Immunotherapy combined with targeted therapy in advanced small cell carcinoma of the ovary of hypercalcemic type: A case of overall survival lasting for over 5 years.

Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare but highly aggressive ovarian malignant neoplasm lacking a unified clinical management process. Most patients are diagnosed at an advanced stage and have an extremely poor prognosis with an overall probability of survival less than 10 %. Here, we describe the case of a patient with advanced SCCOHT achieved a survival of over 5 years after receiving multiple cycles of immunotherapy combined with anti-angiogenic therapy or CDK4/6 inhibitors. At the same time, we also summarized the case reports and clinical trials of immunotherapy in SCCOHT.

Whole-tumor apparent diffusion coefficient histogram analysis for preoperative risk stratification in endometrial endometrioid adenocarcinoma.

OBJECTIVE: To investigate the application of whole-tumor apparent diffusion coefficient (ADC) histogram metrics for preoperative risk stratification in endometrial endometrioid adenocarcinoma (EEA). METHODS: Preoperative MRI of 502 EEA patients were retrospectively analyzed. Whole tumor ADC histogram analysis was performed with regions of interest drawn on all tumor slices of diffusion-weighted imaging scans. Risk stratification was based on ESMO-ESTRO-ESP guidelines: low-, intermediate-, high-intermediate-, and high-risk. Univariable analysis was used to compare ADC histogram metrics (tumor volume, minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC [recorded as P10, P25, P50, P75, and P90 ADC, respectively]; skewness; and kurtosis) between different risk EEAs, and multivariable logistic regression analysis to determine the optimal metric or combined model for risk stratifications. Receiver operating characteristic curve analysis with the area under the curve (AUC) was used for diagnostic performance evaluation. RESULTS: A decreasing tendency in multiple ADC values was observed from the low- to high-intermediate-risk EEAs. The (low + intermediate)-risk EEAs and low-risk EEAs had significantly smaller tumor volumes and higher minADCs, meanADCs, P10, P25, P50, P75, and P90 ADCs than the (high-intermediate + high)-risk EEAs and non-low-risk EEAs (all P < 0.05), respectively. The combined models of the (meanADC + volume) and the (P75 ADC + volume) yielded the largest AUCs of 0.775 and 0.780 in identifying the (low + intermediate)- and the low-risk EEAs from the other EEAs, respectively. CONCLUSION: Whole-tumor ADC histogram metrics might be helpful for preoperatively identifying low- and (low + intermediate)-risk EEAs, facilitating personalized therapeutic planning.

Physiological response and transcriptome analyses of leguminous Indigofera bungeana Walp. to drought stress.

Objective: Indigofera bungeana is a shrub with high quality protein that has been widely utilized for forage grass in the semi-arid regions of China. This study aimed to enrich the currently available knowledge and clarify the detailed drought stress regulatory mechanisms in I. bungeana, and provide a theoretical foundation for the cultivation and resistance breeding of forage crops. Methods: This study evaluates the response mechanism to drought stress by exploiting multiple parameters and transcriptomic analyses of a 1-year-old seedlings of I. bungeana in a pot experiment. Results: Drought stress significantly caused physiological changes in I. bungeana. The antioxidant enzyme activities and osmoregulation substance content of I. bungeana showed an increase under drought. Moreover, 3,978 and 6,923 differentially expressed genes were approved by transcriptome in leaves and roots. The transcription factors, hormone signal transduction, carbohydrate metabolism of regulatory network were observed to have increased. In both tissues, genes related to plant hormone signaling transduction pathway might play a more pivotal role in drought tolerance. Transcription factors families like basic helix-loop-helix (bHLH), vian myeloblastosis viral oncogene homolog (MYB), basic leucine zipper (bZIP) and the metabolic pathway related-genes like serine/threonine-phosphatase 2C (PP2C), SNF1-related protein kinase 2 (SnRK2), indole-3-acetic acid (IAA), auxin (AUX28), small auxin up-regulated rna (SAUR), sucrose synthase (SUS), sucrosecarriers (SUC) were highlighted for future research about drought stress resistance in Indigofera bungeana. Conclusion: Our study posited I. bungeana mainly participate in various physiological and metabolic activities to response severe drought stress, by regulating the expression of the related genes in hormone signal transduction. These findings, which may be valuable for drought resistance breeding, and to clarify the drought stress regulatory mechanisms of I. bungeana and other plants.

