scholarly journals Diagnostic Performance of International Ovarian Tumor Analysis Logistic Regression Model LR2 for Adnexal Masses Classification at a Tertiary Gynecology Center in Singapore

2021 ◽  
Vol 10 (3) ◽  
pp. 67-72
Author(s):  
Pamela Partana ◽  
Sook Ling Lee ◽  
Wei Ching Tan
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Ovarian Tumor ◽  
Diagnostic Performance ◽  
Logistic Regression Model ◽  
Adnexal Masses

Logistic regression model to predict malignancy in adnexal masses experience using Doppler energy

2003 ◽  
Vol 29 (5) ◽  
pp. S25-S26
Author(s):  
H. Marret ◽  
B. Giraudeau ◽  
F. Golfier ◽  
R. Ecochard ◽  
D. Raudrant ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Adnexal Masses

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.

Intravoxel Incoherent Motion for Differentiating Spinal Metastasis: A Pilot Study

2021 ◽  
Author(s):  
Yuan Li ◽  
Xiaoying Xing ◽  
Enlong Zhang ◽  
Siyuan Qin ◽  
Huishu Yuan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Diffusion Coefficient ◽  
Regression Model ◽  
Diagnostic Performance ◽  
Logistic Regression Model ◽  
Spinal Metastasis ◽  
Exponential Model ◽  
Intravoxel Incoherent Motion ◽  
Model Parameters ◽  
Tuberculous Spondylitis

Abstract Background: To investigate the value of an intravoxel incoherent motion (IVIM) MRI for discriminating spinal metastasis from tuberculous spondylitis.Methods: This study included 50 patients with spinal metastasis (32 lung cancer, 7 breast cancer, 11 renal cancer), and 20 tuberculous spondylitis. All patients underwent IVIM MRI at 3.0T before treatment. The IVIM parameters including single-index model ( Apparent diffusion coefficient (ADC)-stand), double exponential model (ADCslow，ADCfast and f) and stretched-exponential model parameters (distributed diffusion coefficient (DDC) and α) were acquired. Two radiologists separately measured these parameters for each lesion through drawing region of interest. Receiver operating characteristic (ROC) and the area under the ROC curve analysis was used to evaluate the diagnostic performance. Each parameter was substituted into the Logistic regression model to determine the meaningful parameters, and the combined diagnostic performance was evaluated.Results: The ADCfast and f showed significant differences between spinal metastasis and tuberculous spondylitis. (for all, p < 0.05). The Logistic regression model results showed that ADCfast and f were independent factors affecting the conclusion (P<0.05). The AUC values of ADCfast and f were 0.823 (95%CI:0.719 to 0.927) and 0.876 (95%CI: 0.782 to 0.969), respectively. ADCfast combined with f showed the highest AUC value of 0.925 (95%CI: 0.858 to 0.992). Additional significant differences were found in ADCstand, ADCslow, DDC and α among different metastasis type.Conclusions: IVIM MR imaging may be helpful for differentiating spinal metastasis from tuberculous spondylitis and may be used to detect the origin tumor for those patients who could not identify primary tumors, and provide help for clinical treatment.

Sign in / Sign up

Export Citation Format

Share Document