Differentiation between benign and malignant ovarian masses using multiparametric MRI

PurposeTo investigate the capabilities of multiparametric MRI including dynamic contrast enhanced (DCE) perfusion and diffusion-weighted imaging (DWI) to discriminate between benign and malignant ovarian masses.MethodsA total of 43 women with a total of 43 ovarian masses were retrospectively included. They had a mean age of 51.26 ± 18.05 (SD) years (range: 20–88 years). Twenty women had benign and 23 had malignant ovarian tumors. All women had multiparametric MRI examinations including DWI (b50-b800) and DCE perfusion imaging at 1–5 T. Results of DWI (apparent diffusion coefficient [ADC], b-800) and DCE imaging (volume transfer coefficient [K^trans], rate constant [K_ep], interstitial volume [V_e], initial area under the curve [iAUC]) were compared between benign and malignant ovarian masses.ResultsMean ADC was significantly lowed in malignant tumors (0.92 ± 0.25 [SD] ×10⁻³ mm²/s (range: 0.6–1.6 × 10⁻³ mm²/s) than in benign tumors (1.37 ± 0.69 [SD] × 10⁻³ mm²/s; range: 0.4–2.9 × 10⁻³ mm²/s) (P = 0.011). B-800 was significantly greater in malignant tumors (80.61 ± 24.73 [SD] s/mm²; range: 24–110 s/mm²) than in benign ones (61.15 ± 22.17 [SD] s/mm²; range: 38–155 s/mm²) (P = 0.010).  K^trans was lower in benign tumors (0.13 ± 0.06 [SD] min⁻¹; range: 0–0.2 min⁻¹) than in malignant ones (0.25 ± 0.16 [SD] min⁻¹; range: 0.1–0.8 min⁻¹) (P = 0.002).  K_ep was significantly greater in malignant tumors (0.55 ± 0.19 [SD] min⁻¹; range: 0.1–1.9 min⁻¹) than in benign ones (0.44 ± 0.38 [SD] min⁻¹; range: 0.2–1.1 min⁻¹) (P = 0.003). iAUC was greater in malignant tumors (15.59 ± 7.98 [SD] mM/min; range: 6.6–42.1 mM/min) than in benign ones (7.98 ± 5.06 [SD] mM/min; range: 0.2–17.7 mM/min) (P = 0.001). No differences in V_e were found between benign and malignant masses (P = 0.084). The area under the ROC curve was significant for all parameters but V_e. Logistic regression analysis revealed 5.590 and 11.637 times higher malignancy risk for an ADC ≤ 0.93 × 10⁻³ mm²/s and an iAUC ≥ 13.88 mM/min, respectively.ConclusionMultiparametric MRI has high accuracy in discrimination between benign and malignant ovarian masses. Therefore, adding these methods to the more common MRI protocol can help select the best treatment option in women with ovarian mass.     

