FEMaLe: The use of machine learning for early diagnosis of endometriosis based on patient self-reported data-Study protocol of a multicenter trial.

INTRODUCTION: Endometriosis is a chronic disease that affects up to 190 million women and those assigned female at birth and remains unresolved mainly in terms of etiology and optimal therapy. It is defined by the presence of endometrium-like tissue outside the uterine cavity and is commonly associated with chronic pelvic pain, infertility, and decreased quality of life. Despite the availability of various screening methods (e.g., biomarkers, genomic analysis, imaging techniques) intended to replace the need for invasive surgery, the time to diagnosis remains in the range of 4 to 11 years. AIMS: This study aims to create a large prospective data bank using the Lucy mobile health application (Lucy app) and analyze patient profiles and structured clinical data. In addition, we will investigate the association of removed or restricted dietary components with quality of life, pain, and central pain sensitization. METHODS: A baseline and a longitudinal questionnaire in the Lucy app collects real-world, self-reported information on symptoms of endometriosis, socio-demographics, mental and physical health, economic factors, nutritional, and other lifestyle factors. 5,000 women with confirmed endometriosis and 5,000 women without diagnosed endometriosis in a control group will be enrolled and followed up for one year. With this information, any connections between recorded symptoms and endometriosis will be analyzed using machine learning. CONCLUSIONS: We aim to develop a phenotypic description of women with endometriosis by linking the collected data with existing registry-based information on endometriosis diagnosis, healthcare utilization, and big data approach. This may help to achieve earlier detection of endometriosis with pelvic pain and significantly reduce the current diagnostic delay. Additionally, we may identify dietary components that worsen the quality of life and pain in women with endometriosis, upon which we can create real-world data-based nutritional recommendations.

Endometriosis: Age at diagnosis and the severity of the disease.

BACKGROUND: Endometriosis is often described as a progressive disease beginning with menarche and menstrual bleeding. However, this is controversial, as the origin of the disease remains unknown. The objective of this study was to investigate the association between the severity of endometriosis and age at initial diagnosis. MATERIAL AND METHODS: This prospective multicenter cohort study included 964 patients, newly and histologically diagnosed with endometriosis during laparoscopic treatment. The main outcome measures were diagnostic lesions with staging according to rAFS (revised American Fertility Society) scores, intensity of pain based on VAS (Visual Analog Scale) scores and quality of life from SF-36 questionnaire results. Age was treated as a categorical variable. Univariate and multivariable analyses (linear and multinomial ordinal) were conducted taking into account two by two multiple comparisons. The results were expressed with effect sizes and 95 % confidence intervals. MAIN FINDINGS: No significant association between age and rAFS stage was found (p = 0.053). The percentage of patients with a deep nodule ≥ 2 cm was respectively 20 %, 25 %, 21 %, 18 % and 9 % for patients aged ≤ 25, 26-30, 31-35, 36-40 and >40 years respectively (p = 0.005). For dysmenorrhea, 64 %, 61 %, 54 %, 51 % and 32 % of patients aged ≤25, 26-30, 31-35, 36-40 and >40 years respectively reported a VAS score of ≥7 (p ≤ 0.001). CONCLUSION: Prospective data obtained in patients newly diagnosed with endometriosis, revealed that the rAFS score is not related to patient age, and that the percentage of patients with a nodule ≥ 2 cm and severe dysmenorrhea did not increase with age. These percentages were significantly lower in patients aged > 40.

Automatic Smoke Analysis in Minimally Invasive Surgery by Image-Based Machine Learning.

