Value of deep-learning image reconstruction at submillisievert CT for evaluation of the female pelvis.

AIM: To assess the value of deep-learning reconstruction (DLR) at submillisievert computed tomography (CT) for the evaluation of the female pelvis, with standard dose (SD) hybrid iterative reconstruction (IR) images as reference. MATERIALS AND METHODS: The present study enrolled 50 female patients consecutively who underwent contrast-enhanced abdominopelvic CT for clinically indicated reasons. Submillisievert pelvic images were acquired using a noise index of 15 for low-dose (LD) scans, which were reconstructed with DLR (body and body sharp), hybrid-IR, and model-based IR (MBIR). Additionally, SD scans were reconstructed with a noise index of 7.5 using hybrid-IR. Radiation dose, quantitative image quality, overall image quality, image appearance using a five-point Likert scale (1-5: worst to best), and lesion evaluation in both SD and LD images were analysed and compared. RESULTS: The submillisievert pelvic CT examinations showed a 61.09 ± 4.13% reduction in the CT dose index volume compared to SD examinations. Among the LD images, DLR (body sharp) had the highest quantitative quality, followed by DLR (body), MBIR, and hybrid-IR. LD DLR (body) had overall image quality comparable to the reference (p=0.084) and favourable image appearance (p=0.209). In total, 40 pelvic lesions were detected in both SD and LD images. LD DLR (body and body sharp) exhibited similar diagnostic confidence (p=0.317 and 0.096) compared with SD hybrid-IR. CONCLUSION: DLR algorithms, providing comparable image quality and diagnostic confidence, are feasible in submillisievert abdominopelvic CT. The DLR (body) algorithm with favourable image appearance is recommended in clinical settings.

[Predictive value of MRI pelvic measurements for "difficult pelvis" during total mesorectal excision].

Objective: Total mesorectal resection (TME) is difficult to perform for rectal cancer patients with anatomical confines of the pelvis or thick mesorectal fat. This study aimed to evaluate the ability of pelvic dimensions to predict the difficulty of TME, and establish a nomogram for predicting its difficulty. Methods: The inclusion criteria for this retrospective study were as follows: (1) tumor within 15 cm of the anal verge; (2) rectal cancer confirmed by preoperative pathological examination; (3) adequate preoperative MRI data; (4) depth of tumor invasion T1-4a; and (5) grade of surgical difficulty available. Patients who had undergone non-TME surgery were excluded. A total of 88 patients with rectal cancer who underwent TME between March 2019 and November 2021 were eligible for this study. The system for scaling difficulty was as follows: Grade I, easy procedure, no difficulties; Grade II, difficult procedure, but no impact on specimen quality (complete TME); Grade III, difficult procedure, with a slight impact on specimen quality (near-complete TME); Grade IV: very difficult procedure, with remarkable impact on specimen quality (incomplete TME). We classified Grades I-II as no surgical difficulty and grades III-IV as surgical difficulty. Pelvic parameters included pelvic inlet length, anteroposterior length of the mid-pelvis, pelvic outlet length, pubic tubercle height, sacral length, sacral depth, distance from the pubis to the pelvic floor, anterior pelvic depth, interspinous distance, and inter-tuberosity distance. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with the difficulty of TME, and a nomogram predicting the difficulty of the procedure was established. Results: The study cohort comprised 88 patients, 30 (34.1%) of whom were classified as having undergone difficult procedures and 58 (65.9%) non-difficult procedures. The median age was 64 years (56-70), 51 patients were male and 64 received neoadjuvant therapy. The median pelvic inlet length, anteroposterior length of the mid-pelvis, pelvic outlet length, pubic tubercle height, sacral length, sacral depth, distance from the pubis to the pelvic floor, anterior pelvic depth, interspinous distance, and inter-tuberosity distance were 12.0 cm, 11.0 cm, 8.6 cm, 4.9 cm, 12.6 cm, 3.7 cm, 3.0 cm, 13.3 cm, 10.2 cm, and 12.2 cm, respectively. Multivariable analyses showed that preoperative chemoradiotherapy (OR=4.97,95% CI: 1.25-19.71, P=0.023), distance between the tumor and the anal verge (OR=1.31, 95% CI: 1.02-1.67, P=0.035) and pubic tubercle height (OR=3.36, 95% CI: 1.56-7.25, P=0.002) were associated with surgical difficulty. We then built and validated a predictive nomogram based on the above three variables (AUC = 0.795, 95%CI: 0.696-0.895). Conclusion: Our research demonstrated that our system for scaling surgical difficulty of TME is useful and practical. Preoperative chemoradiotherapy, distance between tumor and anal verge, and pubic tubercle height are risk factors for surgical difficulty. These data may aid surgeons in planning appropriate surgical procedures.

