scholarly journals Differentiation between Germinoma and Craniopharyngioma Using Radiomics-Based Machine Learning

2022 ◽  
Vol 12 (1) ◽  
pp. 45
Author(s):  
Boran Chen ◽  
Chaoyue Chen ◽  
Yang Zhang ◽  
Zhouyang Huang ◽  
Haoran Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Clinical Manifestations ◽  
Treatment Strategies ◽  
Region Of Interest ◽  
Imaging Features ◽  
Contrast Enhanced ◽  
Accurate Preoperative Diagnosis ◽  
The Difference

For the tumors located in the anterior skull base, germinoma and craniopharyngioma (CP) are unusual types with similar clinical manifestations and imaging features. The difference in treatment strategies and outcomes of patients highlights the importance of making an accurate preoperative diagnosis. This retrospective study enrolled 107 patients diagnosed with germinoma (n = 44) and CP (n = 63). The region of interest (ROI) was drawn independently by two researchers. Radiomic features were extracted from contrast-enhanced T1WI and T2WI sequences. Here, we established the diagnosis models with a combination of three selection methods, as well as three classifiers. After training the models, their performances were evaluated on the independent validation cohort and compared based on the index of the area under the receiver operating characteristic curve (AUC) in the validation cohort. Nine models were established and compared to find the optimal one defined with the highest AUC in the validation cohort. For the models applied in the contrast-enhanced T1WI images, RFS + RFC and LASSO + LDA were observed to be the optimal models with AUCs of 0.91. For the models applied in the T2WI images, DC + LDA and LASSO + LDA were observed to be the optimal models with AUCs of 0.88. The evidence of this study indicated that radiomics-based machine learning could be potentially considered as the radiological method in the presurgical differential diagnosis of germinoma and CP with a reliable diagnostic performance.

scholarly journals Clinical Characteristics and CT Manifestations of 143 Patients With 2019 Novel Coronavirus Disease (COVID-19) in Taizhou City, Zhejiang, China

2020 ◽  
Author(s):  
Yani Kuang ◽  
Susu He ◽  
Shuangxiang Lin ◽  
Rui Zhu ◽  
Rongzhen Zhou ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Protein A ◽  
Clinical Manifestations ◽  
Imaging Features ◽  
First Case ◽  
Severe Group ◽  
Laboratory Examinations ◽  
The Difference ◽  
Severity Of The Disease ◽  
Novel Coronavirus

Abstract Background: In December 2019, the first case of pneumonia associated with the SARS-CoV-2 was found in Wuhan and rapidly spread throughout China, so data are needed on the affected patients. The purpose of our study was to find the clinical manifestations and CT features of COVID-19.Methods: All patients with COVID-19 in Taizhou city were retrospectively included and divided into non-severe group and severe group according to the severity of the disease. The clinical manifestations, laboratory examinations and imaging features of COVID-19 patients were analyzed, and the differences between the two groups were compared.Results: A total of 143 laboratory-confirmed cases were included in the study, including 110 non-severe patients and 33 severe patients. The median age of patients was 47 (range 4–86 years). Fever (73.4%) and cough (63.6%) were the most common initial clinical symptoms. Between two groups of cases, the results of aspartate transaminase, creatine kinase and lactate dehydrogenase, serum albumin, CR, glomerular filtration rate, amyloid protein A, fibrinogen, calcitonin level and oxygen partial pressure, IL – 10, absolute value of CD3, CD4, CD8 were different, and the difference was statistically significant (P < 0.05). Therefore, these quantitative indicators can be used to help assess the severity. On admission, the CT showed that the lesions were mostly distributed in the periphery of the lung or subpleural (135 cases (98%)), and most of lesions presented as patchy (81%), mixed density (63%) shadow. Consolidation (68% vs 41%), bronchial inflation signs (59% vs 41%), and bronchiectasis (71% vs 39%) were more common in the severe group.Conclusions: Most of the cases of COVID-19 in Taizhou have mild symptoms and no death. In addition to clinical symptoms, some laboratory tests (such as absolute values of CD4 and CD8) and CT findings can be used to assess the severity of the disease.

scholarly journals The evolution of time-intensity curves of contrast enhanced ultrasonography in early arthritis patients with wrist involvement.

