Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer
This study aims to use a deep learning method to develop a signature extract from preoperative magnetic resonance imaging (MRI) and to evaluate its ability as a non-invasive recurrence risk prognostic marker in patients with advanced high-grade serous ovarian cancer (HGSOC). Our study comprises a to...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/4/748 |
| _version_ | 1797621479705149440 |
|---|---|
| author | Lili Liu Haoming Wan Li Liu Jie Wang Yibo Tang Shaoguo Cui Yongmei Li |
| author_facet | Lili Liu Haoming Wan Li Liu Jie Wang Yibo Tang Shaoguo Cui Yongmei Li |
| author_sort | Lili Liu |
| collection | DOAJ |
| description | This study aims to use a deep learning method to develop a signature extract from preoperative magnetic resonance imaging (MRI) and to evaluate its ability as a non-invasive recurrence risk prognostic marker in patients with advanced high-grade serous ovarian cancer (HGSOC). Our study comprises a total of 185 patients with pathologically confirmed HGSOC. A total of 185 patients were randomly assigned in a 5:3:2 ratio to a training cohort (n = 92), validation cohort 1 (n = 56), and validation cohort 2 (n = 37). We built a new deep learning network from 3839 preoperative MRI images (T2-weighted images and diffusion-weighted images) to extract HGSOC prognostic indicators. Following that, a fusion model including clinical and deep learning features is developed to predict patients’ individual recurrence risk and 3-year recurrence likelihood. In the two validation cohorts, the consistency index of the fusion model was higher than both the deep learning model and the clinical feature model (0.752, 0.813 vs. 0.625, 0.600 vs. 0.505, 0.501). Among the three models, the fusion model had a higher AUC than either the deep learning model or the clinical model in validation cohorts 1 or 2 (AUC = was 0.986, 0.961 vs. 0.706, 0.676/0.506, 0.506). Using the DeLong method, the difference between them was statistically significant (<i>p</i> < 0.05). The Kaplan–Meier analysis distinguished two patient groups with high and low recurrence risk (<i>p</i> = 0.0008 and 0.0035, respectively). Deep learning may be a low-cost, non-invasive method for predicting risk for advanced HGSOC recurrence. Deep learning based on multi-sequence MRI serves as a prognostic biomarker for advanced HGSOC, which provides a preoperative model for predicting recurrence in HGSOC. Additionally, using the fusion model as a new prognostic analysis means that can use MRI data can be used without the need to follow-up the prognostic biomarker. |
| first_indexed | 2024-03-11T08:56:31Z |
| format | Article |
| id | doaj.art-05e941c2358a4a0f97d917ab951ec847 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T08:56:31Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-05e941c2358a4a0f97d917ab951ec8472023-11-16T20:02:25ZengMDPI AGDiagnostics2075-44182023-02-0113474810.3390/diagnostics13040748Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian CancerLili Liu0Haoming Wan1Li Liu2Jie Wang3Yibo Tang4Shaoguo Cui5Yongmei Li6Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, ChinaCollege of Computer and Information Science, Chongqing Normal University, Chongqing 400016, ChinaDepartment of Radiology, The People’s Hospital of Yubei District of Chongqing, Chongqing 401120, ChinaDepartment of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, ChinaCollege of Computer and Information Science, Chongqing Normal University, Chongqing 400016, ChinaCollege of Computer and Information Science, Chongqing Normal University, Chongqing 400016, ChinaDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, ChinaThis study aims to use a deep learning method to develop a signature extract from preoperative magnetic resonance imaging (MRI) and to evaluate its ability as a non-invasive recurrence risk prognostic marker in patients with advanced high-grade serous ovarian cancer (HGSOC). Our study comprises a total of 185 patients with pathologically confirmed HGSOC. A total of 185 patients were randomly assigned in a 5:3:2 ratio to a training cohort (n = 92), validation cohort 1 (n = 56), and validation cohort 2 (n = 37). We built a new deep learning network from 3839 preoperative MRI images (T2-weighted images and diffusion-weighted images) to extract HGSOC prognostic indicators. Following that, a fusion model including clinical and deep learning features is developed to predict patients’ individual recurrence risk and 3-year recurrence likelihood. In the two validation cohorts, the consistency index of the fusion model was higher than both the deep learning model and the clinical feature model (0.752, 0.813 vs. 0.625, 0.600 vs. 0.505, 0.501). Among the three models, the fusion model had a higher AUC than either the deep learning model or the clinical model in validation cohorts 1 or 2 (AUC = was 0.986, 0.961 vs. 0.706, 0.676/0.506, 0.506). Using the DeLong method, the difference between them was statistically significant (<i>p</i> < 0.05). The Kaplan–Meier analysis distinguished two patient groups with high and low recurrence risk (<i>p</i> = 0.0008 and 0.0035, respectively). Deep learning may be a low-cost, non-invasive method for predicting risk for advanced HGSOC recurrence. Deep learning based on multi-sequence MRI serves as a prognostic biomarker for advanced HGSOC, which provides a preoperative model for predicting recurrence in HGSOC. Additionally, using the fusion model as a new prognostic analysis means that can use MRI data can be used without the need to follow-up the prognostic biomarker.https://www.mdpi.com/2075-4418/13/4/748high-grade serous ovarian cancerdeep learningmagnetic resonance imaging |
| spellingShingle | Lili Liu Haoming Wan Li Liu Jie Wang Yibo Tang Shaoguo Cui Yongmei Li Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer Diagnostics high-grade serous ovarian cancer deep learning magnetic resonance imaging |
| title | Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer |
| title_full | Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer |
| title_fullStr | Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer |
| title_full_unstemmed | Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer |
| title_short | Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer |
| title_sort | deep learning provides a new magnetic resonance imaging based prognostic biomarker for recurrence prediction in high grade serous ovarian cancer |
| topic | high-grade serous ovarian cancer deep learning magnetic resonance imaging |
| url | https://www.mdpi.com/2075-4418/13/4/748 |
| work_keys_str_mv | AT lililiu deeplearningprovidesanewmagneticresonanceimagingbasedprognosticbiomarkerforrecurrencepredictioninhighgradeserousovariancancer AT haomingwan deeplearningprovidesanewmagneticresonanceimagingbasedprognosticbiomarkerforrecurrencepredictioninhighgradeserousovariancancer AT liliu deeplearningprovidesanewmagneticresonanceimagingbasedprognosticbiomarkerforrecurrencepredictioninhighgradeserousovariancancer AT jiewang deeplearningprovidesanewmagneticresonanceimagingbasedprognosticbiomarkerforrecurrencepredictioninhighgradeserousovariancancer AT yibotang deeplearningprovidesanewmagneticresonanceimagingbasedprognosticbiomarkerforrecurrencepredictioninhighgradeserousovariancancer AT shaoguocui deeplearningprovidesanewmagneticresonanceimagingbasedprognosticbiomarkerforrecurrencepredictioninhighgradeserousovariancancer AT yongmeili deeplearningprovidesanewmagneticresonanceimagingbasedprognosticbiomarkerforrecurrencepredictioninhighgradeserousovariancancer |