Texture Analysis of 68Ga-DOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors
<b>Objectives</b>: The aim of our study is to evaluate whether texture analysis of 68Ga-DOTATOC PET/CT images can predict clinical outcome in patients with neuroendocrine tumors (NET). <b>Methods</b>: Forty-seven NET patients who had undergone 68Ga-DOTATOC PET/CT were studied...
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2025-05-01
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| author | Sara Pellegrino Mariarosaria Panico Roberto Bologna Rocco Morra Alberto Servetto Roberto Bianco Silvana Del Vecchio Rosa Fonti |
| author_facet | Sara Pellegrino Mariarosaria Panico Roberto Bologna Rocco Morra Alberto Servetto Roberto Bianco Silvana Del Vecchio Rosa Fonti |
| author_sort | Sara Pellegrino |
| collection | DOAJ |
| description | <b>Objectives</b>: The aim of our study is to evaluate whether texture analysis of 68Ga-DOTATOC PET/CT images can predict clinical outcome in patients with neuroendocrine tumors (NET). <b>Methods</b>: Forty-seven NET patients who had undergone 68Ga-DOTATOC PET/CT were studied. Primary tumors were localized in the gastroenteropancreatic (n = 35), bronchopulmonary (n = 8), and other (n = 4) districts. NET lesions were segmented using an automated contouring program and subjected to texture analysis, thus obtaining the conventional parameters SUVmax and SUVmean, volumetric parameters of the primary lesion, such as Receptor-Expressing Tumor Volume (RETV) and Total Lesion Receptor Expression (TLRE), volumetric parameters of the lesions in the whole-body, such as wbRETV and wbTLRE, and texture features such as Coefficient of Variation (CoV), HISTO Skewness, HISTO Kurtosis, HISTO Entropy-log<sub>10</sub>, GLCM Entropy-log<sub>10</sub>, GLCM Dissimilarity, and NGLDM Coarseness. Patients were subjected to a mean follow-up period of 17 months, and survival analysis was performed using the Kaplan–Meier method and log-rank tests. <b>Results</b>: Forty-seven primary lesions were analyzed. Survival analysis was performed, including clinical variables along with conventional, volumetric, and texture imaging features. At univariate analysis, overall survival (OS) was predicted by age (<i>p</i> = 0.0079), grading (<i>p</i> = 0.0130), SUVmax (<i>p</i> = 0.0017), SUVmean (<i>p</i> = 0.0011), CoV (<i>p</i> = 0.0037), HISTO Entropy-log<sub>10</sub> (<i>p</i> = 0.0039), GLCM Entropy-log<sub>10</sub> (<i>p</i> = 0.0044), and GLCM Dissimilarity (<i>p</i> = 0.0063). At multivariate analysis, only GLCM Entropy-log<sub>10</sub> was retained in the model (χ<sup>2</sup> = 7.7120, <i>p</i> = 0.0055). Kaplan–Meier curves showed that patients with GLCM Entropy-log<sub>10</sub> >1.28 had a significantly better OS than patients with GLCM Entropy-log<sub>10</sub> ≤1.28 (χ<sup>2</sup> = 10.6063, <i>p</i> = 0.0011). <b>Conclusions</b>: Texture analysis of 68Ga-DOTATOC PET/CT images, by revealing the heterogeneity of somatostatin receptor expression, can predict the clinical outcome of NET patients. |
| format | Article |
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| institution | Kabale University |
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| publishDate | 2025-05-01 |
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| spelling | doaj-art-345aec8707884c8fa4044118370cd61e2025-08-20T03:32:32ZengMDPI AGBiomedicines2227-90592025-05-01136128610.3390/biomedicines13061286Texture Analysis of 68Ga-DOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine TumorsSara Pellegrino0Mariarosaria Panico1Roberto Bologna2Rocco Morra3Alberto Servetto4Roberto Bianco5Silvana Del Vecchio6Rosa Fonti7Department of Advanced Biomedical Sciences, University Federico II, 80131 Naples, ItalyInstitute of Biostructures and Bioimages, National Research Council, 80145 Naples, ItalyDepartment of Advanced Biomedical Sciences, University Federico II, 80131 