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To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set. In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively). The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.

Rationale and Objectives To investigate the feasibility of apparent diffusion coefficient (ADC) histogram analysis of primary advanced high-grade serous ovarian cancer (HGSOC) to predict patient response to platinum-based chemotherapy. Materials and Methods A total of 70 patients with 102 advanced stage HGSOCs (International Federation of Gynecology and Obstetrics (FIGO) stages III-IV) who received standard treatment of primary debulking surgery followed by the first line of platinum-based chemotherapy were retrospectively enrolled. Patients were grouped as platinum-resistant and platinum-sensitive according to whether relapse occurred within 6 months. Clinical characteristics, including age, pretherapy CA125 level, International Federation of Gynecology and Obstetrics stage, residual tumor, and histogram parameters derived from whole tumor and solid component such as ADCmean; 10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th percentiles; skewness and kurtosis, were compared between platinum-resistant and platinum-sensitive groups. Results No significantly different clinical characteristics were observed between platinum-sensitive and platinum-resistant patients. There were no significant differences in any whole-tumor histogram-derived parameters between the two groups. Significantly higher ADCmean and percentiles and significantly lower skewness and kurtosis from the solid-component histogram parameters were observed in the platinum-sensitive group when compared with the platinum-resistant group. ADCmean, skewness and kurtosis showed moderate prediction performances, with areas under the curve of 0.667, 0.733 and 0.616, respectively. Skewness was an independent risk factor for platinum resistance. Conclusion Pretreatment ADC histogram analysis of primary tumors has the potential to allow prediction of response to platinum-based chemotherapy in patients with advanced HGSOC.

Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image–based radiomics model to differentiate between type I and type II EOC. In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation. The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection. MR image–based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis. • Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively). • The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS. • The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.

Post-stroke depression (PSD) is one of the complications after stroke, with an incidence of about 31%. There are different opinions on the pathogenesis of PSD, but it is still unclear and needs further study. At present, in addition to clinical research, the establishment and research of PSD animal model is an important part of the basic research of PSD. Therefore, it is very important to establish an effective animal model. In this paper, combined with the current situation at home and abroad, the relevant models are summarized and summarized.

Purpose The aim of this study is to analyze retrospectively the computed tomography and magnetic resonance imaging (MRI) features of pediatric vaginal endodermal sinus tumor (EST) and rhabdomyosarcoma (RMS) in a case series. Methods Seven children with vaginal EST and 2 children with vaginal RMS underwent MRI and/or computed tomography examination before the biopsy and treatment. Images were evaluated by 2 observers for the following features: (a) tumor extent; (b) margin; (c) tumor shape; (d) tumor size; (e) architecture (solid, cystic, or mixed cystic-solid); (f) hemorrhage, necrosis, and calcification; (g) magnetic resonance T2 signal intensity; (h) pattern of the enhancement; and (i) signal intensity on diffusion-weighted imaging and apparent diffusion coefficient (ADC) value. Results All of the 7 ESTs showed as a round-shaped solid mass with a location limited to the vagina without vulva and bladder invasion, whereas RMSs have an irregular grape-like appearance filling the whole vagina with vulva and bladder invasion. The invasion to the cervix was not observed in EST or RMS. Both EST and RMS were heterogeneously hyperintense on T2-weighted magnetic resonance images and enhanced remarkably and heterogeneously. Heterogeneous high signal on diffusion-weighted imaging were observed in vaginal EST and RMS. The ESTs showed a lower ADC value (mean, 1.04×10 mm/s), while RMSs showed a relatively high ADC value (mean, 1.51 × 10 mm/s). Conclusions Computed tomography and MRI may be useful tools in diagnosing the vaginal RMS and EST.