INTRODUCTION: Minimally Invasive Surgery uses electrosurgical tools that generate smoke. This smoke reduces the visibility of the surgical site and spreads harmful substances with potential hazards for the surgical staff. Automatic image analysis may provide assistance. However, the existing studies are restricted to simple clear versus smoky image classification. MATERIALS AND METHODS: We propose a novel approach using surgical image analysis with machine learning, including deep neural networks. We address three tasks: 1) smoke quantification, which estimates the visual level of smoke, 2) smoke evacuation confidence, which estimates the level of confidence to evacuate smoke, and 3) smoke evacuation recommendation, which estimates the evacuation decision. We collected three datasets with expert annotations. We trained end-to-end neural networks for the three tasks. We also created indirect predictors using task 1 followed by linear regression to solve task 2 and using task 2 followed by binary classification to solve task 3. RESULTS: We observe a reasonable inter-expert variability for tasks 1 and a large one for tasks 2 and 3. For task 1, the expert error is 17.61 percentage points (pp) and the neural network error is 18.45 pp. For tasks 2, the best results are obtained from the indirect predictor based on task 1. For this task, the expert error is 27.35 pp and the predictor error is 23.60 pp. For task 3, the expert accuracy is 76.78% and the predictor accuracy is 81.30%. CONCLUSIONS: Smoke quantification, evacuation confidence, and evaluation recommendation can be achieved by automatic surgical image analysis with similar or better accuracy as the experts.

A deep learning-based interactive medical image segmentation framework with sequential memory.

BACKGROUND AND OBJECTIVE: Image segmentation is an essential component in medical image analysis. The case of 3D images such as MRI is particularly challenging and time consuming. Interactive or semi-automatic methods are thus highly desirable. However, existing methods do not exploit the typical sequentiality of real user interactions. This is due to the interaction memory used in these systems, which discards ordering. In contrast, we argue that the order of the user corrections should be used for training and lead to performance improvements. METHODS: We contribute to solving this problem by proposing a general multi-class deep learning-based interactive framework for image segmentation, which embeds a base network in a user interaction loop with a user feedback memory. We propose to model the memory explicitly as a sequence of consecutive system states, from which the features can be learned, generally learning from the segmentation refinement process. Training is a major difficulty owing to the network's input being dependent on the previous output. We adapt the network to this loop by introducing a virtual user in the training process, modelled by dynamically simulating the iterative user feedback. RESULTS: We evaluated our framework against existing methods on the complex task of multi-class semantic instance female pelvis MRI segmentation with 5 classes, including up to 27 tumour instances, using a segmentation dataset collected in our hospital, and on liver and pancreas CT segmentation, using public datasets. We conducted a user evaluation, involving both senior and junior medical personnel in matching and adjacent areas of expertise. We observed an annotation time reduction with 5'56" for our framework against 25' on average for classical tools. We systematically evaluated the influence of the number of clicks on the segmentation accuracy. A single interaction round our framework outperforms existing automatic systems with a comparable setup. We provide an ablation study and show that our framework outperforms existing interactive systems. CONCLUSIONS: Our framework largely outperforms existing systems in accuracy, with the largest impact on the smallest, most difficult classes, and drastically reduces the average user segmentation time with fast inference at 47.2±6.2 ms per image.

The impact of laparoscopic gynecological surgery training on the technicity index of a developing country center.

PURPOSE: To compare laparoscopic gynecological surgery training between a developed country's reference center (host center) and a public reference service in a developing country (home center), and use the technicity index (TI) to compare outcomes and to determine the impact of laparoscopic gynecological surgery fellowship training on the home center's TI. METHODS: The impact of training on the home center was assessed by comparing surgical performance before and after training. TI was assessed in 2017 in the host center, and before and after training in the home center. Epidemiological and clinical data, and information on reason for surgery, preoperative images, estimated intraoperative bleeding, operative time, surgical specimen weight, hospital stay length, complication and reintervention rates were collected from both institutions. Home center pre-training data were retrospectively collected between 2010 and 2013, while post-training data were prospectively collected between 2015 and 2017. A two-tail Z-score was used for TI comparison. RESULTS: The analysis included 366 hysterectomies performed at the host center in 2017, and 663 hysterectomies performed at the home center between 2015 and 2017. TI in the host center was 82.5%, while in the home center it was 6% before training and 22% after training. There were no statistical differences in length of hospital stay, preoperative uterine volume, surgical specimen weight and complication rate between centers. However, significantly shorter mean operative time and lower blood loss during surgery were observed in the host center. CONCLUSIONS: High-quality laparoscopic training in a world-renowned specialized center allowed standardizing laparoscopic hysterectomy procedures and helped to significantly improve TI in the recipient's center with comparable surgical outcomes.

Multicenter External Validation of the Deep Pelvic Endometriosis Index Magnetic Resonance Imaging Score.

