TORSION OF SUBMUCOUS MYOMA ON HYSTEROSCOPY

Transvaginal Ultrasonography and Saline Infusion Sonography Compared to Hysteroscopy for Diagnosis of Intracavity Lesion: A 4-Year Experience in Suburban Referral Hospital

Journal of the Medical Association of Thailand ◽

10.35755/jmedassocthai.2020.06.10240 ◽

2020 ◽

Vol 103 (6) ◽

pp. 585-593

Keyword(s):

Predictive Value ◽

Final Diagnosis ◽

Saline Infusion ◽

Transvaginal Ultrasonography ◽

Histopathological Diagnosis ◽

Endometrial Polyps ◽

Saline Infusion Sonography ◽

Submucous Myoma ◽

Kappa Value ◽

Sensitivity Specificity

Objective: To evaluate the accuracy of transvaginal ultrasonography (TVS) and saline infusion sonography (SIS) in use for the diagnosis of endometrial polyps and submucous myoma compared to hysteroscopy. Histopathology was considered as the gold standard for final diagnosis. Materials and Methods: The present retrospective study was conducted at Bhumibol Adulyadej Hospital, Bangkok, Thailand between January 2014 and December 2017. Medical records of 150 patients who attended for hysteroscopy and histopathological diagnosis were reviewed. The accuracy of TVS and SIS for the diagnosis of endometrial polyps and submucous myoma were determined. Results: Out of 150 enrolled cases, endometrial polyp was the most frequent hysteroscopic finding in participants of the present study (92/150). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of TVS, SIS, and hysteroscopy compared to pathologic reports for detection endometrial polyps were 71.7% versus 93.5% versus 97.8%, 38.5% versus 52.2% versus 68.2%, 80.5% versus 88.7% versus 92.8%, 27.8% versus 66.7% versus 88.2%, and 64.4% versus 85.2% versus 92.1%, respectively. The sensitivity, specificity, PPV, NPV, and accuracy of TVS, SIS, and hysteroscopy for detection of submucous myoma were 81.6% versus 92.1% versus 94.7%, 66.7% versus 86.9% versus 100%, 77.5% versus 92.1% versus 100%, 72.0% versus 86.9% versus 90.9%, and 75.4% versus 90.2% versus 96.6%, respectively. The kappa value from TVS, SIS, and hysteroscopy when the histopathologic reports were overall intrauterine abnormalities, endometrial polyps and submucous myoma were 0.45/0.43/0.72, 0.77/0.76/0.89, and 0.92/0.92/1.00, respectively. Conclusion: Sensitivity, specificity, PPV, NPV, accuracy, and kappa value of SIS for detecting endometrial polyps and submucous myoma were better than TVS. Keywords: Ultrasonography, Saline infusion sonography, Hysteroscopy, Accuracy

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Deep learning model for classifying endometrial lesions

Journal of Translational Medicine ◽

10.1186/s12967-020-02660-x ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

YunZheng Zhang ◽

ZiHao Wang ◽

Jin Zhang ◽

CuiCui Wang ◽

YuShan Wang ◽

...

Keyword(s):

Endometrial Cancer ◽

Endometrial Hyperplasia ◽

Histogram Equalization ◽

Atypical Hyperplasia ◽

Endometrial Polyps ◽

Submucous Myoma ◽

Objective Model ◽

Medical University ◽

Deep Learning Model ◽

Better Than

Abstract Background Hysteroscopy is a commonly used technique for diagnosing endometrial lesions. It is essential to develop an objective model to aid clinicians in lesion diagnosis, as each type of lesion has a distinct treatment, and judgments of hysteroscopists are relatively subjective. This study constructs a convolutional neural network model that can automatically classify endometrial lesions using hysteroscopic images as input. Methods All histopathologically confirmed endometrial lesion images were obtained from the Shengjing Hospital of China Medical University, including endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyps, and submucous myomas. The study included 1851 images from 454 patients. After the images were preprocessed (histogram equalization, addition of noise, rotations, and flips), a training set of 6478 images was input into a tuned VGGNet-16 model; 250 images were used as the test set to evaluate the model’s performance. Thereafter, we compared the model’s results with the diagnosis of gynecologists. Results The overall accuracy of the VGGNet-16 model in classifying endometrial lesions is 80.8%. Its sensitivity to endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyp, and submucous myoma is 84.0%, 68.0%, 78.0%, 94.0%, and 80.0%, respectively; for these diagnoses, the model’s specificity is 92.5%, 95.5%, 96.5%, 95.0%, and 96.5%, respectively. When classifying lesions as benign or as premalignant/malignant, the VGGNet-16 model’s accuracy, sensitivity, and specificity are 90.8%, 83.0%, and 96.0%, respectively. The diagnostic performance of the VGGNet-16 model is slightly better than that of the three gynecologists in both classification tasks. With the aid of the model, the overall accuracy of the diagnosis of endometrial lesions by gynecologists can be improved. Conclusions The VGGNet-16 model performs well in classifying endometrial lesions from hysteroscopic images and can provide objective diagnostic evidence for hysteroscopists.