Background Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT vs. MEOT) is challenging and can significantly impact surgical management. Purpose To develop a multiple instance convolutional neural network (MICNN) that can differentiate BEOT from MEOT, and to compare its diagnostic performance with that of radiologists. Study type Retrospective study of eight clinical centers. Subjects Between January 2010 and June 2018, a total of 501 women (mean age, 48.93 ± 14.05 years) with histopathologically confirmed BEOT (N = 165) or MEOT (N = 336) were divided into the training (N = 342) and validation cohorts (N = 159). Field strength/sequence Three axial sequences from 1.5 or 3 T scanner were used: fast spin echo T2-weighted imaging with fat saturation (T2WI FS), echo planar diffusion-weighted imaging, and 2D volumetric interpolated breath-hold examination of contrast-enhanced T1-weighted imaging (CE-T1WI) with FS. Assessment Three monoparametric MICNN models were built based on T2WI FS, apparent diffusion coefficient map, and CE-T1WI. Based on these monoparametric models, we constructed an early multiparametric (EMP) model and a late multiparametric (LMP) model using early and late information fusion methods, respectively. The diagnostic performance of the models was evaluated using the receiver operating characteristic (ROC) curve and compared to the performance of six radiologists with varying levels of experience. Statistical tests We used DeLong test, chi-square test, Mann-Whitney U-test, and t-test, with significance level of 0.05. Results Both EMP and LMP models differentiated BEOT from MEOT, with an area under the ROC curve (AUC) of 0.855 (95% CI, 0.795-0.915) and 0.884 (95% CI, 0.831-0.938), respectively. The AUC of the LMP model was significantly higher than the radiologists' pooled AUC (0.884 vs. 0.797). Data conclusion The developed MICNN models can effectively differentiate BEOT from MEOT and the diagnostic performances (AUCs) were more superior than that of the radiologists' assessments. Level of evidence 3 TECHNICAL EFFICACY STAGE: 2.

Serous borderline ovarian tumors (SBOTs) behave between benign cystadenomas and carcinomas, and the effective detection and clinical management of SBOTs remain clinical challenges. Because it is difficult to isolate and enrich borderline tumor cells, a borderline animal model is in need. 7,12-dimethylbenz[a]anthracene (DMBA) is capable of inducing the initiation, promotion, and progression of serous ovarian tumors. This study aims to investigate the proper dosage and induction time of DMBA for rat models of SBOTs, and explore their morphological features demonstrated by magnetic resonance (MR) imaging and molecular genetic characteristics. Rats were randomly divided into six groups (1 mg/70 D, 2 mg/70 D, 3 mg/70 D, 2 mg/50 D, 2 mg/90 D, and 2 mg/110 D). The 3 mg/70 D group induced the most SBOTs (50.0%, 12/24). The micropapillary projections were shown on MR imaging, which was the characteristic of SBOTs. The Cyclin D1 characterizing an early pathogenetic event strongly expressed in induced serous benign tumors (SBTs). The immunoreactivity staining scores of P53 expression significantly increased from SBTs, SBOTs to serous ovarian carcinomas (SCAs), which elucidate that P53 might be a promising biomarker to grade serous ovarian tumors. Based on morphological and molecular genetic similarities, this rodent SBOT model was suitable for investigating the pathogenesis of serous ovarian tumors and developing an early detection strategy.

To investigate the magnetic resonance (MR) imaging morphological relationship between adnexal tumors and the ipsilateral ovaries to characterize the origin and malignancy of tumors.Clinical and MR imaging data of 496 adnexal tumors confirmed by histology (ovary tumors, n = 400, non-ovarian tumors, n = 96; benign tumors, n = 183, borderline tumors, n = 120, and malignant tumors, n = 193) were retrospectively analyzed. The presence and shape of the ipsilateral ovaries within the context of adnexal tumors of different origins, malignancies and configurations were evaluated. The relationships between the presence of the ipsilateral ovary and patient age, menstrual status and tumor size were also analyzed.The ipsilateral ovary was detected on MRI in 23% (90/400) of ovarian tumors and in 45% (43/96) of non-ovarian tumors (p 0.001). A normal ovoid morphology of the ipsilateral ovary was found in only 7% (26/400) of ovarian tumors and in 26% (25/96) of non-ovarian tumors (p 0.001). The ipsilateral ovary was detectable in 38% (69/183) of benign tumors, 35% (42/120) of borderline tumors, and 11% (22/193) of malignant tumors (p 0.001); in 24% (24/101) of cystic tumors, 27% (49/179) of mixed cystic-solid tumors and 28% (60/216) of solid tumors (p = 0.737); and in 40% (120/303) of adnexal tumors in premenopausal patients and 7% (13/193) of adnexal tumors in postmenopausal patients (p 0.001).Detection of the ipsilateral ovary contributes to the localization and characterization of adnexal tumors. The ipsilateral ovary can be detected more frequently in non-ovarian tumors and in benign or borderline ovarian tumors.

