Mucin-producing tumors of the ovary--preoperative differentiation between metastatic ovarian mucinous carcinoma and primary mucinous malignant tumors.

OBJECTIVE: To investigate the clinical and magnetic resonance imaging (MRI) features for preoperatively discriminating  primary ovarian mucinous malignant tumors (POMTs) and metastatic mucinous carcinomas involving the ovary (MOMCs). METHODS: This retrospective multicenter study enrolled 61 patients with 22 POMTs and 49 MOMCs, which were pathologically proved between November 2014 to Jane 2023. The clinical and MRI features were evaluated and compared between POMTs and MOMCs. Univariate and multivariate analyses were performed to identify the significant variables between the two groups, which were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance. RESULTS: 35.9% patients with MOMCs were discovered synchronously with the primary carcinomas; 25.6% patients with MOMCs were bilateral, and all of the patients with POMTs were unilateral. The biomarker CEA was significantly different between the two groups (p = 0.002). There were significant differences in the following MRI features: tumor size, configuration, enhanced pattern, the number of cysts, honeycomb sign, stained-glass appearance, ascites, size diversity ratio, signal diversity ratio. The locular size diversity ratio (p = 0.005, OR = 1.31), and signal intensity diversity ratio (p = 0.10, OR = 4.01) were independent predictors for MOMCs. The combination of above independent criteria yielded the largest area under curve of 0.922 with a sensitivity of 82.3% and specificity of 88.9%. CONCLUSIONS: Patients with MOMCs were more commonly bilaterally and having higher levels of CEA, but did not always had a malignant tumor history. For ovarian mucin-producing tumors, the uniform locular sizes and signal intensities were more predict MOMCs.

Supragastric lesser sac: an insidious site for surgical exploration during the debulking surgery in advanced ovarian cancer.

OBJECTIVE: Metastases in the supragastric lesser sac (SGLS) are not only occult but are also barriers to complete resection of ovarian cancer. We describe a cohort of patients with SGLS disease undergoing debulking surgery. METHODS: We identified all patients who underwent evaluation and eventual resection of SGLS disease as part of cytoreductive surgery for stage IIIC-IVB high-grade epithelial ovarian cancer at our institution from January 2018 to August 2022. RESULTS: Thirty-three of 286 patients (11.5%) underwent resection of SGLS disease. Metastases in the SGLS were identified by preoperative imaging in 4 of 33 patients (12.1%). The median peritoneal cancer index score was 22 (range, 9-33). Through surgical exploration, metastases were frequently seen in the right diaphragm (100%), hepatorenal recess (97%), lesser omentum (81.8%), left diaphragm (78.8%), supracolic omentum (75.8%), anterior transverse mesocolon (72.7%), splenic hilum (63.6%), ligamentum teres hepatis (60.6%), and gallbladder fossa (51.5%). The lesser omentum was normal in 6 of 33 (18.2%) patients, despite metastases within the SGLS. A total of 54.5% of patients underwent complex surgery (surgical complexity scores; median, 8; range, 3-14). Complete resections were achieved in 19 (57.6%) patients. No complications were related to the resection of SGLS disease. The median length of progression-free survival was 24.8 months (95% confidence interval=16.6-32.9). CONCLUSION: Metastases to the SGLS are not uncommon in advanced ovarian cancer, particularly those with widely disseminated disease. Disease in this recess is rarely identified by preoperative imaging and deserves systematic surgical exploration to attain complete cytoreduction.

The Added Value of ADC-based Nomogram in Assessing the Depth of Myometrial Invasion of Endometrial Endometrioid Adenocarcinoma.