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Towards uterine inversion: Illustration of a gradual process through three cases of submucous myoma

Nepal Journal of Obstetrics and Gynaecology ◽

10.3126/njog.v1i2.2398 ◽

1970 ◽

Vol 1 (2) ◽

pp. 51-54

Author(s):

Kesang D Bista ◽

Ashma Rana ◽

Geeta Gurung ◽

Neelam Pradhan ◽

Archana Amatya

Keyword(s):

Total Abdominal Hysterectomy ◽

Abdominal Hysterectomy ◽

Menopausal Woman ◽

Post Menopausal Woman ◽

Submucous Myoma ◽

Uterine Inversion ◽

Gradual Process ◽

Post Menopausal ◽

Uterine Fundus ◽

Vaginal Myomectomy

How the largest of the large (> 10 x 8 cms) sub mucous myoma arising from the fundus uteri successively promotes the occurrence of non puerperal uterine inversion over the years as depicted through 3 different illustrations imitating a gradual process; first by forming an indentation in the uterine fundus then progressively causing more dimpling in the verge of uterine inversion until finally giving rise to a full blown picture of complete uterine inversion where the uterine fundus is driven beyond the level of introitus with the consequences of prolapsed incarcerated myoma in a post menopausal woman. A total abdominal hysterectomy and bilateral salpingoophorectomy were performed on all of these 3 women 2 perimenopausal and a postmenopausal; the latter was first facilitated by vaginal myomectomy further supplemented by division of the inversion ring posteriorly as described by Haultain. Key words: Non puerperal uterine inversion, submucous fundal myoma, vaginal myomectomy. doi:10.3126/njog.v1i2.2398 N. J. Obstet. Gynaecol Vol. 1, No. 2, p. 51-54 Nov-Dec 2006

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Submucous Myoma: Laparoscopic Myomectomy

10.5005/jp-journals-10058-0029 ◽

2018 ◽

Vol 2 (1) ◽

pp. 35-37

Author(s):

Vandana Jain ◽

Nutan Jain ◽

Parima Jain

Keyword(s):

Laparoscopic Myomectomy ◽

Submucous Myoma

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Febrile morbidity post laparoscopic myomectomy for symptomatic large submucous myoma in an adolescent girl

International Journal of Reproduction Contraception Obstetrics and Gynecology ◽

10.18203/2320-1770.ijrcog20213871 ◽

2021 ◽

Vol 10 (10) ◽

pp. 3970

Author(s):

Prabha Agrawal ◽

Rahul Agrawal ◽

Sri Varshini Muthineni

Keyword(s):

Adolescent Girl ◽

Urine Culture ◽

Abnormal Uterine Bleeding ◽

Uterine Bleeding ◽

Laparoscopic Myomectomy ◽

Postoperative Fever ◽

Submucous Myoma ◽

Inflammatory Changes ◽

Antibiotic Coverage ◽

Febrile Morbidity

The occurrence of post-operative fever after myomectomy without an apparent infectious cause has been reported in various studies. We here in report a case of an 18-year girl with abnormal uterine bleeding and severe anemia resulting from a large 5 cm submucosal fibroid. She underwent laparoscopic myomectomy. Surgery was uneventful. Post-operative she had fever of 100.4°F which lasted for 48 hours. Antibiotic coverage was continued for 48 hours. Her urine culture and hemogram were negative for infection. Early postoperative fever is most commonly caused by inflammatory changes rather than infectious causes. Large submucous fibroids can be managed by laparoscopy with excellent clinical outcomes and minimal morbidity.

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