Background To investigate the spectrum of CT and MRI findings of dysgerminoma of the ovary. Methods CT and MRI imaging of 12 patients with 13 histologically proven dysgerminomas of the ovary were retrospectively reviewed. Patients, ages ranged from 6 ~ 27 years (mean, 17.2 years). Two observers evaluated the following CT and MRI features of the tumor by consensus: (i) location, shape, and size; (ii) attenuation, T2 signal intensity, and ADC value; (iii) patterns of contrast enhancement; (iv) presence of fibrovascular septa; (v) presence of necrosis, hemorrhage, and calcification; (vi) presence of “ovarian vascular pedicle” sign. We also noted the extent or stage of the tumors. Results 75% lesions arised in the right ovary. Bilateral ovaries were involved in one case. Tumors displayed as a purely or predominantly solid mass (mean size, 17.0 ± 7.8 cm). Ten tumors were shaped multilobulated. The mean ADC value of lesions was 0.830 ± 0.154 × 10− 3 mm2/s. Characteristic fibrovascular septa were observed in all lesions. Among them, classic septa were present in 69% lesions. They were thin, hypointense on T2WI with a linear intense enhancement indicating the blood vessels in septa. Due to the stromal edema, fibrovascular septa may become thick even amorphous in shape, hyperintense on T2WI and even low attenuation on CT with a slight enhancement except for a bright blood vessel on the edge. Massive necrosis was observed only in one lesion. Calcification was present in 3 of the 5 tumors on CT. “Ovarian vascular pedicle” sign was present in 12 lesions. Lymphadenopathy, retroperitoneal spread, and distant metastases combined with an implantation in Douglas’ cul-de-sac were present in one patient respectively. Conclusion On CT and MR images, ovarian dysgerminoma often appears as a large solid mass. The edematous condition of characteristic fibrovascular septa can be well displayed by imaging which then can guide the radiologists to make an accurate diagnosis. Calcifications often occur in the tumor. Nonspecific low ADC value and “ovarian vascular pedicle” sign may narrow the differential diagnosis.

Background Differentiation of borderline tumors from early ovarian cancer has recently received increasing attention, since borderline tumors often affect young women of childbearing age who desire to preserve fertility. However, previous studies have demonstrated that non-enhanced magnetic resonance imaging (MRI) sequences cannot sufficiently differentiate these tumors. Purpose To investigate the value of dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating serous borderline ovarian tumors (SBOT) from early serous ovarian cancers (eSOCA). Material and Methods Twenty SBOT and 20 eSOCA rat models were performed with DCE-MRI and IVIM-DWI at 3.0-T MR scanner. Qualitative and quantitative parameters of DCE-MRI were acquired and compared between two groups and correlated with the microvessel density (MVD). The receiver operating characteristic (ROC) curve analyses were conducted to determine their differentiating performances. Results SBOTs presented significantly lower values of the initial area under the enhancement curve (iAUC), volume transfer constant (Ktrans), and extracellular extravascular volume fraction (ve) ( P < 0.05) and a significantly higher value of true diffusion (D) ( P = 0.001) compared with eSOCAs. The diagnostic effectiveness of ve combined with D was significantly better than that of ve or Ktrans alone ( P ≤ 0.039). Conclusion DCE-MRI may represent a promising tool for differentiating SBOTs from eSOCAs and may not be replaced by IVIM-DWI. Combining DCE-MRI with DWI may improve the diagnostic performance of ovarian tumors.