2015 ◽  
Vol 17 (3) ◽  
pp. 345 ◽  
Author(s):  
Maria Magdalena Tamas ◽  
Cosmina Ioana Bondor ◽  
Nicolae Rednic ◽  
Linda Jessica Ghib ◽  
Simona Rednic
Keyword(s):  
Conventional Treatment ◽  
Power Doppler ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
Early Arthritis ◽  
Synovial Hypertrophy ◽  
Synthetic Drugs ◽  
Grey Scale ◽  
Contrast Enhanced ◽  
The Difference

Aims: The aim of the study was to assess the evolution of time-intensity curves parameters of contrast-enhanced ultra- sonography (CEUS) after 6 months of conventional treatment in early arthritis patients with wrist involvement. Material and methods: Patients diagnosed with early rheumatoid arthritis or undifferentiated arthritis on the basis of 2010 ACR/EU- LAR classification criteria, with bilateral wrist arthritis and both radiocarpal (RC) and intercarpal (IC) synovial hypertrophy identified by grey-scale ultrasonography, were enrolled. Synovial hypertrophy was semi-quantitatively scored (grade 0-3) by grey-scale and by Power Doppler at wrist level. CEUS was performed using Sonovue. The region of interest was selected as the area corresponding to the synovial hypertrophy of the RC and IC joints. Time-intensity curves parameters were cal- culated with Contrast Dynamic Software. The minimum and the maximum values of Peak, area under the curve (AUC), and slope were selected for each patient at baseline and after 6 months of conventional treatment. The difference between the visits was noted as “Δ”. Results: Eleven patients fulfilled the inclusion criteria. Maximum time-intensity curves parameters’ difference significantly decreased at 6 months: Peak (30.00±5.90% vs 23.22±5.22%, p=0.008), AUC (1206.08±216.91%s vs 949.13±280.12%s, p=0.04) and slope (1.6 (1.4;2.3) %/s vs 1(0.7;1.2) %/s, p=0.03). Moderate correlations were found between maximum ΔPeak, maximum ΔAUC and maximum ΔPower Doppler grade (r=0.44, p=0.17; r=0.46, p=0.16, respec- tively). Conclusions: Peak and AUC for joints that had high baseline values significantly decreased following treatment with conventional synthetic drugs in EA patients with wrist arthritis. This decrease in Peak and AUC was moderately correlated with a decrease in US parameters. The joint with the highest values of these parameters may be used for evaluation of EA patients at follow-up.

Clinical Characteristics of Coronavirus Disease 2019: A Review on Laboratory Tests, Comorbidity, and Complication

2020 ◽  
pp. 85-99 ◽  
Author(s):  
Xiaowei Qiu ◽  
Yehong Tian ◽  
Xin Jiang ◽  
Qiaoli Zhang ◽  
Jinchang Huang
Keyword(s):  
Laboratory Tests ◽  
Clinical Manifestations ◽  
Treatment Strategies ◽  
Cardiac Biomarkers ◽  
Respiratory Syndrome Virus ◽  
Imaging Features ◽  
Chest Imaging ◽  
Disease Heterogeneity ◽  
Blood Tests ◽  
Therapeutic Development

Coronavirus Disease 2019 (COVID-19), a new respiratory disease caused by severe acute respiratory syndrome virus 2, has emerged as an ongoing pandemic and global health emergency. This article primarily aims to describe laboratory tests, comorbidities, and complications, specifically comprise 1) the incubation period and basic epidemiological parameters, 2) clinical manifestations, 3) laboratory tests, including routine blood tests, inflammatory biomarkers, cardiac biomarkers, liver and renal function, and blood coagulation function, 4) chest imaging features, 5) significant comorbidities and complications. This information on the disease conditions would help dissect the disease heterogeneity for appropriately selecting clinical treatment strategies and therapeutic development.

scholarly journals A Nomogram for Preoperatively Predicting the Ki-67 Index of a Pituitary Tumor: A Retrospective Cohort Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangming Cai ◽  
Junhao Zhu ◽  
Jin Yang ◽  
Chao Tang ◽  
Feng Yuan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Treatment Strategies ◽  
Predictive Performance ◽  
Maximum Dimension ◽  
Ki 67 ◽  
Training Cohort ◽  
Index Level