Comparison of the effect of levonorgestrel-intrauterine system with or without oral megestrol acetate on fertility-preserving treatment in patients with atypical endometrial hyperplasia: A prospective, open-label, randomized controlled phase II study.

OBJECTIVE: To compare the effects of levonorgestrel-intrauterine system (LNG-IUS) with or without oral megestrol acetate (MA) versus MA alone on fertility-preserving treatment in patients with atypical endometrial hyperplasia (AEH). METHODS: This was a single-center phase II study with an open-label, randomized, controlled trial conducted between July 2017 and June 2020 at the Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China. A total of 180 patients (18-45 years) with primary AEH were randomly assigned (1:1:1) to the MA (N = 60), LNG-IUS (N = 60), or MA + LNG-IUS (N = 60) groups, in which the patients received MA (160 mg orally daily), LNG-IUS, or MA + LNG-IUS (MA 160 mg orally daily plus LNG-IUS), respectively. The primary endpoint was complete response (CR) rate at 16 weeks of treatment. The secondary endpoints were CR rate at 32 weeks of treatment, adverse events, and recurrence and pregnancy rates. All analyses were conducted in a modified intention to treat (ITT) population who underwent randomization and in whom treatment was initiated. RESULTS: The Kaplan-Meier estimate of 16-week CR rates (with 95% confidence interval) were 19.2% (9.0-29.4%) in the MA group, 35.0% (22.8-47.2%) in the LNG-IUS group, and 29.4% (17.2-41.6%) in the MA + LNG-IUS groups. Side effects such as weight gain, increased nocturnal urine, night sweat, insomnia and edema face seemed to occur less frequently in LNG-IUS group compared with MA group. No difference was found among groups regarding second endpoints. CONCLUSIONS: LNG-IUS or LNG-IUS plus MA did not show significant therapeutic benefit compared with MA alone. Further studies including sufficient sample-size are needed to validate these findings due to the underpowered design of this trial. FUNDING: This study was supported by the National Key Research and Development Program of China (Grant No 2019YFC1005200 and 2019YFC1005204), Shanghai Medical Centre of Key Programs for Female Reproductive Diseases (Grant No. 2017ZZ010616), Shanghai sailing program (Grant No. 19YF1404200), and Shen Kang clinical project (SHDC22021219). Trial registrationClinicalTrials.govNCT03241888. https://www. CLINICALTRIALS: gov/ct2/show/NCT03241888?term=NCT03241888&draw=2&rank=1.

Case Report: A case of COL1A1-PDGFB fusion uterine sarcoma at cervix and insights into the clinical management of rare uterine sarcoma.

COL1A1-PDGFB gene fusion uterine sarcoma is an especially rare malignant mesenchymal tumor that was previously classified as an undifferentiated uterine sarcoma due to the lack of specific features of differentiation. Till now, only five cases have been reported, and here we presented another case recently diagnosed in a Chinese woman who had vaginal bleeding. She presented with a cervical mass at the anterior lip of the cervix invading the vagina and was treated with laparoscopic total hysterectomy plus bilateral salpingo-oophorectomy (TH+BSO) and partial vaginal wall resection with the final pathology of COL1A1-PDGFB fusion uterine sarcoma. Our aim is to emphasize the importance of differential diagnosis of this rare tumor, as early precise diagnosis may allow patients to benefit from the targeted therapy imatinib. This article also serves as further clinical evidence of this disease, serving to increase clinical awareness of this rare sarcoma to avoid misdiagnosis.

Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma.

