scholarly journals Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0250964
Author(s):  
Shaswati Roy ◽  
Pradipta Maji
Keyword(s):  
Predictive Value ◽  
Early Stage ◽  
Research Area ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Correct Prediction ◽  
Texture Gradient ◽  
Tumor Type ◽  
Wavelet Coefficients ◽  
Mr Sequences

Brain tumor is not most common, but truculent type of cancer. Therefore, correct prediction of its aggressiveness nature at an early stage would influence the treatment strategy. Although several diagnostic methods based on different modalities exist, a pre-operative method for determining tumor malignancy state still remains as an active research area. In this regard, the paper presents a new method for the assessment of tumor grades using conventional MR sequences namely, T1, T1 with contrast enhancement, T2 and FLAIR. The proposed method for tumor gradation is mainly based on feature extraction using multiresolution image analysis and classification using support vector machine. Since the wavelet features of different tumor subregions, obtained from single MR sequence, do not carry equally important information, a wavelet fusion technique is proposed based on the texture information content of each voxel. The concept of texture gradient, used in the proposed algorithm, fuses the wavelet coefficients of the given MR sequences. The feature vector is then derived from the co-occurrence of fused wavelet coefficients. As each wavelet subband contains distinct detail information, a novel concept of multispectral co-occurrence of wavelet coefficients is introduced to capture the spatial correlation among different subbands. It enables to convey more informative features to characterize the tumor type. The effectiveness of the proposed method is analyzed, with respect to six classification performance indices, on BRATS 2012 and BRATS 2014 data sets. The classification accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under curve assessed by the ten-fold cross-validation are 91.3%, 96.8%, 66.7%, 92.4%, 88.4%, and 92.0%, respectively, on real brain MR data.

scholarly journals Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

2021 ◽  
Author(s):  
Le-le Song ◽  
Shun-jun Chen ◽  
Wang Chen ◽  
Zhan Shi ◽  
Xiao-dong Wang ◽  
...  
Keyword(s):  
Pleomorphic Adenoma ◽  
Predictive Value ◽  
Current Knowledge ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Imaging Data ◽  
Clinical Model

Abstract Background: Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods: The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared.Results: The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity= 0.90 and 0.88, specificity=0.82 and 0.80, positive predictive value=0.86 and 0.84, negative predictive value=0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p>0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p<0.001) and validation (0.90 vs. 0.68, p=0.001) cohorts.Conclusions: The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

scholarly journals Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

2020 ◽  
Author(s):  
Le-le Song ◽  
Shun-jun Chen ◽  
Wang Chen ◽  
Zhan Shi ◽  
Xiao-dong Wang ◽  
...  
Keyword(s):  
Pleomorphic Adenoma ◽  
Predictive Value ◽  
Current Knowledge ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Imaging Data ◽  
Clinical Model

Abstract Background: Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods: The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared.Results: The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity= 0.90 and 0.88, specificity=0.82 and 0.80, positive predictive value=0.86 and 0.84, negative predictive value=0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p>0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p=0.000) and validation (0.90 vs. 0.68, p=0.001) cohorts.Conclusions: The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

scholarly journals Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Le-le Song ◽  
Shun-jun Chen ◽  
Wang Chen ◽  
Zhan Shi ◽  
Xiao-dong Wang ◽  
...  
Keyword(s):  
Pleomorphic Adenoma ◽  
Predictive Value ◽  
Current Knowledge ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Diagnostic Value ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Imaging Data ◽  
Clinical Model

Abstract Background Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared. Results The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity = 0.90 and 0.88, specificity = 0.82 and 0.80, positive predictive value = 0.86 and 0.84, negative predictive value = 0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p > 0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p < 0.001) and validation (0.90 vs. 0.68, p = 0.001) cohorts. Conclusions The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

The Accuracy of ACR TI-RADS Classification of Neck Ultrasound as a First-Line Diagnostic Approach for Thyroid Neoplasms in Pediatric Patients: A Retrospective Study

2018 ◽  
Vol 5 (1) ◽  
pp. 13-23
Author(s):  
Nikolai S. Grachev ◽  
Elena V. Feoktistova ◽  
Igor N. Vorozhtsov ◽  
Natalia V. Babaskina ◽  
Ekaterina Yu. Iaremenko ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Predictive Value ◽  
Thyroid Nodules ◽  
Pediatric Patients ◽  
Thyroid Neoplasms ◽  
Diagnostic Methods ◽  
Diagnostic Approach ◽  
First Line ◽  
Neck Ultrasound