BackgroundThe Ki-67 index is an indicator of proliferation and aggressive behavior in pituitary adenomas (PAs). This study aims to develop and validate a predictive nomogram for forecasting Ki-67 index levels preoperatively in PAs.MethodsA total of 439 patients with PAs underwent PA resection at the Department of Neurosurgery in Jinling Hospital between January 2018 and October 2020; they were enrolled in this retrospective study and were classified randomly into a training cohort (n = 300) and a validation cohort (n = 139). A range of clinical, radiological, and laboratory characteristics were collected. The Ki-67 index was classified into the low Ki-67 index (&lt;3%) and the high Ki-67 index (≥3%). Least absolute shrinkage and selection operator algorithm and uni- and multivariate logistic regression analyses were applied to identify independent risk factors associated with Ki-67. A nomogram was constructed to visualize these risk factors. The receiver operation characteristic curve and calibration curve were computed to evaluate the predictive performance of the nomogram model.ResultsAge, primary-recurrence subtype, maximum dimension, and prolactin were included in the nomogram model. The areas under the curve (AUCs) of the nomogram model were 0.694 in the training cohort and 0.658 in the validation cohort. A well-fitted calibration curve was also generated for the nomogram model. A subgroup analysis revealed stable predictive performance for the nomogram model. A correlation analysis revealed that age (R = −0.23; p &lt; 0.01), maximum dimension (R = 0.17; p &lt; 0.01), and prolactin (R = 0.16; p &lt; 0.01) were all significantly correlated with the Ki-67 index level.ConclusionsAge, primary-recurrence subtype, maximum dimension, and prolactin are independent predictors for the Ki-67 index level. The current study provides a novel and feasible nomogram, which can further assist neurosurgeons to develop better, more individualized treatment strategies for patients with PAs by predicting the Ki-67 index level preoperatively.

scholarly journals Machine learning-based CT radiomics model Distinguishes COVID-19 from other viral pneumonia

2020 ◽  
Author(s):  
Hui Juan Chen ◽  
Yang Chen ◽  
Li Yuan ◽  
Fei Wang ◽  
Li Mao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensitivity And Specificity ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Region Of Interest ◽  
Viral Pneumonia ◽  
Final Model ◽  
Radiological Features ◽  
Svm Model ◽  
Rapid Spread

Abstract Purpose To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spread Coronavirus disease 2019 (COVID-19).Methods In this retrospective study, a machine learning-based CT radiomics model was developed to extract features from chest CT exams for the detection of COVID-19. Other viral-pneumonia CT exams of the corresponding period were also included. The radiomics features extracted from the region of interest (ROI), the radiological features evaluated by the radiologists, the quantity features calculated by the AI segmentation and evaluation, and the clinical parameters including clinical symptoms, epidemiology history and biochemical results were enrolled in this study. The SVM model was built and the performance on the testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results For the SVM model that built on the radiomics features only, it reached an AUC of 0.688(95% CI 0.496 to 0.881) on the testing cohort. After the radiological features were enrolled, the AUC achieved 0.696(95% CI 0.501 to 0.892), then the AUC reached 0.753(95% CI 0.596 to 0.910) after the quantity features were included. Our final model employed all the features, reached the per-exam sensitivity and specificity for differentiating COVID-19 was 29 of 38 (0.763, 95% CI: 0.598 to 0.886]) and 12 of 13 (0.923, 95% CI: 0.640 to 0.998]), respectively, with an AUC of 0.968(95% CI 0.911 to 1.000). Conclusion The machine learning-based CT radiomics models may accurately detect COVID-19 and differentiate it from other viral pneumonia.

scholarly journals An Approach Based on Mammographic Imaging and Radiomics for Distinguishing Male Benign and Malignant Lesions: A Preliminary Study

2021 ◽  
Vol 10 ◽  
Author(s):  
Yan Huang ◽  
Qin Xiao ◽  
Yiqun Sun ◽  
Zhe Wang ◽  
Qin Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Sensitivity And Specificity ◽  
Validation Cohort ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
Imaging Features ◽  
Clinical Value ◽  
Oblique View ◽  
Imaging Model ◽  
Imaging Characteristics

PurposeTo develop and validate an imaging-radiomics model for the diagnosis of male benign and malignant breast lesions.MethodsNinety male patients who underwent preoperative mammography from January 2011 to December 2018 were enrolled in this study (63 in the training cohort and 27 in the validation cohort). The region of interest was segmented into a mediolateral oblique view, and 104 radiomics features were extracted. The minimum redundancy and maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) methods were used to exclude radiomics features to establish the radiomics score (rad-score). Mammographic features were evaluated by two radiologists. Univariate logistic regression was used to select for imaging features, and multivariate logistic regression was used to construct an imaging model. An imaging-radiomics model was eventually established, and a nomogram was developed based on the imaging-radiomics model. Area under the curve (AUC) and decision curve analysis (DCA) were applied to assess the clinical value.ResultsThe AUC based on the imaging model in the validation cohort was 0.760, the sensitivity was 0.750, and the specificity was 0.727. The AUC, sensitivity and specificity based on the radiomics in the validation cohort were 0.820, 0.750, and 0.867, respectively. The imaging-radiomics model was better than the imaging and radiomics models; the AUC, sensitivity, and specificity of the imaging-radiomics model in the validation cohort were 0.870, 0.824, and 0.900, respectively.ConclusionThe imaging-radiomics model created by the imaging characteristics and radiomics features exhibited a favorable discriminatory ability for male breast cancer.