[The Chinese guidelines for the diagnosis and treatment of pancreatic neuroendocrine neoplasms (2020)].

Pancreatic neuroendocrine neoplasms (pNENs) are highly heterogeneous, and the management of pNENs patients can be intractable. To address this challenge, an expert committee was established on behalf of the Group of Pancreatic Surgery, Chinese Society of Surgery, Chinese Medical Association, which consisted of surgical oncologists, gastroenterologists, medical oncologists, endocrinologists, radiologists, pathologists, and nuclear medicine specialists. By reviewing the important issues regarding the diagnosis and treatment of pNENs, the committee concluded evidence-based statements and recommendations in this article, in order to further improve the management of pNENs patients in China.

[Analysis of pancreatic segmentation algorithm based on deep learning to improve pancreatic critical region segmentation ability on dual-phase CT].

Objective: To investigate the segmentation effects of the deep learning method on CT in the arterial phase and venous phase respectively by using subjective and objective evaluation system, and to investigate the factors that affect the difference between arterial phase and venous phase pancreas segmentation and the related factors affecting the venous pancreas segmentation. Method: A total of 218 cases of pancreatic CT scan data in the Department of Radiology of Peking Union Medical College Hospital from January to November 2019 were retrospectively collected. Each case contained images of arterial and venous phases, and the data were randomly divided into training set (139 cases), validation set (20 cases) and test set (59 cases) according to the ratio of the training and verification set to the test set of 7∶3. The two-stage global local progressive fusion network was trained on the training set, the model parameters of the optimal segmentation effect were found on the validation set, and the test set was predicted and the results were evaluated subjectively and objectively. The Likert 5-point scale was used for subjective evaluation based on the critical regions between pancreas and peripheral organs, while the Dice similarity coefficient (DSC) was used for objective evaluation. The paired t test or Wilcoxon paired rank test was used to compare the differences of subjective and objective scores of the arterial phase and venous phase. Results: For the critical regions of the pancreas at the duodenum, duodenal jejunal flexure, left adrenal gland, portal vein, superior mesenteric vein, splenic artery and splenic vein, the median number of subjective scores in arterial phase were 4(4, 5), 5(4, 5), 5(4, 5), 4(4, 5), 5(4, 5), 5(5, 5) and 4(3, 5)points respectively, the median number(first quartile, third quartile) of subjective scores in venous phase were 4(4, 4), 5(4, 5), 5(4, 5), 5(4, 5), 5(5, 5), 4(3, 4) and 5(5, 5) points respectively,there were statistically significant differences of the median number(first quartile, third quartile) of the subjective scores between the arterial and venous phase for the critical regions of the pancreas at the organs described above (all P<0.05). DSC in the venous phase was slightly higher than that in the arterial phase and the difference was not statistically significant (DSC: 0.932 vs 0.921, P=0.952). Subjective scores in venous phase of the pancreas and duodenal jejunum, stomach, and left adrenal gland with fat gaps were 4.64,4.68 and 4.63 points respectively, and those of the group without fat gaps were 4.56,4.62 and 4.56 points respectively, there were statistically significant differences of the subjective scores in venous phase of the groups with fat gaps or not between the pancreas and the organs described above (t=2.147, 2.112, 2.277, all P<0.05). Except the spleen, the density differences between the critical regions of the pancreas and other surrounding organs were statistically significant in arterial phase and venous phase segmentation (all P<0.05). Conclusion: Dual-phase CT was used to construct a deep learning automatic pancreas segmentation model, and the segmentation effect was evaluated subjectively and objectively. Subjective evaluation was helpful to improve the ability to segment the critical regions of the pancreas in the future.

