Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer
The accurate prediction of the status of PLNM preoperatively plays a key role in treatment strategy decisions in early-stage cervical cancer. The aim of this study was to develop and validate a radiomics-based nomogram for the preoperative prediction of pelvic lymph node metastatic status in early-s...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/12/10/2446 |
| _version_ | 1797473794664693760 |
|---|---|
| author | Xueming Xia Dongdong Li Wei Du Yu Wang Shihong Nie Qiaoyue Tan Qiheng Gou |
| author_facet | Xueming Xia Dongdong Li Wei Du Yu Wang Shihong Nie Qiaoyue Tan Qiheng Gou |
| author_sort | Xueming Xia |
| collection | DOAJ |
| description | The accurate prediction of the status of PLNM preoperatively plays a key role in treatment strategy decisions in early-stage cervical cancer. The aim of this study was to develop and validate a radiomics-based nomogram for the preoperative prediction of pelvic lymph node metastatic status in early-stage cervical cancer. One hundred fifty patients were enrolled in this study. Radiomics features were extracted from T2-weighted MRI imaging (T2WI). Based on the selected features, a support vector machine (SVM) algorithm was used to build the radiomics signature. The radiomics-based nomogram was developed incorporating radiomics signature and clinical risk factors. In the training cohort (AUC = 0.925, accuracy = 81.6%, sensitivity = 70.3%, and specificity = 92.0%) and the testing cohort (AUC = 0.839, accuracy = 74.2%, sensitivity = 65.7%, and specificity = 82.8%), clinical models that combine stromal invasion depth, FIGO stage, and MTD perform poorly. The combined model had the highest AUC in the training cohort (AUC = 0.988, accuracy = 95.9%, sensitivity = 92.0%, and specificity = 100.0%) and the testing cohort (AUC = 0.922, accuracy = 87.1%, sensitivity = 85.7%, and specificity = 88.6%) when compared to the radiomics and clinical models. The study may provide valuable guidance for clinical physicians regarding the treatment strategies for early-stage cervical cancer patients. |
| first_indexed | 2024-03-09T20:22:28Z |
| format | Article |
| id | doaj.art-8fc63a39e0f048fea38c01ca79e95c43 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T20:22:28Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-8fc63a39e0f048fea38c01ca79e95c432023-11-23T23:45:27ZengMDPI AGDiagnostics2075-44182022-10-011210244610.3390/diagnostics12102446Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical CancerXueming Xia0Dongdong Li1Wei Du2Yu Wang3Shihong Nie4Qiaoyue Tan5Qiheng Gou6Department of Head and Neck Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, ChinaDepartment of Network Engineering, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, ChinaDepartment of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, ChinaDepartment of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 402103, ChinaDepartment of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, ChinaDepartment of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, ChinaDepartment of Head and Neck Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, ChinaThe accurate prediction of the status of PLNM preoperatively plays a key role in treatment strategy decisions in early-stage cervical cancer. The aim of this study was to develop and validate a radiomics-based nomogram for the preoperative prediction of pelvic lymph node metastatic status in early-stage cervical cancer. One hundred fifty patients were enrolled in this study. Radiomics features were extracted from T2-weighted MRI imaging (T2WI). Based on the selected features, a support vector machine (SVM) algorithm was used to build the radiomics signature. The radiomics-based nomogram was developed incorporating radiomics signature and clinical risk factors. In the training cohort (AUC = 0.925, accuracy = 81.6%, sensitivity = 70.3%, and specificity = 92.0%) and the testing cohort (AUC = 0.839, accuracy = 74.2%, sensitivity = 65.7%, and specificity = 82.8%), clinical models that combine stromal invasion depth, FIGO stage, and MTD perform poorly. The combined model had the highest AUC in the training cohort (AUC = 0.988, accuracy = 95.9%, sensitivity = 92.0%, and specificity = 100.0%) and the testing cohort (AUC = 0.922, accuracy = 87.1%, sensitivity = 85.7%, and specificity = 88.6%) when compared to the radiomics and clinical models. The study may provide valuable guidance for clinical physicians regarding the treatment strategies for early-stage cervical cancer patients.https://www.mdpi.com/2075-4418/12/10/2446cervical cancerpelvic lymph node metastasisradiomicsnomogram |
| spellingShingle | Xueming Xia Dongdong Li Wei Du Yu Wang Shihong Nie Qiaoyue Tan Qiheng Gou Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer Diagnostics cervical cancer pelvic lymph node metastasis radiomics nomogram |
| title | Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer |
| title_full | Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer |
| title_fullStr | Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer |
| title_full_unstemmed | Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer |
| title_short | Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer |
| title_sort | radiomics based on nomogram predict pelvic lymphnode metastasis in early stage cervical cancer |
| topic | cervical cancer pelvic lymph node metastasis radiomics nomogram |
| url | https://www.mdpi.com/2075-4418/12/10/2446 |
| work_keys_str_mv | AT xuemingxia radiomicsbasedonnomogrampredictpelviclymphnodemetastasisinearlystagecervicalcancer AT dongdongli radiomicsbasedonnomogrampredictpelviclymphnodemetastasisinearlystagecervicalcancer AT weidu radiomicsbasedonnomogrampredictpelviclymphnodemetastasisinearlystagecervicalcancer AT yuwang radiomicsbasedonnomogrampredictpelviclymphnodemetastasisinearlystagecervicalcancer AT shihongnie radiomicsbasedonnomogrampredictpelviclymphnodemetastasisinearlystagecervicalcancer AT qiaoyuetan radiomicsbasedonnomogrampredictpelviclymphnodemetastasisinearlystagecervicalcancer AT qihenggou radiomicsbasedonnomogrampredictpelviclymphnodemetastasisinearlystagecervicalcancer |