Background Preoperative prediction of the recurrence of epithelial ovarian carcinoma (EOC) can guide the clinical treatment and improve the prognosis. However, there are still no reliable predictive biomarkers. Purpose To evaluate whether whole solid tumor volume histogram parameters measured from quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the recurrence in patients with EOC. Material and Methods We followed up 56 patients with surgical and histopathologically diagnosed EOC who underwent quantitative DCE-MRI scans. The differences of the histogram parameters between patients with and without recurrence were compared. Mann–Whitney U test, Pearson’s Chi-squared test, or Fisher’s exact test, and receiver operating characteristic (ROC) curves were used for statistical analysis. Results All histogram parameters of Ktrans, kep, and ve were not significantly different between EOC patients with and without recurrence ( P>0.05). For 30 patients with high-grade serous ovarian carcinoma (HGSOC), the histogram parameters of Ktrans (mean and 5th, 10th, 25th, 50th, 75th percentiles) and kep (mean and 50th percentile) in 12 patients with recurrence were significantly lower than those in 18 patients without recurrence (all Ptrans had the largest area under the curve (AUC) of 0.792 for predicting the recurrence in patients with HGSOC. When the threshold value was ≤0.0263/min, the sensitivity, specificity, and accuracy were 100%, 66.7%, and 80%, respectively. Conclusion Instead of predicting the recurrence of EOC, whole solid tumor volume quantitative DCE-MRI histogram parameters could predict the recurrence of HGSOC and may be potential biomarkers for the prediction of HGSOC recurrence.

PURPOSE To investigate the use of diffusion kurtosis imaging (DKI) in differentiating borderline from malignant epithelial ovarian tumors (MEOTs) and to correlate DKI parameters with Ki-67 expression. MATERIALS AND METHODS Fifty-two consecutive patients with epithelial ovarian tumors (17 borderline epithelial ovarian tumors, BEOTs; 35 MEOTs) were prospectively evaluated using DKI with b values of 0, 500, 1000, 1500, 2000, and 2500 s/mm2 and standard diffusion-weighted imaging (DWI) with b values of 0 and 1000 s/mm2 using a 1.5T magnetic resonance imaging (MRI) unit. The kurtosis (K) and diffusion coefficient (D) from DKI and apparent diffusion coefficient (ADC) from standard DWI were measured, compared, and correlated with Ki-67 expression between the two groups. Statistical analyses were performed using the Mann-Whitney U-test, receiver operating characteristic (ROC) curves, and Spearman's correlation. RESULTS The K value was significantly lower in BEOTs than in MEOTs (0.55 ± 0.09 vs. 0.9 ± 0.2), while the D and ADC values were significantly higher in BEOTs than in MEOTs (2.27 ± 0.35 vs. 1.39 ± 0.37 and 1.72 ± 0.36 vs. 1.1 ± 0.25, respectively) (P < 0.001). For differentiating between BEOTs and MEOTs, the sensitivity, specificity, and accuracy were 88.2%, 94.3%, and 92.3% for K value; 88.2%, 91.4%, and 90.4% for D value; and 88.2%, 88.6%, and 88.5% for ADC value, respectively. However, there were no differences in the diagnostic performances among the three parameters above (K vs. ADC, P = 0.203; D vs. ADC, P = 0.148; K vs. D, P = 0.904). The K value was positively correlated with Ki-67 expression (r = 0.699), while the D and ADC values were negatively correlated with Ki-67 expression (r = -0.680, -0.665, respectively). CONCLUSION Preliminary findings demonstrate that DKI is an alternative tool for differentiating BEOTs from MEOTs, and is correlated with Ki-67 expression. However, no added value is found for DKI compared with standard DWI. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1499-1506.

BACKGROUND Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results. PURPOSE To develop and validate an objective MRI-based machine-learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation. STUDY TYPE Retrospective study of eight clinical centers. POPULATION In all, 501 women with histopathologically-confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159). FIELD STRENGTH/SEQUENCE Preoperative MRI within 2 weeks of surgery. Single- and multiparameter (MP) machine-learning assessment models were built utilizing the following four MRI sequences: T2 -weighted imaging (T2 WI), fat saturation (FS), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced (CE)-T1 WI. ASSESSMENT Diagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early-stage MEOT was made. Six radiologists of varying experience also interpreted the MR images. STATISTICAL TESTS Mann-Whitney U-test: significance of the clinical characteristics; chi-square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC). RESULTS The MP-ST model performed better than the MP-WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early-stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679-0.924) and external (mean AUC = 0.797; range, 0.744-0.867) validation cohorts. DATA CONCLUSION Performance of the MRI-based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women. LEVEL OF EVIDENCE Level 4. TECHNICAL EFFICACY Stage 2. J. Magn. Reson. Imaging 2020;52:897-904.