Simultaneous multi-slice accelerated diffusion-weighted imaging with higher spatial resolution for patients with liver metastases from neuroendocrine tumours.

AIM: To evaluate the imaging characteristics of simultaneous multi-slice (SMS) accelerated diffusion-weighted imaging (DWI) with decreased section thickness, with and without motion correction, in comparison to conventional DWI (cDWI) for the detection of lesions in patients with neuroendocrine tumour (NET) liver metastases. MATERIALS AND METHODS: Fifteen patients with NET liver metastases underwent cDWI (section thickness [SL]=4 mm) and SMS-DWI (SL=2 mm). Non-linear motion-corrected (Moco)-SMS-DWI was generated in addition to the original series. Qualitative imaging characteristics (five-point Likert scale), the number of high signal lesions, and the detectability and delineation of lesions were evaluated and compared using the Friedman and the Dunn-Bonferroni tests. The test-retest variability (TRV) of the cDWI and SMS-DWI techniques was investigated among 11 healthy volunteers who underwent cDWI (SL=4 mm) and SMS-DWI (SL=4 mm) twice. The Friedman and the Dunn-Bonferroni post-hoc tests were used to compare the mean apparent diffusion coefficient (ADC) and the TRV in different liver regions between the three series. RESULTS: Moco-SMS-DWI demonstrated significantly superior overall image quality (p<0.001) with significantly fewer artefacts (p=0.003) than cDWI. The number of lesions detected by cDWI, SMS-DWI, and Moco-SMS-DWI were 348, 504, and 523, respectively. The detectability and delineation of the lesions and the ADC values were significantly higher on the SMS-DWI and Moco-SMS-DWI images than on the cDWI images (all p<0.001). Moco-SMS-DWI showed significantly higher TRV than cDWI in regions near the liver edge (p=0.018). CONCLUSIONS: SMS-DWI achieves higher spatial resolution than cDWI within the same acquisition time, detects more lesions, and provides better lesion delineation. By applying motion correction, the TRV of DWI could be enhanced in regions near the liver edge.

Visualising the boundary sharpness of uterine zonal structures using high-resolution T2-weighted images during the menstrual cycle.

AIM: To investigate whether there is an optimal time in the menstrual cycle to obtain the best image quality of uterine zonal structures with high-field magnetic resonance imaging (MRI). MATERIALS AND METHODS: Thirty-eight normal volunteers with regular menstrual cycles underwent pelvic 3 T high-resolution T2-weighted three-dimensional (3D) turbo spin echo (TSE) with variable flip angle MRI examinations during the menstrual phase (MP), follicular phase (FP), peri-ovulatory phase (OP), and luteal phase (LP). Two radiologists blinded evaluated the boundary sharpness of the three zonal structures of the uterine corpus and cervix on mid-sagittal images using a three-point Likert-scale. The signal intensity (SI) on T2-weighted sequences of each zonal structure was measured and the ratio between the SI of adjacent structures was calculated. Paired Wilcoxon's test and repeated measurement analysis of variance were used to investigate the differences among the four phases. RESULTS: No variation during the menstrual cycle was found in 10.5% (4/38) of volunteers and their boundaries were all well-defined. The OP exhibited the clearest boundaries of the corpus zonal structures. For the endometrium to junctional zone, mean scores of boundary sharpness from high to low were 3 (OP), 2.97 (FP), 2.76 (LP), 2.74 (MP); that for the junctional zone to myometrium were 2.76 (OP), 2.42 (FP), 2.32 (LP), 2.11 (MP); which were consistent with the SI ratio results. The results for the cervix showed no statistical difference during the menstrual cycle (p>0.05), and was well-defined throughout. CONCLUSIONS: The OP is recommended as the best phase to investigate zonal-related uterine corpus diseases due to the best contrast. For cervical diseases, imaging could be performed when necessary at any time point, due to the limited influence of menstrual phases on cervical zone delineation.

