A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules

Hu, Tian; Wang, Sheng Ping; Huang, Lv; Wang, Jia Zhou; Shi, De Bing; Li, Yuan; Tong, Tong; Peng, Weijun

doi:10.1007/s00330-018-5539-3

Cited by 73 publications

(52 citation statements)

References 27 publications

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“…According to previous studies [19][20][21], whose sample size is comparable with ours, the ratio between primary and validation cohort is 7:3. In this study, a total of 136 patients were divided into primary (n = 98) and validation (n = 38) cohorts, close to 7:3.…”

Section: Patientssupporting

confidence: 64%

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

et al. 2020

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Objectives Rapid and accurate diagnosis of coronavirus disease 2019 is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. Methods A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student's t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram. Results Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1-3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits. Conclusions Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19.Xiaofeng Chen and Yanyan Tang contributed equally to this work.Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-06829-2) contains supplementary material, which is available to authorized users. Key Points• Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.

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Section: Patientssupporting

confidence: 64%

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

et al. 2020

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“…In order to avoid overfitting, 10-fold cross-validation with minimum criteria was used. These two-dimensional reduction methods have been well-used in some radiomics studies ( 16 , 20 ).…”

Section: Methodsmentioning

confidence: 99%

“…In order to avoid overfitting, 10-fold cross-validation with minimum criteria was used. These two-dimensional reduction methods have been well-used in some radiomics studies (16,20). In machine learning modeling, according to the selected features, the logistic regression models of T2WI and PET were constructed using machine learning methods.…”

Section: Radiomics Analysismentioning

confidence: 99%

Radiomics Analysis and Correlation With Metabolic Parameters in Nasopharyngeal Carcinoma Based on PET/MR Imaging

Feng

Liang²,

Wang

et al. 2020

Front. Oncol.

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Objective: Accurate staging is of great importance in treatment selection for patients with nasopharyngeal carcinoma (NPC). The aims of this study were to construct radiomic models of NPC staging based on positron emission tomography (PET) and magnetic resonance (MR) images and to investigate the correlation between metabolic parameters and radiomic features. Methods: A total of 100 consecutive cases of NPC (70 in training and 30 in the testing cohort) with undifferentiated carcinoma confirmed pathologically were recruited. Metabolic parameters of the local lesions of NPC were measured. A total of 396 radiomic features based on PET and MRI images were calculated [including histogram, Haralick, shape factor, gray level co-occurrence matrix (GLCM), and run length matrix (RLM)] and selected [using maximum relevance and minimum redundancy (mRMR) and least shrinkage and selection operator (LASSO)], respectively. The logistic regression models were established according to these features. Finally, the relationship between the metabolic parameters and radiomic features was analyzed. Results: We selected the nine most relevant radiomic features (six from MR images and three from PET images) from local NPC lesions. In the PET model, the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and the specificity of the training group were 0.84, 0.75, 0.90, and 0.69, respectively. In the MR model, those metrics were 0.85, 0.83, 0.75, and 0.86, respectively. Pearson's correlation analysis showed that the metabolic parameters had different degrees of correlation with the selected radiomic features. Conclusion: The PET and MR radiomic models were helpful in the diagnosis of NPC staging. There were correlations between the metabolic parameters and radiomic features of primary NPC based on PET/MR. In the future, PET/MR-based radiomic Feng et al. NPC Radiomics Based on PET/MR Imaging models, with further improvement and validation, can be a more useful and economical tool for predicting local invasion and distant metastasis of NPC.

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“…Comparative cross-assessment between risk factors evaluated in the umbrella review and risk predictors applied in existing prediction models Prediction models for CRC metastasis Twelve prognostic models have been developed for prediction of CRC metastasis [22][23][24][25][26][27][28][29][30][31][32][33] (Table 3). The median number of included predictors was four (range 3-9), and 27 unique predictors were included in at least one model.…”

Section: Sensitivity Analysis Of Redefying the Disease Outcome Groupsmentioning

confidence: 99%

Risk factors and risk prediction models for colorectal cancer metastasis and recurrence: an umbrella review of systematic reviews and meta-analyses of observational studies

Wang

et al. 2020

BMC Med

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Background: There is a clear need for systematic appraisal of models/factors predicting colorectal cancer (CRC) metastasis and recurrence because clinical decisions about adjuvant treatment are taken on the basis of such variables. Methods: We conducted an umbrella review of all systematic reviews of observational studies (with/without metaanalysis) that evaluated risk factors of CRC metastasis and recurrence. We also generated an updated synthesis of risk prediction models for CRC metastasis and recurrence. We cross-assessed individual risk factors and risk prediction models. Results: Thirty-four risk factors for CRC metastasis and 17 for recurrence were investigated. Twelve of 34 and 4/17 risk factors with p < 0.05 were estimated to change the odds of the outcome at least 3-fold. Only one risk factor (vascular invasion for lymph node metastasis [LNM] in pT1 CRC) presented convincing evidence. We identified 24 CRC risk prediction models. Across 12 metastasis models, six out of 27 unique predictors were assessed in the umbrella review and four of them changed the odds of the outcome at least 3-fold. Across 12 recurrence models, five out of 25 unique predictors were assessed in the umbrella review and only one changed the odds of the outcome at least 3-fold. Conclusions: This study provides an in-depth evaluation and cross-assessment of 51 risk factors and 24 prediction models. Our findings suggest that a minority of influential risk factors are employed in prediction models, which indicates the need for a more rigorous and systematic model construction process following evidence-based methods.

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A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules

Abstract: • Clinical features can predict lung metastasis of colorectal cancer patients. • Radiomics analysis outperformed clinical features in assessing the risk of pulmonary metastasis. • A clinical-radiomics nomogram can help clinicians predict lung metastasis in colorectal cancer patients.

Cited by 73 publications

References 27 publications

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

Radiomics Analysis and Correlation With Metabolic Parameters in Nasopharyngeal Carcinoma Based on PET/MR Imaging

Risk factors and risk prediction models for colorectal cancer metastasis and recurrence: an umbrella review of systematic reviews and meta-analyses of observational studies

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