scholarly journals Automating Three-dimensional Osteoarthritis Histopathological Grading of Human Osteochondral Tissue using Machine Learning on Contrast-Enhanced Micro-Computed Tomography

2019 ◽  
Author(s):  
S.J.O. Rytky ◽  
A. Tiulpin ◽  
T. Frondelius ◽  
M.A.J. Finnilä ◽  
S.S. Karhula ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Three Dimensional ◽  
Micro Computed Tomography ◽  
Test Set ◽  
Source Codes ◽  
Contrast Enhanced ◽  
Histopathological Grading

AbstractObjectiveTo develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEμCT).DesignOsteochondral cores from 24 total knee arthroplasty patients and 2 asymptomatic cadavers (n = 34, Ø = 2 mm; n = 45, Ø = 4 mm) were imaged using CEμCT with phosphotungstic acid-staining. Volumes-of-interest (VOI) in surface (SZ), deep (DZ) and calcified (CZ) zones were extracted depthwise and subjected to dimensionally reduced Local Binary Pattern-textural feature analysis. Regularized Ridge and Logistic regression (LR) models were trained zone-wise against the manually assessed semi-quantitative histopathological CEμCT grades (Ø = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (Ø = 4 mm samples). The performance was assessed using Spearman’s correlation, Average Precision (AP) and Area under the Receiver Operating Characteristic Curve (AUC).ResultsHighest performance on cross-validation was observed for SZ, both on Ridge regression (ρ = 0.68, p < 0.0001) and LR (AP = 0.89, AUC = 0.92). The test set evaluations yielded decreased Spearman’s correlations on all zones. For LR, performance was almost similar in SZ (AP = 0.89, AUC = 0.86), decreased in CZ (AP = 0.71→0.62, AUC = 0.77→0.63) and increased in DZ (AP = 0.50→0.83, AUC = 0.72→0.72).ConclusionWe showed that the ML-based automatic 3D histopathological grading of osteochondral samples is feasible from CEμCT. The developed method can be directly applied by OA researchers since the grading software and all source codes are publicly available.

scholarly journals Usefulness of the advanced monoenergetic image reconstruction in dual-energy computed tomography for detecting the perforator vein of lower extremity varix

2020 ◽  
Vol 9 (4) ◽  
pp. 205846012091619
Author(s):  
Hidekazu Matsumae ◽  
Motoo Nakagawa ◽  
Yoshiyuki Ozawa ◽  
Miki Asano ◽  
Masashi Shimohira ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lower Extremity ◽  
Region Of Interest ◽  
Dual Energy ◽  
Reconstruction Technique ◽  
Ct Number ◽  
Dual Energy Computed Tomography ◽  
Contrast Enhanced ◽  
The Difference ◽  
Monoenergetic Images

Background Identification of the perforator vein is important for treating lower extremity varix. Purpose We evaluated the ability of 40-keV advanced monoenergetic images to depict the perforator vein in patients with lower extremity varix. Material and Methods Thirty-three patients aged 52–86 years were examined with contrast-enhanced dual-energy computed tomography (CT) and advanced virtual monoenergetic images (40 keV) were reconstructed. For evaluating enhancement of a lower extremity vein and the difference in CT number between the vein and muscle, we set the region of interest on the popliteal vein (PV). We also evaluated the ability of 100-kVp and 40-keV volume-rendering (VR) images to depict the perforator veins. Results The mean CT numbers of the PV at 100 kVp and 40 keV were 113 ± 16 and 321 ± 63 HU, respectively ( P < 0.01). In 40-keV transverse images of 33 patients, 84 of the perforator veins were detected. In those 84 veins, 70 (83%) were depicted and 14 (17%) were not depicted on VR images that were reconstructed from 40-keV transverse images. At 100 kVp, 10 (12%) of the perforator veins could be depicted in VR images because the muscles buried them or the PVs were blurred due to insufficient enhancement. Conclusion The advanced monoenergetic reconstruction technique is useful for evaluating the perforator vein in patients with lower extremity varix.