Naples, ItalyDepartment of Clinical Medicine and Surgery, University Federico II, 80131 Naples, ItalyDepartment of Clinical Medicine and Surgery, University Federico II, 80131 Naples, ItalyDepartment of Clinical Medicine and Surgery, University Federico II, 80131 Naples, ItalyDepartment of Advanced Biomedical Sciences, University Federico II, 80131 Naples, ItalyDepartment of Advanced Biomedical Sciences, University Federico II, 80131 Naples, Italy<b>Objectives</b>: The aim of our study is to evaluate whether texture analysis of 68Ga-DOTATOC PET/CT images can predict clinical outcome in patients with neuroendocrine tumors (NET). <b>Methods</b>: Forty-seven NET patients who had undergone 68Ga-DOTATOC PET/CT were studied. Primary tumors were localized in the gastroenteropancreatic (n = 35), bronchopulmonary (n = 8), and other (n = 4) districts. NET lesions were segmented using an automated contouring program and subjected to texture analysis, thus obtaining the conventional parameters SUVmax and SUVmean, volumetric parameters of the primary lesion, such as Receptor-Expressing Tumor Volume (RETV) and Total Lesion Receptor Expression (TLRE), volumetric parameters of the lesions in the whole-body, such as wbRETV and wbTLRE, and texture features such as Coefficient of Variation (CoV), HISTO Skewness, HISTO Kurtosis, HISTO Entropy-log<sub>10</sub>, GLCM Entropy-log<sub>10</sub>, GLCM Dissimilarity, and NGLDM Coarseness. Patients were subjected to a mean follow-up period of 17 months, and survival analysis was performed using the Kaplan–Meier method and log-rank tests. <b>Results</b>: Forty-seven primary lesions were analyzed. Survival analysis was performed, including clinical variables along with conventional, volumetric, and texture imaging features. At univariate analysis, overall survival (OS) was predicted by age (<i>p</i> = 0.0079), grading (<i>p</i> = 0.0130), SUVmax (<i>p</i> = 0.0017), SUVmean (<i>p</i> = 0.0011), CoV (<i>p</i> = 0.0037), HISTO Entropy-log<sub>10</sub> (<i>p</i> = 0.0039), GLCM Entropy-log<sub>10</sub> (<i>p</i> = 0.0044), and GLCM Dissimilarity (<i>p</i> = 0.0063). At multivariate analysis, only GLCM Entropy-log<sub>10</sub> was retained in the model (χ<sup>2</sup> = 7.7120, <i>p</i> = 0.0055). Kaplan–Meier curves showed that patients with GLCM Entropy-log<sub>10</sub> >1.28 had a significantly better OS than patients with GLCM Entropy-log<sub>10</sub> ≤1.28 (χ<sup>2</sup> = 10.6063, <i>p</i> = 0.0011). <b>Conclusions</b>: Texture analysis of 68Ga-DOTATOC PET/CT images, by revealing the heterogeneity of somatostatin receptor expression, can predict the clinical outcome of NET patients.https://www.mdpi.com/2227-9059/13/6/1286texture analysis68Ga-peptide PET/CTneuroendocrine tumorsprognosis |
| spellingShingle | Sara Pellegrino Mariarosaria Panico Roberto Bologna Rocco Morra Alberto Servetto Roberto Bianco Silvana Del Vecchio Rosa Fonti Texture Analysis of 68Ga-DOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors Biomedicines texture analysis 68Ga-peptide PET/CT neuroendocrine tumors prognosis |
| title | Texture Analysis of 68Ga-DOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors |
| title_full | Texture Analysis of 68Ga-DOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors |
| title_fullStr | Texture Analysis of 68Ga-DOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors |
| title_full_unstemmed | Texture Analysis of 68Ga-DOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors |
| title_short | Texture Analysis of 68Ga-DOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors |
| title_sort | texture analysis of 68ga dotatoc pet ct images for the prediction of outcome in patients with neuroendocrine tumors |
| topic | texture analysis 68Ga-peptide PET/CT neuroendocrine tumors prognosis |
| url | https://www.mdpi.com/2227-9059/13/6/1286 |
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