Importance: Preoperative mapping of deep pelvic endometriosis (DPE) is crucial as surgery can be complex and the quality of preoperative information is key. Objective: To evaluate the Deep Pelvic Endometriosis Index (dPEI) magnetic resonance imaging (MRI) score in a multicenter cohort. Design, Setting, and Participants: In this cohort study, the surgical databases of 7 French referral centers were retrospectively queried for women who underwent surgery and preoperative MRI for DPE between January 1, 2019, and December 31, 2020. Data were analyzed in October 2022. Intervention: Magnetic resonance imaging scans were reviewed using a dedicated lexicon and classified according to the dPEI score. Main outcomes and measures: Operating time, hospital stay, Clavien-Dindo-graded postoperative complications, and presence of de novo voiding dysfunction. Results: The final cohort consisted of 605 women (mean age, 33.3; 95% CI, 32.7-33.8 years). A mild dPEI score was reported in 61.2% (370) of the women, moderate in 25.8% (156), and severe in 13.1% (79). Central endometriosis was described in 93.2% (564) of the women and lateral endometriosis in 31.2% (189). Lateral endometriosis was more frequent in severe (98.7%) vs moderate (48.7%) disease and in moderate vs mild (6.7%) disease according to the dPEI (P < .001). Median operating time (211 minutes) and hospital stay (6 days) were longer in severe DPE than in moderate DPE (operating time, 150 minutes; hospital stay 4 days; P < .001), and in moderate than in mild DPE (operating time; 110 minutes; hospital stay, 3 days; P < .001). Patients with severe disease were 3.6 times more likely to experience severe complications than patients with mild or moderate disease (odds ratio [OR], 3.6; 95% CI, 1.4-8.9; P = .004). They were also more likely to experience postoperative voiding dysfunction (OR, 3.5; 95% CI, 1.6-7.6; P = .001). Interobserver agreement between senior and junior readers was good (κ = 0.76; 95% CI, 0.65-0.86). Conclusions and Relevance: The findings of this study suggest the ability of the dPEI to predict operating time, hospital stay, postoperative complications, and de novo postoperative voiding dysfunction in a multicenter cohort. The dPEI may help clinicians to better anticipate the extent of DPE and improve clinical management and patient counseling.

Operative Hysteroscopy vs Vacuum Aspiration for Incomplete Spontaneous Abortion: A Randomized Clinical Trial.

Importance: Vacuum aspiration is commonly used to remove retained products of conception in patients with incomplete spontaneous abortion. Scarring of the uterine cavity may occur, potentially impairing future fertility. A procedural alternative, operative hysteroscopy, has gained popularity with a presumption of better future fertility. Objective: To assess the superiority of hysteroscopy to vacuum aspiration for subsequent pregnancy in patients with incomplete spontaneous abortion who intend to have future pregnancy. Design, Setting, and Participants: The HY-PER randomized, controlled, single-blind trial included 574 patients between November 6, 2014, and May 3, 2017, with a 2-year duration of follow-up. This multicenter trial recruited patients in 15 French hospitals. Individuals aged 18 to 44 years and planned for surgery for an incomplete spontaneous abortion with plans to subsequently conceive were randomized in a 1:1 ratio. Interventions: Surgical treatment by hysteroscopy (n = 288) or vacuum aspiration (n = 286). Main Outcomes and Measures: The primary outcome was a pregnancy of at least 22 weeks' duration during 2-year follow-up. Results: The intention-to-treat analyses included 563 women (mean [SD] age, 32.6 [5.4] years). All aspiration procedures were completed. The hysteroscopic procedure could not be completed for 19 patients (7%), 18 of which were converted to vacuum aspiration (8 with inability to completely resect, 7 with insufficient visualization, 2 with anesthetic complications that required a shortened procedure, 1 with equipment failure). One hysteroscopy failed due to a false passage during cervical dilatation. During the 2-year follow-up, 177 patients (62.8%) in the hysteroscopy group and 190 (67.6%) in the vacuum aspiration (control) group achieved the primary outcome (difference, -4.8% [95% CI, -13% to 3.0%]; P = .23). The time-to-event analyses showed no statistically significant difference between groups for the primary outcome (hazard ratio, 0.87 [95% CI, 0.71 to 1.07]). Duration of surgery and hospitalization were significantly longer for hysteroscopy. Rates of new miscarriages, ectopic pregnancies, Clavien-Dindo surgical complications of grade 3 or above (requiring surgical, endoscopic, or radiological intervention or life-threatening event or death), and reinterventions to remove remaining products of conception did not differ between groups. Conclusions and Relevance: Surgical management by hysteroscopy of incomplete spontaneous abortions in patients intending to conceive again was not associated with more subsequent births or a better safety profile than vacuum aspiration. Moreover, operative hysteroscopy was not feasible in all cases. Trial Registration: ClinicalTrials.gov Identifier: NCT02201732.

