohort, 545; test cohort, 121) PCa patients from four centers between January 2015 to March 2023 were retrospectively included. To predict BCR, extracapsular extension (ECE), pelvic lymph node metastasis (PLNM), and Gleason Grade group (GG), the pred-BCR, pred-ECE, pred-PLNM, and pred-GG models were developed, respectively. Subsequently, a logistic regression algorithm was used to combine one or more radiomics models and clinicopathology variables into radiomics-clinicopathology combined models (M1, M2) and radiomics-clinical combined model without pathology results (M3) for predicting BCR.

Results: In the test cohort, the AUCs for the pred-BCR, pred-ECE, pred-PLNM, and pred-GG models were 0.841, 0.764, 0.896, and 0.698. Of the three combined models, M3 has the best prediction performance with an AUC of 0.884, M2 is the following with an AUC of 0.863, and M1 has the lowest performance with an AUC of 0.838 (95% CI 0.750-0.925) in the test cohort. Delong's test showed that the M3 was significantly higher (M1 vs. M3, p = 0.028; M2 vs. M3, p = 0.044).

Conclusion: The combined model developed in this study, which is not dependent on pathologic biopsies, can noninvasively predict postoperative histopathology and BCR after PCa, therefore may provide decision support for follow-up and treatment strategies for patients in the postoperative period.

Purpose: Magnetic resonance elastography (MRE) measures liver stiffness for fibrosis staging, but its utility can be hindered by quality control (QC) challenges and measurement variability. The objective of the study was to fully automate liver MRE QC and liver stiffness measurement (LSM) using a deep learning (DL) method.

Methods: In this retrospective, single center, IRB-approved human study, a curated dataset involved 897 MRE magnitude slices from 146 2D MRE scans [1.5 T and 3 T MRI, 2D Gradient Echo (GRE), and 2D Spin Echo-Echo Planar Imaging (SE-EPI)] of 69 patients (37 males, mean age 51.6 years). A SqueezeNet-based binary QC model was trained using combined and individual inputs of MRE magnitude slices and their 2D Fast-Fourier transforms to detect artifacts from patient motion, aliasing, and blurring. Three independent observers labeled MRE magnitude images as 0 (non-diagnostic quality) or 1 (diagnostic quality) to create a reference standard. A 2D U-Net segmentation model was trained on diagnostic slices with liver masks to support LSM. Intersection over union between the predicted segmentation and confidence masks identified measurable areas for LSM on elastograms. Cohen's unweighted Kappa coefficient, mean LSM error (%), and intra-class correlation coefficient were calculated to compare the DL-assisted approach with the observers' annotations. An efficiency analysis compared the DL-assisted vs manual LSM durations.

Results: The top QC ensemble model (using MRE magnitude alone) achieved accuracy, precision, and recall of 0.958, 0.982, and 0.886, respectively. The mean LSM error between the DL-assisted approach and the reference standard was 1.9% ± 4.6%. DL-assisted approach completed LSM for 29 diagnostic slices in under 1 s, compared to 20 min manually.

Conclusion: An automated DL-based classification of liver MRE diagnostic quality, liver segmentation, and LSM approach demonstrates a promising high performance, with potential for clinical adoption.

Background: To evaluate the predictive value of a nomogram based on the vascular entry sign for lymphovascular invasion in gastric cancer.

Methods: A total of 135 patients with histopathologically confirmed gastric cancer from August 2021 to November 2022 were enrolled. All patients underwent contrast-enhanced CT scans. Utilizing a random number method, patients were randomly assigned to either a training dataset (n = 96) or a validation dataset (n = 39) in a 7:3 ratio. CT images and clinical characteristics of the patients were collected. Both univariate and multivariate analyses were conducted to identify independent factors influencing lymphovascular invasion in gastric cancer. A nomogram model was developed, and its diagnostic performance and clinical utility were assessed using receiver operating characterist (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results: The multivariate analysis revealed that the vascular entry sign, clinical T stage, and clinical N stage independently influenced the occurrence of factors for lymphovascular invasion in gastric cancer (P < 0.05). A predictive nomogram model was developed for determining LVI status in gastric cancer. The AUC of the nomogram model in the training dataset and validation dataset were 0.878 (95% CI: 0.808-0.948) and 0.866 (95% CI: 0.723-1.000), respectively. The calibration curve and decision curve showed that the model had good reliability and good clinical validity.

Conclusion: The model established based on the factors of vascular entry sign, clinical T stage, and clinical N stage can effectively predict lymphovascular invasion in gastric cancer.

Objective: To prospectively determine the ability of visible lesions on multiparametric MRI (PI-RADS 4-5) and commonly used biomarkers to predict disease upgrading on rebiopsy in men with low-risk prostate cancer (PCa) enrolled in active surveillance (AS).

Materials and methods: For this prospective study, approved by the Institutional Review Board (IRB), we selected consecutive patients with low-risk, low-grade, and localized prostate cancer (PCa) from our active surveillance (AS) program, who were enrolled between March 2014 and December 2020. Patients who had undergone previous prostate surgery, hormonal treatment, had contraindications for mpMRI, or transrectal ultrasound-guided (TRUS) biopsy were excluded from this study. All eligible patients underwent mpMRI at least 3 months after the initial biopsy, followed by MRI-targeted TRUS-guided re-biopsy within 12 months after enrollment. The mpMRI studies were evaluated by an experienced radiologist using the PI-RADS v2 classification. Statistical significance was determined by comparing the results from the MRI with the pathology data from rebiopsy.

Results: There were 240 patients included. Overall upgrading rate was 41.2% (99/240), higher among patients classified as PIRADS 4 or 5 (77%). MRI sensitivity was 77.7% and specificity was 83.6% on re-biopsy. Visible lesion on mpMRI, PSA density and 3 + /12 positive cores at the first biopsy were good predictors of disease upgrade on rebiopsy. On our predictive model, patients with PI-RADS 4 or 5, PSA density > 0.15 ng/mL/cm³, and 3 + /12 positive cores at first biopsy had 92.4% chance of having clinically significant PCa.

Conclusion: Patients in AS with PI-RADS 4 or 5 lesions, PSA density > 0.15 ng/mL/cm³ and 3 + /12 positive cores at first biopsy have a high probability of having significant PCa on re-biopsy.