Deep Infiltrating Endometriosis and Adenomyosis: Implications on Pregnancy and Outcome

Endometriosis (EM), especially deep infiltrating endometriosis (DIE) and adenomyosis (AM), are known to cause pain and sterility in young women. More recently, they have also been described as risk factors for obstetric complications. While the pathophysiology is not yet completely understood, they seem to share a common origin: archimetrosis. Methods: A systematic literature review was conducted to summarize the existing evidence on DIE and AM as risk factors for obstetric complications. Results: Preterm birth, caesarean section delivery (CS) and placental abnormalities are associated with the diagnosis of DIE and AM. Women with AM seem to experience more often hypertensive pregnancy disorders, premature rupture of membranes and their children are born with lower birth weights than in the control groups. However, many of the studies tried to evaluate AM, EM and DIE as separate risk factors. Moreover, often they did not adjust for important confounders such as multiple pregnancies, parity, mode of conception and maternal age. Therefore, prospective studies with larger numbers of cases and appropriate adjustment for confounders are needed to explore the pathophysiology and to prove causality.

Endometriosis intestinal, una causa de suboclusión intestinal poco sospechada. Serie de casos

Endometriosis is defined as the presence of endometrial glands in extrauterine sites. Intestinal deep infiltrating endometriosis is considered when the endometriotic lesion is located more than 5 mm below the peritoneum. Intestinal obstruction due to endometriosis is rare, occurring in approximately 1% of cases. We present three cases with intestinal occlusion as deep infiltrating endometriosis debut. Its symptomatic presentation can mimic multiple gastroenterological entities, mainly irritable bowel syndrome or inflammatory bowel disease so its diagnosis can be difficult. However, suspecting this pathology and diagnosing it is important to define therapeutic behavior and improve the quality of life of our patients.

Predictive Value of Preoperative MRI Using the #ENZIAN Classification Score in Patients with Deep Infiltrating Endometriosis

Purpose The aim of this study is to investigate the correlation between the magnetic resonance imaging (MRI) and intraoperative findings of deep infiltrating endometriosis using the #ENZIAN score. Methods This retrospective study included 64 patients who underwent surgery for deep infiltrating endometriosis between January 2017 and August 2020. Preoperative abdominopelvic MRI assessment was evaluated and scored using the #ENZIAN classification. Operative scores were considered the gold standard, and the sensitivity, specificity, and positive and negative predictive values (PPV and NPV) of MRI for each category were calculated. Results MRI has higher sensitivity and specificity in showing the lesions of the compartments O (ovarian lesions), A (rectovaginal septum and posterior vaginal fornix), and B (uterosacral ligaments and parametrium) (100–100%, 100–100%, and 97–100%, respectively, p<0.001) compared to the other compartments. The lowest sensitivity, specificity, accuracy, and PPV of the MRI was found in compartment P (14%, 76%, 70%, and 7%, respectively). Conclusion We demonstrated that the #ENZIAN classification in MRI reports has significant sensitivity and specificity in compartments A, B (uterosacral ligaments and parametrium), and O. Furthermore, the determination of peritoneal lesions via MRI is inadequate.

Symptoms based endometriosis prediction using machine learning

Endometriosis a painful disorder that stripes the uterus both inside and outside. Endometriosis can be diagnosed by the medical practitioners with the help of traditional scanning procedures. Laparoscopic surgery is the authentic method for identifying the advanced stages of endometriosis. The statistical approach is a state-of-art method for identifying the various stages of endometriosis using laparoscopic images. The paper focuses on a well-known statistical method known as chi-square and correlation coefficients are implemented for identifying the symptoms that are correlated with various stages of endometriosis. Chi-square analysis performs the association between symptoms and stages of endometriosis. With these analysis, an algorithm was proposed known as endometriosis prediction factor algorithm (EPF). The EPF algorithm predicts the presence of endometriosis if the derived value is greater than 1. From the chi-square analysis, it is identified that mild endometriosis is influenced 34% by menstrual flow, minimal endometriosis is influenced 40% by dysmenorrhea, where moderate endometriosis is influenced 31% by tenderness and deep infiltrating endometriosis is influenced 22% by adnexal mass.