SurgAI3.8K: A Labeled Dataset of Gynecologic Organs in Laparoscopy with Application to Automatic Augmented Reality Surgical Guidance.

STUDY OBJECTIVE: We focus on explaining the concepts underlying artificial intelligence (AI), using Uteraug, a laparoscopic surgery guidance application based on Augmented Reality (AR), to provide concrete examples. AI can be used to automatically interpret the surgical images. We are specifically interested in the tasks of uterus segmentation and uterus contouring in laparoscopic images. A major difficulty with AI methods is their requirement for a massive amount of annotated data. We propose SurgAI3.8K, the first gynaecological dataset with annotated anatomy. We study the impact of AI on automating key steps of Uteraug. DESIGN: We constructed the SurgAI3.8K dataset with 3800 images extracted from 79 laparoscopy videos. We created the following annotations: the uterus segmentation, the uterus contours and the regions of the left and right fallopian tube junctions. We divided our dataset into a training and a test dataset. Our engineers trained a neural network from the training dataset. We then investigated the performance of the neural network compared to the experts on the test dataset. In particular, we established the relationship between the size of the training dataset and the performance, by creating size-performance graphs. SETTING: University. PATIENTS: Not available. INTERVENTION: Not available. MEASUREMENTS AND MAIN RESULTS: The size-performance graphs show a performance plateau at 700 images for uterus segmentation and 2000 images for uterus contouring. The final segmentation scores on the training and test dataset were 94.6% and 84.9% (the higher, the better) and the final contour error were 19.5% and 47.3% (the lower, the better). These results allowed us to bootstrap Uteraug, achieving AR performance equivalent to its current manual setup. CONCLUSION: We describe a concrete AI system in laparoscopic surgery with all steps from data collection, data annotation, neural network training, performance evaluation, to final application.

A survey of augmented reality methods to guide minimally invasive partial nephrectomy.

INTRODUCTION: Minimally invasive partial nephrectomy (MIPN) has become the standard of care for localized kidney tumors over the past decade. The characteristics of each tumor, in particular its size and relationship with the excretory tract and vessels, allow one to judge its complexity and to attempt predicting the risk of complications. The recent development of virtual 3D model reconstruction and computer vision has opened the way to image-guided surgery and augmented reality (AR). OBJECTIVE: Our objective was to perform a systematic review to list and describe the different AR techniques proposed to support PN. MATERIALS AND METHODS: The systematic review of the literature was performed on 12/04/22, using the keywords "nephrectomy" and "augmented reality" on Embase and Medline. Articles were considered if they reported surgical outcomes when using AR with virtual image overlay on real vision, during ex vivo or in vivo MIPN. We classified them according to the registration technique they use. RESULTS: We found 16 articles describing an AR technique during MIPN procedures that met the eligibility criteria. A moderate to high risk of bias was recorded for all the studies. We classified registration methods into three main families, of which the most promising one seems to be surface-based registration. CONCLUSION: Despite promising results, there do not exist studies showing an improvement in clinical outcomes using AR. The ideal AR technique is probably yet to be established, as several designs are still being actively explored. More clinical data will be required to establish the potential contribution of this technology to MIPN.

Surgical complications and their impact for gynecological surgeons.