Background.Ultrasound (US)-guided fine-needle aspiration biopsy (FNAB) is the gold standard in diagnosing the pathological nature of undetermined thyroid nodules. However, in some instances limitations and shortcomings arise, making it insufficient for determining a specific diagnosis.Objective.Our aim was to evaluate the effectiveness of ACR TI-RADS classification of neck ultrasound as a first-line diagnostic approach for thyroid neoplasms in pediatric patients.Methods.A retrospective analysis was made of FNA and US protocols in 70 patients who underwent the examination and treatment at Dmitry Rogachev National Research Center between January 2012 and August 2017. In the retrospective series 70% (49/70) of patients undergone FNA and 43% (30/70) of them undergone repeated FNA. All US protocols were interpreted according to ACR TI-RADS system by the two independent experts. The clinical judgment was assessed using the concordance test and the reliability of preoperative diagnostic methods was analized.Results.According to histologic examination protocols, benign nodules reported greater multimorbidity 29% (20/70), compared with thyroid cancer 17% (12/70), complicating FNA procedure. A statistically significant predictor of thyroid cancer with a tumor size ACR TI-RADS showed a significant advantage of ACR TI-RADS due to higher sensitivity (97.6 vs 60%), specificity (78.6 vs 53.8%), positive predictive value (87.2 vs 71.4%), and negative predictive value (95.7 vs 41.2%). Concordance on the interpreted US protocols according to ACR TI-RADS classification between two experts was high, excluding accidental coincidence.Conclusion.The data support the feasibility of US corresponding to the ACR TI-RADS classification as a first-line diagnostic approach for thyroid neoplasm reducing the number of unnecessary biopsies for thyroid nodules.

On the effect of the periodicity of diagnostic measurements on improving the reliability of high-voltage transformers

2019 ◽  
Vol 12 (1) ◽  
pp. 18-21
Author(s):  
V. A. Tikhonov
Keyword(s):  
Service Life ◽  
High Voltage ◽  
Chromatographic Analysis ◽  
Aluminum Industry ◽  
Early Stage ◽  
Diagnostic Methods ◽  
Transformer Oil ◽  
Operating Voltage ◽  
Dissolved Gases ◽  
The Aluminum Industry

The influence of the periodicity of diagnostic measurements on the operational state of high-voltage transformers is considered. Examples of defects of switching devices of converter transformers and methods for their detection are given. The rationale for the importance of recognition of defects at an early stage of their occurrence is given. The influence of the multiplicity of overvoltages on the service life of converter transformers in the aluminum industry is investigated. Based on the analysis of the service life of converter transformers of one of the powerful aluminum plants, where 83% of converter transformers have exhausted their standard service life, it is shown that in 40% of cases it would be possible to avoid their failures, with timely detection and elimination of emerging defects. Examples of defects of OLR (on-load regulators) of converter transformers and methods for their detection are given. The importance of recognition of defects at an early stage of their occurrence is substantiated. A method for chromatographic analysis of dissolved gases in transformer oil has been developed for the qualitative determination of defects and ways to eliminate them. Examples of diagnostics of converter transformers at operating voltage and working load are given, providing the best quality operational characteristics of converter transformers. The periodicity of diagnostic measurements and the reduction of defects and failures has been substantiated. The question of diagnosing the state of the converter transformer TDNP-40000/10 at an enterprise of the aluminum industry is investigated. Currently, diagnostic methods are being developed based on chromatographic analysis of dissolved gases in transformer oil. The presented method of evaluating the operating parameters of transformers allows for the safe operation of high-voltage transformers and enables to increase the reliability of the power supply scheme of aluminum industry plants.

scholarly journals Automated Assay of a Four-Protein Biomarker Panel for Improved Detection of Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 325
Author(s):  
Christopher Walker ◽  
Tuan-Minh Nguyen ◽  
Shlomit Jessel ◽  
Ayesha B. Alvero ◽  
Dan-Arin Silasi ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Detection ◽  
Predictive Value ◽  
Early Stage ◽  
Ca 125 ◽  
Healthy Controls ◽  
Classification Models ◽  
Serum Samples ◽  
Protein Biomarker ◽  
Biomarker Panel

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10−9). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.