RATIONALE AND OBJECTIVES: To explore the potential value of the apparent diffusion coefficient (ADC)-based nomogram models in preoperatively assessing the depth of myometrial invasion of endometrial endometrioid adenocarcinoma (EEA). MATERIALS AND METHODS: Preoperative magnetic resonance imaging (MRI) of 210 EEA patients were retrospectively analyzed. ADC histogram metrics derive from the whole-tumor regions of interest. Univariate and multivariate analyses were used to screen the ADC histogram metrics and clinical characteristics for nomogram model building. The diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of two radiologists without and with the assistance of models were calculated and compared. RESULTS: Two nomogram models were developed for predicting no myometrial invasion (NMI) and deep myometrial invasion (DMI) with area under the curves of 0.85 and 0.82, respectively. With the assistance of models, the overall accuracies were significantly improved [radiologist_1, 73.3% vs 86.2% (p = 0.001); radiologist_2, 80.0% vs 91.0% (p = 0.002)]. In determining NMI, the sensitivity and PPV were greatly improved but not significant for radiologist_1 (51.9% vs 77.8% and 46.7% vs 75.0%, p = 0.229 and 0.511), and under/near the significance level for radiologist_2 (59.3% vs 88.9% and 57.1% vs 82.8%, p = 0.041 and 0.065), while the specificity, accuracy, and NPV were significantly improved (all p < 0.001). In determining DMI, all sensitivity, specificity, accuracy, PPV, and NPV were significantly improved (all p < 0.001). CONCLUSION: The ADC-based nomogram models can improve the diagnostic performance of radiologist in preoperatively assessing the depth of myometrial invasion and facilitate optimizing clinical individualized treatment decisions.

Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram.

OBJECTIVE: This study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS: In this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine-based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models. RESULTS: Five features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan-Meier survival analysis showed that the radiomics nomogram-defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC. CONCLUSIONS: The radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC. KEY POINTS: • The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC. • The radiomics-clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC. • The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.

Development of a prediction model for gross residual in high-grade serous ovarian cancer by combining preoperative assessments of abdominal and pelvic metastases and multiparametric MRI.

RATIONALE AND OBJECTIVES: To preoperatively predict residual tumor (RT) in patients with high-grade serous ovarian carcinoma (HGSOC) via a radiomic-clinical nomogram. METHODS: A total of 128 patients with advanced HGSOC were enrolled (training cohort: n=106; validation cohort: n=22). Serum cancer antigen-125 (CA125), serum human epididymis protein 4 (HE-4) level, and neutrophil-to-lymphocyte ratio (NLR) were obtained from the medical records. Metastases in abdomen and pelvis (MAP) of HGSOC patients was evaluated and scored based on preoperative abdominal and pelvic enhanced CT, MRI and/or PET-CT. A volume of interest (VOI) of each tumor was manually contoured along the boundary slice-by-slice. Radiomic features were extracted from the T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images. Univariate and multivariate analyses were used to determine the independent predictors of RT status. Least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select optimal features and construct radiomic models. A radiomic-clinical nomogram incorporating radiomic signature and clinical parameters was developed and evaluated in training and validation cohorts. RESULTS: MAP score (p = 0.002), HE-4 level (p = 0.001) and NLR (p = 0.008) were independent predictors of RT status. The final radiomic-clinical nomogram showed satisfactory prediction performance in training (AUC = 0.936), cross validation (AUC = 0.906) and separate validation cohorts (AUC = 0.900), and fitted well in calibration curves (p > 0.05). Decision curve further confirmed the clinical application value of the nomogram. CONCLUSION: The proposed MRI-based radiomic-clinical nomogram achieved excellent preoperative prediction of the RT status in HGSOC.

Accuracy and reproducibility of the O-RADS MRI risk stratification system based on enhanced non-DCE MRI in the assessment of adnexal masses.

