1. A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method
- Author
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Massimo Ippolito, Albert Comelli, Igor Daskalovski, Valentina Bravatà, Gaetano Savoca, Alessandro Stefano, Giorgio Ivan Russo, Stefano Barone, Maria Gabriella Sabini, Stefano A., Comelli A., Bravata V., Barone S., Daskalovski I., Savoca G., Sabini M.G., Ippolito M., and Russo G.
- Subjects
Male ,Positron emission tomography ,Computer science ,Lesion volume ,lcsh:Computer applications to medicine. Medical informatics ,Biochemistry ,030218 nuclear medicine & medical imaging ,Lesion ,03 medical and health sciences ,0302 clinical medicine ,Radiomics ,Structural Biology ,Artificial Intelligence ,medicine ,Humans ,Segmentation ,Neoplasm Metastasis ,lcsh:QH301-705.5 ,Molecular Biology ,Cancer ,Active contour model ,medicine.diagnostic_test ,Brain Neoplasms ,business.industry ,Applied Mathematics ,Research ,Pattern recognition ,Middle Aged ,Prognosis ,medicine.disease ,Computer Science Applications ,Cancer treatment ,Biological target volume ,lcsh:Biology (General) ,Feature (computer vision) ,030220 oncology & carcinogenesis ,Positron-Emission Tomography ,Fully automatic ,lcsh:R858-859.7 ,Female ,Active contour ,Artificial intelligence ,medicine.symptom ,Radiomic ,business - Abstract
BackgroundPositron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure.This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between patients who respond to treatment or not. For these purposes, 31 brain metastases, for predictive evaluation, and 25 ones, for follow-up evaluation after treatment, were delineated using the proposed method. Successively, 11C-MET PET studies and related volumetric segmentations were used to extract 108 features to investigate the potential application of radiomics analysis in patients with brain metastases. A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification.ResultsFor predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUVmean, SULpeak, SUVmin, SULpeakprod-surface-area, SUVmeanprod-sphericity, surface mean SUV 3, SULpeakprod-sphericity, and second angular moment) were selected with optimal performance in discriminant analysis classification (sensitivity 86.28%, specificity 87.75%, and accuracy 86.57%) outperforming the use of all features.ConclusionsThe proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.
- Published
- 2020