Can quantitative CT texture analysis be used to differentiate subtypes of renal cell carcinoma?

AIM: To investigate whether computed tomography (CT) texture analysis (TA) can be used to differentiate non-clear-cell renal cell carcinoma (non-ccRCC) from clear-cell RCC (ccRCC) and classify non-ccRCC subtypes. MATERIALS AND METHODS: One hundred ccRCC and 27 non-ccRCC (12 papillary and 15 chromophobe) were analysed. Texture parameters quantified from multiphasic CT images were compared for the objectives. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was calculated. The optimal discriminative texture parameters were used to produce support vector machine (SVM) classifiers. Diagnostic accuracy and 10-fold cross-validation was performed. RESULTS: Compared to ccRCC, non-ccRCC had significantly lower mean grey-level intensity (mean), standard deviation (SD), entropy, mean of positive pixels (MPP), and higher kurtosis (p<0.001). A model incorporating SD, entropy, MPP, and kurtosis produced an AUC of 0.94±0.03 with an accuracy of 87% (sensitivity=89%, specificity=92%) to identify non-ccRCC from ccRCC. Compared to chromophobe RCC, papillary RCC had significantly lower mean and MPP (p=0.002). A model incorporating SD, MPP, and skewness resulted in an AUC of 0.96±0.04 with an accuracy of 78% (sensitivity=87%, specificity=92%) to differentiate between papillary and chromophobe RCC. CONCLUSION: CT TA could potentially be used as a less invasive tool to classify histological subtypes of RCC.

[The 465th case: intestinal obstruction, gastrointestinal hemorrhage and duodenal fistula].

This is a complicated and difficult case. The onset symptom of a 62-year-old male was recurrent intestinal obstruction. Ileocecal and ileocolic operation was done twice. Massive gastrointestinal bleeding occurred due to giant fistula of descending duodenum, which connected to ileocolic anastomosis. After consultation by multidisciplinary team, jejunal-feeding tube was placed to provide enteral nutrition. With general condition improving, duodenal fistula repair and involved bowel resection were performed. Postoperative pathology confirmed Crohn's disease. The patient was treated with thalidomide and recovered well during follow-up.

Uric acid versus non-uric acid urinary stones: differentiation with single energy CT texture analysis.

AIM: To evaluate the accuracy of computed tomography (CT) texture analysis (TA) to differentiate uric acid (UA) stones from non-UA stones on unenhanced CT in patients with urinary calculi with ex vivo Fourier transform infrared spectroscopy (FTIR) as the reference standard. MATERIALS AND METHODS: Fourteen patients with 18 UA stones and 31 patients with 32 non-UA stones were included. All the patients had preoperative CT evaluation and subsequent surgical removal of the stones. CTTA was performed on CT images using commercially available research software. Each texture feature was evaluated using the non-parametric Mann-Whitney test. Receiver operating characteristic (ROC) curves were created and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different. The features were used to train support vector machine (SVM) classifiers. Diagnostic accuracy was evaluated. RESULTS: Compared to non-UA stones, UA stones had significantly lower mean, standard deviation and mean of positive pixels but higher kurtosis (p<0.001) on both unfiltered and filtered texture scales. There were no significant differences in entropy or skewness between UA and non-UA stones. The average SVM accuracy of texture features for differentiating UA from non-UA stones ranged from 88% to 92% (after 10-fold cross validation). A model incorporating standard deviation, skewness, and kurtosis from unfiltered texture scale images resulted in an AUC of 0.965±00.029 with a sensitivity of 94.4% and specificity of 93.7%. CONCLUSION: CTTA can be used to accurately differentiate UA stones from non-UA stones in vivo using unenhanced CT images.