OBJECTIVE: Evaluation of the diagnostic performance and reproducibility of the Ovarian-Adnexal Reporting and Data System (O-RADS) Magnetic Resonance Imaging (MRI) risk stratification system based on enhanced non-dynamic contrast-enhanced (non-DCE) MRI in the diagnosis of adnexal masses. METHODS: Patients who underwent conventional pelvic enhanced non-DCE MRI examination within one month prior to surgery formed the study population. Two experienced radiologists independently evaluated the images and assigned a score according to the O-RADS MRI risk stratification system. One of the radiologists reviewed the images and reassigned the scores after three months. Intra- and inter-observer agreement was evaluated with the k coefficient value. The adnexal masses that attained scores between 1 and 3 were considered benign, while those with scores of 4 or 5 were considered malignant. Analyses were conducted to determine the sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curve, which were then used for evaluating the diagnostic efficacy of the developed system based on enhanced non-DCE MRI scan. The reference standard was histology. RESULTS: A total of 308 patients (mean age: 42.09 ± 12.42 years, age range: 20-84 years) were enrolled in the study. Among the 362 adnexal masses from the included patients, there were 320 benign masses and 42 malignant masses. In the case of three readers, there were no malignant tumors scored 1-2. The O-RADS MRI score ≥ 4 was associated with malignancy resulted in a good diagnostic efficacy with the areas under the curve (AUC) values of 0.918 (95 % CI, 0.864-0.972), 0.905 (95 % CI, 0.842-0.968), and 0.882 (95 % CI, 0.815-0.950), the sensitivity values of 90.5 % (95 % CI, 87.5-93.5 %), 85.7 % (95 % CI, 82.1-89.3 %), and 83.3 % (95 % CI, 79.5-87.2 %), and the specificity values of 93.1 % (95 % CI, 90.5-95.7 %), 95.3 % (95 % CI, 93.1-97.5 %), and 93.1 % (95 % CI, 90.5-95.7 %) obtained for the three readers, respectively. Excellent intra-observer agreement and inter-observer agreement were observed with the k values of 0.883 (95 % CI, 0.814-0.952) and 0.848 (95 % CI, 0.770-0.926), respectively. CONCLUSIONS: The O-RADS MRI risk stratification system based on enhanced non-DCE MRI scans exhibited high accuracy and reproducibility in the prediction of adnexal masses malignancy. Enhanced non-DCE MRI scan may offer an alternative diagnostic tool when DCE is not possible.

Magnetic Resonance Imaging and Diffusion Weighted Imaging-Based Histogram in Predicting Mesenchymal Transition High-Grade Serous Ovarian Cancer.

RATIONALE AND OBJECTIVES: To investigate the value of magnetic resonance imaging (MRI) including diffusion-weighted imaging (DWI) findings in predicting mesenchymal transition (MT) high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS: Patients with HGSOC were enrolled from May 2017 to December 2020, who underwent pelvic MRI including DWI (b = 0,1000 s/mm2) before surgery, and were assigned to the MT HGSOC or non-MT HGSOC group according to histopathology results. Clinical characteristics and MRI features including DWI-based histogram metrics were assessed and compared between the two groups. Univariate and multivariate analyses were performed to identify the significant variables associated with MT HGSOC - these variables were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance. RESULTS: A total of 81 consecutive patients were recruited for pelvic MRI before surgery, including 37 (45.7%) MT patients and 44 (54.3%) non-MT patients. At univariate analysis, the features significantly related to MT HGSOC were identified as absence of discrete primary ovarian mass, pouch of Douglas implants, ovarian mass size, tumor volume, mean, SD, median, and 95th percentile apparent diffusion coefficient (ADC) values (all p < 0.05). At multivariate analysis, the absence of discrete primary ovarian mass {odds ratio (OR): 46.477; p = 0.025}, mean ADC value ≤ 1.105 (OR: 1.023; p = 0.009), and median ADC value ≤ 1.038 (OR: 0.982; p = 0.034) were found to be independent risk factors associated with MT HGSOC. The combination of all independent criteria yielded the largest AUC of 0.82 with a sensitivity of 83.87% and specificity of 66.67%, superior to any of the single predictor alone (p ≤ 0.012). The predictive C-index nomogram performance of the combination was 0.82. CONCLUSION: The combination of absence of discrete primary ovarian mass, lower mean ADC value, and median ADC value may be helpful for preoperatively predicting MT HGSOC.

Automatic vs manual coronary CT angiography reconstruction for whole-heart coverage CT scanner: a comparison study in general patient population.

