T2-Weighted Image-Based Radiomics Signature for Discriminating Between Seminomas and Nonseminoma

Zhang, Peipei; Cai, Wei; You, Huijuan; Fan, Chanyuan; Lv, Wenzhi; Min, Xiangde; Wang, Liang

doi:10.3389/fonc.2019.01330

Cited by 24 publications

(29 citation statements)

References 36 publications

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“…First, all the variables were compared between non-survivors and discharged patients using the Mann–Whitney U test for non-normally distributed features or the independent t test for normally distributed features 16 , 17 . Features with P < 0.05 were considered significant variables and selected 16 , 17 . Second, Spearman’s correlation coefficient was used to compute the relevance and redundancy of the features 16 , 17 .…”

Section: Methodsmentioning

confidence: 99%

“…Features with P < 0.05 were considered significant variables and selected 16 , 17 . Second, Spearman’s correlation coefficient was used to compute the relevance and redundancy of the features 16 , 17 . Third, we applied the maximum relevance minimum redundancy (mRMR) algorithm to assess the relevance and redundancy of the features 16 , 17 .…”

Section: Methodsmentioning

confidence: 99%

“…Second, Spearman’s correlation coefficient was used to compute the relevance and redundancy of the features 16 , 17 . Third, we applied the maximum relevance minimum redundancy (mRMR) algorithm to assess the relevance and redundancy of the features 16 , 17 . The features were ranked according to their mRMR scores 16 , 17 .…”

Section: Methodsmentioning

confidence: 99%

“…Thus we retrospectively collected laboratory findings of discharged patients and non-survivors. These data were dealt with a ML method similar to radiomics 16 , 17 . We aim to establish a prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory data.…”

Section: Introductionmentioning

confidence: 99%

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A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings

Zhou

Wang

et al. 2020

Sci Rep

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings

Zhou

Wang

et al. 2020

Sci Rep

Self Cite

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show abstract

“…These data were dealt with a ML method similar to radiomics. 16,17 We aim to establish a prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory data.…”

Section: Introductionmentioning

confidence: 99%

Machine learning: a predication model of outcome of SARS-CoV-2 pneumonia

Zhou

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) logistic regression model were used for selection of laboratory features. Seven laboratory features selected by machine learning were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicating outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia with machine learning.

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Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study

Yan

Hao

et al. 2021

Magnetic Resonance Imaging

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Background Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed. Purpose To develop and test an magnetic resonance imaging (MRI)‐based radiomics nomogram for predicting the grade of STS (low‐grade vs. high grade). Study Type Retrospective Population One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N = 109; external validation set, N = 71). Field Strength/Sequence Unenhanced T1‐weighted (T1WI) and fat‐suppressed T2‐weighted images (FS‐T2WI) were acquired at 1.5 T and 3.0 T. Assessment Clinical‐MRI characteristics included age, gender, tumor‐node‐metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression‐free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS‐T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS‐T1， RS‐FST2, and RS‐Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors. Statistical Tests Clinical‐MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS‐T1 model, RS‐FST2 model, and RS‐Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS‐Combined model had AUCs of 0.916 (95%CI, 0.866–0.966, training set) and 0.879 (95%CI, 0.791–0.967, external validation set), and demonstrated good calibration and good clinical utility. Data Conclusion The proposed noninvasive MRI‐based radiomics models showed good performance in differentiating low‐grade from high‐grade STSs. Level of Evidence 3 Technical Efficacy Stage 2

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T2-Weighted Image-Based Radiomics Signature for Discriminating Between Seminomas and Nonseminoma

Cited by 24 publications

References 36 publications

A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings

A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings

Machine learning: a predication model of outcome of SARS-CoV-2 pneumonia

Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study

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