scholarly journals NIMG-14. MACHINE LEARNING-BASED EVALUATION OF STATIC AND DYNAMIC FET-PET FOR THE DETECTION OF PSEUDOPROGRESSION IN PATIENTS WITH IDH-WILDTYPE GLIOBLASTOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii149-ii150
Author(s):  
Sied Kebir ◽  
Teresa Schmidt ◽  
Matthias Weber ◽  
Lazaros Lazaridis ◽  
Norbert Galldiks ◽  
...  
Keyword(s):  
Machine Learning ◽  
Roc Curve ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Response Assessment ◽  
Linear Discriminant ◽  
Fet Pet ◽  
Contrast Enhanced ◽  
The Mean ◽  
Dynamic Fet Pet

Abstract BACKGROUND Pseudoprogression (PSP) detection in glioblastoma has important clinical implications and remains a challenging task. With the significant advances provided by machine learning (ML) in health care, we investigated the potential of ML in improving the performance of PET using O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) for differentiation of tumor progression from PSP in IDH-wildtype glioblastoma. METHODS We retrospectively evaluated the PET data of patients with newly diagnosed IDH-wildtype glioblastoma following chemoradiation. All patients presented imaging findings suspected of PSP/TP on contrast-enhanced MRI. For further diagnostic evaluation, patients underwent subsequently an additional dynamic FET-PET scan. The modified Response Assessment in Neuro-Oncology (RANO) criteria served to diagnose PSP. To develop a robust ML model, we trained a Linear Discriminant Analysis (LDA)-based classifier using FET-PET derived features on a training cohort and validated the results on a separate test cohort. The results of the ML model were compared with a conventional FET-PET analysis using the receiver-operating-characteristic (ROC) curve. RESULTS Of the 44 patients included in this study, 14 patients were diagnosed with PSP. The mean (TBRmean) and maximum tumor-to-brain ratios (TBRmax) were significantly higher in the TP group as compared to the PSP group (p=0.010 and p=0.047, respectively). The area under the ROC curve (AUC) for TBRmax and TBRmean was 0.68 and 0.74, respectively. Using the LDA-based algorithm, the AUC (0.93) was significantly higher than the AUC for TBRmax. CONCLUSIONS This study shows that in IDH-wildtype glioblastoma, ML-based PSP detection leads to better diagnostic performance compared to conventional ROC analysis.

scholarly journals A Preliminary Study on Machine Learning-Based Evaluation of Static and Dynamic FET-PET for the Detection of Pseudoprogression in Patients with IDH-Wildtype Glioblastoma

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3080
Author(s):  
Sied Kebir ◽  
Teresa Schmidt ◽  
Matthias Weber ◽  
Lazaros Lazaridis ◽  
Norbert Galldiks ◽  
...  
Keyword(s):  
Machine Learning ◽  
Roc Curve ◽  
Response Assessment ◽  
Linear Discriminant ◽  
Fet Pet ◽  
Contrast Enhanced ◽  
The Mean ◽  
Preliminary Study ◽  
Validation Set ◽  
Dynamic Fet Pet

Pseudoprogression (PSP) detection in glioblastoma remains challenging and has important clinical implications. We investigated the potential of machine learning (ML) in improving the performance of PET using O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) for differentiation of tumor progression from PSP in IDH-wildtype glioblastoma. We retrospectively evaluated the PET data of patients with newly diagnosed IDH-wildtype glioblastoma following chemoradiation. Contrast-enhanced MRI suspected PSP/TP and all patients underwent subsequently an additional dynamic FET-PET scan. The modified Response Assessment in Neuro-Oncology (RANO) criteria served to diagnose PSP. We trained a Linear Discriminant Analysis (LDA)-based classifier using FET-PET derived features on a hold-out validation set. The results of the ML model were compared with a conventional FET-PET analysis using the receiver-operating-characteristic (ROC) curve. Of the 44 patients included in this preliminary study, 14 patients were diagnosed with PSP. The mean (TBRmean) and maximum tumor-to-brain ratios (TBRmax) were significantly higher in the TP group as compared to the PSP group (p = 0.014 and p = 0.033, respectively). The area under the ROC curve (AUC) for TBRmax and TBRmean was 0.68 and 0.74, respectively. Using the LDA-based algorithm, the AUC (0.93) was significantly higher than the AUC for TBRmax. This preliminary study shows that in IDH-wildtype glioblastoma, ML-based PSP detection leads to better diagnostic performance.