PURPOSE: To investigate the feasibility of whole-tumor apparent diffusion coefficient (ADC) histogram analysis for improving the differentiation of endometriosis-related tumors: seromucinous borderline tumor (SMBT), clear cell carcinoma (CCC) and endometrioid carcinoma (EC). METHODS: Clinical features, solid component ADC (ADCSC) and whole-tumor ADC histogram-derived parameters (volume, the ADCmean, 10th, 50th and 90th percentile ADCs, inhomogeneity, skewness, kurtosis and entropy) were compared among 22 SMBTs, 42 CCCs and 21 ECs. Statistical analyses were performed using chi-square test, one-way ANOVA or Kruskal-Wallis test, and receiver operating characteristic curves. RESULTS: A significantly higher ADCSC and smaller volume were associated with SMBT than with CCC/EC. The ADCmean was significantly higher in CCC than in EC. The 10th percentile ADC was significantly lower in EC than in SMBT/CCC. The 50th and 90th percentile ADCs were significantly higher in CCC than in SMBT/EC. For differentiating SMBT from CCC, AUCs of the ADCSC, volume, and 50th and 90th percentile ADCs were 0.97, 0.86, 0.72 and 0.81, respectively. For differentiating SMBT from EC, AUCs of the ADCSC, volume and 10th percentile ADC were 0.97, 0.71 and 0.72, respectively. For differentiating CCC from EC, AUCs of the ADCmean and 10th, 50th and 90th percentile ADCs were 0.79, 0.72, 0.81 and 0.85, respectively. CONCLUSION: Whole-tumor ADC histogram analysis was valuable for differentiating endometriosis-related tumors, and the 90th percentile ADC was optimal in differentiating CCC from EC.

Radiomics feature as a preoperative predictive of lymphovascular invasion in early-stage endometrial cancer: A multicenter study.

Background: The presence of lymphovascular space invasion (LVSI) has been demonstrated to be significantly associated with poor outcome in endometrial cancer (EC). No effective clinical tools could be used for the prediction of LVSI preoperatively in early-stage EC. A radiomics nomogram based on MRI was established to predict LVSI in patients with early-stage EC. Methods: This retrospective study included 339 consecutive patients with early-stage EC with or without LVSI from five centers. According to the ratio of 2:1, 226 and 113 patients were randomly assigned to a training group and a test group, respectively. Radiomics features were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), contrast-enhanced (CE), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. The radiomics signatures were constructed by using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm in the training group. The radiomics nomogram was developed using multivariable logistic regression analysis by incorporating radiomics signatures and clinical risk factors. The sensitivity, specificity, and AUC of the radiomics signatures, clinical risk factors, and radiomics nomogram were also calculated. Results: The individualized prediction nomogram was constructed by incorporating the radiomics signatures with the clinical risk factors (age and cancer antigen 125). The radiomics nomogram exhibited a good performance in discriminating between negative and positive LVSI patients with AUC of 0.89 (95% CI: 0.83-0.95) in the training group and of 0.85 (95% CI: 0.75-0.94) in the test group. The decision curve analysis indicated that clinicians could be benefit from the using of radiomics nomogram to predict the presence of LVSI preoperatively. Conclusion: The radiomics nomogram could individually predict LVSI in early-stage EC patients. The nomogram could be conveniently used to facilitate the treatment decision for clinicians.

Radiomics Nomogram in Assisting Lymphadenectomy Decisions by Predicting Lymph Node Metastasis in Early-Stage Endometrial Cancer.

Background: Lymph node metastasis (LNM) is an important risk factor affecting treatment strategy and prognosis for endometrial cancer (EC) patients. A radiomics nomogram was established in assisting lymphadenectomy decisions preoperatively by predicting LNM status in early-stage EC patients. Methods: A total of 707 retrospective clinical early-stage EC patients were enrolled and randomly divided into a training cohort and a test cohort. Radiomics features were extracted from MR imaging. Three models were built, including a guideline-recommended clinical model (grade 1-2 endometrioid tumors by dilatation and curettage and less than 50% myometrial invasion on MRI without cervical infiltration), a radiomics model (selected radiomics features), and a radiomics nomogram model (combing the selected radiomics features, myometrial invasion on MRI, and cancer antigen 125). The predictive performance of the three models was assessed by the area under the receiver operating characteristic (ROC) curves (AUC). The clinical decision curves, net reclassification index (NRI), and total integrated discrimination index (IDI) based on the total included patients to assess the clinical benefit of the clinical model and the radiomics nomogram were calculated. Results: The predictive ability of the clinical model, the radiomics model, and the radiomics nomogram between LNM and non-LNM were 0.66 [95% CI: 0.55-0.77], 0.82 [95% CI: 0.74-0.90], and 0.85 [95% CI: 0.77-0.93] in the training cohort, and 0.67 [95% CI: 0.56-0.78], 0.81 [95% CI: 0.72-0.90], and 0.83 [95% CI: 0.74-0.92] in the test cohort, respectively. The decision curve analysis, NRI (1.06 [95% CI: 0.81-1.32]), and IDI (0.05 [95% CI: 0.03-0.07]) demonstrated the clinical usefulness of the radiomics nomogram. Conclusions: The predictive radiomics nomogram could be conveniently used for individualized prediction of LNM and assisting lymphadenectomy decisions in early-stage EC patients.