OBJECTIVES: Major clinical incidents can impact the healthcare professionals involved. This is of particular relevance in surgery, with the operating room being a high-risk zone for complications; however, there is few available data on how surgeons may be affected. The current study examined the impact of surgical complications on surgeon traumatic stress levels, emotional state, job performance, and coping strategies. METHODS: A questionnaire to evaluate the impact of surgical complications on surgeon traumatic stress levels, emotional state, and job performance was developed by our team and communicated via an online link to gynecological surgeons. RESULTS: A total of 72 gynecologic surgeons completed the questionnaire. Five percent had a Peritraumatic Distress Inventory (PDI) score of ≥15, revealing a high stress level, and 12% had an Impact of Event Scale-Revised (IES-R) score of ≥36, indicating acute traumatic stress. Our results show that following surgical incidents, surgeons receive support primarily from another surgical team member and that surgical practice may be impacted, leading in some cases to detrimental effects on patient care, notably reduced radicality in some surgical procedures. CONCLUSION: Surgeons may experience acute traumatic stress after serious surgical complications. Increased awareness of the negative consequences on surgeon emotional well-being is required, as well as improved access to support mechanisms. The study was approved by local ethics committee (IRB00013412, "CHU de Clermont Ferrand IRB #1," institutional review board number 2022-CF004).

The Advances in Computer Vision That Are Enabling More Autonomous Actions in Surgery: A Systematic Review of the Literature.

This is a review focused on advances and current limitations of computer vision (CV) and how CV can help us obtain to more autonomous actions in surgery. It is a follow-up article to one that we previously published in Sensors entitled, "Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?" As opposed to that article that also discussed issues of machine learning, deep learning and natural language processing, this review will delve deeper into the field of CV. Additionally, non-visual forms of data that can aid computerized robots in the performance of more autonomous actions, such as instrument priors and audio haptics, will also be highlighted. Furthermore, the current existential crisis for surgeons, endoscopists and interventional radiologists regarding more autonomy during procedures will be discussed. In summary, this paper will discuss how to harness the power of CV to keep doctors who do interventions in the loop.

Perioperative parameters to consider for enhanced recovery in surgery (ERS) in gynecology (excluding breast surgery).

The current review explores the Enhanced Rehabilitation in Surgery (ERS) approach in the specific context of gynecological surgery. Implementation of an ERS protocol in gynecological surgery reduces postoperative complications and length of stay without increasing morbidity. An ERS approach is based on maintaining an adequate diet and hydration before the operation, according to the recommended time frame, to reduce the phenomenon of insulin resistance, and to optimize patient comfort. On the other hand, the use of anxiolytic treatment as premedication is not recommended. Systematic preoperative digestive preparation, a source of patient discomfort, is not associated with an improvement in the postoperative functional outcome or with a reduction in the rate of complications. A minimally invasive surgical approach is preferrable in the context of ERS. Prevention of surgical site infection includes measures such as optimized antibiotic prophylaxis, skin disinfection with alcoholic chlorhexidine, reduction in the use of drainage of the surgical site, and prevention of hypothermia. Early removal of the bladder catheter is associated with a reduction in the risk of urinary tract infection and a reduction in the length of hospital stay. Prevention of postoperative ileus is based on early refeeding, and prevention of postoperative nausea-vomiting in a multimodal strategy to be initiated during the intraoperative period. Intraoperative hydration should be aimed at achieving euvolemia. Pain control is based on a multimodal strategy to spare morphine use and may include locoregional analgesia. Medicines should be administered orally during the postoperative period to hasten the resumption of the patient's autonomy. The prevention of thromboembolic risk is based on a strategy combining drug prophylaxis, when indicated, and mechanical restraint, as well as early mobilization. However, the eclectic nature of the implementation of these measures as reported in the literature renders their interpretation difficult. Furthermore, beyond the application of one of these measures in isolation, the best benefit on the postoperative outcome is achieved by a combination of measures which then constitutes a global strategy allowing the objectives of the ERS to be met.

To what extent is enhanced recovery in surgery used in French obstetrics and gynecology departments?

