Prediction model for extrathyroidal extension in thyroid papillary carcinoma based on ultrasound radiomics
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| Argitaratua izan da: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 36200-36208 |
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Nature Publishing Group
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| Sarrera elektronikoa: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1038/s41598-025-19908-5 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Yuan, Sha-Sha |u School of Gongli Hospital Medical Technology, University of Shanghai for Science and Technology, 200093, Shanghai, China (ROR: https://ror.org/00ay9v204) (GRID: grid.267139.8) (ISNI: 0000 0000 9188 055X) | |
| 245 | 1 | |a Prediction model for extrathyroidal extension in thyroid papillary carcinoma based on ultrasound radiomics | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study aimed to construct preoperative prediction models for extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) based on ultrasonic radiomics and explore their clinical application value. This retrospective study included PTC patients treated across three centers from 2015 to 2023. Data for 609 cases from two centers were utilized for model construction and divided 4:1 into a training set (n = 487; 144 with ETE and 343 without ETE) and test set (n = 122; 58 with ETE and 64 without ETE). The external validation set comprised 109 PTC patients from the third center (n = 109; 55 with ETE and 54 without ETE). Image features were extracted using Pyradiomics. Feature selection and dimensionality reduction were performed using the least absolute shrinkage and selection operator and principal component analysis to construct radiomics models. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis, and clinical benefit was assessed by decision curve analysis. A total of 806 radiomics features were extracted from the training set data. After feature selection and dimensionality reduction, six significant features were included in the models, including one gray-level size zone matrix feature, one shape feature, one first-order feature, one gray-level run-length matrix feature, and two gray-level co-occurrence matrix features. The extreme gradient boosting (XGB) model showed the best performance in both the test and external validation sets, with area under the ROC curve values of 0.841 and 0.814, respectively. In conclusion, the XGB preoperative ETE prediction model for PTC based on ultrasonic radiomics offers good clinical application value for decision-making regarding therapeutic strategies. | |
| 653 | |a Radiomics | ||
| 653 | |a Patients | ||
| 653 | |a Software | ||
| 653 | |a Thyroid cancer | ||
| 653 | |a Medical prognosis | ||
| 653 | |a Metastasis | ||
| 653 | |a Surgery | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Cancer therapies | ||
| 653 | |a Decision making | ||
| 653 | |a Hospitals | ||
| 653 | |a Feature selection | ||
| 653 | |a Thyroid | ||
| 653 | |a Papillary thyroid carcinoma | ||
| 653 | |a Ultrasonic imaging | ||
| 653 | |a Prediction models | ||
| 653 | |a Variance analysis | ||
| 653 | |a Therapeutic applications | ||
| 653 | |a Social | ||
| 700 | 1 | |a Zhang, Xin-Ran |u School of Gongli Hospital Medical Technology, University of Shanghai for Science and Technology, 200093, Shanghai, China (ROR: https://ror.org/00ay9v204) (GRID: grid.267139.8) (ISNI: 0000 0000 9188 055X) | |
| 700 | 1 | |a Yu, Xiao-Qin |u Ultrasound Department of Gongli Hospital of Shanghai Pudong New Area, 200135, New Area, Shanghai, China | |
| 700 | 1 | |a Hu, Jiao-Jiao |u Ultrasound Department of Gongli Hospital of Shanghai Pudong New Area, 200135, New Area, Shanghai, China | |
| 700 | 1 | |a Chen, Qing-Qing |u Ultrasound Department of Gongli Hospital of Shanghai Pudong New Area, 200135, New Area, Shanghai, China | |
| 700 | 1 | |a Lu, Feng |u Center of Ultrasonography, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, 201203, Shanghai, China (ROR: https://ror.org/00z27jk27) (GRID: grid.412540.6) (ISNI: 0000 0001 2372 7462) | |
| 700 | 1 | |a Xiao, Yang-Jie |u Department of Ultrasound, Shengjing Hospital of China Medical University, 110004, Shenyang, Liaoning Province, China (ROR: https://ror.org/04wjghj95) (GRID: grid.412636.4) | |
| 700 | 1 | |a Huang, Ying-Fei |u School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, 200093, Shanghai, China (ROR: https://ror.org/00ay9v204) (GRID: grid.267139.8) (ISNI: 0000 0000 9188 055X) | |
| 700 | 1 | |a Fu, Xiao-Hong |u Ultrasound Department of Gongli Hospital of Shanghai Pudong New Area, 200135, New Area, Shanghai, China | |
| 700 | 1 | |a Shen, Yan |u Ultrasound Department of Gongli Hospital of Shanghai Pudong New Area, 200135, New Area, Shanghai, China | |
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 36200-36208 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3261988542/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3261988542/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3261988542/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |