Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram

Borguezan, Bruno Max; Lopes, Agnaldo José; Saito, Eduardo Haruo; Higa, Cláudio; Silva, Aristófanes Corrêa; Nunes, Rodolfo Acatauassú

doi:10.1155/2019/4071762

“…Some authors have discussed the theoretical basis of skewness and kurtosis and questioned the reliability and usability of those parameters 14,22 . On the other hand, several recently published studies have used these metrics (e.g., for tumor differentiation) and, upon evaluating their repeatability and correlations with clinical findings, have reported promising results 23–25 . Based on these findings, kurtosis may be regarded as a reliable biomarker with certain clinical potential if the analyzed data set is sufficiently large.…”

Section: Discussionmentioning

confidence: 99%

MR Diffusion Properties of Cervical Spinal Cord as a Predictor of Progression to Multiple Sclerosis in Patients with Clinically Isolated Syndrome

Dostál

¹

,

Keřkovský

²

,

Stulík

³

et al. 2020

Journal of Neuroimaging

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BACKGROUND AND PURPOSE This study's aim was to investigate diffusion properties of the cervical spinal cord in patients with clinically isolated syndrome (CIS) through analysis of diffusion tensor imaging (DTI) data and thereby to assess the capacity of this technique for predicting the progression of CIS to clinically definite multiple sclerosis (CDMS). METHODS The study groups were comprised of 47 patients with CIS (15 of them with progression to CDMS within 2 years of follow‐up) and 57 asymptomatic controls. All patients and controls had undergone magnetic resonance imaging (MRI) of the cervical spine including DTI and brain MRI. Methodological approaches included histogram analysis of the cervical cord's diffusion parameters and evaluation of T2 hyperintense lesions of the spinal cord and brain. All parameters were compared between the study groups. Sensitivity and specificity calculations were then performed with a view to predicting conversion to CDMS. RESULTS The patient subgroups defined by progression to CDMS differed significantly in values of fractional anisotropy (FA) kurtosis measured within white matter (WM) and normal‐appearing WM (NAWM). The same parameters also differed significantly when patients with progression to CDMS were compared to healthy controls. Receiver operating characteristic (ROC) analysis revealed sensitivity and specificity of FA kurtosis of WM and NAWM of 93% and 72%, respectively, in terms of predicting CIS to CDMS progression. CONCLUSION This study presents evidence that histogram analysis of diffusion parameters of the cervical spinal cord in patients with CIS may be helpful in predicting conversion to CDMS.

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“…There were no validation sets and area under the curve (AUC) were high, ranging between 0.864-0.93. A more recent study looking at only kurtosis and skewness showed differences between benign and malignant nodules; however, AUC was only approximately 0.7 for both features and no combined model was explored (52).…”

Section: Predicting Benign Vs Malignant Nodulesmentioning

confidence: 92%

Narrative review of radiomics for classifying pulmonary nodules and potential impact on lung cancer screening

Stephens¹

2023

Curr Chall Thorac Surg

0

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Lung cancer screening has proven to be a useful tool for identifying early stage lung cancers, however, the overall accuracy can sometimes lead to false positive and negatives that have potential adverse effects on patient outcomes. Advancement in computational methods have allowed for quantification of pulmonary nodule imaging features, referred to as radiomics, which have the potential to increase lung cancer screening accuracy and improve patient management. The initial part of this review covers common radiomic features and the challenges in deriving them. The second part of this review systematically evaluates literature relating to radiomics and lung cancer finding articles in areas that might have the potential to change management in lung cancer screening. Pertinent literature included initial nodule classification as benign or malignant, classifying subsolid nodules as invasive or noninvasive, and prediction of tumor recurrence after surgical resection. The reviewed articles evaluating use of radiomics are mostly limited due to small sample sizes and lack of a validation cohort. These studies show potential for radiomic features to improve pulmonary nodule classification and change the way patients are managed, however, comparison between studies is limited due to variabilities in the way these features are derived. To make these features useful will require further research and standardization of the workflows that derive these features.

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“…There were no validation sets and area under the curve (AUC) were high, ranging between 0.864-0.93. A more recent study looking at only kurtosis and skewness showed differences between benign and malignant nodules; however, AUC was only approximately 0.7 for both features and no combined model was explored (52). Some early studies also looked at derived features from unsupervised methods.…”

Section: Predicting Benign Vs Malignant Nodulesmentioning

confidence: 99%

Narrative review of radiomics for classifying pulmonary nodules and potential impact on lung cancer screening

Stephens¹

2021

Curr Chall Thorac Surg

0

View full text Add to dashboard Cite

Lung cancer screening has proven to be a useful tool for identifying early stage lung cancers, however, the overall accuracy can sometimes lead to false positive and negatives that have potential adverse effects on patient outcomes. Advancement in computational methods have allowed for quantification of pulmonary nodule imaging features, referred to as radiomics, which have the potential to increase lung cancer screening accuracy and improve patient management. The initial part of this review covers common radiomic features and the challenges in deriving them. The second part of this review systematically evaluates literature relating to radiomics and lung cancer finding articles in areas that might have the potential to change management in lung cancer screening. Pertinent literature included initial nodule classification as benign or malignant, classifying subsolid nodules as invasive or noninvasive, and prediction of tumor recurrence after surgical resection. The reviewed articles evaluating use of radiomics are mostly limited due to small sample sizes and lack of a validation cohort. These studies show potential for radiomic features to improve pulmonary nodule classification and change the way patients are managed, however, comparison between studies is limited due to variabilities in the way these features are derived. To make these features useful will require further research and standardization of the workflows that derive these features.

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Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram

Cited by 3 publications

References 39 publications

MR Diffusion Properties of Cervical Spinal Cord as a Predictor of Progression to Multiple Sclerosis in Patients with Clinically Isolated Syndrome

MR Diffusion Properties of Cervical Spinal Cord as a Predictor of Progression to Multiple Sclerosis in Patients with Clinically Isolated Syndrome

Narrative review of radiomics for classifying pulmonary nodules and potential impact on lung cancer screening

Narrative review of radiomics for classifying pulmonary nodules and potential impact on lung cancer screening

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