Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy
Abstract This study aimed to assess the feasibility of using magnetic resonance imaging (MRI)-based Delta radiomics characteristics extrapolated from the Ax LAVA + C series to identify intermediary- and high-risk factors in patients with cervical cancer undergoing surgery following neoadjuvant chemo...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46621-y |
| _version_ | 1797630213067112448 |
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| author | Rong-Rong Wu Yi-Min Zhou Xing-Yun Xie Jin-Yang Chen Ke-Run Quan Yu-Ting Wei Xiao-Yi Xia Wen-Juan Chen |
| author_facet | Rong-Rong Wu Yi-Min Zhou Xing-Yun Xie Jin-Yang Chen Ke-Run Quan Yu-Ting Wei Xiao-Yi Xia Wen-Juan Chen |
| author_sort | Rong-Rong Wu |
| collection | DOAJ |
| description | Abstract This study aimed to assess the feasibility of using magnetic resonance imaging (MRI)-based Delta radiomics characteristics extrapolated from the Ax LAVA + C series to identify intermediary- and high-risk factors in patients with cervical cancer undergoing surgery following neoadjuvant chemoradiotherapy. A total of 157 patients were divided into two groups: those without any intermediary- or high-risk factors and those with one intermediary-risk factor (negative group; n = 75). Those with any high-risk factor or more than one intermediary-risk factor (positive group; n = 82). Radiomics characteristics were extracted using Ax-LAVA + C MRI sequences. The data was divided into training (n = 126) and test (n = 31) sets in an 8:2 ratio. The training set data features were selected using the Mann–Whitney U test and the Least Absolute Shrinkage and Selection Operator (LASSO) test. The best radiomics features were then analyzed to build a preoperative predictive radiomics model for predicting intermediary- and high-risk factors in cervical cancer. Three models—the clinical model, the radiomics model, and the combined clinic and radiomics model—were developed in this study utilizing the random forest Algorithm. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity were used to assess the predictive efficacy and clinical benefits of each model. Three models were developed in this study to predict intermediary- and high-risk variables associated with postoperative pathology for patients who underwent surgery after receiving neoadjuvant radiation. In the training and test sets, the AUC values assessed using the clinical model, radiomics model, and combined clinical and radiomics models were 0.76 and 0.70, 0.88 and 0.86, and 0.91 and 0.89, respectively. The use of machine learning algorithms to analyze Delta Ax LAVA + C MRI radiomics features can aid in the prediction of intermediary- and high-risk factors in patients with cervical cancer receiving neoadjuvant therapy. |
| first_indexed | 2024-03-11T11:05:00Z |
| format | Article |
| id | doaj.art-0f1d7a1824474e018c7577aca0f380c5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-11T11:05:00Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0f1d7a1824474e018c7577aca0f380c52023-11-12T12:14:46ZengNature PortfolioScientific Reports2045-23222023-11-011311810.1038/s41598-023-46621-yDelta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapyRong-Rong Wu0Yi-Min Zhou1Xing-Yun Xie2Jin-Yang Chen3Ke-Run Quan4Yu-Ting Wei5Xiao-Yi Xia6Wen-Juan Chen7Department of Radiation Oncology, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalSchool of Nuclear Science and Technology, University of South ChinaDepartment of Radiation Oncology, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalCollege of Computer and Cyber Security, Fujian Normal UniversityDepartment of Radiation Oncology, Xiangtan City Central Hospital XiangtanDepartment of Radiation Oncology, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Radiation Oncology, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Radiation Oncology, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalAbstract This study aimed to assess the feasibility of using magnetic resonance imaging (MRI)-based Delta radiomics characteristics extrapolated from the Ax LAVA + C series to identify intermediary- and high-risk factors in patients with cervical cancer undergoing surgery following neoadjuvant chemoradiotherapy. A total of 157 patients were divided into two groups: those without any intermediary- or high-risk factors and those with one intermediary-risk factor (negative group; n = 75). Those with any high-risk factor or more than one intermediary-risk factor (positive group; n = 82). Radiomics characteristics were extracted using Ax-LAVA + C MRI sequences. The data was divided into training (n = 126) and test (n = 31) sets in an 8:2 ratio. The training set data features were selected using the Mann–Whitney U test and the Least Absolute Shrinkage and Selection Operator (LASSO) test. The best radiomics features were then analyzed to build a preoperative predictive radiomics model for predicting intermediary- and high-risk factors in cervical cancer. Three models—the clinical model, the radiomics model, and the combined clinic and radiomics model—were developed in this study utilizing the random forest Algorithm. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity were used to assess the predictive efficacy and clinical benefits of each model. Three models were developed in this study to predict intermediary- and high-risk variables associated with postoperative pathology for patients who underwent surgery after receiving neoadjuvant radiation. In the training and test sets, the AUC values assessed using the clinical model, radiomics model, and combined clinical and radiomics models were 0.76 and 0.70, 0.88 and 0.86, and 0.91 and 0.89, respectively. The use of machine learning algorithms to analyze Delta Ax LAVA + C MRI radiomics features can aid in the prediction of intermediary- and high-risk factors in patients with cervical cancer receiving neoadjuvant therapy.https://doi.org/10.1038/s41598-023-46621-y |
| spellingShingle | Rong-Rong Wu Yi-Min Zhou Xing-Yun Xie Jin-Yang Chen Ke-Run Quan Yu-Ting Wei Xiao-Yi Xia Wen-Juan Chen Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy Scientific Reports |
| title | Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy |
| title_full | Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy |
| title_fullStr | Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy |
| title_full_unstemmed | Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy |
| title_short | Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy |
| title_sort | delta radiomics analysis for prediction of intermediary and high risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy |
| url | https://doi.org/10.1038/s41598-023-46621-y |
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