Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study
Abstract Background Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients w...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-04-01
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| Series: | Cancer Imaging |
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| Online Access: | https://doi.org/10.1186/s40644-024-00697-5 |
| _version_ | 1827276993254653952 |
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| author | Zhen Tian Yifan Cheng Shuai Zhao Ruiqi Li Jiajie Zhou Qiannan Sun Daorong Wang |
| author_facet | Zhen Tian Yifan Cheng Shuai Zhao Ruiqi Li Jiajie Zhou Qiannan Sun Daorong Wang |
| author_sort | Zhen Tian |
| collection | DOAJ |
| description | Abstract Background Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection. Methods A total of 179 patients who had undergone surgery for the treatment of histologically confirmed RLS were retrospectively recruited from two tertiary sarcoma centers. Semantic segmentation features derived from a convolutional neural network deep learning model as well as conventional hand-crafted radiomics features were extracted from preoperative three-phase CECT images to quantify the sarcoma phenotypes. A conventional radiomics signature (RS) and a deep learning radiomics signature (DLRS) that incorporated hand-crafted radiomics and deep learning features were developed to predict the risk of MDM. Additionally, a deep learning radiomics nomogram (DLRN) was established to evaluate the incremental prognostic significance of the DLRS in combination with clinico-radiological predictors. Results The comparison of the area under the curve (AUC) values in the external validation set, as determined by the DeLong test, demonstrated that the integrated DLRN, DLRS, and RS models all exhibited superior predictive performance compared with that of the clinical model (AUC 0.786 [95% confidence interval 0.649–0.923] vs. 0.822 [0.692–0.952] vs. 0.733 [0.573–0.892] vs. 0.511 [0.359–0.662]; both P < 0.05). The decision curve analyses graphically indicated that utilizing the DLRN for risk stratification provided greater net benefits than those achieved using the DLRS, RS and clinical models. Good alignment with the calibration curve indicated that the DLRN also exhibited good performance. Conclusions The novel CECT-based DLRN developed in this study demonstrated promising performance in the preoperative prediction of the risk of MDM following curative resection in patients with RLS. The DLRN, which outperformed the other three models, could provide valuable information for predicting surgical efficacy and tailoring individualized treatment plans in this patient population. Trial registration : Not applicable. |
| first_indexed | 2024-04-24T07:12:17Z |
| format | Article |
| id | doaj.art-d2af5ea38c4145c88a2c2f3b7e9f98be |
| institution | Directory Open Access Journal |
| issn | 1470-7330 |
| language | English |
| last_indexed | 2024-04-24T07:12:17Z |
| publishDate | 2024-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj.art-d2af5ea38c4145c88a2c2f3b7e9f98be2024-04-21T11:28:51ZengBMCCancer Imaging1470-73302024-04-0124111310.1186/s40644-024-00697-5Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric studyZhen Tian0Yifan Cheng1Shuai Zhao2Ruiqi Li3Jiajie Zhou4Qiannan Sun5Daorong Wang6Northern Jiangsu People’s Hospital, Clinical Teaching Hospital of Medical School, Nanjing UniversityNorthern Jiangsu People’s Hospital, Clinical Teaching Hospital of Medical School, Nanjing UniversityNorthern Jiangsu People’s Hospital, Clinical Teaching Hospital of Medical School, Nanjing UniversityNorthern Jiangsu People’s Hospital, Clinical Teaching Hospital of Medical School, Nanjing UniversityNorthern Jiangsu People’s Hospital, Clinical Teaching Hospital of Medical School, Nanjing UniversityDepartment of General Surgery, Northern Jiangsu People’s HospitalNorthern Jiangsu People’s Hospital, Clinical Teaching Hospital of Medical School, Nanjing UniversityAbstract Background Combining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection. Methods A total of 179 patients who had undergone surgery for the treatment of histologically confirmed RLS were retrospectively recruited from two tertiary sarcoma centers. Semantic segmentation features derived from a convolutional neural network deep learning model as well as conventional hand-crafted radiomics features were extracted from preoperative three-phase CECT images to quantify the sarcoma phenotypes. A conventional radiomics signature (RS) and a deep learning radiomics signature (DLRS) that incorporated hand-crafted radiomics and deep learning features were developed to predict the risk of MDM. Additionally, a deep learning radiomics nomogram (DLRN) was established to evaluate the incremental prognostic significance of the DLRS in combination with clinico-radiological predictors. Results The comparison of the area under the curve (AUC) values in the external validation set, as determined by the DeLong test, demonstrated that the integrated DLRN, DLRS, and RS models all exhibited superior predictive performance compared with that of the clinical model (AUC 0.786 [95% confidence interval 0.649–0.923] vs. 0.822 [0.692–0.952] vs. 0.733 [0.573–0.892] vs. 0.511 [0.359–0.662]; both P < 0.05). The decision curve analyses graphically indicated that utilizing the DLRN for risk stratification provided greater net benefits than those achieved using the DLRS, RS and clinical models. Good alignment with the calibration curve indicated that the DLRN also exhibited good performance. Conclusions The novel CECT-based DLRN developed in this study demonstrated promising performance in the preoperative prediction of the risk of MDM following curative resection in patients with RLS. The DLRN, which outperformed the other three models, could provide valuable information for predicting surgical efficacy and tailoring individualized treatment plans in this patient population. Trial registration : Not applicable.https://doi.org/10.1186/s40644-024-00697-5Retroperitoneal leiomyosarcomaDistant metastasisDeep learningRadiomics |
| spellingShingle | Zhen Tian Yifan Cheng Shuai Zhao Ruiqi Li Jiajie Zhou Qiannan Sun Daorong Wang Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study Cancer Imaging Retroperitoneal leiomyosarcoma Distant metastasis Deep learning Radiomics |
| title | Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study |
| title_full | Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study |
| title_fullStr | Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study |
| title_full_unstemmed | Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study |
| title_short | Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study |
| title_sort | deep learning radiomics based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma a bicentric study |
| topic | Retroperitoneal leiomyosarcoma Distant metastasis Deep learning Radiomics |
| url | https://doi.org/10.1186/s40644-024-00697-5 |
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