Purpose To compare the diagnostic performance of normalized apparent diffusion coefficients (nADCs) of different references with that of ADCs at differentb factors in differentiating borderline epithelial ovarian tumors (BEOTs) from malignant epithelial ovarian tumors (MEOTs). Method This retrospective study included 53 BEOTs and 148 MEOTs. Conventional magnetic resonance and diffusion-weighted imaging withb factors of 800 and 1000s/mm2 were performed. ADC was measured three times at solid components of tumors, gluteus maximus muscle (GMM), iliopsoas muscle (IM) and urine and then averaged. ADCtumor, nADCs were then obtained. Differences and the diagnostic performance of ADCtumor and nADCs between BEOTs and MEOTs with different b factors were compared. Results ADCtumor, nADCs regardless of b factors were significantly higher in BEOTs than MEOTs. The diagnostic performance of nADCurine regardless of b factors was significantly larger than that of nADCGMM and nADCIM. There was no significant difference in the diagnostic performance between ADCtumor and nADCurine regardless of b factors. A significantly lower ADCtumor and a larger diagnostic performance for ADCtumor was found with a b factor of 1000s/mm2 than 800 s/mm2. There were no significant differences in nADCurine of BEOTs or MEOTs or in the diagnostic performance of nADCurine with b factors between 800 and 1000s/mm2. Conclusions ADCtumor and nADCs were both capable of differentiating BEOTs from MEOTs. nADCurine was the best of all nADCs and was superior to ADCtumor because of its stable performance in differentiating BEOTs from MEOTs, regardless of b factors.

Marine aerosols were measured in real-time by an on-board signal particle aerosol mass spectrometer(SPAMS) over the Southeast China Sea. The chemical compositions and size distribution characteristics of aerosol particles were obtained, and the sources and ion spectra were analyzed. The results showed that particle number concentration decreased with the distance apart from the coastal area. In the coastal area, the aerosol compositions were mainly determined by the emissions of industry, such as vessel, traffic and coal combustion etc. When it was far from the continent, aerosols were mainly affected by the sea sources. Aerosol particles during the observation period disturbed singly with a peak diameter value of 0.5 μm. Most of the particles were in the size range of 0.2 μm to 0.8 μm. High signal intensity of EC with high K

Heavy metal aerosol particles were first measured with Single Particle Aerosol Mass Spectrometry (SPAMS) in north suburb of Nanjing from January to December, 2013. Using the ART-2a neural network algorithm, we studied the chemical characteristics of aerosol particles and found that the main sources of heavy metal aerosols in Nanjing were industrial emissions, biomass burning, traffic emissions, fuel combustion and dust, accounting for 35.7%, 34.45%, 13.6%, 11.03% and 4.07% respectively. Pb, Cd and Cr-containing aerosols mainly came from industrial emissions. Cu, Co and Hg-containing aerosols mainly came from biomass burning. V, Zn and Ba-containing aerosols mainly came from traffic emissions. As and Ni-containing aerosols mainly came from fuel combustion.

This study aimed to investigate the diagnostic performance of quantitative DCE-MRI for characterizing ovarian tumors. We prospectively assessed the differences of quantitative DCE-MRI parameters (Ktrans, kep, and ve) among 15 benign, 28 borderline, and 66 malignant ovarian tumors; and between type I (n = 28) and type II (n = 29) of epithelial ovarian carcinomas (EOCs). DCE-MRI data were analyzed using whole solid tumor volume region of interest (ROI) method, and quantitative parameters were calculated based on a modified Tofts model. The non-parametric Kruskal–Wallis test, Mann–Whitney U test, Pearson’s chi-square test, intraclass correlation coefficient (ICC), variance test, and receiver operating characteristic curves (ROC) were used for statistical analysis. The largest Ktrans and kep values were observed in ovarian malignant tumors, followed by borderline and benign tumors (all P