[A comparison between the revision of Atlanta classification and determinant-based classification in acute pancreatitis].

Objective: To compare the performance of the revision of Atlanta classification (RAC) and determinant-based classification (DBC) in acute pancreatitis. Methods: Consecutive patients with acute pancreatitis admitted to a single center from January 2001 to January 2015 were retrospectively analyzed. Patients were classified into mild, moderately severe and severe categories based on RAC and were simultaneously classified into mild, moderate, severe and critical grades according to DBC. Disease severity and clinical outcomes were compared between subgroups. The receiver operating curve (ROC) was used to compare the utility of RAC and DBC by calculating the area under curve (AUC). Results: Among 1 120 patients enrolled, organ failure occurred in 343 patients (30.6%) and infected necrosis in 74 patients(6.6%). A total of 63 patients (5.6%) died. Statistically significant difference of disease severity and outcomes was observed between all the subgroups in RAC and DBC (P<0.001). The category of critical acute pancreatitis (with both persistent organ failure and infected necrosis) had the most severe clinical course and the highest mortality (19/31, 61.3%). DBC had a larger AUC (0.73, 95%CI 0.69-0.78) than RAC (0.68, 95%CI 0.65-0.73) in classifying ICU admissions (P=0.031), but both were similar in predicting mortality(P=0.372) and prolonged ICU stay (P=0.266). Conclusions: DBC and RAC perform comparably well in categorizing patients with acute pancreatitis regarding disease severity and clinical outcome. DBC is slightly better than RAC in predicting prolonged hospital stay. Persistent organ failure and infected necrosis are risk factors for poor prognosis and presence of both is associated with the most dismal outcome.

Image quality of mean temporal arterial and mean temporal portal venous phase images calculated from low dose dynamic volume perfusion CT datasets in patients with hepatocellular carcinoma and pancreatic cancer.

PURPOSE: Dynamic volume perfusion CT (dVPCT) provides valuable information on tissue perfusion in patients with hepatocellular carcinoma (HCC) and pancreatic cancer. However, currently dVPCT is often performed in addition to conventional CT acquisitions due to the limited morphologic image quality of dose optimized dVPCT protocols. The aim of this study was to prospectively compare objective and subjective image quality, lesion detectability and radiation dose between mean temporal arterial (mTA) and mean temporal portal venous (mTPV) images calculated from low dose dynamic volume perfusion CT (dVPCT) datasets with linearly blended 120-kVp arterial and portal venous datasets in patients with HCC and pancreatic cancer. MATERIALS AND METHODS: All patients gave written informed consent for this institutional review board-approved HIPAA compliant study. 27 consecutive patients (18 men, 9 women, mean age, 69.1 years±9.4) with histologically proven HCC or suspected pancreatic cancer were prospectively enrolled. The study CT protocol included a dVPCT protocol performed with 70 or 80kVp tube voltage (18 spiral acquisitions, 71.2s total acquisition times) and standard dual-energy (90/150kVpSn) arterial and portal venous acquisition performed 25min after the dVPCT. The mTA and mTPV images were manually reconstructed from the 3 to 5 best visually selected single arterial and 3 to 5 best single portal venous phases dVPCT dataset. The linearly blended 120-kVp images were calculated from dual-energy CT (DECT) raw data. Image noise, SNR, and CNR of the liver, abdominal aorta (AA) and main portal vein (PV) were compared between the mTA/mTPV and the linearly blended 120-kVp dual-energy arterial and portal venous datasets, respectively. Subjective image quality was evaluated by two radiologists regarding subjective image noise, sharpness and overall diagnostic image quality using a 5-point Likert Scale. In addition, liver lesion detectability was performed for each liver segment by the two radiologists using the linearly blended120-kVp arterial and portal venous datasets as the reference standard. RESULTS: Image noise, SNR and CNR values of the mTA and mTPV were significantly higher when compared to the corresponding linearly blended arterial and portal venous 120-kVp datasets (all p<0.001) except for image noise within the PV in the portal venous phases (p=0.136). OBJECTIVE: image quality of mTA and mTPV were rated significantly better when compared to the linearly blended 120-kVp arterial and portal venous datasets. Both readers were able to detect all liver lesions found on the linearly blended 120-kVp arterial and portal venous datasets using the mTA and mTPV datasets. The effective radiation dose of the dVPCT was 27.6mSv for the 80kVp protocol and 14.5mSv for the 70kVp protocol. The mean effective radiation dose for the linearly blended 120-kVp arterial and portal venous CT protocol together of the upper abdomen was 5.60mSv±1.48mSv. CONCLUSION: Our preliminary data suggest that subjective and objective image quality of mTA and mTPV datasets calculated from low-kVp dVPCT datasets is non-inferior when compared to linearly blended 120-kVp arterial and portal venous acquisitions in patients with HCC and pancreatic cancer. Thus, dVPCT could be used as a stand-alone imaging technique without additionally performed conventional arterial and portal venous CT acquisitions.