Volumetric apparent diffusion coefficient (ADC) histogram metrics as imaging biomarkers for pretreatment predicting response to fertility-sparing treatment in patients with endometrial cancer.

OBJECTIVES: To investigate the feasibility of volumetric apparent diffusion coefficient (ADC) histogram analysis for prediction of fertility-sparing treatment (FST) response in patients with endometrial cancer (EC). METHODS: Pretreatment data of 54 EC patients with FST were retrospectively analyzed. Treatment response at each follow-up was pathologically evaluated. The associations of ADC histogram metrics (volume, minADC, maxADC, meanADC; 10th, 25th, 50th, 75th and 90th ADC percentiles; skewness; kurtosis) and baseline clinical characteristics with complete response (CR) at the second and third follow-ups, two-consecutive CR, and recurrence at the final follow-up were evaluated by uni- and multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was used for diagnostic performance evaluation. RESULTS: Compared with non-CR patients, CR patients had significantly higher minADC and 10th and 25th ADC percentiles at the second follow-up (P = 0.008, 0.039, and 0.034, respectively) and higher minADC, older age, lower HE4 level, and higher overweight rate at the third follow-up (P = 0.001, 0.040, 0.021, and 0.004, respectively). Patients with two-consecutive CR had a significantly higher minADC than those without (P = 0.018). There was no association between ADC metrics or clinical characteristics and recurrence (all P > 0.05). MinADC yielded the largest AUC in predicting CR (0.688 and 0.735 at the second and third follow-up, respectively) and the presence of two-consecutive CR (0.753). When combined with patient age and HE4 level, the prediction of CR could be further improved at the third follow-up, with an AUC of 0.786. CONCLUSION: Pretreatment minADC could be a potential imaging biomarker for predicting FST response. Clinical characteristics may have incremental value to minADC in predicting CR.

An MRI radiomics nomogram improves the accuracy in identifying eligible candidates for fertility-preserving treatment in endometrioid adenocarcinoma.

It is difficult to identify eligible candidates for fertility-preserving treatment (FPT) among endometrioid adenocarcinoma (EAC) and atypical hyperplasia (AH) patients. Therefore, new approaches for improving the accuracy of candidate selection are warranted. From December 2014 to January 2020, 236 EAC/AH patients (age <50 and premenopausal) were retrospectively reviewed and randomly divided into the primary group (n=158) and validation group 1 (n=78). From February 2020 to December 2021, 51 EAC/AH patients were prospectively enrolled and formed the validation group 2. From the primary group, 385 features were extracted using pyradiomics from multiparameter magnetic resonance imaging (MRI) (including T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, and contrast enhancement sequences) and 13 radiomics features were selected using a least absolute shrinkage and selection operator. A clinical model based on clinical information (myometrial invasion on MRI and tumor grade in curettage) and a radiomics nomogram by integrating clinical information with the radiomics features was developed to identify eligible candidates of FPT. For identifying eligible candidates of FPT, the areas under the receiver operating characteristic curve (AUCs) were 0.63 (95% confidence interval [CI]: 0.53-0.73) in the primary group, and 0.62 (95% CI: 0.45-0.78) and 0.69 (95% CI: 0.53-0.86) in validation groups 1 and 2, respectively, for the clinical model; were 0.86 (95% CI: 0.80-0.93) in the primary group, and 0.82 (95% CI: 0.71-0.93) and 0.94 (95% CI: 0.87-1.0) in validation groups 1 and 2, respectively, for the radiomics nomogram. With the help of radiomics nomogram, the treatment decision determined from the clinical model was revised in 45 EAC/AH patients. The net reclassification index (NRI) was 0.80 and integrated discrimination improvement (IDI) was 0.17, indicating that the nomogram could improve the accuracy in identifying eligible EAC/AH candidates for FPT.

Multiparametric MRI radiomics nomogram for predicting lymph-vascular space invasion in early-stage cervical cancer.

