scholarly journals OP16.03: Reproducibility of different patterns of benign ovarian masses assessed by transvaginal ultrasonography evaluated by several operators with different degree of experience

2007 ◽  
Vol 30 (4) ◽  
pp. 509-510
Author(s):  
S. Guerriero ◽  
J. L. Alcazar ◽  
M. A. Pascual ◽  
S. Ajossa ◽  
S. Floris ◽  
...  
Keyword(s):  
Transvaginal Ultrasonography ◽  
Ovarian Masses

Efficacy of the echo pattern classification of ovarian tumors 2000 in conjunction with transvaginal ultrasonography for diagnosis of ovarian masses

2015 ◽  
Vol 43 (2) ◽  
pp. 249-255 ◽  
Author(s):  
Atsushi Tajima ◽  
Chikako Suzuki ◽  
Iwaho Kikuchi ◽  
Hanako Kasahara ◽  
Akari Koizumi ◽  
...  
Keyword(s):  
Pattern Classification ◽  
Ovarian Tumors ◽  
Transvaginal Ultrasonography ◽  
Ovarian Masses

scholarly journals A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Valentina Chiappa ◽  
Matteo Interlenghi ◽  
Giorgio Bogani ◽  
Christian Salvatore ◽  
Francesca Bertolina ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Clinical Decision Making ◽  
Transvaginal Ultrasonography ◽  
Ca 125 ◽  
Individual Risk ◽  
Risk Of Malignancy ◽  
Ovarian Masses

Abstract Background To evaluate the performance of a decision support system (DSS) based on radiomics and machine learning in predicting the risk of malignancy of ovarian masses (OMs) from transvaginal ultrasonography (TUS) and serum CA-125. Methods A total of 274 consecutive patients who underwent TUS (by different examiners and with different ultrasound machines) and surgery, with suspicious OMs and known CA-125 serum level were used to train and test a DSS. The DSS was used to predict the risk of malignancy of these masses (very low versus medium-high risk), based on the US appearance (solid, liquid, or mixed) and radiomic features (morphometry and regional texture features) within the masses, on the shadow presence (yes/no), and on the level of serum CA-125. Reproducibility of results among the examiners, and performance accuracy, sensitivity, specificity, and area under the curve were tested in a real-world clinical setting. Results The DSS showed a mean 88% accuracy, 99% sensitivity, and 77% specificity for the 239 patients used for training, cross-validation, and testing, and a mean 91% accuracy, 100% sensitivity, and 80% specificity for the 35 patients used for independent testing. Conclusions This DSS is a promising tool in women diagnosed with OMs at TUS, allowing to predict the individual risk of malignancy, supporting clinical decision making.

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

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

scholarly journals Proptosis and ovarian masses: extramedullary manifestations of acute myeloid leukaemia

2021 ◽  
Vol 14 (4) ◽  
pp. e243045
Author(s):  
Sebastian Alejandro Mikulic ◽  
Michael Chahin ◽  
Ketav Desai ◽  
Hardik Chhatrala
Keyword(s):  
Acute Myeloid Leukaemia ◽  
Myeloid Leukaemia ◽  
Ovarian Masses ◽  
Acute Myeloid

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

2021 ◽  
pp. 016173462199809
Author(s):  
Dhurgham Al-karawi ◽  
Hisham Al-Assam ◽  
Hongbo Du ◽  
Ahmad Sayasneh ◽  
Chiara Landolfo ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Empirical Evaluation ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Simple Majority ◽  
Ultrasound Scan ◽  
Histograms Of Oriented Gradients ◽  
The Individual ◽  
Ovarian Masses

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k ( k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

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