Prospective prediction of the major component of urinary stone composition with dual-source dual-energy CT in vivo.

AIM: To prospectively evaluate the diagnostic accuracy of dual-source dual-energy computed tomography (DSDECT) for predicting the major component and determining the composition of urinary calculi in patients with urolithiasis, using postoperative in vitro Fourier transform infrared spectroscopy (FT-IR) analysis as the reference standard. MATERIALS AND METHODS: Patients with known urolithiasis underwent preoperative DSDECT evaluation, and subsequently, underwent surgical removal of the stones. All patients were examined using the dual-energy renal stone protocol. Material-specific chromatic images were made using dedicated post-processing software. The final determination of stone composition was made using FT-IR postoperatively. Diagnostic parameters of DSDECT for predicting the major component and detecting the presence of four composition types were calculated. RESULTS: A total of 81 urinary calculi were included in this study. Forty-three were pure stones and 38 were mixed stones according to FT-IR. DSDECT correctly identified the major component of all pure stones and 36 mixed stones. The major component of two mixed stones with uric acid as the major component was falsely interpreted as calcium oxalate. The overall accuracy of DSDECT for predicting the major component of stones was 97.5% (79/81). The accuracy of DSDECT for detecting the presence of four types of composition, uric acid, cysteine, hydroxyapatite, and calcium oxalate, was 97.5% (79/81), 93.8% (76/81), 80.2% (65/81), and 93.8% (76/81), respectively. CONCLUSION: DSDECT could accurately predict the major component of urinary calculi and detect uric acid, cysteine, and calcium oxalate with a satisfactory accuracy.

[Clinical and radiologic characteristics of craniomaxillofacial primary tumor induced osteomalacia: a retrospective analysis].

OBJECTIVE: To investigate the diagnosis strategy, radiology and clinical pathology feature of craniomaxillofacial primary tumor induced osteomalacia. METHODS: Twelve cases of craniomaxillofacial primary tumor induced osteomalacia were reviewed, including 5 male and 7 female with a age range from 16 to 69 years. The clinical characteristics, radiology examinations and pathological features were analyzed. RESULTS: The craniomaxillofacial primary tumor induced osteomalacia occurred in different ages, with 2 to 30 years of medical history. Seven of the twelve cases were octreotide scintigraphy positive, nine of the twelve cases invaded bone, exhibiting destruction of the adjacent cortex, ten of the twelve showed gingival lesions of local thickening or epulis. The tumor arises from mesenchymal tissue, infiltrating local gingiva and bone trabecular with spindle like fibroblasts and dental epithelial rests. CONCLUSIONS: The concealed nature of the tumor induced osteomalacia requires multiple methods to locate the primary tumor and the tumors located in craniomaxillofacial region have unique clinical and pathological features.