腹壁缝合钳辅助单孔腹腔镜卵巢囊肿剥除术的疗效分析

目的探讨腹壁缝合钳辅助单孔腹腔镜卵巢囊肿剥除术的疗效。 方法回顾性分析2018年1月至2019年2月于青岛大学附属青岛妇女儿童医院行微创卵巢囊肿剥除术145例患者的临床资料,其中50例行腹壁缝合钳辅助单孔腹腔镜卵巢囊肿剥除术(腹壁缝合钳辅助单孔组)、45例行单孔腹腔镜卵巢囊肿剥除术(单孔组)、50例行多孔腹腔镜卵巢囊肿剥除术(多孔组)。比较3组的一般资料、术中指标、术后指标及围手术期并发症。 结果腹壁缝合钳辅助单孔组、单孔组、多孔组患者的手术时间分别为(53.3±6.5)min 、(70.8±6.6)min、(52.3±6.2)min,腹壁缝合钳辅助单孔组的手术时间短于单孔组,差异有统计学意义(P<0.001);腹壁缝合钳辅助单孔组与多孔组比较,差异无统计学意义(P>0.05)。腹壁缝合钳辅助单孔组、单孔组、多孔组患者的住院费用分别为(11 850.7±142.2)元、(12 934.6±138.9)元、(11 883.1±131.9)元,腹壁缝合钳辅助单孔组的住院费用低于单孔组,差异有统计学意义(P<0.001);腹壁缝合钳辅助单孔组与多孔组比较,差异无统计学意义(P>0.05)。腹壁缝合钳辅助单孔组、单孔组、多孔组患者的术后24 h疼痛视觉模拟评分(visual analogue scale, VAS)分别为(2.0±0.8)分、(1.9±0.7)分、(2.5±0.8)分,腹壁缝合钳辅助单孔组的术后24 h VAS低于多孔组,差异有统计学意义(P<0.001);腹壁缝合钳辅助单孔组与单孔组比较,差异无统计学意义(P>0.05)。腹壁缝合钳辅助单孔组、单孔组、多孔组患者的术后1个月切口美容评分(cosmetic score, CS)分别为(21.1±0.9)分、(21.1±0.9)分、(17.5±0.6)分,腹壁缝合钳辅助单孔组的术后1个月CS高于多孔组,差异有统计学意义(P<0.001);腹壁缝合钳辅助单孔组与单孔组比较,差异无统计学意义(P>0.999)。3组患者的术中出血量、术后肛门排气时间、术后住院时间、术后1个月术后体象量表评分及围手术期并发症发生率比较,差异无统计学意义。 结论腹壁缝合钳辅助单孔腹腔镜卵巢囊肿剥除术是安全、可行的,与单孔腹腔镜卵巢囊肿剥除术比较,具有手术时间缩短、住院费用降低的优势;与多孔腹腔镜卵巢囊肿剥除术比较,具有减轻疼痛、美观的优势;可作为一种符合经济效益的手术方式在基层医院推广应用。     

长链非编码RNA PVT1调控miR-551通过Wnt信号通路对卵巢癌迁移和侵袭的影响

目的:探讨长链非编码RNA PVT1在卵巢癌组织中的表达情况及其在卵巢癌细胞迁移和侵袭过程中的作用及机制。方法:q PCR检测卵巢癌和正常卵巢组织及不同卵巢癌细胞中PVT1的表达情况;Transwell侵袭实验和细胞划痕实验分别检测沉默PVT1后卵巢癌细胞侵袭和迁移能力的变化;双萤光素酶报告基因检测PVT1与微小RNA(miR)-551的相互作用;Transwell侵袭实验和细胞划痕实验分别检测沉默PVT1后miR-551-inhibitor对卵巢癌细胞侵袭和迁移能力的影响;Western blot法检测沉默PVT1后Wnt信号通路相关蛋白的表达情况。裸鼠皮下成瘤实验检测沉默PVT1对卵巢癌成瘤重量及体积的影响。结果:与正常卵巢组织相比,卵巢瘤组织中PVT1表达明显增高(P0.05);卵巢癌细胞株ES-2中PVT1表达水平最高(P0.05);沉默PVT1可以抑制卵巢癌细胞侵袭和迁移能力;PVT1能与miR-551的位点特异性结合;沉默PVT1后,miR-551-inhibitor可以促进卵巢癌细胞侵袭和迁移能力;沉默PVT1后Wnt信号通路蛋白的表达相应下调;与阴性对照组相比,PVT1-siRNA组荷瘤小鼠肿瘤体积和重量都明显减小(P0.05)。结论:PVT1在卵巢癌发生发展过程中起重要作用,它可以靶向调节miR-551,通过Wnt信号通路调控卵巢癌细胞的侵袭和迁移能力。     

