Differentiation between endometrial carcinoma and atypical endometrial hyperplasia with transvaginal sonographic elastography

Metin, Muzaffer; Aydın, Hasan; Ünal, Özlem; Akçay, Yasemin; Duymuş, Mahmut; Türkyılmaz, Esengül; Avcu, Serhat

doi:10.1016/j.diii.2015.11.007

Cited by 23 publications

(23 citation statements)

References 42 publications

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“…The etiology of EC has not been clear up to now. Metin et al [ 11 ] employed the radiomics to establish a preoperative predictive model of EC. The radiomics preoperative prediction research also had been reported in the early cervical cancer [ 12 ] and bladder cancer [ 13 ]; the radiomics models of these research were the images based on different sequences of CT or MRI.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Intelligent Recognition and Prediction of Endometrial Cancer

Zhang

Gong

Zheng

et al. 2021

Journal of Healthcare Engineering

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The aim of the study was to investigate the intelligent recognition of radiomics based on the convolutional neural network (CNN) in predicting endometrial cancer (EC). In this study, 158 patients with EC in hospital were selected as the research objects and divided into a training group and a test group. All the patients underwent magnetic resonance imaging (MRI) before surgery. Based on the CNN, the imaging model of EC prediction was constructed according to the characteristics. Besides, the comprehensive prediction model was established through the clinical information and imaging parameters. The results showed that the area under the working characteristic curve (AUC) of the radiomics model and comprehensive prediction model was 0.897 and 0.913 in the training group, respectively. In addition, the AUC of the radiomics model was 0.889 in the test group and that of the comprehensive prediction model was 0.897. The comprehensive prediction model was established through specific imaging parameters and clinical pathological information, and its prediction performance was good, indicating that radiomics parameters could be applied as noninvasive markers to predict EC.

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Section: Discussionmentioning

confidence: 99%

Deep Learning for Intelligent Recognition and Prediction of Endometrial Cancer

Zhang

Gong

Zheng

et al. 2021

Journal of Healthcare Engineering

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“…For any quantitative parameter, high accuracy and reproducibility are essential. Due to its cost-effectiveness and lack of radiation, ultrasound-based elastography is the most widely used and validated imaging method to measure tumor stiffness in women with EC [ 35 , 36 ]. However, ultrasound-based elastography has several limitations, such as operator dependence, interobserver variation, and measurement difficulties in patients with severe obesity or ascites.…”

Section: Discussionmentioning

confidence: 99%

Tumor stiffness measured by 3D magnetic resonance elastography can help predict the aggressiveness of endometrial carcinoma: preliminary findings

Zhang

Liu

Nijiati³

et al. 2021

Cancer Imaging

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Background Preoperative evaluation of aggressiveness, including tumor histological subtype, grade of differentiation, Federation International of Gynecology and Obstetrics (FIGO) stage, and depth of myometrial invasion, is significant for treatment planning and prognosis in endometrial carcinoma (EC). The purpose of this study was to evaluate whether three-dimensional (3D) magnetic resonance elastography (MRE) can help predict the aggressiveness of EC. Methods From August 2015 to January 2019, 82 consecutive patients with suspected uterine tumors underwent pelvic MRI and MRE scans, and 15 patients with confirmed EC after surgical resection were enrolled. According to pathological results (tumor grade, histological subtype, FIGO stage, and myometrial invasiveness), the patients were divided into two subgroups. The independent-samples t-test or Mann-Whitney U test was used to compare the stiffness between different groups. The diagnostic performance was determined with receiver operating characteristic (ROC) curve analysis. Results The stiffness of EC with ≥ 50 % (n = 6) myometrial invasion was significantly higher than that with < 50 % (n = 9) myometrial invasion (3.68 ± 0.59 kPa vs. 2.61 ± 0.72 kPa, p = 0.009). Using a stiffness of 3.04 kPa as a cutoff value resulted in 100 % sensitivity and 77.8 % specificity for differentiating ≥ 50 % myometrial invasion from < 50 % myometrial invasion of EC. The stiffness of poorly differentiated EC (n = 8) was significantly higher than that of well/moderately differentiated EC (n = 7) (3.47 ± 0.64 kPa vs. 2.55 ± 0.82 kPa, p = 0.028). Using a stiffness of 3.04 kPa as a cutoff value resulted in 75 % sensitivity and 71.4 % specificity for differentiating poorly differentiated from well/moderately differentiated EC. The stiffness of FIGO stage II/III EC was significantly higher than that of FIGO stage I EC (3.69 ± 0.65 kPa vs. 2.72 ± 0.76 kPa, p = 0.030). Using a stiffness of 3.04 kPa as a cutoff value resulted in 100 % sensitivity and 70 % specificity for differentiating FIGO stage I EC from FIGO stage II/III EC. The tumor stiffness value in type II (n = 3) EC was higher than that in type I (n = 12) EC (3.67 ± 0.59 kPa vs. 2.88 ± 0.85 kPa), but the difference was not significant (p = 0.136). Conclusions Tumor stiffness measured by 3D MRE may be potentially useful for predicting tumor grade, FIGO stage and myometrial invasion of EC and can aid in the preoperative risk stratification of EC.