OBJECTIVE: To develop a radiomics nomogram based on multiparametric MRI (mpMRI) to pre-operatively predict lymph-vascular space invasion (LVSI) in patients with early-stage cervical cancer. METHODS: This retrospective study included 233 consecutive patients with Stage IB-IIB cervical cancer. According to the ratio of 2:1, 154 patients and 79 patients were randomly assigned to the primary and validation cohorts, respectively. Features with intraclass and interclass correlation coefficient (ICCs) greater than 0.75 were selected for radiomics features. The significant features for predicting LVSI were selected using the least absolute shrinkage and selection operator (LASSO) algorithm based on the primary cohort. The rad-score for each patient was constructed via a linear combination of selected features that were weighted by their respective coefficients. The radiomics nomogram was developed using multivariable logistic regression analysis by incorporating the rad-score and clinical risk factors. RESULTS: A total of 19 radiomics features and 3 clinical risk factors were selected. The rad-score exhibited a good performance in discriminating LVSI with a C-index of 0.76 and 0.81 in the primary and validation cohorts, respectively. The radiomics nomogram also exhibited a good discriminating performance in two cohorts (C-index of 0.78 and 0.82). The calibration curve of the radiomics nomogram demonstrated no significant differences was found between prediction and observation outcomes for the probability of LVSI in two cohorts (p = 0.86 and 0.98, respectively). The decision curve analysis indicated that clinician and patients could benefit from the use of radiomics nomogram and rad-score. CONCLUSION: The nomogram and rad-score could be used conveniently and individually to predict LVSI in patients with early-stage cervical cancer and facilitate the treatment decision for clinician and patients. ADVANCES IN KNOWLEDGE: The nomogram could pre-operatively predict LVSI in early-stage cervical cancer.

Volumetric ADC histogram analysis for preoperative evaluation of LVSI status in stage I endometrioid adenocarcinoma.

OBJECTIVES: To investigate the value of volumetric ADC histogram metrics for evaluating lymphovascular space invasion (LVSI) status in stage I endometrioid adenocarcinoma (EAC). METHODS: Preoperative MRI of 227 patients with stage I EAC were retrospectively analyzed. ADC histogram data were derived from the whole tumor with ROIs drawn on all slices of DWI scans (b = 0, 1000 s/mm2). The Student t-test was performed to compare ADC histogram metrics (minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC; skewness; and kurtosis) between the LVSI-positive and LVSI-negative groups, as well as between stage Ia and Ib EACs. ROC curve analysis was carried out to evaluate the diagnostic performance of ADC histogram metrics in predicting LVSI status in EAC. RESULTS: The minADC and meanADC and 10th, 25th, 50th, 75th, and 90th percentiles of ADC were significantly lower in LVSI-positive EACs compared with those in the LVSI-negative groups for stage I, Ia, and Ib EACs (all p < 0.05). MeanADC ≤ 0.857 × 10-3 mm2/s, meanADC ≤ 0.854 × 10-3 mm2/s, and the 90th percentile of ADC ≤ 1.06 × 10-3 mm2/s yielded the largest AUC of 0.844, 0.844, and 0.849 for evaluating LVSI positivity in stage I, Ia, and Ib tumors, respectively, with sensitivity of 75.4%, 75.0%, and 76.2%; specificity of 80.0%, 83.1%, and 82.1%; and accuracy of 79.3%, 81.5%, and 79.6%, respectively. CONCLUSION: Volumetric ADC histogram metrics might be helpful for the preoperative evaluation of LVSI status and personalized clinical management in patients with stage I EAC. KEY POINTS: • Volumetric ADC histogram analysis helps evaluate LVSI status preoperatively. • LVSI-positive EAC is associated with a reduction in multiple volumetric ADC histogram metrics. • MeanADC and the 90th percentile of ADC were shown to be best in evaluating LVSI- positivity in stage Ia and Ib EACs, respectively.

Whole-tumor histogram analysis of apparent diffusion coefficient for differentiating adenosquamous carcinoma and adenocarcinoma from squamous cell carcinoma in patients with cervical cancer.