scholarly journals Clinical Characteristics and CT Manifestations of 143 Patients With 2019 Novel Coronavirus Disease (COVID-19) in Taizhou City, Zhejiang, China

2020 ◽  
Author(s):  
Yani Kuang ◽  
Susu He ◽  
Shuangxiang Lin ◽  
Rui Zhu ◽  
Rongzhen Zhou ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Protein A ◽  
Clinical Manifestations ◽  
Imaging Features ◽  
First Case ◽  
Severe Group ◽  
Laboratory Examinations ◽  
The Difference ◽  
Severity Of The Disease ◽  
Novel Coronavirus

Abstract Background In December 2019, the first case of pneumonia associated with the SARS-CoV-2 was found in Wuhan and rapidly spread throughout China, so data are needed on the affected patients. The purpose of our study was to find the clinical manifestations and CT features of COVID-19.Methods All patients with COVID-19 in Taizhou city were retrospectively included and divided into non-severe group and severe group according to the severity of the disease. The clinical manifestations, laboratory examinations and imaging features of COVID-19 patients were analyzed, and the differences between the two groups were compared.Results A total of 143 laboratory-confirmed cases were included in the study, including 110 non-severe patients and 33 severe patients. The median age of patients was 47 (range 4–86 years). Fever (73.4%) and cough (63.6%) were the most common initial clinical symptoms. Between two groups of cases, the results of aspartate transaminase, creatine kinase and lactate dehydrogenase, serum albumin, CPR, glomerular filtration rate, amyloid protein A, fibrinogen, calcitonin level and oxygen partial pressure, red protein, IL – 10, absolute value of CD3, CD4, CD8 were different, and the difference was statistically significant (P < 0.05). On admission, the CT showed that the lesions were mostly distributed in the external lung or under the pleura (135 cases (98%)), and most of lesions presented as patchy (81%), heterogeneous (73%) and mixed density (63%) shadow. Consolidation (68% vs 41%), bronchial inflation signs (59% vs 41%), and bronchiectasis (71% vs 39%) were more common in the severe group.Conclusions Most of the cases of COVID-19 in Taizhou have mild symptoms and no death. In addition to clinical symptoms, some laboratory tests (such as absolute values of CD4 and CD8) and CT findings can be used to assess the severity of the disease.

scholarly journals Development and Validation of A Nomogram for Predicting the Overall Survival of Patients with Testicular Cancer

2021 ◽  
Author(s):  
Haohui Yu ◽  
Bin Feng ◽  
Yunrui Zhang ◽  
Jun Lyu
Keyword(s):  
Testicular Cancer ◽  
Cancer Patients ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Treatment Strategies ◽  
Staging System ◽  
Patient Treatment ◽  
Training Cohort ◽  
Ajcc Staging System ◽  
Ajcc Staging

Abstract Background The purpose of this study was to develop and validate a nomogram containing multiple predictors for the survival of testicular cancer patients. Methods Testicular cancer patients diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were selected for this study. A random sampling method was used to divide patients into training and validation cohorts, which accounted for 30% and 70% of the total sample, respectively. The nomogram was developed using the training cohort and evaluated using the C index, calibration chart, and area under the receiver operating characteristic curve (AUC). The same method was applied to the validation cohort to verify the nomogram. Results Seven risk factors that affect the survival of testicular cancer patients (AJCC stage, marital status, age at diagnosis, race, SEER historic stage A, surgery status, and origin) were identified using Cox proportional hazard regression analysis. The training cohort was used to construct a nomogram. The nomogram has a higher C index (0.897) and AUC when compared with the AJCC staging system. The validation cohort was used to verify the nomogram, which has a higher C index and AUC when compared with the AJCC staging system. The results of the calibration chart of the nomogram show that the predicted survival of testicular cancer patients at 3, 5, and 10 years after diagnosis is very close to their actual survival. Conclusions We developed and validated a nomogram for predicting the survival rate of testicular cancer patients at 3, 5, and 10 years after diagnosis. This nomogram has better discrimination, calibration, and clinical validity than the AJCC staging system. This indicates that the nomogram can be used to predict the survival of testicular cancer patients effectively, and provide a reference for patient treatment strategies.

Sign in / Sign up

Export Citation Format

Share Document