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“…Gerinim elastografisinde ise kullanıcı tarafından manüel olarak kompresyonlar ve dekompresyonlar uygulanır ve elde edilen değişken gerinim oranı ya da gerinim indeksidir. Gerinim indeksi lezyonun ve komşu normal dokunun oranıdır (8)(9)(10).…”

Section: Discussionunclassified

Real Ti̇me Elastografi̇ni̇n Etki̇nli̇ği̇: Korti̇komedüller Strai̇n Orani Üri̇ner Obstrüksi̇yonun Tani Ve Taki̇bi̇nde Kullanilabi̇li̇r Mi̇?

Çoban¹,

Kurtoğlu²,

Türkoğlu³

et al. 2020

Yeni Üroloji Dergisi

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Üriner obstrüksiyonun tanı ve takibinde kortikomedüller strain oranının (SR) kullanılabilirliğini araştırmak. Gereç ve Yöntemler: Hastanemiz üroloji kliniğine renal kolikle başvuran hastalardan taşa bağlı unilateral üriner obstrüksiyon tanısı BT ile konulmuş 18-60 yaş arası 40 gönüllü (26 erkek, 14 kadın) hasta çalışmaya alındı. Hastalar iki gruba ayrıldı. Taşa sekonder hidronefrotik böbrek grup 1 iken yine aynı hastaların kontralateral sağlam böbrekleri kontrol grubu (grup 2) olarak dahil edildi. Hastaların yaş, cinsiyet, ne kadar zamandır renal koliği olduğu, tansiyon, body mass indexi (BMI) kaydedildi. Hastalara, abdominal radyolojide 8 yıl deneyimli, tek radyoloji uzmanı tarafından renal ultrasonografi, doppler ultrasonografi ve Real Time Elastografi (RTE) incelemesi yapıldı. Bu üç ölçümün ortalaması istatistiksel analizde kullanıldı. Dekompresyon fazında böbreğin cilde en yakın kesiminden B modda belirlenen medulla ve korteksde ilgilenilen bölge işaretlenerek gerinim oranı (SR) ölçüldü. Bulgular: Hem aynı hastanın taşa sekonder hidronefrotik böbreğine (grup 1), hem de kontralateral sağlam böbreğe (grup 2) bakılması nedeniyle gruplar arasında yaş farkı yoktu. Renal pelvis çapı ile obstrükte böbrekteki resistive index (RI) arasında pozitif korelasyon bulduk (p=0.006 ve r=0.446). Hidronefrotik böbreklerin ortalama kortikomedüller SR değerleri (0,225(0,01-2,40)) ile kontralateral normal böbreklerin ortalama SR değer

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Differentiation between endometrial carcinoma and atypical endometrial hyperplasia with transvaginal sonographic elastography

Abstract: Our results indicate that TSE can provide important information that help discriminate between endometrial carcinoma and endometrial hyperplasia.

Cited by 23 publications

References 42 publications

Deep Learning for Intelligent Recognition and Prediction of Endometrial Cancer

Deep Learning for Intelligent Recognition and Prediction of Endometrial Cancer

Tumor stiffness measured by 3D magnetic resonance elastography can help predict the aggressiveness of endometrial carcinoma: preliminary findings

Real Ti̇me Elastografi̇ni̇n Etki̇nli̇ği̇: Korti̇komedüller Strai̇n Orani Üri̇ner Obstrüksi̇yonun Tani Ve Taki̇bi̇nde Kullanilabi̇li̇r Mi̇?

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