Background To investigate MRI for differentiating benign from malignant sex cord-stromal tumors of the ovary (SCSTs) emphasizing on the value of diffusion-weighted (DW) magnetic resonance (MR) imaging. Methods This retrospective study included 29 benign SCSTs in 28 patients and 13 malignant SCSTs in 13 patients. DW imaging as well as conventional MR imaging was performed. Signal intensity on DW imaging was assessed and apparent diffusion coefficient (ADC) value was measured. In addition, T2 signal intensity and contrast enhancement pattern were also assessed and compared between benign and malignant SCSTs. Results Both of the T2 hypointensity and mild enhancement were specific to benign SCSTs. The majority of malignant SCSTs showed high signal intensity on DW imaging, whereas most benign SCSTs showed low or moderate signal intensity (p = 0.000). Fibromas were the tumors with the lowest observed ADC value (0.470 × 10− 3 mm2/s). Sclerosing stromal tumors were the tumors with the highest observed ADC value (2.291 × 10− 3 mm2/s). ADC value of solid component was significantly lower in malignant SCSTs (0.825 ± 0.129 × 10− 3 mm2/s) than in benign SCSTs (1.343 ± 0.528 × 10− 3 mm2/s) when fibromas were excluded (p = 0.024). T2, DCE and DW imaging has a limited value on the differential diagnosis of the benign and malignant SCSTs with an accuracy of 69.0%,71.4% and 78.1% respectively. Combination of T2, DCE and DW imaging permitted the distinction with an accuracy of 88.0%. Conclusions It is more helpful for distinction of the benign and malignant SCSTs by combining of T2, DCE and DW imaging than using each of the three sequences independently.

The purpose of this article is to investigate the proton MR spectroscopy ((1)H-MRS) features of solid adnexal tumors and to evaluate the efficacy of (1)H-MRS for differentiating benign from malignant solid adnexal tumors.Sixty-nine patients with surgically and histologically proven solid adnexal tumors (27 benign and 42 malignant) underwent conventional MRI and (1)H-MRS. Single-voxel spectroscopy was performed using the point-resolved spectroscopy localization technique with a voxel size of 2 × 2 × 2 cm(3). Resonance peak integrals of choline, N-acetyl aspartate (NAA), creatine, lactate, and lipid were analyzed, and the choline-tocreatine, NAA-to-creatine, lactate-to-creatine, and lipid-to-creatine ratios were recorded and compared between benign and malignant tumors.A choline peak was detected in all 69 cases (100%), NAA peak in 67 cases (97%, 25 benign and 42 malignant), lipid peak in 47 cases (17 benign and 30 malignant), and lactate peak in eight cases (four benign and four malignant). The mean (± SD) choline-tocreatine ratio was 5.13 ± 0.6 in benign tumors versus 8.90 ± 0.5 in malignant solid adnexal tumors, a statistically significant difference (p = 0.000). There were no statistically significant differences between benign and malignant tumors in the NAA-to-creatine and lipid-to-creatine ratios (p = 0.263 and 0.120, respectively). When the choline-to-creatine threshold was 7.46 for differentiating between benign and malignant tumors, the sensitivity, specificity, and accuracy were 94.1%, 97.1%, and 91.2%, respectively.Our preliminary study shows that the (1)H-MRS patterns of benign and malignant solid adnexal tumors differ. The choline-to-creatine ratio can help clinicians differentiate benign from malignant tumors.

Purpose To identify the MRI features of borderline epithelial ovarian tumors (BEOTs) and to differentiate BEOTs from malignant epithelial ovarian tumors (MEOTs). Materials and methods The clinical and MRI data of 89 patients with a BEOT and 109 patients with a MEOT proven by surgery and histopathology were retrospectively reviewed. MRI features, including bilaterality, size, shape, margin, cystic-solid interface, configuration, papillae or nodules, signal intensity, enhancement, presence of an ipsilateral ovary, peritoneal implants and ascites were analyzed and compared. Based on the odds ratio (OR) values, the significant risk features for BEOTs were scored as 3 (OR ≈ ∞), 2 (5 ≤ OR Results There were 89 BEOT patients with 113 tumors [mean size of (13 ± 6.7) cm], with bilateral ovary involvement in 24 cases. There were 109 MEOT patients with 142 tumors [(9.3 ± 4.2) cm] with bilateral ovary involvement in 33 cases. There were eight significant risk factors for BEOTs, including round or oval shape (OR = 2.714), well-defined margins (OR = 3.318), clear cystic-solid interfaces (OR = 5.593), purely cystic (OR = 15.206), predominantly cystic with papillae or nodules (OR = 2.579), exophytic papillae or nodules (OR = 5.351), branching papilla (OR ≈ ∞) and the presence of an ipsilateral ovary (OR ≈ ∞). Based on the scoring of the eight risk factors, a cut-off score of 3.5 yielded a differential sensitivity, specificity, and accuracy of 82%, 85% and 84%, respectively. Conclusion In contrast to MEOTs, BEOTs frequently had the following features on MRI: round or oval, with well-defined margins and clear cystic-solid interfaces, purely cystic or predominantly cystic with papillae or nodules, branching or exophytic papillae, with the presence of an ipsilateral ovary. MRI can reveal the distinct morphological features of BEOTs and MEOTs and facilitate their discrimination.