转化生长因子-β和Smad4在绝经过渡期大鼠卵巢颗粒细胞中的表达

目的 探讨转化生长因子-β（TGF-β）、Smad4 在绝经过渡期大鼠卵巢颗粒细胞中的表达,分析其与卵巢功能衰退的关系。 方法 雌性SD大鼠分为对照组（C组,6月龄,阴道涂片筛选,n=9）、绝经过渡期组（MT组,12~14月龄,阴道涂片筛选,n=8）,大鼠麻醉处死后迅速取出卵巢,采用机械分离方法释放卵泡颗粒细胞,于CO₂培养箱中培养,利用免疫细胞化学法检测促卵泡刺激素受体（FSHR）蛋白的表达,鉴定卵巢颗粒细胞;免疫细胞化学法检测TGF-β、Smad4蛋白在两组卵巢颗粒细胞的表达;采用Real-time PCR的方法检测各组颗粒细胞中TGF-β、Smad4 mRNA的表达。 结果 免疫细胞化学显示分离培养的颗粒细胞纯度＞95%,MT组TGF-β、Smad4蛋白表达水平低于对照组（P<0.05）;Real-time PCR结果显示,两组均有TGF-β、Smad4 mRNA的表达,MT组TGF-β、Smad4 mRNA表达水平显著低于对照组（P<0.05）。 结论 TGF-β、Smad4参与绝经过渡期大鼠卵巢功能的衰退过程,绝经过渡期大鼠功能衰退的部分原因可能与卵巢颗粒细胞中TGF-β、Smad4 的表达降低有关。     

促性腺激素释放激素拮抗剂方案中卵巢反应性相关因素分析

目的研究促性腺激素释放激素拮抗剂（GnRH—ant）方案中卵巢反应性的影响因素及其与妊娠结局的关系。方法分析因输卵管因素和（或）男方因素在我院生殖中心进行体外受精／卵胞浆内单精子注射（IVF／ICSI）助孕的患者共452个周期,按患者的卵巢反应性分为卵巢低反应组、正常反应组和高反应组,比较拮抗剂方案中患者的一般情况、内分泌激素水平、促性腺激素（Gn）用药时间和剂量、获卵数、受精率及临床妊娠结局,并分析上述因素与卵巢反应性的关系。结果（1）在卵巢低反应组、正常反应组和高反应组中,患者的年龄、基础卵泡刺激素（bFSH）水平、基础睾酮（T）水平、窦卵泡计数（AFC）之间差异有统计学意义（P〈O．05）。（2）三组患者人绒毛膜促性腺激素（HCG）日的雌二醇（E：）水平、Gn的用量、取卵数、移植数、冷冻数及流产率之间差异有统计学意义（P〈O．05）;Gn的用药时间、子宫内膜厚度之间差异无统计学意义（P〉O．05）;卵巢低反应组的临床妊娠率（30．28％）明显低于其他两组,差异有统计学意义（P〈0．05）。（3）多因素Logistic回归显示,年龄、bFSH水平及总Gn用量与卵巢反应性呈负相关,取卵数与卵巢反应性呈正相关。结论拮抗剂方案中,卵巢反应性与患者的年龄、bFSH水平及Gn用量有关,对卵巢反应性进行评估应当结合患者的一般情况、超声学检查及内分泌特点等多因素进行综合分析。     

Ask Rosa – The making of a digital genetic conversation tool,a chatbot,about hereditary breast and ovarian cancer

ObjectiveWe aimed at developing a pilot version of an app (Rosa) that can perform digital conversations with breast or ovarian cancer patients about genetic BRCA testing, using chatbot technology, to identify best practices for future patient-focused chatbots.MethodsWe chose a commercial chatbot platform and participatory methodology with a team of patient representatives, IT engineers, genetic counselors and clinical geneticists, within a nationwide collaboration. An iterative approach ensured extensive user and formal usability testing during the development process.ResultsThe development phase lasted for two years until the pilot version was completed in December 2019. The iteration steps disclosed major challenges in the artificial intelligence (AI)-based matching of user provided questions with predefined information in the database, leading initially to high level of fallback answers. We therefore developed strategies to reduce potential language ambiguities (e.g. BRCA1 vs BRCA2) and overcome dialogue confusion. The first prototype contained a database with 500 predefined questions and 67 corresponding predefined answers, while the final version included 2257 predefined questions and 144 predefined answers. Despite the limited AI functionality of the chatbot, the testing revealed that the users liked the layout and found the chatbot trustworthy and reader friendly.ConclusionsBuilding a health chatbot is challenging, expensive and time consuming with today’s technology. The users had a positive attitude to the chatbot, and would use it in a real life setting, if given to them by health care personnel.Practice implicationsWe here present a framework for future health chatbot initiatives. The participatory methodology in combination with an iterative approach ensured that the patient perspective was incorporated at every level of the development process. We strongly recommend this approach in patient-centered health innovations.