Cyclic changes of the junctional zone on 3 T MRI images in young and middle-aged females during the menstrual cycle.

AIM: To evaluate the cyclic changes of the junctional zone in different age groups during the menstrual cycle using 3 T magnetic resonance imaging (MRI), and to investigate the correlation with basic female hormone levels. MATERIALS AND METHODS: Thirty-eight normal volunteers (age range, 20-40 years; mean age, 29 years: 20-30 years, n=22; 31-40 years, n=16) with regular menstrual cycles underwent a pelvic 3 T MRI examination on the 2nd or 3rd days of their menstrual phase (MP), follicular phase (FP), peri-ovulatory phase (OP), and luteal phase (LP), respectively, including a T2-weighted three-dimensional (3D) turbo spin-echo (TSE) with variable flip angle ("SPACE") sequence, a T2-weighted mapping sequence, and diffusion tensor imaging (DTI). The thickness, T2, fractional anisotropy (FA), and apparent diffusion coefficient (ADC) values of the junctional zone on mid-sagittal images were separately measured by two radiologists on the post-processed workstation. The linear mixed model and one-way analysis of variance were used to evaluate the differences between the two age groups during the four phases. The serum levels of oestradiol (E), progesterone (P), luteinising hormone (LH), and follicle-stimulating hormone (FSH) were measured during the MP and compared with anatomical and functional MRI values using Pearson's correlation analysis. RESULTS: The thickness of the anterior and posterior junctional zone increased with age (p<0.05). In the 20-30 year age group, during the MP the junctional zone was significantly thicker than at the other three phases (p<0.05). Serum E levels correlated moderately with variation in thickness during the menstrual cycle. In the 30-40 year age group, no statistical difference in the thickness was found during the menstrual cycle. As age increased, the ADC values of the junctional zone decreased (p=0.02). In both groups, the ADC and T2 values of the junctional zone showed significant differences between the MP and LP (p<0.05), whereas no statistical difference in FA values were found during the menstrual cycle. Serum E, P, and LH levels correlated moderately with variation of ADC and T2 values during the menstrual cycle. CONCLUSIONS: The thickness and ADC values of the junctional zone showed significant differences between the two age groups. Cyclic changes in the thickness, ADC, and T2 values of the junctional zone were found during the menstrual cycle, which correlated moderately with the serum female hormone levels.

Dual-source dual-energy computed tomography angiography for active gastrointestinal bleeding: a preliminary study.

AIM: To assess the utility of dual-source dual-energy computed tomography angiography (DSDECTA) in the diagnosis of active gastrointestinal bleeding (GIB). MATERIALS AND METHODS: From June 2010 to September 2011, 58 consecutive patients with clinical signs of active GIB underwent DSDECTA. Two radiologists, blinded to clinical data, interpreted images from DSDECTA independently, with discordant interpretation resolved by consensus. The standards of reference included digital subtraction angiography, endoscopy, surgery, or final pathology reports. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values, and accuracy of DSDECTA for detection of active GIB were evaluated. Receiver-operating characteristic (ROC) analysis was undertaken and the area under the curve (AUC) calculated. RESULTS: Active GIB source was identified in 39 of 58 patients (67.2%), all of which were confirmed by one or more reference standard. Negative DSDECTA results were obtained in 19 patients (32.8%). Of these, 15 patients did not require any further intervention and were discharged without incident. The overall sensitivity, specificity, PPV, NPV, and accuracy of DSDECTA was 88.6, 100, 100, 73.7, and 91.4%, respectively. The AUC was 0.935 ± 0.063. The dose reduction of a dual-phase DSDECTA protocol was approximately 30%, compared with that of a triple-phase protocol used in a previous study. CONCLUSION: DSDECTA can act as an accurate method for detection and localization of active GIB and has a relatively low radiation dose.