Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach

Romeo, Valeria; Cuocolo, Renato; Ricciardi, Carlo; Ugga, Lorenzo; Cocozza, Sirio; Verde, Francesco; Stanzione, Arnaldo; Napolitano, Valeria; Russo, Daniela; Improta, Giovanni; Elefante, Andrea; Staibano, Stefania; Brunetti, Arturo

doi:10.21873/anticanres.13949

Cited by 86 publications

(55 citation statements)

References 40 publications

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“…It has been proved the correlation between radiomics features and tumor phenotype (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22). Many studies have found Adc can be predicted by radiomics (22)(23)(24)(25)(26)(27)(28). Tang et al (27) developed a radiomics model to discriminate Adc from squamous cell carcinoma (Sqc) with an AUC of 0.82, Yang et al (24) developed an LR model to predict lymph node metastasis in solid Adc with an AUC of 0.86.…”

Section: Discussionmentioning

confidence: 99%

A Non-invasive Method to Diagnose Lung Adenocarcinoma

Yan

Wang

2020

Front. Oncol.

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Purpose: To find out the CT radiomics features of differentiating lung adenocarcinoma from another lung cancer histological type.Methods: This was a historical cohort study, three independent lung cancer cohorts included. One cohort was used to evaluate the stability of radiomics features, one cohort was used to feature selection, and the last was used to construct and evaluate classification models. The research is divided into four steps: region of interest segmentation, feature extraction, feature selection, and model building and validation. The feature selection methods included the intraclass correlation coefficient, ReliefF coefficient, and Partition-Membership filter. The performance metrics of the classification model included accuracy (Acc), precision (Pre), area under curve (AUC), and kappa statistics.

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Section: Discussionmentioning

confidence: 99%

A Non-invasive Method to Diagnose Lung Adenocarcinoma

Yan

Wang

2020

Front. Oncol.

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“…Many studies have shown that radiomics features have great potential to be the maker for tumor phenotype (8)(9)(10)(11)(12)(13)(14)(15)(16)(17), and found Adc can be differentiated from Sqc by radiomics (17)(18)(19)(20)(21)(22)(23). However, The data sets of those studies only included Adc and Sqc, that is to say, the accuracy of those models will be affected by other histological subtypes of lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Yan

Wang

2020

Front. Oncol.

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Purpose: To develop a diagnostic model for histological subtypes in lung cancer combined CT and FDG PET. Methods: Machine learning binary and four class classification of a cohort of 445 lung cancer patients who have CT and PET simultaneously. The outcomes to be predicted were primary, metastases (Mts), adenocarcinoma (Adc), and squamous cell carcinoma (Sqc). The classification method is a combination of machine learning and feature selection that is a Partition-Membership. The performance metrics include accuracy (Acc), precision (Pre), area under curve (AUC) and kappa statistics. Results: The combination of CT and PET radiomics (CPR) binary model showed more than 98% Acc and AUC on predicting Adc, Sqc, primary, and metastases, CPR fourclass classification model showed 91% Acc and 0.89 Kappa. Conclusion: The proposed CPR models can be used to obtain valid predictions of histological subtypes in lung cancer patients, assisting in diagnosis and shortening the time to diagnostic.

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“…The Knime Analytics Platform (version 3.7.1) was used for variable selection and the implementation of the algorithms. The Knime Analytics Platform version 3.7.1 was chosen since it is a well-known analytics platform already used in previous studies 58,59 and it resulted as the best choice for advanced users in a comparison with other platforms and programming languages 60 . It allows the users to create workflows of ML analyses by combining nodes and is integrated with other software, thus allowing other researchers a high reproducibility of the analysis.…”

Section: Methodsmentioning

confidence: 99%

Machine learning to predict mortality after rehabilitation among patients with severe stroke

Scrutinio

Ricciardi

Donisi

et al. 2020

Sci Rep

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Stroke is among the leading causes of death and disability worldwide. Approximately 20–25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decision-making. Machine learning (ML) methods have gained increasing popularity in the setting of biomedical research. The aim of this study was twofold: assessing the performance of ML tree-based algorithms for predicting three-year mortality model in 1207 stroke patients with severe disability who completed rehabilitation and comparing the performance of ML algorithms to that of a standard logistic regression. The logistic regression model achieved an area under the Receiver Operating Characteristics curve (AUC) of 0.745 and was well calibrated. At the optimal risk threshold, the model had an accuracy of 75.7%, a positive predictive value (PPV) of 33.9%, and a negative predictive value (NPV) of 91.0%. The ML algorithm outperformed the logistic regression model through the implementation of synthetic minority oversampling technique and the Random Forests, achieving an AUC of 0.928 and an accuracy of 86.3%. The PPV was 84.6% and the NPV 87.5%. This study introduced a step forward in the creation of standardisable tools for predicting health outcomes in individuals affected by stroke.

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Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach

Cited by 86 publications

References 40 publications

A Non-invasive Method to Diagnose Lung Adenocarcinoma

A Non-invasive Method to Diagnose Lung Adenocarcinoma

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Machine learning to predict mortality after rehabilitation among patients with severe stroke

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