BACKGROUND: Differentiating adenosquamous carcinoma (ASC) and adenocarcinoma (AC) from squamous cell carcinoma (SCC) precisely is crucial for treatment strategy and prognosis prediction in patients with cervical cancer (CC). PURPOSE: To differentiate ASC and AC from SCC in patients with CC using the apparent diffusion coefficient (ADC) histogram analysis. MATERIAL AND METHODS: A total of 118 patients with histologically diagnosed ASC, AC, and SCC were included. The ADC histogram parameters were extracted from ADC maps. Receiver operating characteristic analysis was performed to evaluate the diagnostic performance of each ADC histogram parameter in differentiating the subtypes of CC. The predictors for histologic subtypes were further selected using univariate and multivariate logistic regression analyses. RESULTS: The ADCmean, ADCmax, ADCP10, ADCP25, ADCP75, ADCP90, ADCmedian, and ADCmode of the ASC were significantly lower than those of the AC; and ADCkurtosis and ADCskewness of the ASC were lower than those of the SCC. The ADCmean, ADCmax, ADCP10, ADCP25, ADCP75, ADCP90, ADCmedian, and ADCmode of AC were significantly higher than those of the SCC. The ADCP10 and ADCP10 + diameter yielded the AUCs of 0.753 and 0.778 in differentiating ASC from AC. The ADCmedian and ADCmedian + diameter yielded the AUCs of 0.807 and 0.838 in differentiating AC from SCC. The ADCskewness yielded the AUC of 0.713 in differentiating ASC from SCC. CONCLUSION: The ADCP10 and ADCP10 + diameter, ADCmedian, and ADCmedian + diameter performed well in differentiating ASC from AC and AC from SCC, respectively. However, ADCskewness exhibited a limited ability in differentiating ASC from SCC.

Evaluation of the Depth of Myometrial Invasion of Endometrial Carcinoma: Comparison of Orthogonal Pelvis-axial Contrast-enhanced and Uterus-axial Dynamic Contrast-enhanced MRI Protocols.

RATIONALE AND OBJECTIVES: To compare the diagnostic performance of orthogonal pelvis-axial (OPA) contrast-enhanced (CE) and orthogonal uterus-axial (OUA) dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) protocols in assessing the depth of myometrial invasion (MI) of endometrial carcinoma (EC). MATERIALS AND METHODS: Preoperative MRI of 398 consecutive EC patients (197 patients with OPA CE-MRI protocol and 201 patients with OUA DCE-MRI protocol) was analyzed. Two radiologists independently interpreted the depth of MI, with postoperative histopathology as the reference standard. The chi-square test, Fisher's exact test, and receiver operating characteristic curve analysis were used for diagnostic performance comparison. RESULTS: OUA DCE-MRI showed a significantly larger area under the curve than OPA CE-MRI in detecting the presence of MI for radiologist 1 (0.71 versus 0.49, p < 0.05) but not for radiologist 2 or deep MI (all p > 0.05). Compared to OPA CE-MRI, OUA DCE-MRI significantly improved the diagnostic accuracy of non-MI and superficial MI (radiologist 1: 45.5% versus 0 and 88.7% versus 86.4%, p = 0.045 and 0.567, respectively; radiologist 2: 45.5% versus 12.5% and 88.7% versus 78.8%, p = 0.177 and 0.027, respectively) and of EC with adenomyosis/submucous myomas, cornual tumor, and antero-posterior diameter ≤ 10 mm (radiologist 1: 86.4% versus 71.4%, 91.2% versus 67.7%, and 90.1% versus 81.1%, p = 0.048, 0.018, and 0.081, respectively; radiologist 2: 86.4% versus 64.3%, 88.2% versus 64.5%, and 87.0% versus 71.6%, p = 0.006, 0.023, and 0.019, respectively). CONCLUSION: The OUA DCE-MRI protocol was superior to the OPA CE-MRI protocol in assessing the depth of MI of EC.

The role of volumetric ADC histogram analysis in preoperatively evaluating the tumour subtype and grade of endometrial cancer.