scholarly journals The analysis of sacrum and coccyx length measured with computerized tomography images depending on sex

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rukiye Sumeyye Bakici ◽  
Zulal Oner ◽  
Serkan Oner
Keyword(s):  
Forensic Anthropology ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Sex Estimation ◽  
Biological Profile ◽  
Linear Discriminant ◽  
Discrimination Rate ◽  
Length Measurements ◽  
The Mean ◽  
Fisher’S Linear Discriminant

Abstract Background Sex estimation is vital in establishing an accurate biological profile from the human skeleton, as sex influences the analysis of other elements in both Physical and Forensic Anthropology and Legal Medicine. The present study was conducted to analyze the sex differences between the sacrum and coccyx length based on the measurements calculated with computed tomography (CT) images. One hundred case images (50 females, 50 males) who were between the ages of 25 and 50 and admitted by the emergency department between September 2018 and June 2019 and underwent CT were included in the study. Eighteen lengths, 4 curvature lengths, and 2 regions were measured in sagittal, coronal and transverse planes with orthogonal adjustment for three times. Results It was stated that the mean anterior and posterior sacral length, anterior and posterior sacrococcygeal length, anterior and posterior sacral curvature length, anterior coccygeal curvature length, sacral area, lengths of transverse lines 1, 2, 3 and 4, sacral first vertebra transverse and sagittal length measurements were longer in males when compared to females (p < 0.05). It was noted that the parameter with the highest discrimination value in the receiver operating characteristic (ROC) analysis was the sacral area (AUC = 0.88/Acc = 0.82). Based on Fisher’s linear discriminant analysis findings, the discrimination rate was 96% for males, 92% for females and the overall discrimination rate was 94%. Conclusions It was concluded that the fourteen parameters that were indicated as significant in the present study could be used in anthropology, Forensic Medicine and Anatomy to predict sex.

Anomalies Detection Based on the ROC Analysis using Classifiers in Tactical Cognitive Radio Systems: A survey

2016 ◽  
Vol 5 (3) ◽  
pp. 105
Author(s):  
Ahmed Moumena
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Cognitive Radio ◽  
Anomaly Detection ◽  
Roc Curve ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Cognitive Radio Systems ◽  
Radio Systems

Receiver operating characteristic (ROC) curve is an important technique for organizing classifiers and visualizing their performance in tactical systems in the presence of jamming signal. ROC curves are commonly used to evaluate the performance of classifiers for anomalies detection. This paper gives a survey of ROC analysis based on the anomaly detection using classifiers for using them in research. In recent years have been increasingly adopted in the machine learning and data mining research communities. This survey gives definitions of the anomaly detection theory and how to use one ROC curve, what a ROC curve, when we use ROC curves.

scholarly journals FET PET Radiomics for Differentiating Pseudoprogression from Early Tumor Progression in Glioma Patients Post-Chemoradiation

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3835
Author(s):  
Philipp Lohmann ◽  
Mai A. Elahmadawy ◽  
Robin Gutsche ◽  
Jan-Michael Werner ◽  
Elena K. Bauer ◽  
...  
Keyword(s):  
Tumor Progression ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Data Augmentation ◽  
Response Assessment ◽  
Lower Sensitivity ◽  
Fet Pet ◽  
Early Tumor ◽  
Dynamic Fet Pet

