Predictive Value of <sup>18</sup>F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer
Background: To develop and validate a radiomics model based on <sup>18</sup>F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients...
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MDPI AG
2022-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/4/997 |
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| author | Kun Chen Guotao Yin Wengui Xu |
| author_facet | Kun Chen Guotao Yin Wengui Xu |
| author_sort | Kun Chen |
| collection | DOAJ |
| description | Background: To develop and validate a radiomics model based on <sup>18</sup>F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients (mean age, 55 years; range, 31–82 years) with pathologically proven IDC and a preoperative <sup>18</sup>F-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, <i>t</i>-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661–0.929 (mean AUC, 0.817), and the accuracy range was 65.3–93.9% (mean accuracy, 81.2%). The <i>p</i>-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative <sup>18</sup>F-FDG PET/CT radiomic features. |
| first_indexed | 2024-03-09T10:56:34Z |
| format | Article |
| id | doaj.art-b68af402472c46538c3ea475a103890f |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T10:56:34Z |
| publishDate | 2022-04-01 |
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| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-b68af402472c46538c3ea475a103890f2023-12-01T01:35:25ZengMDPI AGDiagnostics2075-44182022-04-0112499710.3390/diagnostics12040997Predictive Value of <sup>18</sup>F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast CancerKun Chen0Guotao Yin1Wengui Xu2Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, ChinaDepartment of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, ChinaDepartment of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, ChinaBackground: To develop and validate a radiomics model based on <sup>18</sup>F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients (mean age, 55 years; range, 31–82 years) with pathologically proven IDC and a preoperative <sup>18</sup>F-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, <i>t</i>-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661–0.929 (mean AUC, 0.817), and the accuracy range was 65.3–93.9% (mean accuracy, 81.2%). The <i>p</i>-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative <sup>18</sup>F-FDG PET/CT radiomic features.https://www.mdpi.com/2075-4418/12/4/997radiomics<sup>18</sup>F-FDG PET/CTinvasive ductal breast cancerclinically negative axillary lymph node |
| spellingShingle | Kun Chen Guotao Yin Wengui Xu Predictive Value of <sup>18</sup>F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer Diagnostics radiomics <sup>18</sup>F-FDG PET/CT invasive ductal breast cancer clinically negative axillary lymph node |
| title | Predictive Value of <sup>18</sup>F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer |
| title_full | Predictive Value of <sup>18</sup>F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer |
| title_fullStr | Predictive Value of <sup>18</sup>F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer |
| title_full_unstemmed | Predictive Value of <sup>18</sup>F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer |
| title_short | Predictive Value of <sup>18</sup>F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer |
| title_sort | predictive value of sup 18 sup f fdg pet ct based radiomics model for occult axillary lymph node metastasis in clinically node negative breast cancer |
| topic | radiomics <sup>18</sup>F-FDG PET/CT invasive ductal breast cancer clinically negative axillary lymph node |
| url | https://www.mdpi.com/2075-4418/12/4/997 |
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