BACKGROUND: Due to the limited temporal resolution and cardiac motion, coronary computed tomography angiography (CCTA) exam is one of the most challenging CT protocols which may require operating radiologist to apply additional phase adjustment or motion correction for image reconstruction. OBJECTIVE: To evaluate image quality between automatic and manual CCTA reconstruction in a 0.25 second rotation time, 16 cm coverage, single-beat, CT scanner with automated phase selection and AI-assisted motion correction. METHODS: CCTA exams of 535 consecutive patients were included. All exams were first reconstructed with an automatically selected phase. If there was an unacceptable motion artifact, a manual reconstruction process was performed by radiologists. Additionally, automatic image series which consist of auto-phase selection and a follow-up motion correction were reconstructed. For these two manual and automatic image series, a four-point Likert scale rating system was used to evaluate image quality of the coronary artery segment by two experienced radiologists, according to the 18-segment model. RESULTS: Fifty-one patients (9.5%) did not have satisfactory image quality after auto-phase selection. In these patients, the heart rate during scanning was higher (78.3±18.4 bpm) than in the remaining 484 patients (68.9±13.1 bpm). Overall, 734 out of the 918 vessel segments were identified for quality evaluation among 51 patients. Automatic and manual image series were rated as having average Likert scores of 3.48±0.62 and 3.32±0.67 (P < 0.001), respectively. CONCLUSIONS: Using a 0.25 second rotation speed, 16 cm z-coverage, CT scanner installed with an AI-assisted motion correction algorithm, the automatic image reconstruction with scanner equipped auto-phase-selection and motion correction algorithm outperforms manually controlled image reconstruction by radiologists. This suggests that the traditional CCTA exam reconstruction workflow could be altered allowing less radiologist involvement and becoming more efficient.

MRI-Based Multiple Instance Convolutional Neural Network for Increased Accuracy in the Differentiation of Borderline and Malignant Epithelial Ovarian Tumors.

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.

Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram.

OBJECTIVES: 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). METHODS: 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. RESULTS: 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). CONCLUSIONS: The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. KEY POINTS: • 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.

Perfusion-based functional magnetic resonance imaging for differentiating serous borderline ovarian tumors from early serous ovarian cancers in a rat model.

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.

MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: MR image-based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis. KEY POINTS: • 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.

MRI-Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study.

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.

Dynamic contrast enhanced­magnetic resonance imaging for the early evaluation of the response to docetaxel in rats with epithelial ovarian cancer.

Dynamic contrast enhanced­magnetic resonance imaging (DCE­MRI) contributes to the early detection and prediction of responses to chemotherapy in cancer. The aim of the present study was to investigate the feasibility of quantitative DCE­MRI parameters for noninvasively predicting the early response to DTX in epithelial ovarian cancer (EOC). In the present study, using 7,12­dimethylbenz (A) anthracene, orthotopic EOC was induced in Sprague Dawley rats. Rats with EOC were treated with docetaxel (DTX) on day 0. DCE­MRI was applied on days 0, 3, 7, 14 and 21. On day 21, the treated tumor types were categorized into sensitive and insensitive groups according to their change in size. Quantitative DCE­MRI parameters were used to assess the early response to therapy. The experiment was performed again, the treatment group was divided into sensitive and insensitive groups according to their initially obtained cut-off values, and histopathological analyses were performed. Comparing the sensitive group with the insensitive group, there were significant differences in the percentage change in the volume transfer constant (Ktrans), rate constant (kep) and initial area under the curve (IAUC) from day 3 and tumor size from day 14. During the early stages of treatment (on day 3), the percentage change of Ktrans combined with kep produced an AUC of 1, and a sensitivity and specificity of 100 and 100%, respectively, using a cut-off value of a 17.59% reduction in Ktrans and kep. From day 7, there were significant differences in the quantitative index percentage change in angiogenesis in the sensitive group compared with the insensitive group. The percentage change in Ktrans, kep and IAUC were positively correlated with the percentage of change in tumor size and angiogenesis, and negatively correlated with the percentage of change in necrosis. The results of the present study indicated that quantitative DCE­MRI parameters were superior to imaging tumor size for the early detection and prediction of the response to DTX chemotherapy in EOC.

A rat model of serous borderline ovarian tumors induced by 7,12-dimethylbenz[a]anthracene.

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.

Incidence of insulin resistance and diabetes in patients with portosystemic shunts without liver dysfunction.