The objective of the present study was to evaluate the implementation of Enhanced Recovery in Surgery (ERS) in French obstetrics and gynecology departments. To achieve this objective, we drafted an online questionnaire about ERS protocols for cesarian sections and hysterectomies with a benign indication and put a hyperlink on the 'French National College of Gynecologists and Obstetricians' (Collège National des Gynécologues et Obstétriciens Français) website. We obtained 112 analyzable responses. Respectively 66% and 34% of the surveyed departments had established ERS protocols for cesarean sections and for hysterectomies with a benign indication. However, not all of the key ERS items were sufficiently implemented: despite the establishment of written protocols, the degree of compliance with the guidelines issued by the French-Speaking Group for Enhanced Recovery After Surgery (Groupement Francophone de Réhabilitation Améliorée Après Chirurgie) was variable. There are few published data on the implementation of ERS in obstetrics and gynecology departments worldwide. In 2010, the Enhanced Recovery After Surgery® Society issued guidelines and a checklist for an ERS protocol. The literature data suggest that for most surgical disciplines, the main ERS criteria are not well known or not widely applied. ERS protocols are still not widespread in French gynecologic surgery departments. Moreover, the application of some of the major ERS items differs markedly from one ERS program to other, which is likely to reduce the level of effectiveness. It therefore appears to be essential to formalize and promote ERS protocols in gynecological surgery.

Definition, general principles and expected benefits of enhanced recovery in surgery.

A multimodal approach to promoting recovery from surgery was first described by Henrik Kehlet in 1995. This approach has since been significantly developed and refined, and is now referred to as Enhanced Recovery in Surgery (ERS). The goal of ERS is to enable a patient to regain his/her pre-surgery physical and psychological state after a surgical procedure - notably by reducing the stress and the inflammatory response inevitably triggered by surgery. ERS protocols include anesthesia-related items (such as reducing the use of morphine) and surgical items (such as the use of minimally invasive routes, and limiting the postoperative use of drains and probes). Each step is essential - from patient information, education and adherence during the preoperative phase to involvement of the family circle and the attending physician with a view to early discharge. The term ERS corresponds to a set of principles for optimizing pre-, per- and postoperative care, the aim of which is to improve the post-operative course and the patient's experience by decreasing per- and postoperative complications and accelerating a return to the patient's pre-operative physical and psychological state. The use of ERS protocols is associated with a lower complication rate and a shorter hospital stay, regardless of the patient's age and comorbidities.

Combining prehabilitation with enhanced recovery programs in gynecological surgery.

Prehabilitation is a multimodal approach to preoperative care based on physical exercise, dietary/nutritional interventions, smoking and alcohol cessation, and psychological care. The goal is to reduce stress and apprehension, encourage general well-being, and thus optimize the patient's state of health before surgery. Prehabilitation encompasses all the actions undertaken between the diagnosis of the disease and the initiation of surgery to reduce the morbidity attributable to the latter. Although there are few literature data on prehabilitation in gynecological surgery, the management of moderate-to-severe undernutrition prior to gynecological oncology surgery reduces the risk of postoperative complications and increases the overall survival rate.

[Avenues of reflection for endometriosis research in France]. / Des pistes de réflexion pour la recherche sur l'endométriose en France.

Endometriosis is a chronic disease in which lesions resembling endometrial tissue are found outside the uterus, mainly in the pelvis or abdomen. It may affect 10% of women of childbearing age. It is the cause of a significant alteration in quality of life and a major cost to the health system. Few research teams are working on this subject, and its pathophysiology is still poorly understood. This article proposes avenues of reflection for research on endometriosis in France, notably based on the mobilization of related scientific communities (involved in cancer, development, epigenetics, and neurosciences research studies).

Title: Des pistes de réflexion pour la recherche sur l'endométriose en France. Abstract: L'endométriose est une maladie chronique dans laquelle des lésions ressemblant à du tissu endométrial se retrouvent hors de l'utérus, principalement dans la cavité abdomino-pelvienne. Cette maladie pourrait toucher 10 % des femmes en âge de procréer. Elle est à l'origine d'une importante altération de la qualité de vie et d'un coût majeur pour le système de santé. Peu d'équipes de recherche sont mobilisées sur ce sujet, et la physiopathologie de la maladie reste mal comprise. Nous proposons dans cet article des pistes de réflexion pour la recherche sur l'endométriose en France, fondées notamment sur la mobilisation de communautés scientifiques connexes (notamment celles impliquées dans la recherche sur le cancer, la biologie du développement, l'épigénétique, les neurosciences).

Management of peritoneal pseudomyxoma in pregnant women: A case report and review of the literature.

