What is it about?
The paper aims to establish the potential for using fractal analysis of tumour complexity in non-small cell lung cancer (NSCLC) as assessed by Computed Tomography (CT) to provide an independent marker of survival. The possibility that computer analysis of lung tumour fractal characteristics providing prognostic information is suggested by recent studies reporting an association for tumour texture with tumour stage and glucose metabolism, both recognised markers of prognosis.
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Why is it important?
Quantitative analysis of tumour tissue from CT scans is challenging since changes in tumour texture between stages are subtle, and visual analysis of tumour volumes could be tedious and time-consuming. Also, there is marked variability in computerised methods, parameters reported, and strength of associations with biologic correlates. This calls for meaningful biomarkers that can be associated with inherent underlying physiology of tissue properties. CT fractal analysis, an emerging area of radiomics, can extract and interpret quantitative imaging features in relation to underlying physiology. It allows objective assessment of lesion and organ heterogeneity beyond what is possible with subjective visual interpretation, and may reflect information about the tissue micro-structure. It has shown promise in lesion characterisation, such as differentiating benign from malignant or more biologically aggressive lesions. Recently, there is an increasing interest in the use of computerised fractal analysis of CT images to improve lung nodules detection and characterisation as benign or malignant. However, the ability for fractal analysis to provide prognostic information for patients with NSCLC is largely unexplored. The paper should be of interest to readers in the area of chest radiology; particularly, in the field of tumour characterisation, fractal and image analysis.
Perspectives
Initial results of this study indicate higher FD in CT images of NSCLC is associated with advanced stage and greater FDG uptake on PET. Measurements of tumor fractal analysis on conventional non-contrast CT examinations could potentially be used as a prognostic marker and/or to select patients for PET.
Dr Omar S Al-Kadi
University of Jordan
Read the Original
This page is a summary of: Prediction of FDG-PET stage and uptake for non-small cell lung cancer on non-contrast enhanced CT scans via fractal analysis, Clinical Imaging, September 2020, Elsevier,
DOI: 10.1016/j.clinimag.2020.03.005.
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Graphical abstract
Graphical abstract
Preprint
Purpose: To investigate whether the FD of non-small cell lung cancer (NSCLC) on CT predicts tumor stage and uptake on 18F-fluorodeoxyglucose positron emission tomography. Material and Methods: The FD within a tumor region was determined using a box counting algorithm and compared to the lymph node involvement (NI) and metastatic involvement (MI) and overall stage as determined from PET. A Mann-Whitney U test was applied to the extracted FD features for the NI and the MI. Results: The two tests showed good significance with p< 0.05 (pNI= 0.0139, pMI= 0.0194). Also after performing fractal analysis to all cases, it was found that for those who had a CT of stage I or II had a higher likelihood of the NI and/or MI stage being upstaged by PET, Odds Ratio 5.38 (95% CI 0.99 -29.3). For those who are CT stage III or IV had an increased likelihood of the NI and/or MI stage being down staged by PET, Odds Ratio: 7.33 (95% CI 0.48 -111.2). Conclusion: Initial results of this study indicate higher FD in CT images of NSCLC is associated with advanced stage and greater FDG uptake on PET. Measurements of tumor fractal analysis on conventional non-contrast CT examinations could potentially be used as a prognostic marker and/or to select patients for PET.
Article source
Highlights • Fractal analysis can quantify complexity within image texture. • Conventional non-contrast enhanced CT scans are employed. • Fractal dimension (FD) estimated within a tumor region using box counting algorithm • FD values correlate with PET lymph node, metastasis and overall tumor stage. • Fractal analysis could potentially be used as a prognostic marker and/or to select patients for PET.
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