Factors Influencing on Pain in Patients Undergoing Pipelle Endometrial Biopsy for Abnormal Uterine Bleeding: Why a Personalized Approach Should Be Applied?

Terzić, Milan; Aimagambetova, Gulzhanat; Ukybassova, Talshyn; Bapayeva, Gauri; Kaiyrlykyzy, Aiym; Foster, Faye; Linkov, Faina

doi:10.3390/jpm12030431

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…With Pipelle sampling, pain score is operator dependent: less pain with the more experienced operator. 16 The masked clinicians who performed the procedure in this study were credentialled residents.…”

Section: Discussionmentioning

confidence: 99%

Pipelle Endometrial Sampling With a Full Bladder Compared With Standard Care

Hashim,

Hong,

Woo

et al. 2024

Obstetrics &Amp; Gynecology

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OBJECTIVE: To compare insertion failure rates for Pipelle endometrial sampling with a full bladder compared with the standard process (not taking into account bladder status) without cervical manipulation. METHODS: A single-masked randomized trial was conducted in a single tertiary care center from July 2021 to January 2022. Two hundred fourteen participants aged 18 years or older who were scheduled for outpatient Pipelle endometrial sampling were randomized: 107 each to having a full bladder (by oral water intake) or standard process (without delayed sampling and bladder status not taken into account). Women with known cervical stenosis, gynecologic malignancy, uterine anomalies, leiomyoma distorting the uterine cavity, acute cervicitis, urge bladder dysfunction, intense anxiety, need for anesthesia or analgesic before the procedure, positive pregnancy test, or previous failed office endometrial sampling were excluded. The primary outcome was the insertion failure rate of endometrial sampling at first attempt. Secondary outcomes were pain during procedure, satisfaction score, analgesia use, procedure duration, and need for cervical manipulation. Factoring in a baseline insertion failure rate of 30.0%, relative risk of 0.45, α of 0.05, 80.0% power, and a dropout rate of 10.0%, we needed 107 participants in each arm. RESULTS: The insertion failure rate was significantly lower in the full bladder group compared with standard process: 25 of 107 (23.4%) compared with 45 of 107 (42.1%) (relative risk 0.56, 95% CI, 0.37–0.84; number needed to treat to benefit 6.0, 95% CI, 3.20–15.70). Pain score (median) during the procedure (interquartile range) was 4 (3–6) compared with 5 (3–8) (P=.004); patient satisfaction score was 8 (7–9) compared with 7 (4–8) (P<.001); and mean±SD procedure duration was 3.0±2.4 compared with 4.7±2.9 minutes (P<.001) for the full bladder and standard process arm, respectively. Other secondary outcomes of cervical laceration, analgesia use, and adequacy of endometrial tissue for histopathologic assessment were not significantly different between groups. CONCLUSION: Pipelle endometrial sampling with a full bladder reduces the initial insertion failure rate, procedure-related pain, and duration of sampling and increases patient satisfaction compared with the standard process. CLINICAL TRIAL REGISTRATION: ISRCTN, ISRCTN33938192.

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Section: Discussionmentioning

confidence: 99%

Pipelle Endometrial Sampling With a Full Bladder Compared With Standard Care

Hashim,

Hong,

Woo

et al. 2024

Obstetrics &Amp; Gynecology

View full text Add to dashboard Cite

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Artificial intelligence model for enhancing the accuracy of transvaginal ultrasound in detecting endometrial cancer and endometrial atypical hyperplasia

Capasso,

Cucinella,

Wright

et al. 2024

Int J Gynecol Cancer

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ObjectivesTransvaginal ultrasound is typically the initial diagnostic approach in patients with postmenopausal bleeding for detecting endometrial atypical hyperplasia/cancer. Although transvaginal ultrasound demonstrates notable sensitivity, its specificity remains limited. The objective of this study was to enhance the diagnostic accuracy of transvaginal ultrasound through the integration of artificial intelligence. By using transvaginal ultrasound images, we aimed to develop an artificial intelligence based automated segmentation model and an artificial intelligence based classifier model.MethodsPatients with postmenopausal bleeding undergoing transvaginal ultrasound and endometrial sampling at Mayo Clinic between 2016 and 2021 were retrospectively included. Manual segmentation of images was performed by four physicians (readers). Patients were classified into cohort A (atypical hyperplasia/cancer) and cohort B (benign) based on the pathologic report of endometrial sampling. A fully automated segmentation model was developed, and the performance of the model in correctly identifying the endometrium was compared with physician made segmentation using similarity metrics. To develop the classifier model, radiomic features were calculated from the manually segmented regions-of-interest. These features were used to train a wide range of machine learning based classifiers. The top performing machine learning classifier was evaluated using a threefold approach, and diagnostic accuracy was assessed through the F1 score and area under the receiver operating characteristic curve (AUC-ROC).Results302 patients were included. Automated segmentation–reader agreement was 0.79±0.21 using the Dice coefficient. For the classification task, 92 radiomic features related to pixel texture/shape/intensity were found to be significantly different between cohort A and B. The threefold evaluation of the top performing classifier model showed an AUC-ROC of 0.90 (range 0.88–0.92) on the validation set and 0.88 (range 0.86–0.91) on the hold-out test set. Sensitivity and specificity were 0.87 (range 0.77–0.94) and 0.86 (range 0.81–0.94), respectively.ConclusionsWe trained an artificial intelligence based algorithm to differentiate endometrial atypical hyperplasia/cancer from benign conditions on transvaginal ultrasound images in a population of patients with postmenopausal bleeding.

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Factors Influencing on Pain in Patients Undergoing Pipelle Endometrial Biopsy for Abnormal Uterine Bleeding: Why a Personalized Approach Should Be Applied?

Cited by 2 publications

References 26 publications

Pipelle Endometrial Sampling With a Full Bladder Compared With Standard Care

Pipelle Endometrial Sampling With a Full Bladder Compared With Standard Care

Artificial intelligence model for enhancing the accuracy of transvaginal ultrasound in detecting endometrial cancer and endometrial atypical hyperplasia

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