To investigate the use of diffusion kurtosis imaging (DKI) in differentiating borderline from malignant epithelial ovarian tumors (MEOTs) and to correlate DKI parameters with Ki-67 expression.Fifty-two consecutive patients with epithelial ovarian tumors (17 borderline epithelial ovarian tumors, BEOTs; 35 MEOTs) were prospectively evaluated using DKI with b values of 0, 500, 1000, 1500, 2000, and 2500 s/mmThe K value was significantly lower in BEOTs than in MEOTs (0.55 ± 0.09 vs. 0.9 ± 0.2), while the D and ADC values were significantly higher in BEOTs than in MEOTs (2.27 ± 0.35 vs. 1.39 ± 0.37 and 1.72 ± 0.36 vs. 1.1 ± 0.25, respectively) (P0.001). For differentiating between BEOTs and MEOTs, the sensitivity, specificity, and accuracy were 88.2%, 94.3%, and 92.3% for K value; 88.2%, 91.4%, and 90.4% for D value; and 88.2%, 88.6%, and 88.5% for ADC value, respectively. However, there were no differences in the diagnostic performances among the three parameters above (K vs. ADC, P = 0.203; D vs. ADC, P = 0.148; K vs. D, P = 0.904). The K value was positively correlated with Ki-67 expression (r = 0.699), while the D and ADC values were negatively correlated with Ki-67 expression (r = -0.680, -0.665, respectively).Preliminary findings demonstrate that DKI is an alternative tool for differentiating BEOTs from MEOTs, and is correlated with Ki-67 expression. However, no added value is found for DKI compared with standard DWI.1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1499-1506.

Background The study aimed to investigate the utility of dynamic contrast enhanced MRI (DCE-MRI) in the differentiation of malignant, borderline, and benign complex ovarian tumors. Methods DCE-MRI data of 102 consecutive complex ovarian tumors (benign 15, borderline 16, and malignant 71), confirmed by surgery and histopathology, were analyzed retrospectively. The patterns (I, II, and III) of time-signal intensity curve (TIC) and three semi-quantitative parameters, including enhancement amplitude (EA), maximal slope (MS), and time of half rising (THR), were evaluated and compared among benign, borderline, and malignant ovarian tumors. The types of TIC were compared by Pearson Chi-square χ 2 between malignant and benign, borderline tumors. The mean values of EA, MS, and THR were compared using one-way ANOVA or nonparametric Kruskal-Wallis test. Results Fifty-nine of 71 (83%) malignant tumors showed a type-III TIC; 9 of 16 (56%) borderline tumors showed a type-II TIC, and 10 of 15 (67%) benign tumors showed a type-II TIC, with a statistically significant difference between malignant and benign tumors (P 0.05). Conclusions DCE-MRI is helpful for characterizing complex ovarian tumors; however, semi-quantitative parameters perform poorly when distinguishing malignant from borderline tumors.