Currently, a reliable diagnostic test for differentiating pseudoprogression from early tumor progression is lacking. We explored the potential of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) positron emission tomography (PET) radiomics for this clinically important task. Thirty-four patients (isocitrate dehydrogenase (IDH)-wildtype glioblastoma, 94%) with progressive magnetic resonance imaging (MRI) changes according to the Response Assessment in Neuro-Oncology (RANO) criteria within the first 12 weeks after completing temozolomide chemoradiation underwent a dynamic FET PET scan. Static and dynamic FET PET parameters were calculated. For radiomics analysis, the number of datasets was increased to 102 using data augmentation. After randomly assigning patients to a training and test dataset, 944 features were calculated on unfiltered and filtered images. The number of features for model generation was limited to four to avoid data overfitting. Eighteen patients were diagnosed with early tumor progression, and 16 patients had pseudoprogression. The FET PET radiomics model correctly diagnosed pseudoprogression in all test cohort patients (sensitivity, 100%; negative predictive value, 100%). In contrast, the diagnostic performance of the best FET PET parameter (TBRmax) was lower (sensitivity, 81%; negative predictive value, 80%). The results suggest that FET PET radiomics helps diagnose patients with pseudoprogression with a high diagnostic performance. Given the clinical significance, further studies are warranted.

Value of multimodality imaging in the diagnosis of breast lesions with calcification: A retrospective study

2020 ◽  
Vol 76 (1) ◽  
pp. 85-98 ◽  
Author(s):  
Wenjuan Tong ◽  
Xiaoling Zhang ◽  
Jia Luo ◽  
Fushun Pan ◽  
Jinyu Liang ◽  
...  
Keyword(s):  
Roc Curve ◽  
Operating Characteristic ◽  
Multimodality Imaging ◽  
Curve Analysis ◽  
Breast Lesions ◽  
Roc Curve Analysis ◽  
Diagnostic Efficacy ◽  
Contrast Enhanced ◽  
Conventional Ultrasound ◽  
Group A

PURPOSE: To assess the value of conventional ultrasound (US), contrast-enhanced ultrasound (CEUS) and mammography in the diagnosis of breast lesions with calcifications. METHODS: A total of 87 breast lesions with calcification were subjected to US, CEUS and mammography and divided into 3 groups: Group A (all cases), Group A1 (31 cases who underwent US and CEUS first followed by mammography), and Group A2 (56 cases who underwent mammography first followed by US and CEUS). A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic efficacy of different methods in different groups. RESULTS: In Group A, the area under the ROC curve (AUROC) of CEUS were 0.937, which were significantly higher than that of mammography (p < 0.05). In Group A1, the AUROC of CEUS were 0.842, which were not significantly different from that of US and mammography (p > 0.05). In Group A2, the AUROC of CEUS were 0.987, which were significantly higher than that of mammography and US (p < 0.05). CONCLUSION: Based on the mammography results, the combination of US and CEUS might improve the diagnostic efficacy in breast lesions with calcification.

scholarly journals Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Mou Li ◽  
Ling Yang ◽  
Yufeng Yue ◽  
Jingxu Xu ◽  
Chencui Huang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Performance ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Combined Model ◽  
Contrast Enhanced ◽  
Apparent Diffusion ◽  
Validation Set

ObjectiveTo investigate whether a radiomics model can help to improve the performance of PI-RADS v2.1 in prostate cancer (PCa).MethodsThis was a retrospective analysis of 203 patients with pathologically confirmed PCa or non-PCa between March 2015 and December 2016. Patients were divided into a training set (n = 141) and a validation set (n = 62). The radiomics model (Rad-score) was developed based on multi-parametric MRI including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast enhanced (DCE) imaging. The combined model involving Rad-score and PI-RADS was compared with PI-RADS for the diagnosis of PCa by using the receiver operating characteristic curve (ROC) analysis.ResultsA total of 112 (55.2%) patients had PCa, and 91 (44.8%) patients had benign lesions. For PCa versus non-PCa, the Rad-score had a significantly higher area under the ROC curve (AUC) [0.979 (95% CI, 0.940–0.996)] than PI-RADS [0.905 (0.844–0.948), P = 0.002] in the training set. However, the AUC between them was insignificant in the validation set [0.861 (0.749–0.936) vs. 0.845 (0.731–0.924), P = 0.825]. When Rad-score was added to PI-RADS, the performance of the PI-RADS was significantly improved for the PCa diagnosis (AUC = 0.989, P &lt; 0.001 for the training set and AUC = 0.931, P = 0.038 for the validation set).ConclusionsThe radiomics based on multi-parametric MRI can help to improve the diagnostic performance of PI-RADS v2.1 in PCa.

