Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics
ObjectiveHigh-grade serous ovarian cancer (HGSOC) has the highest mortality rate among female reproductive system tumors. Accurate preoperative assessment is crucial for treatment planning. This study aims to develop multitask prediction models for HGSOC using radiomics analysis based on preoperativ...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1334062/full |
| _version_ | 1797323601368580096 |
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| author | Le Fu Wenjing Wang Lingling Lin Feng Gao Jiani Yang Yunyun Lv Ruiqiu Ge Meixuan Wu Lei Chen Aie Liu Enhui Xin Jianli Yu Jiejun Cheng Yu Wang |
| author_facet | Le Fu Wenjing Wang Lingling Lin Feng Gao Jiani Yang Yunyun Lv Ruiqiu Ge Meixuan Wu Lei Chen Aie Liu Enhui Xin Jianli Yu Jiejun Cheng Yu Wang |
| author_sort | Le Fu |
| collection | DOAJ |
| description | ObjectiveHigh-grade serous ovarian cancer (HGSOC) has the highest mortality rate among female reproductive system tumors. Accurate preoperative assessment is crucial for treatment planning. This study aims to develop multitask prediction models for HGSOC using radiomics analysis based on preoperative CT images.MethodsThis study enrolled 112 patients diagnosed with HGSOC. Laboratory findings, including serum levels of CA125, HE-4, and NLR, were collected. Radiomic features were extracted from manually delineated ROI on CT images by two radiologists. Classification models were developed using selected optimal feature sets to predict R0 resection, lymph node invasion, and distant metastasis status. Model evaluation was conducted by quantifying receiver operating curves (ROC), calculating the area under the curve (AUC), De Long’s test.ResultsThe radiomics models applied to CT images demonstrated superior performance in the testing set compared to the clinical models. The area under the curve (AUC) values for the combined model in predicting R0 resection were 0.913 and 0.881 in the training and testing datasets, respectively. De Long’s test indicated significant differences between the combined and clinical models in the testing set (p = 0.003). For predicting lymph node invasion, the AUCs of the combined model were 0.868 and 0.800 in the training and testing datasets, respectively. The results also revealed significant differences between the combined and clinical models in the testing set (p = 0.002). The combined model for predicting distant metastasis achieved AUCs of 0.872 and 0.796 in the training and test datasets, respectively. The combined model displayed excellent agreement between observed and predicted results in predicting R0 resection, while the radiomics model demonstrated better calibration than both the clinical model and combined model in predicting lymph node invasion and distant metastasis. The decision curve analysis (DCA) for predicting R0 resection favored the combined model over both the clinical and radiomics models, whereas for predicting lymph node invasion and distant metastasis, DCA favored the radiomics model over both the clinical model and combined model.ConclusionThe identified radiomics signature holds potential value in preoperatively evaluating the R0, lymph node invasion and distant metastasis in patients with HGSC. The radiomics nomogram demonstrated the incremental value of clinical predictors for surgical outcome and metastasis estimation. |
| first_indexed | 2024-03-08T05:30:59Z |
| format | Article |
| id | doaj.art-82e9999ea9a7411687b8ac56f7fe986a |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-03-08T05:30:59Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-82e9999ea9a7411687b8ac56f7fe986a2024-02-06T04:57:17ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2024-02-011110.3389/fmed.2024.13340621334062Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomicsLe Fu0Wenjing Wang1Lingling Lin2Feng Gao3Jiani Yang4Yunyun Lv5Ruiqiu Ge6Meixuan Wu7Lei Chen8Aie Liu9Enhui Xin10Jianli Yu11Jiejun Cheng12Yu Wang13Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaShanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaShanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaShanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaDepartment of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaDepartment of Obstetrics and Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaObjectiveHigh-grade serous ovarian cancer (HGSOC) has the highest mortality rate among female reproductive system tumors. Accurate preoperative assessment is crucial for treatment planning. This study aims to develop multitask prediction models for HGSOC using radiomics analysis based on preoperative CT images.MethodsThis study enrolled 112 patients diagnosed with HGSOC. Laboratory findings, including serum levels of CA125, HE-4, and NLR, were collected. Radiomic features were extracted from manually delineated ROI on CT images by two radiologists. Classification models were developed using selected optimal feature sets to predict R0 resection, lymph node invasion, and distant metastasis status. Model evaluation was conducted by quantifying receiver operating curves (ROC), calculating the area under the curve (AUC), De Long’s test.ResultsThe radiomics models applied to CT images demonstrated superior performance in the testing set compared to the clinical models. The area under the curve (AUC) values for the combined model in predicting R0 resection were 0.913 and 0.881 in the training and testing datasets, respectively. De Long’s test indicated significant differences between the combined and clinical models in the testing set (p = 0.003). For predicting lymph node invasion, the AUCs of the combined model were 0.868 and 0.800 in the training and testing datasets, respectively. The results also revealed significant differences between the combined and clinical models in the testing set (p = 0.002). The combined model for predicting distant metastasis achieved AUCs of 0.872 and 0.796 in the training and test datasets, respectively. The combined model displayed excellent agreement between observed and predicted results in predicting R0 resection, while the radiomics model demonstrated better calibration than both the clinical model and combined model in predicting lymph node invasion and distant metastasis. The decision curve analysis (DCA) for predicting R0 resection favored the combined model over both the clinical and radiomics models, whereas for predicting lymph node invasion and distant metastasis, DCA favored the radiomics model over both the clinical model and combined model.ConclusionThe identified radiomics signature holds potential value in preoperatively evaluating the R0, lymph node invasion and distant metastasis in patients with HGSC. The radiomics nomogram demonstrated the incremental value of clinical predictors for surgical outcome and metastasis estimation.https://www.frontiersin.org/articles/10.3389/fmed.2024.1334062/fullradiomicspreoperative evaluationserous ovarian cancercomputer tomographynomogram |
| spellingShingle | Le Fu Wenjing Wang Lingling Lin Feng Gao Jiani Yang Yunyun Lv Ruiqiu Ge Meixuan Wu Lei Chen Aie Liu Enhui Xin Jianli Yu Jiejun Cheng Yu Wang Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics Frontiers in Medicine radiomics preoperative evaluation serous ovarian cancer computer tomography nomogram |
| title | Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics |
| title_full | Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics |
| title_fullStr | Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics |
| title_full_unstemmed | Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics |
| title_short | Multitask prediction models for serous ovarian cancer by preoperative CT image assessments based on radiomics |
| title_sort | multitask prediction models for serous ovarian cancer by preoperative ct image assessments based on radiomics |
| topic | radiomics preoperative evaluation serous ovarian cancer computer tomography nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1334062/full |
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