scholarly journals Clinicopathological features and prognostic significance of CTNNB1 mutation in low-grade, early-stage endometrial endometrioid carcinoma

2021 ◽  
Author(s):  
Ignacio Ruz-Caracuel ◽  
Álvaro López-Janeiro ◽  
Victoria Heredia-Soto ◽  
Jorge L. Ramón-Patino ◽  
Laura Yébenes ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Mismatch Repair ◽  
Predictive Value ◽  
Early Stage ◽  
Disease Free Survival ◽  
Low Grade ◽  
Beta Catenin ◽  
Free Survival ◽  
Disease Free ◽  
Risk Of Relapse

AbstractLow-grade and early-stage endometrioid endometrial carcinomas (EECs) have an overall good prognosis but biomarkers identifying patients at risk of relapse are still lacking. Recently, CTNNB1 exon 3 mutation has been identified as a potential risk factor of recurrence in these patients. We evaluate the prognostic value of CTNNB1 mutation in a single-centre cohort of 218 low-grade, early-stage EECs, and the correlation with beta-catenin and LEF1 immunohistochemistry as candidate surrogate markers. CTNNB1 exon 3 hotspot mutations were evaluated by Sanger sequencing. Immunohistochemical staining of mismatch repair proteins (MLH1, PMS2, MSH2, and MSH6), p53, beta-catenin, and LEF1 was performed in representative tissue microarrays. Tumours were also reviewed for mucinous and squamous differentiation, and MELF pattern. Nineteen (8.7%) tumours harboured a mutation in CTNNB1 exon 3. Nuclear beta-catenin and LEF1 were significantly associated with CTNNB1 mutation, showing nuclear beta-catenin a better specificity and positive predictive value for CTNNB1 mutation. Tumours with CTNNB1 exon 3 mutation were associated with reduced disease-free survival (p = 0.010), but no impact on overall survival was found (p = 0.807). The risk of relapse in tumours with CTNNB1 exon 3 mutation was independent of FIGO stage, tumour grade, mismatch repair protein expression, or the presence of lymphovascular space invasion. CTNNB1 exon 3 mutation has a negative impact on disease-free survival in low-grade, early-stage EECs. Nuclear beta-catenin shows a higher positive predictive value than LEF1 for CTNNB1 exon 3 mutation in these tumours. Graphical abstract

scholarly journals The Emerging Factors and Treatment Options for NAFLD-Related Hepatocellular Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3740
Author(s):  
Chunye Zhang ◽  
Ming Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Fatty Liver ◽  
Early Stage ◽  
Primary Liver Cancer ◽  
Treatment Options ◽  
Underlying Mechanism ◽  
Diagnostic Methods ◽  
T Cell Therapy ◽  
Nonalcoholic Fatty Liver

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, followed by cholangiocarcinoma (CCA). HCC is the third most common cause of cancer death worldwide, and its incidence is rising, associated with an increased prevalence of obesity and nonalcoholic fatty liver disease (NAFLD). However, current treatment options are limited. Genetic factors and epigenetic factors, influenced by age and environment, significantly impact the initiation and progression of NAFLD-related HCC. In addition, both transcriptional factors and post-transcriptional modification are critically important for the development of HCC in the fatty liver under inflammatory and fibrotic conditions. The early diagnosis of liver cancer predicts curative treatment and longer survival. However, clinical HCC cases are commonly found in a very late stage due to the asymptomatic nature of the early stage of NAFLD-related HCC. The development of diagnostic methods and novel biomarkers, as well as the combined evaluation algorithm and artificial intelligence, support the early and precise diagnosis of NAFLD-related HCC, and timely monitoring during its progression. Treatment options for HCC and NAFLD-related HCC include immunotherapy, CAR T cell therapy, peptide treatment, bariatric surgery, anti-fibrotic treatment, and so on. Overall, the incidence of NAFLD-related HCC is increasing, and a better understanding of the underlying mechanism implicated in the progression of NAFLD-related HCC is essential for improving treatment and prognosis.

scholarly journals Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Zhong Xin ◽  
Lin Hua ◽  
Xu-Hong Wang ◽  
Dong Zhao ◽  
Cai-Guo Yu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Predictive Value ◽  
Early Stage ◽  
Current Model ◽  
External Validation ◽  
Area Under The Curve ◽  
Decision Tree Model ◽  
Tree Model ◽  
Chinese Adult ◽  
Significant Difference

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.

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