scholarly journals Comparison of IOTA three-step strategy and logistic regression model LR2 for discriminating between benign and malignant adnexal masses

2020 ◽  
Author(s):  
Juan José Hidalgo ◽  
Antoni Llueca ◽  
Irene Zolfaroli ◽  
Nadia Veiga ◽  
Ester Ortiz ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Transvaginal Ultrasound ◽  
Ovarian Tumour ◽  
Diagnostic Strategy ◽  
Diagnostic Systems ◽  
Likelihood Ratios ◽  
Adnexal Masses

Aims: To compare the diagnostic performance of two ultrasound-based diagnostic systems for the classification of benign or malignant adnexal masses, the three-step strategy and the predictive logistic regression model LR2, both proposed by the International Ovarian Tumour Analysis (IOTA) Group. Material and methods: Prospective observational study at a single centre that included patients diagnosed with a persistent adnexal mass by transvaginal ultrasound over a period of two years. They were evaluated by a non-expert sonographer by applying the three-step diagnostic strategy and the LR2 predictive model to classify the masses as benign or malignant. Patients were treated surgically or followed up for at least one year, taking as the standard reference for benignity or malignancy the histological diagnosis of the lesion or ultrasound changes suggestive of malignancy during the follow-up period. Sensitivity, specificity, positive and negative likelihood ratios and overall accuracy of both systems was calculated and compared. Results: One hundred patients were included, with a mean age of 50.6 years (range 18-87). Surgery was performed on 62 (62%) patients and 38 (38%) were managed expectantly. Eighty-three (83%) lesions were benign and 17 (17%) were malignant. The IOTA three-step strategy presented sensitivity of 94.1% (95%CI, 86.7-98.3%) and specificity 97.6% (95%CI, 94.8-99%). The LR2 logistic regression model showed sensitivity 94.1% (95%CI, 73-98.9%) and specificity 81.9% (95%CI 72.3-88.7%). Comparison of the two systems showed a statistically significant dif-ference in specificity in favour of the three-step strategy. Conclusions: The IOTA three-step strategy, in addition to being sim-ple to use in clinical practice, has a high diagnostic accuracy for the classification of benignity and malignancy of the adnexal masses, overtaking that of other predictive models such as the LR2 logistic regression model.

scholarly journals Selected Robust Logistic Regression Specification for Classification of Multi‑dimensional Functional Data in Presence of Outlier

2018 ◽  
Vol 2 (334) ◽  
Author(s):  
Mirosław Krzyśko ◽  
Łukasz Smaga
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Functional Data ◽  
Logistic Regression Model ◽  
Binary Classification ◽  
Classification Problem ◽  
Classification Rule ◽  
Unknown Parameters ◽  
Explanatory Variables

In this paper, the binary classification problem of multi‑dimensional functional data is considered. To solve this problem a regression technique based on functional logistic regression model is used. This model is re‑expressed as a particular logistic regression model by using the basis expansions of functional coefficients and explanatory variables. Based on re‑expressed model, a classification rule is proposed. To handle with outlying observations, robust methods of estimation of unknown parameters are also considered. Numerical experiments suggest that the proposed methods may behave satisfactory in practice.

Classification of array CGH data using smoothed logistic regression model

2009 ◽  
Vol 28 (30) ◽  
pp. 3798-3810 ◽  
Author(s):  
Jian Huang ◽  
Agus Salim ◽  
Kaibin Lei ◽  
Kathleen O'Sullivan ◽  
Yudi Pawitan
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Array Cgh

Moving Shadow Detection Method Using Logistic Regression

2014 ◽  
Vol 543-547 ◽  
pp. 2724-2727
Author(s):  
Liu Yang ◽  
Jiang Yan Dai ◽  
Miao Qi ◽  
Qing Ji Guan
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Moving Objects ◽  
Detection Method ◽  
Experimental Results ◽  
Shadow Detection ◽  
Video Frames ◽  
Moving Shadow Detection

We present a novel moving shadow detection method using logistic regression in this paper. First, several types of features are extracted from pixels in foreground images. Second, the logistic regression model is constructed by random pixels selected from video frames. Finally, for a new frame in one video, we take advantage of the constructed regression model to implement the classification of moving shadows and objects. To verify the performance of the proposed method, we test it on several different surveillance scenes and compare it with some well-known methods. Extensive experimental results indicate that the proposed method not only can separate moving shadows from moving objects accurately, but also is superior to several existing methods.

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