PURPOSE: To assess the value of volumetric ADC histogram metrics in evaluating the histological subtype and grade of endometrial cancer. METHOD: Preoperative MRI datasets of 317 patients with endometrial cancer were used to obtain volumetric ADC histogram metrics (tumour volume; minADC, maxADC and meanADC; 10th, 25th, 50th, 75th and 90th percentiles of ADC; skewness; and kurtosis). The Mann-Whitney test or Student's t-test was used to compare the difference in ADC histogram metrics between endometrioid adenocarcinomas (EACs) and serous endometrial cancers (SECs) and between different tumour grades (G1, G2, G3). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the performance of ADC histogram metrics or combined models in predicting the tumour subtype and grade. RESULTS: SECs showed a significantly larger tumour volume (P < 0.001) and lower meanADC, 50th, 75th and 90th percentiles of ADC than EACs (all P < 0.05). MinADC, maxADC, meanADC, 10th, 25th, 50th, 75th, 90th percentiles of ADC were significantly higher in G1 than in G2 and G3 EACs (all P < 0.05), while were not significantly different between G2 and G3 EACs (all P > 0.05). A tumour volume ≥ 7.752 cm3 allowed for the prediction of SECs, with an AUC of 0.765 (0.714-0.810). A meanADC ≥ 0.892 × 10-3  mm2/s enabled to discriminate G1 from G2 and G3 EACs, with an AUC of 0.818 (0.769-0.861). CONCLUSION: Volumetric ADC histogram analysis is helpful for non-invasive preoperatively predicting the subtype of endometrial cancer and differentiating G1 from G2 and G3 EACs.

Alginate Oligosaccharide Prevents against D-galactose-mediated Cataract in C57BL/6J Mice via Regulating Oxidative Stress and Antioxidant System.

PURPOSE: Alginate oligosaccharides (AOS), obtained from depolymerizing alginate, has multiple pharmacological benefits. Cataract is a common disease caused by turbidity of the lens protein due to lens metabolism disorders. This study aimed to test the effects and the underlying mechanisms of AOS on D-galactose (D-gal)-mediated cataract. MATERIALS AND METHODS: A total of 45 8-week-old C57BL/6 J male mice were randomly divided into 5 groups. After eight weeks' intervention, the score of cataract was calculated depending on the turbidity of the lens. Hematoxylin and eosin (HE) and transmission electron microscope (TEM) images were observed. Superoxide dismutase (SOD) activity and malondialdehyde (MDA) level were measured by corresponding detection kits, respectively. SOD1, SOD2, catalase (CAT) and p53 protein expressions were examined by Western blot. Nuclear factor erythroid-2 related factor (Nrf2) and heme oxygenase-1 (HO-1) mRNA expressions were examined by Quantitative Real Time-PCR (RT-qPCR). RESULTS: The score of the turbidity of the lens showed that AOS significantly delayed the cataractogenesis. HE staining and TEM imaging showed that AOS decreased the damage and senescence of lenses in D-gal-induced C57BL/6 J mice. We further detected aging marker p53 expression in crystalline lenses, and our result showed that AOS significantly inhibited p53 protein expression in D-gal-induced mice. In addition, SOD activity and MDA level detection results showed that AOS significantly increased the activity of SOD, and decreased the level of MDA in crystalline lenses homogenates of D-gal-induced aging mice. Western blot results showed that AOS attenuated the damage of D-gal in the protein expressions of antioxidative enzymes SOD1, SOD2 and CAT. RT-qPCR results showed that AOS suppressed the down-regulation of Nrf2 and HO-1 mRNA expressions induced by D-gal. CONCLUSIONS: AOS prevents against D-gal-mediated cataract in C57BL/6 J mice via inhibiting oxidative stress and up-regulating antioxidant system. Consequently, our results suggest that AOS may be an effective therapeutic strategy against cataract.

MicroRNA-374 is a potential diagnostic biomarker for atherosclerosis and regulates the proliferation and migration of vascular smooth muscle cells.

BACKGROUND: The occurrence and development of atherosclerosis (AS) are closely related to the abnormality of vascular smooth muscle cells (VSMCs), and multiple microRNAs (miRNAs) have been reported to participate in the pathogenesis of AS. This study explored the expression and clinical value of miR-374 in the serum of AS patients, and analyzed its effect on the proliferation and migration of VSMCs. METHODS: The expression levels of miR-374 in the serum of 102 asymptomatic patients with AS and 89 healthy patients were detected by fluorescence quantitative PCR. The diagnostic value of miR-374 was evaluated through the receiver operating characteristic (ROC) curve. What's more, CCK-8 and Transwell assays were used to analyze the effects of miR-374 on the proliferation and migration of VSMCs. RESULTS: The expression level of miR-374 in the serum of AS patients was significantly higher than that of the control group. At the same time, the expression of miR-374 in AS patients was positively correlated with carotid intima-media thickness (CIMT). The area under the ROC curve is 0.824. Furthermore, overexpression of miR-374 significantly promoted the proliferation and migration of VSMCs, whereas reducing miR-374 inhibited the proliferation and migration of VSMCs. CONCLUSIONS: The high expression of miR-374 may be a potential diagnostic marker for AS, and overexpression of miR-374 may play a role in AS by promoting the proliferation and migration of VSMCs.

