Quality of Life after Hysterectomy and Uterus-Sparing Hysteroscopic Management of Abnormal Uterine Bleeding or Heavy Menstrual Bleeding

Hysteroscopy as a Therapeutic Tool: A Vision to Spare the Uterus in Premenopausal Abnormal Uterine Bleeding (AUB)/Heavy Menstrual Bleeding (HMB), an Update

Heavy menstrual bleeding (HMB) or abnormal uterine bleeding (AUB) is a common cause of gynecological complaints in perimenopausal women. The treatment chosen by most of the women having HMB/AUB in the perimenopausal age group when medical therapy fails is hysterectomy, which unfortunately has significant issues related to safety and long-term quality of life apart from being a burden on the health care system and cost to patients. Minimal access surgical techniques like hysteroscopic targeted therapies are available which are conservative and spare the uterus and major surgery and its complications and sequelae. Hysteroscopic management of HMB is a minimally invasive and targeted approach in diagnosing and treating the pathological lesions causing the symptoms and not radical like hysterectomy which has more chances of occurrence of adverse events both intra and postoperatively. In terms of health-related quality of life, women suffering from HMB who underwent a hysteroscopic conservative approach had better scores when compared to those with hysterectomy. The present review aims to review the evidence generated to compare the two surgical modalities, hysteroscopic targeted therapy and hysterectomy, in terms of their effectiveness, safety, and effect on the quality of life of these perimenopausal women having HMB/AUB.

Endometrial ablation and resection versus hysterectomy for heavy menstrual bleeding: an updated systematic review and meta-analysis of effectiveness and complications

To evaluate the clinical efficacy, safety, and cost-effectiveness of endometrial ablation or resection (E:A/R) compared to hysterectomy for the treatment of heavy menstrual bleeding. Literature search was conducted, and randomized control trials (RCTs) comparing (E:A/R) versus hysterectomy were reviewed. The search was last updated in November 2022. Twelve RCTs with 2,028 women (hysterectomy: n=977 vs. [E:A/R]: n=1,051) were included in the analyzis. The meta-analysis revealed that the hysterectomy group showed improved patient-reported and objective bleeding symptoms more than those of the (E:A/R) group, with risk ratios of (mean difference [MD], 0.75; 95% confidence intervals [CI], 0.71 to 0.79) and (MD, 44.00; 95% CI, 36.09 to 51.91), respectively. Patient satisfaction was higher post-hysterectomy than (E:A/R) at 2 years of follow-up, but this effect was absent with long-term follow-up. (E:A/R) is considered an alternative to hysterectomy as a surgical management for heavy menstrual bleeding. Although both procedures are highly effective, safe, and improve the quality of life, hysterectomy is significantly superior at improving bleeding symptoms and patient satisfaction for up to 2 years. However, it is associated with longer operating and recovery times and a higher rate of postoperative complications. The initial cost of (E:A/R) is less than the cost of hysterectomy, but further surgical requirements are common; therefore, there is no difference in the cost for long-term follow-up.

Quality of Life after Risk-Reducing Hysterectomy for Endometrial Cancer Prevention: A Systematic Review

BACKGROUND

Risk-reducing hysterectomy (RRH) is the gold-standard prevention for endometrial cancer (EC). Knowledge of the impact on quality-of-life (QoL) is crucial for decision-making. This systematic review aims to summarise the evidence.

METHODS

We searched major databases until July 2022 (CRD42022347631). Given the paucity of data on RRH, we also included hysterectomy as treatment for benign disease. We used validated quality-assessment tools, and performed qualitative synthesis of QoL outcomes.

RESULTS

Four studies (64 patients) reported on RRH, 25 studies (1268 patients) on hysterectomy as treatment for uterine bleeding. There was moderate risk-of-bias in many studies. Following RRH, three qualitative studies found substantially lowered cancer-worry, with no decision-regret. Oophorectomy (for ovarian cancer prevention) severely impaired menopause-specific QoL and sexual-function, particularly without hormone-replacement. Quantitative studies supported these results, finding low distress and generally high satisfaction. Hysterectomy as treatment of bleeding improved QoL, resulted in high satisfaction, and no change or improvements in sexual and urinary function, although small numbers reported worsening.

CONCLUSIONS

There is very limited evidence on QoL after RRH. Whilst there are benefits, most adverse consequences arise from oophorectomy. Benign hysterectomy allows for some limited comparison; however, more research is needed for outcomes in the population of women at increased EC-risk.

A Novel Deep Learning-Based Mitosis Recognition Approach and Dataset for Uterine Leiomyosarcoma Histopathology

Uterine leiomyosarcoma (ULMS) is the most common sarcoma of the uterus, with both a high malignant potential and poor prognosis. Its diagnosis is sometimes challenging owing to its resemblance to leiomyosarcoma, often being accompanied by benign smooth muscle neoplasms of the uterus. Pathologists diagnose and grade leiomyosarcoma based on three biomarkers (i.e., mitosis count, necrosis, and nuclear atypia). Among these biomarkers, mitosis count is the most important and challenging biomarker. In general, pathologists use the traditional manual counting method for the detection and counting of mitosis. This procedure is very time-consuming, tedious, and subjective. To overcome these challenges, artificial intelligence (AI) based methods have been developed that automatically detect mitosis. In this paper, we propose a new ULMS dataset and an AI-based approach for mitosis detection. We collected our dataset from a local medical facility in collaboration with highly trained pathologists. Preprocessing and annotations are performed using standard procedures, and a deep learning-based method is applied to provide baseline accuracies. The experimental results showed 0.7462 precision, 0.8981 recall, and 0.8151 F1-score. For research and development, the code and dataset have been made publicly available.