Objective To investigate the incidence of insulin resistance (IR) and diabetes in patients with chronic hepatic schistosomiasis japonica (HSJ) and portosystemic shunts (PSS). Methods Pre- and post-contrasted computed tomography images obtained from patients with HSJ and control subjects were reviewed by two radiologists who identified and graded any shunting vessels. Anthropometric measurements, hepatic enzymes, lipid profile, blood levels of albumin, glucose, insulin and homeostasis model assessment (HOMA-2) index of all participants were also assessed. Results Fifty-two patients with HSJ and 30 control subjects were involved in the study. The coronary, short gastric and perisplenic veins were the most common shunting vessels. There were no significant differences between patients and controls in terms of body mass index or liver function. The degree of shunting vessels, blood glucose, oral glucose tolerance test120/0, insulin, HOMA-2 index, glycosylated haemoglobin, cholesterol, high- and low-density lipoprotein, and C-reactive protein were significantly higher in the patients with IR. A positive correlation was found between the degree of the shunting vessels and the HOMA-2 index. Conclusions Patients with chronic HSJ and PSS without liver dysfunction had a high incidence of IR and diabetes. The study showed that PSS and IR are related and therefore patients with PSS should be screened for IR and vice versa.

Magnetic resonance imaging for distinguishing ovarian clear cell carcinoma from high-grade serous carcinoma.

BACKGROUND: To compare the magnetic resonance imaging (MRI) features of ovarian clear cell carcinoma (CCC) and high-grade serous carcinoma (HGSC), to distinguish CCC from HGSC. METHODS: MRI features (laterality, shape, size, configuration, papillary projection, signal intensity, enhancement, peritoneal implant, lymphadenopathy, ascites) of 40 tumors in 37 patients with CCC, confirmed by surgery and pathology, were compared with those of 62 tumors in 40 patients with HGSC. Statistical analysis was performed using Mann-Whitney and Fisher's exact tests. RESULTS: There was a statistically significant difference in the mean maximum diameter, laterality, and FIGO stage (P = 0.002, P < 0.001, P < 0.001, respectively) between CCC and HGSC. Compared to HGSCs, CCCs were more frequently oval (30/40, 75 % vs 12/62, 19 %; P < 0.001), more often cystic (21/40, 53 % vs 8/62, 13 %; P < 0.001) and unilocular (23/29, 79 % vs 7/31, 23 %; P < 0.001), had T1-hyperintense cystic components more often (18/29, 62 % vs 5/29, 17 %; P < 0.001), had larger papillary projections (5.13 ± 0.4 cm vs 2.91 ± 0.3 cm; P < 0.001), were peritoneally implanted less frequently (P = 0.001) and had fewer ascites (P < 0.001). CONCLUSIONS: CCC typically showed an oval, unilocular cystic mass with large papillary projection and T1-hyperintense cystic components. MRI could be helpful for distinguishing CCC from HGSC.

Ovarian Sertoli-Leydig cell tumors: MRI findings and pathological correlation.

BACKGROUND: To investigate the magnetic resonance imaging (MRI) characteristics of ovarian Sertoli-Leydig cell tumors (SLCT). METHODS: The clinical, MRI and pathological findings of five cases of SLCT were reviewed retrospectively. MRI appearances of tumors including laterality, shape and size, architecture, wall, septa and vegetation, signal intensity and contrast-enhancement pattern were evaluated and correlated with pathological findings. RESULTS: Two tumors were solid which appeared as low signal intensity on T1-weighted imaging (T1WI) and moderate on T2-weighted imaging (T2WI) with multiple small cysts in one of them. The remaining three SLCT were multilocular cystic with the irregularly thickened wall and septa, and with solid area and mural nodules in one of them. The cystic components had the same signal intensity as urine. All the solid components were intensely enhanced after administration of contrast medium. All five tumors were pathologically intermediate differentiation and at FIGO stage I. CONCLUSIONS: SLCT demonstrate variable MRI morphological appearances. However, the irregularly thickened wall and septa, the moderate T2WI signal intensity and obvious enhancement in the solid components are three MRI features.