The incidence of neoplasia during pregnancy is low, 1/1000 pregnancies. The most common cancers diagnosed during pregnancy are breast and cervical cancer. Pseudomyxoma peritonei (PMP) is a rare syndrome (1/1 000 000) characterized by the presence of gelatinous ascites and disseminated intra-peritoneal mucinous tumors. The origin of this syndrome is, in most of cases, a tumor of the appendix. A PMP diagnosis during pregnancy is an extremely rare event. We present the medical history of a 34-year-old woman diagnosed with a PMP at 29 weeks of amenorrhea, during the management of an ovarian masse. We preserved the pregnancy until 37 weeks of amenorrhea. She had a vaginal delivery. At 4 weeks post-partum, she had an extensive cytoreduction with intraperitoneal chemotherapy. We present literature review of PMP discover during pregnancy and a discussion about treatment of these PMP. We also discuss management of an ovarian masse diagnosis during pregnancy.

Adherence to French and ESGO Quality Indicators in Ovarian Cancer Surgery: An Ad-Hoc Analysis from the Prospective Multicentric CURSOC Study.

BACKGROUND: Quality Indicators for ovarian cancer (OC) have been developed by the European Society of Gynaecological Oncology (ESGO) and by the French National Cancer Institute (Institut National du Cancer, INCa). The aim of the study was to characterize OC care distribution in France by case-volume and to prospectively evaluate the adherence of high-volume institutions to INCa/ESGO quality indicators. METHODS: The cost-utility of radical surgery in ovarian cancer (CURSOC) trial is a prospective, multicenter, comparative and non-randomized study that includes patients with stage IIIC-IV epithelial OC treated in nine French health care tertiary institutions. Adherence to institutional quality indicators were anonymously assessed by an independent committee. OC care distribution in France were provided by the nationwide database of hospital procedures. RESULTS: More than half of patients are treated in low-volume institutions. Among the nine high-volume centers participating in the study, four (44.4%) met all institutional INCa/ESGO quality indicators. The other five (55.6%) did not fulfil one of the quality indicator criteria. CONCLUSIONS: Access to high-volume OC providers in France is restricted to a minority of patients, and yet half of the referral institutions included in this study failed to meet all recommended institutional quality indicators. It is mandatory that national authorities work both to improve OC centralization and to incorporate quality assurance programs into certified centers.

Autism spectrum disorder and air pollution: A systematic review and meta-analysis.

Despite the widely-known effects of air pollution, pollutants exposure surrounding pregnancy and the risk for autism spectrum disorder (ASD) in newborns remains controversial. The purpose of our study was to carry out a systematic review and meta-analyses of the risk of ASD in newborns following air pollution exposure during the perinatal period (preconception to second year of life). The PubMed, Cochrane Library, Embase and ScienceDirect databases were searched for articles, published up to July 2020, with the keywords "air pollution" and "autism". Three models were used for each meta-analysis: a global model based on all risks listed in included articles, a pessimistic model based on less favorable data only, and an optimistic model based on the most favorable data only. 28 studies corresponding to a total of 758 997 newborns were included (47190 ASD and 703980 controls). Maternal exposure to all pollutants was associated with an increased risk of ASD in newborns by 3.9% using the global model and by 12.3% using the optimistic model, while the pessimistic model found no change. Each increase of 5 µg/m3 in particulate matter <2.5 µm (PM2.5) was associated with an increased risk of ASD in newborns, regardless of the model used (global +7%, pessimistic +5%, optimistic +15%). This risk increased during preconception (global +17%), during pregnancy (global +5%, and optimistic +16%), and during the postnatal period (global +11% and optimistic +16%). Evidence levels were poor for other pollutants (PM10, NOx, O3, metals, solvents, styrene, PAHs, pesticides). PM2.5 was associated with a greater risk than PM10 (coefficient 0.20, 95CI -0.02 to 0.42), NOx (0.29, 0.08 to 0.50) or solvents (0.24, 0.04 to 0.44). All models revealed that exposure to pollutants, notably PM2.5 during pregnancy, was associated with an increased risk of ASD in newborns. Pregnancy and postnatal periods seem to be the most at-risk periods.