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      A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method

      1 , 1 , 2 , , 1 , 3 , 4 , 1 , 5 , 6 , 1 , 5

      BMC Bioinformatics

      BioMed Central

      Annual Meeting of the Bioinformatics Italian Society (BITS 2019) (BITS 2019)

      26-28 June 2019

      Cancer, Active contour, Positron emission tomography, Biological target volume, Radiomics

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          Positron 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.


          For 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 (SUV mean, SUL peak, SUV min, SUL peak prod-surface-area, SUV mean prod-sphericity, surface mean SUV 3, SUL peak prod-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.


          The 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.

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          Most cited references 39

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            Reirradiation of recurrent high-grade gliomas using amino acid PET (SPECT)/CT/MRI image fusion to determine gross tumor volume for stereotactic fractionated radiotherapy.

            To develop a valid treatment strategy for recurrent high-grade gliomas using stereotactic hypofractionated reirradiation based on biologic imaging and temozolomide. The trial included a total of 44 patients with recurrent high-grade gliomas (1 patient with anaplastic oligodendroglioma, 8 with anaplastic astrocytoma, 33 with glioblastoma multiforme, and 2 with gliosarcoma) after previous surgery and postoperative conventional radiotherapy +/- chemotherapy. For fractionated stereotactic radiotherapy (SFRT) treatment planning, the gross tumor volume was defined by (11)C-methionine positron emission tomography (MET-PET) or (123)I-alpha-methyl-tyrosine (IMT) single-photon computed emission tomography (SPECT)/computed tomography (CT)/magnetic resonance imaging (MRI) fusion in 82% of the patients and by CT/T1+gadolinium-MRI image fusion in 18% of the patients. Six fractions of 5 Gy were administered in 6 days. In 29 of 44 patients (66%), chemotherapy with temozolomide (200 mg/m(2) body surface/day) was given in one to two cycles before and four to five cycles after SFRT. The patients were evaluated in follow-up by clinical investigators and MRI or CT every 3 months after SFRT until death. In cases suspicious for radiation necrosis, a MET-PET or IMT-SPECT investigation was performed. The median survival time in the whole group was 8 months. Treatment planning based on PET(SPECT)/CT/MRI imaging was associated with improved survival in comparison to treatment planning using CT/MRI alone: median survival time 9 months vs. 5 months (p = 0.03, log-rank). Median survival time were 11 months for patients who received SFRT based on biologic imaging plus temozolomide and significantly lower, 6 months for patients treated with SFRT without biologic imaging, without temozolomide or without both (p = 0.008, log rank). The most important prognostic factor in univariate analysis was a long interval between initial diagnosis and recurrence (p = 0.0002, log-rank). In the multivariate model, time interval to retreatment (p = 0.006) and temozolomide (p = 0.04) remained statistically significant. No acute neurologic toxicity Grade 3 or higher and no Grade 4 hematologic toxicity was observed. This is the first study of biologic imaging optimized SFRT plus temozolomide in recurrent high-grade gliomas. It demonstrates the feasibility and safety of this approach. The most striking result of the trial is the statistically significant longer survival time in the univariate analysis for patients reirradiated using MET-PET or IMT-SPECT/CT/MRI image fusion in the treatment planning, in comparison to patients treated based on MRI/CT alone. Multivariate analysis confirmed a significant survival benefit from multimodal treatment (i.e., addition of temozolomide), despite the limited number of patients. Whether treatment planning with SPECT/PET independently influences survival has to be studied in a larger series of patients.
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              A review on segmentation of positron emission tomography images.

              Positron Emission Tomography (PET), a non-invasive functional imaging method at the molecular level, images the distribution of biologically targeted radiotracers with high sensitivity. PET imaging provides detailed quantitative information about many diseases and is often used to evaluate inflammation, infection, and cancer by detecting emitted photons from a radiotracer localized to abnormal cells. In order to differentiate abnormal tissue from surrounding areas in PET images, image segmentation methods play a vital role; therefore, accurate image segmentation is often necessary for proper disease detection, diagnosis, treatment planning, and follow-ups. In this review paper, we present state-of-the-art PET image segmentation methods, as well as the recent advances in image segmentation techniques. In order to make this manuscript self-contained, we also briefly explain the fundamentals of PET imaging, the challenges of diagnostic PET image analysis, and the effects of these challenges on the segmentation results.

                Author and article information

                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                16 September 2020
                16 September 2020
                : 21
                Issue : Suppl 8 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
                [1 ]Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy
                [2 ]Ri.MED Foundation, Palermo, Italy
                [3 ]GRID grid.10776.37, ISNI 0000 0004 1762 5517, University of Palermo, ; Palermo, Italy
                [4 ]GRID grid.8158.4, ISNI 0000 0004 1757 1969, Department of Physics and Astronomy, , University of Catania, ; Catania, Italy
                [5 ]GRID grid.413340.1, ISNI 0000 0004 1759 8037, Medical Physics Unit, , Cannizzaro Hospital, ; Catania, Italy
                [6 ]GRID grid.413340.1, ISNI 0000 0004 1759 8037, Nuclear Medicine Department, , Cannizzaro Hospital, ; Catania, Italy
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                Annual Meeting of the Bioinformatics Italian Society (BITS 2019)
                BITS 2019
                Palermo, Italy
                26-28 June 2019
                Funded by: Italian Ministry of Economic Development
                Award ID: Grant No. F/090012/01-02/X36
                Award Recipient :
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                © The Author(s) 2020


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