AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CT
Abstract Background The number of thyroid cancer diagnoses has been increasing for several decades, with a significant part of cases being detected incidentally (thyroid incidentaloma, TI) by imaging studies performed for reasons other than thyroid disease, including PET/CT with [18F]FDG. The chacte...
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SpringerOpen
2025-04-01
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| Series: | EJNMMI Research |
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| Online Access: | https://doi.org/10.1186/s13550-025-01228-4 |
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| author | Krystian Ślusarz Mikolaj Buchwald Adrian Szczeszek Szymon Kupinski Anna Gramek-Jedwabna Wojciech Andrzejewski Juliusz Pukacki Robert Pękal Marek Ruchała Rafał Czepczyński Cezary Mazurek |
| author_facet | Krystian Ślusarz Mikolaj Buchwald Adrian Szczeszek Szymon Kupinski Anna Gramek-Jedwabna Wojciech Andrzejewski Juliusz Pukacki Robert Pękal Marek Ruchała Rafał Czepczyński Cezary Mazurek |
| author_sort | Krystian Ślusarz |
| collection | DOAJ |
| description | Abstract Background The number of thyroid cancer diagnoses has been increasing for several decades, with a significant part of cases being detected incidentally (thyroid incidentaloma, TI) by imaging studies performed for reasons other than thyroid disease, including PET/CT with [18F]FDG. The chacteristics of the detected TI cannot be determined solely on the basis of conventional parameters used in everyday clinical practice, such as SUVmax. In recent years, there has been a growing interest in radiomics, which is a quantitative method of analyzing radiological images based on the analysis of image texture. Textural analysis may be helpful, as it allows to characterize features invisible to the physician with the naked eye. Results Of the 54 patients who presented focal [18F]FDG-avid TI and had subsequent fine needle aspiration biopsy, 4 patients were excluded from the analysis due to the unavailability of the final diagnostic information. Hence, in the final analysis, data from 50 patients were used (39 females and 11 males) with a mean age of 58.5 ± 11.26. Of these 50 patients, 11 (22.0%) [18F]FDG-avid nodules were diagnosed as malignant. The performance of the XGBoost model in assessing [18F]FDG-avid TI was similar (0.846 [confidence interval, CI, 95% 0.737–0.956]) to SUVmax (0.797 [CI 95%: 0.622–0.973]; p = 0.60). Conclusions With an AI-based algorithm using radiomics features it is possible to detect the malignancy of thyroid nodule. However, no statistically significant differences were observed between the AI and radiomics approach, and when using a conventional measure, i.e., SUVmax. |
| format | Article |
| id | doaj-art-d5f1d01ac01f44f5b2acf86513f128e8 |
| institution | DOAJ |
| issn | 2191-219X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EJNMMI Research |
| spelling | doaj-art-d5f1d01ac01f44f5b2acf86513f128e82025-08-20T03:10:10ZengSpringerOpenEJNMMI Research2191-219X2025-04-011511910.1186/s13550-025-01228-4AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CTKrystian Ślusarz0Mikolaj Buchwald1Adrian Szczeszek2Szymon Kupinski3Anna Gramek-Jedwabna4Wojciech Andrzejewski5Juliusz Pukacki6Robert Pękal7Marek Ruchała8Rafał Czepczyński9Cezary Mazurek10Department of Nuclear Medicine, AffideaPoznan Supercomputing and Networking Center, Polish Academy of SciencePoznan Supercomputing and Networking Center, Polish Academy of SciencePoznan Supercomputing and Networking Center, Polish Academy of ScienceDepartment of Nuclear Medicine, AffideaPoznan Supercomputing and Networking Center, Polish Academy of SciencePoznan Supercomputing and Networking Center, Polish Academy of SciencePoznan Supercomputing and Networking Center, Polish Academy of ScienceDepartment of Endocrinology, Metabolism and Internal Medicine, Poznan University of Medical SciencesDepartment of Nuclear Medicine, AffideaPoznan Supercomputing and Networking Center, Polish Academy of ScienceAbstract Background The number of thyroid cancer diagnoses has been increasing for several decades, with a significant part of cases being detected incidentally (thyroid incidentaloma, TI) by imaging studies performed for reasons other than thyroid disease, including PET/CT with [18F]FDG. The chacteristics of the detected TI cannot be determined solely on the basis of conventional parameters used in everyday clinical practice, such as SUVmax. In recent years, there has been a growing interest in radiomics, which is a quantitative method of analyzing radiological images based on the analysis of image texture. Textural analysis may be helpful, as it allows to characterize features invisible to the physician with the naked eye. Results Of the 54 patients who presented focal [18F]FDG-avid TI and had subsequent fine needle aspiration biopsy, 4 patients were excluded from the analysis due to the unavailability of the final diagnostic information. Hence, in the final analysis, data from 50 patients were used (39 females and 11 males) with a mean age of 58.5 ± 11.26. Of these 50 patients, 11 (22.0%) [18F]FDG-avid nodules were diagnosed as malignant. The performance of the XGBoost model in assessing [18F]FDG-avid TI was similar (0.846 [confidence interval, CI, 95% 0.737–0.956]) to SUVmax (0.797 [CI 95%: 0.622–0.973]; p = 0.60). Conclusions With an AI-based algorithm using radiomics features it is possible to detect the malignancy of thyroid nodule. However, no statistically significant differences were observed between the AI and radiomics approach, and when using a conventional measure, i.e., SUVmax.https://doi.org/10.1186/s13550-025-01228-4RadiomicsMachine learningAIThyroid incidentalomaThyroid cancerPET/CT |
| spellingShingle | Krystian Ślusarz Mikolaj Buchwald Adrian Szczeszek Szymon Kupinski Anna Gramek-Jedwabna Wojciech Andrzejewski Juliusz Pukacki Robert Pękal Marek Ruchała Rafał Czepczyński Cezary Mazurek AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CT EJNMMI Research Radiomics Machine learning AI Thyroid incidentaloma Thyroid cancer PET/CT |
| title | AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CT |
| title_full | AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CT |
| title_fullStr | AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CT |
| title_full_unstemmed | AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CT |
| title_short | AI may help to predict thyroid nodule malignancy based on radiomics features from [18F]FDG PET/CT |
| title_sort | ai may help to predict thyroid nodule malignancy based on radiomics features from 18f fdg pet ct |
| topic | Radiomics Machine learning AI Thyroid incidentaloma Thyroid cancer PET/CT |
| url | https://doi.org/10.1186/s13550-025-01228-4 |
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