Multiparametric MRI-Based Radiomics Nomogram for Predicting Lymph Node Metastasis in Early-Stage Cervical Cancer.

BACKGROUND: Lymph node metastasis (LNM) is a critical risk factor affecting treatment strategy and prognosis in patients with early-stage cervical cancer. PURPOSE: To establish a multiparametric MRI (mpMRI)-based radiomics nomogram for preoperatively predicting LNM status. STUDY TYPE: Retrospective. POPULATION: Among 233 consecutive patients, 155 patients were randomly allocated to the primary cohort and 78 patients to the validation cohort. FIELD STRENGTH: Radiomic features were extracted from a 1.5T mpMRI scan (T1 -weighted imaging [T1 WI], fat-saturated T2 -weighted imaging [FS-T2 WI], contrast-enhanced [CE], diffusion-weighted imaging [DWI], and apparent diffusion coefficient [ADC] maps). ASSESSMENT: The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The area under the receiver operating characteristics curve (ROC AUC), accuracy, sensitivity, and specificity were also calculated. STATISTICAL TESTS: The least absolute shrinkage and selection operator (LASSO) method was used for dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the radiomics nomogram. An independent sample t-test and chi-squared test were used to compare the differences in continuous and categorical variables, respectively. RESULTS: The radiomic signature allowed a good discrimination between the LNM and non-LNM groups, with a C-index of 0.856 (95% confidence interval [CI], 0.794-0.918) in the primary cohort and 0.883 (95% CI, 0.809-0.957) in the validation cohort. Additionally, the radiomics nomogram also had a good discriminating performance and yielded good calibration both in the primary and validation cohorts (C-index, 0.882 [95% CI, 0.827-0.937], C-index, 0.893 [95% CI, 0.822-0.964], respectively). Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. DATA CONCLUSION: A radiomics nomogram was developed by incorporating the radiomics signature with the MRI-reported LN status and FIGO stage. This nomogram might be used to facilitate the individualized prediction of LNM in patients with early-stage cervical cancer. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:885-896.

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.

The ability of ADC measurements in the assessment of patients with stage I endometrial carcinoma based on three risk categories.

BACKGROUND: Better selection of patients with intermediate and high-risk stage I endometrial carcinoma (EC) for lymphadenectomy has an important effect on the prognosis. PURPOSE: To investigate the role of apparent diffusion coefficient (ADC) measurements in the assessment of stage I EC patients based on three risk categories. MATERIAL AND METHODS: We retrospectively studied 80 patients with EC and 28 cervical cancer patients with normal endometrium. 1.5-T conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) (b = 0, 1000 s/mm2) were performed, and ADC values were calculated. Sixty-eight stage I EC patients were divided into three groups: low-risk EC (group 1); intermediate-risk EC (group 2); and high-risk EC (group 3). The remaining 12 EC patients were in stages II and III. Intraclass coefficient, Mann-Whitney U test, Kruskal-Wallis test, and receiver operating characteristics were used for statistical analysis. RESULTS: The mean ADC values ( × 10-3 mm2 /s) were 0.851 ± 0.131, 0.734 ± 0.108, and 0.710 ± 0.108 for groups 1, 2 and 3, respectively. Significant statistical differences were achieved for the three groups ( P = 0.0005). The mean ADC values of group 1 were significantly lower than those in group 2 + 3 (0.725 ± 0.106; P = 0.0001). For the prediction of groups 2 + 3, the area under the curve of 0.786 and the cut-off value of ≤ 0.742 were identified, with a sensitivity, specificity, and accuracy of 66.67%, 84.09%, and 73.53%, respectively. CONCLUSION: ADC measurements may have the potential to select intermediate-risk and high-risk stage I EC patients for lymphadenectomy.