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1.
Ecancermedicalscience ; 17: 1591, 2023.
Article in English | MEDLINE | ID: mdl-37799950

ABSTRACT

Background: Malignant transformation in endometriosis was first described by Sampson in 1925. There is now sufficient evidence of its association specifically with endometrioid (EOC) and clear cell ovarian cancer (CCOC). Whether endometriosis-associated ovarian cancer (EAOC) is a distinct clinicopathological entity from non-endometriosis-associated ovarian cancer (NEAOC) remains uncertain. Objectives: This study aimed to assess the impact of endometriosis on clinical characteristics and survival outcomes in EOC and CCOC. Methods: This is a retrospective single-institution analysis of patients diagnosed with CCOC AND EOC between 2010 and 2021. Demographic and clinical presentation data were obtained from medical records. Patients were followed up till March 2023. Statistical analysis was done using the IBM SPSS Statistics 20 Windows. Results: Of the 77 cases of CCOC and EOC ovary, 38 had histopathologically proven endometriosis. There was no difference in age (51.62 and 50.05 years, respectively), body mass index, parity, menopausal status and CA 125 levels at presentation. Ascites was more frequent in the absence of endometriosis (30% versus 8.1%, p = 0.015). However, this did not translate to a statistical difference in the stage, with the majority presenting in the early stage. (94% versus 83%). All 78 patients underwent primary cytoreduction with equal rates of optimal resection.There was no difference in the mean disease-free interval between EAOC and NEAOC (107.6 and 109.4 months, p 0.484). Recurrences were predominantly pelvic in both groups. The disease-specific survival was 111.7 and 120.1 months, respectively, with and without endometriosis. This was however not statistically significant (p 0.751). Conclusion: In the Indian population, endometriosis did not have any impact on the age at presentation, CA 125 levels, stage of the disease and survival outcomes in EOC and CCOC ovary.

3.
J Robot Surg ; 15(2): 215-219, 2021 Apr.
Article in English | MEDLINE | ID: mdl-32452011

ABSTRACT

Robotic-assisted surgery has a shorter learning curve enabling the surgeons to do complex surgeries in a minimally invasive way. This study analyzed how the time taken for robotic-assisted procedures in gynecology and gynecologic oncology has changed over the years in a university teaching institution. Details were taken from a prospectively maintained electronic database after obtaining permission from the hospital ethics committee. All patients who underwent robotic surgery for gynecologic problems at this center from February 2015 till December 2019 were included. The clinical, perioperative, postoperative and pathologic details were collected from the prospectively maintained database. To analyze quantitative data, student t test was used. Chi-square test was performed to compare categorical variables. 655 patients underwent robotic-assisted surgery during this period. The majority of the patients underwent surgery for uterine cancer (49%). There was a significant improvement in total surgical time (250 vs. 165 min), docking time (12.6 vs 8.9 min), and console time (130 vs. 95 min) between the first and second year (2015-16). The next 2 years (2017 and 18) did not show a significant decrease in the total surgery time and console time, but docking times improved in 2017 (5.5 vs 8.5 min) compared to 2016. In 2019, there was a significant improvement in all surgical times compared to previous years. This study shows that robotic surgery has a lot of scope for improvement in surgical performance beyond its first and second years. The surgical performance as seen from the improved surgical times keeps on improving even after many years.


Subject(s)
Genital Neoplasms, Female/surgery , Gynecologic Surgical Procedures/statistics & numerical data , Gynecologic Surgical Procedures/trends , Operative Time , Robotic Surgical Procedures/statistics & numerical data , Robotic Surgical Procedures/trends , Chi-Square Distribution , Data Interpretation, Statistical , Databases, Factual , Female , Gynecologic Surgical Procedures/education , Gynecologic Surgical Procedures/methods , Humans , Learning Curve , Quality Improvement , Robotic Surgical Procedures/education , Robotic Surgical Procedures/methods , Time Factors , Uterine Neoplasms/surgery
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