Purpose To investigate magnetic resonance imaging (MRI) features for differentiating ovarian endometrioid adenocarcinoma (OEC) from high-grade serous adenocarcinoma (HGSC). Materials and methods Twenty-three patients with 25 OECs and 93 patients with 139 HGSCs confirmed by surgery and pathology underwent conventional MRI and diffusion-weighted imaging (DWI). The MRI features of the tumors, including laterality, size, shape, configuration, signal intensity, ADC value of solid component, enhancement, ascites, synchronous primary cancer (SPC) of the ovary and endometrium, and clinical stage, were evaluated and compared between two groups. Results The following characteristics were significantly more common for OECs than HGSCs: unilateral (91.3% vs 50.5%, P

OBJECTIVE The aim of this study was to investigate the magnetic resonance (MR) and diffusion-weighted (DW) imaging characteristics of primary fallopian tube carcinoma (PFTC). METHODS The clinical, MR, and DW imaging characteristics and pathologic findings of 23 patients with 27 tumors were studied retrospectively. The MR and DW imaging appearance of tumors including laterality, size and shape, architecture, signal intensity, apparent diffusion coefficient (ADC) value, enhancement pattern, hydrosalpinx, and intrauterine fluid collection were evaluated and correlated with pathologic findings. RESULTS Histopathologically, all 27 tumors were serous carcinoma with a unilateral tumor in 19 patients and bilateral tumors in 4 patients. Thirteen patients (57%) with PFTC were misdiagnosed preoperatively, 10 of which as epithelial ovarian carcinoma. The mean (SD) largest diameter was 61 (7) mm. The tumor shape was fusiform, sausagelike, or serpentine in 19 patients (70%) and nodular or irregular in 8 patients (30%). Twenty (74%) of the 27 tumors were solid, and 7 (26%) were cystic-solid. The solid components showed hypointensity to isointensity on T1-weighted imaging, and isointensity to slight hyperintensity on T2-weighted imaging. There were obvious hyperintensity on DW imaging; obvious hypointensity on ADC maps with a mean (SD) ADC value of 0.79 (0.22) × 10 mm; and mild (8/27, 30%), moderate (13/27, 48%), and marked (6/27, 22%) enhancement on contrast-enhanced imaging. Ipsilateral hydrosalpinx, intrauterine fluid collection, and ascites were found in 14 tumors (52%) and 7 (30%) and 5 (22%) patients, respectively. CONCLUSIONS The PFTC has some characteristic MR imaging features. The DW imaging, ADC maps, and ADC values are helpful for the detection and differentiation of PFTC from other pelvic masses.

OBJECTIVE This study aimed to investigate the magnetic resonance imaging (MRI) features of ovarian endometrioid adenocarcinoma (OEC) and to evaluate conventional MRI and diffusion-weighted imaging (DWI) for diagnosing OEC. MATERIALS AND METHODS Twenty patients with OEC proven by surgery and pathology underwent MRI. The MRI features of the tumors evaluated included laterality, shape, size, configuration, mural nodules, signal intensity, apparent diffusion coefficient (ADC) values, enhancement, peritoneal implants, ascites, and synchronous primary cancer (SPC) of the ovary and endometrium. RESULTS Unilateral ovarian masses were observed in 18 (90%) of the 20 patients with 22 OEC lesions, whereas the remaining 2 (10%) patients had bilateral masses. Oval, lobulated, and irregular shapes were observed in 13 (59%), 6 (27%), and 3 (14%) tumors, respectively. The maximum diameter of the tumors ranged from 3.7 to 22.5 cm, with a mean of 11.2 ± 5.1 cm. Fifteen (68%) masses were mainly cystic with mural nodules, 5 (23%) were mixed cystic-solid, and 2 (9%) were solid. The solid components of tumors showed isointensity (100%) on T1-weighted imaging (T1WI), heterogeneous hyperintensity on T2-weighted imaging (T2WI) (86%), and hyperintensity on DWI (82%), with a mean ADC value of (0.96 ± 0.20) × 10 mm/s. The cystic components showed isointensity or hyperintensity (85%) on T1WI, hyperintensity on T2WI (100%), and hypointensity on DWI (63%), with a mean ADC value of (2.27 ± 0.27) × 10 mm/s. Ten (50%) of the patients were SPC. The mean ADC values of the solid components were (0.85 ± 0.19) × 10 mm/s and (1.08 ± 0.15) × 10 mm/s in only-OEC and SPC, respectively, with a statistically significant difference (P = 0.012). CONCLUSIONS Ovarian endometrioid adenocarcinoma usually appears as a large, oval, or lobulated cystic mass with mural nodules. Cystic components show isointensity or hyperintensity on T1WI, solid components and hyperintensity on T2WI and DWI. Synchronous primary cancer of the ovary endometrium is another characteristic feature of OEC.