Data Sheet 1_CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules.csv
Background<p>Currently, the computed tomography (CT) radiomics-based models, which can evaluate small (≤ 20 mm) solid pulmonary nodules (SPNs) are lacking. This study aimed to develop a CT radiomics-based model that can differentiate between benign and malignant small SPNs.</p>Methods<...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2025
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| _version_ | 1852022755223928832 |
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| author | Jing-Xi Sun (20717528) |
| author2 | Xuan-Xuan Zhou (300337) Yan-Jin Yu (20717531) Ya-Ming Wei (20717534) Yi-Bing Shi (19748407) Qing-Song Xu (399745) Shuang-Shuang Chen (10683036) |
| author2_role | author author author author author author |
| author_facet | Jing-Xi Sun (20717528) Xuan-Xuan Zhou (300337) Yan-Jin Yu (20717531) Ya-Ming Wei (20717534) Yi-Bing Shi (19748407) Qing-Song Xu (399745) Shuang-Shuang Chen (10683036) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jing-Xi Sun (20717528) Xuan-Xuan Zhou (300337) Yan-Jin Yu (20717531) Ya-Ming Wei (20717534) Yi-Bing Shi (19748407) Qing-Song Xu (399745) Shuang-Shuang Chen (10683036) |
| dc.date.none.fl_str_mv | 2025-02-13T05:11:48Z |
| dc.identifier.none.fl_str_mv | 10.3389/fonc.2025.1502932.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Data_Sheet_1_CT_radiomics_based_model_for_differentiating_malignant_and_benign_small_20mm_solid_pulmonary_nodules_csv/28406558 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Oncology and Carcinogenesis not elsewhere classified CT radiomics pulmonary nodule small prediction |
| dc.title.none.fl_str_mv | Data Sheet 1_CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules.csv |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Background<p>Currently, the computed tomography (CT) radiomics-based models, which can evaluate small (≤ 20 mm) solid pulmonary nodules (SPNs) are lacking. This study aimed to develop a CT radiomics-based model that can differentiate between benign and malignant small SPNs.</p>Methods<p>This study included patients with small SPNs between January 2019 and November 2021. The participants were then randomly categorized into training and testing cohorts with an 8:2 ratio. CT images of all the patients were analyzed to extract radiomics features. Furthermore, a radiomics scoring model was developed based on the features selected in the training group via univariate and multivariate logistic regression analyses. The testing cohort was then used to validate the developed predictive model.</p>Results<p>This study included 210 patients, 168 in the training and 42 in the testing cohorts. Radiomics scores were ultimately calculated based on 9 selected CT radiomics features. Furthermore, traditional CT and clinical risk factors associated with SPNs included lobulation (P < 0.001), spiculation (P < 0.001), and a larger diameter (P < 0.001). The developed CT radiomics scoring model comprised of the following formula: X = -6.773 + 12.0705×radiomics score+2.5313×lobulation (present: 1; no present: 0)+3.1761×spiculation (present: 1; no present: 0)+0.3253×diameter. The area under the curve (AUC) values of the CT radiomics-based model, CT radiomics score, and clinicoradiological score were 0.957, 0.945, and 0.853, respectively, in the training cohort, while that of the testing cohort were 0.943, 0.916, and 0.816, respectively.</p>Conclusions<p>The CT radiomics-based model designed in the present study offers valuable diagnostic accuracy in distinguishing benign and malignant SPNs.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_d7b0f9fde48d6aa440d8ca2189ebfe3d |
| identifier_str_mv | 10.3389/fonc.2025.1502932.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28406558 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Data Sheet 1_CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules.csvJing-Xi Sun (20717528)Xuan-Xuan Zhou (300337)Yan-Jin Yu (20717531)Ya-Ming Wei (20717534)Yi-Bing Shi (19748407)Qing-Song Xu (399745)Shuang-Shuang Chen (10683036)Oncology and Carcinogenesis not elsewhere classifiedCTradiomicspulmonary nodulesmallpredictionBackground<p>Currently, the computed tomography (CT) radiomics-based models, which can evaluate small (≤ 20 mm) solid pulmonary nodules (SPNs) are lacking. This study aimed to develop a CT radiomics-based model that can differentiate between benign and malignant small SPNs.</p>Methods<p>This study included patients with small SPNs between January 2019 and November 2021. The participants were then randomly categorized into training and testing cohorts with an 8:2 ratio. CT images of all the patients were analyzed to extract radiomics features. Furthermore, a radiomics scoring model was developed based on the features selected in the training group via univariate and multivariate logistic regression analyses. The testing cohort was then used to validate the developed predictive model.</p>Results<p>This study included 210 patients, 168 in the training and 42 in the testing cohorts. Radiomics scores were ultimately calculated based on 9 selected CT radiomics features. Furthermore, traditional CT and clinical risk factors associated with SPNs included lobulation (P < 0.001), spiculation (P < 0.001), and a larger diameter (P < 0.001). The developed CT radiomics scoring model comprised of the following formula: X = -6.773 + 12.0705×radiomics score+2.5313×lobulation (present: 1; no present: 0)+3.1761×spiculation (present: 1; no present: 0)+0.3253×diameter. The area under the curve (AUC) values of the CT radiomics-based model, CT radiomics score, and clinicoradiological score were 0.957, 0.945, and 0.853, respectively, in the training cohort, while that of the testing cohort were 0.943, 0.916, and 0.816, respectively.</p>Conclusions<p>The CT radiomics-based model designed in the present study offers valuable diagnostic accuracy in distinguishing benign and malignant SPNs.</p>2025-02-13T05:11:48ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fonc.2025.1502932.s001https://figshare.com/articles/dataset/Data_Sheet_1_CT_radiomics_based_model_for_differentiating_malignant_and_benign_small_20mm_solid_pulmonary_nodules_csv/28406558CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284065582025-02-13T05:11:48Z |
| spellingShingle | Data Sheet 1_CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules.csv Jing-Xi Sun (20717528) Oncology and Carcinogenesis not elsewhere classified CT radiomics pulmonary nodule small prediction |
| status_str | publishedVersion |
| title | Data Sheet 1_CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules.csv |
| title_full | Data Sheet 1_CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules.csv |
| title_fullStr | Data Sheet 1_CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules.csv |
| title_full_unstemmed | Data Sheet 1_CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules.csv |
| title_short | Data Sheet 1_CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules.csv |
| title_sort | Data Sheet 1_CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules.csv |
| topic | Oncology and Carcinogenesis not elsewhere classified CT radiomics pulmonary nodule small prediction |