scholarly journals Radiomic Analysis of Contrast-Enhanced Mammography With Different Image Types: Classification of Breast Lesions

2021 ◽  
Vol 11 ◽  
Author(s):  
Simin Wang ◽  
Ning Mao ◽  
Shaofeng Duan ◽  
Qin Li ◽  
Ruimin Li ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
High Energy ◽  
Breast Lesions ◽  
Wilcoxon Rank Sum Test ◽  
Contrast Enhanced ◽  
Malignant Breast ◽  
The Mean ◽  
Selection Operator

Objective: A limited number of studies have focused on the radiomic analysis of contrast-enhanced mammography (CEM). We aimed to construct several radiomics-based models of CEM for classifying benign and malignant breast lesions.Materials and Methods: The retrospective, double-center study included women who underwent CEM between November 2013 and February 2020. Radiomic analysis was performed using high-energy (HE), low-energy (LE), and dual-energy subtraction (DES) images from CEM. Datasets were randomly divided into the training and testing sets at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) method and least absolute shrinkage and selection operator (LASSO) logistic regression were used to select the radiomic features and construct the best classification models. The performances of the models were assessed by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). Leave-group-out cross-validation (LGOCV) for 100 rounds was performed to obtain the mean AUCs, which were compared by the Wilcoxon rank-sum test and the Kruskal–Wallis rank-sum test.Results: A total of 192 women with 226 breast lesions (101 benign; 125 malignant) were enrolled. The median age was 48 years (range, 22–70 years). For the classification of breast lesions, the AUCs of the best models were 0.931 (95% CI: 0.873–0.989) for HE, 0.897 (95% CI: 0.807–0.981) for LE, 0.882 (95% CI: 0.825–0.987) for DES images and 0.960 (95% CI: 0.910–0.998) for all of the CEM images in the testing set. According to LGOCV, the models constructed with the HE images and all of the CEM images showed the highest mean AUCs for the training (0.931 and 0.938, respectively; P &lt; 0.05 for both) and testing sets (0.892 and 0.889, respectively; P = 0.55 for both), which were significantly higher than those of the two models constructed with the LE and DES images in the training (0.912 and 0.899, respectively; all P &lt; 0.05) and testing sets (0.866 and 0.862, respectively; all P &lt; 0.05).Conclusions: Radiomic analysis of CEM images was valuable for classifying benign and malignant breast lesions. The use of HE images or all three types of CEM images can achieve the best performance.

scholarly journals Evaluation of CSTB and DMBT1 Expression in Saliva of Gastric Cancer Patients and Controls

2021 ◽  
Author(s):  
Maryam Koopaie ◽  
Marjan Ghafourian ◽  
Soheila Manifar ◽  
Shima Younespour ◽  
Mansour Davoudi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastric Cancer ◽  
Statistical Analysis ◽  
Food Intake ◽  
Roc Curve ◽  
Clinical Characteristics ◽  
Demographic Data ◽  
Healthy Individuals ◽  
Diagnostic Algorithms ◽  
The Mean

Abstract Background: Gastric cancer (GC) is the fifth most common cancer and the third cause of cancer deaths globally with late diagnosis, low survival rate and poor prognosis. This case-control study aimed to evaluate the expression of cystatin B (CSTB) and deleted in malignant brain tumor 1 (DMBT1) in the saliva of GC patients with healthy individuals to construct diagnostic algorithms using statistical analysis and machine learning methods.Methods: Demographic data, clinical characteristics and food intake habits of the case and control group were gathered through a standard checklist. Unstimulated whole saliva samples were taken from 31 healthy individuals and 31 GC patients. Through ELISA test and statistical analysis, the expression of salivary CSTB and DMBT1 proteins were evaluated. To construct diagnostic algorithms, we used the machine learning method.Results: The mean salivary expression of CSTB in GC patients was significantly lower (115.55±7.06, p=0.001) and the mean salivary expression of DMBT1 in GC patients was significantly higher (171.88±39.67, p=0.002) than the control. Multiple linear regression analysis demonstrated that GC was significantly correlated with high levels of DMBT1 after controlling the effects of age of participants (R2=0.20, p<0.001). Considering salivary CSTB greater than 119.06 ng/mL as an optimal cut-off value, the sensitivity and specificity of CSTB in the diagnosis of GC was 83.87% and 70.97%, respectively The area under the ROC curve was calculated as 0.728. The optimal cut-off value of DMBT1 for differentiating GC patients from controls was greater than 146.33 ng/mL (sensitivity=80.65% and specificity=64.52%). The area under the ROC curve was up to 0.741. As a result of the machine learning method, the area under the receiver-operating characteristic curve for the diagnostic ability of CSTB, DMBT1, demographic data, clinical characteristics and food intake habits was 0.95. The machine learning model's sensitivity, specificity, and accuracy were 100%, 70.8%, and 80.5%, respectively. Conclusion: Salivary levels of DMBT1 and CSTB may be accurate in diagnosing GCs. Machine learning analyses using salivary biomarkers, demographic, clinical and nutrition habits data simultaneously could provide affordability models with acceptable accuracy for differentiation of GC by a cost-effective and non-invasive method.

scholarly journals Assessment of enamel demineralization using conventional, digital, and digitized radiography

2006 ◽  
Vol 20 (2) ◽  
pp. 114-119 ◽  
Author(s):  
Rívea Inês Ferreira ◽  
Francisco Haiter-Neto ◽  
Cínthia Pereira Machado Tabchoury ◽  
Guilherme Assumpção Neves de Paiva ◽  
Frab Norberto Bóscolo
Keyword(s):  
Roc Analysis ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Chi Square ◽  
Cross Sectional ◽  
Significance Level ◽  
Enamel Demineralization ◽  
Contrast Enhanced ◽  
Chi Square Test ◽  
Approximal Surfaces

This experimental research aimed at evaluating the accuracy of enamel demineralization detection using conventional, digital, and digitized radiographs, as well as to compare radiographs and logarithmically contrast-enhanced subtraction images. Enamel subsurface demineralization was induced on one of the approximal surfaces of 49 sound third molars. Standardized radiographs of the teeth were taken prior to and after the demineralization phase with three digital systems - CygnusRay MPS®, DenOptix® and DIGORA® - and InSight® film. Three radiologists interpreted the pairs of conventional, digital, and digitized radiographs in two different occasions. Logarithmically contrast-enhanced subtraction images were examined by a fourth radiologist only once. Radiographic diagnosis was validated by cross-sectional microhardness profiling in the test areas of the approximal surfaces. Accuracy was estimated by Receiver Operating Characteristic (ROC) analysis. Chi-square test, at a significance level of 5%, was used to compare the areas under the ROC curves (Az) calculated for the different imaging modalities. Concerning the radiographs, the DenOptix® system (Az = 0.91) and conventional radiographs (Az = 0.90) presented the highest accuracy values compared with the other three radiographic modalities. However, logarithmically contrast-enhanced subtraction images (Az = 0.98) were significantly more accurate than conventional, digital, and digitized radiographs (p = 0.0000). It can be concluded that the DenOptix® system and conventional radiographs provide better performance for diagnosing enamel subsurface demineralization. Logarithmic subtraction significantly improves radiographic detection.

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