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Huang Y, Zhu Y, Yang Q, Luo Y, Zhang P, Yang X, Ren J, Ren Y, Lang J, Xu G. Automatic tumor segmentation and metachronous single-organ metastasis prediction of nasopharyngeal carcinoma patients based on multi-sequence magnetic resonance imaging. Front Oncol 2023; 13:953893. [PMID: 37064158 PMCID: PMC10099248 DOI: 10.3389/fonc.2023.953893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 03/07/2023] [Indexed: 03/30/2023] Open
Abstract
BackgroundDistant metastases is the main failure mode of nasopharyngeal carcinoma. However, early prediction of distant metastases in NPC is extremely challenging. Deep learning has made great progress in recent years. Relying on the rich data features of radiomics and the advantages of deep learning in image representation and intelligent learning, this study intends to explore and construct the metachronous single-organ metastases (MSOM) based on multimodal magnetic resonance imaging.Patients and methodsThe magnetic resonance imaging data of 186 patients with nasopharyngeal carcinoma before treatment were collected, and the gross tumor volume (GTV) and metastatic lymph nodes (GTVln) prior to treatment were defined on T1WI, T2WI, and CE-T1WI. After image normalization, the deep learning platform Python (version 3.9.12) was used in Ubuntu 20.04.1 LTS to construct automatic tumor detection and the MSOM prediction model.ResultsThere were 85 of 186 patients who had MSOM (including 32 liver metastases, 25 lung metastases, and 28 bone metastases). The median time to MSOM was 13 months after treatment (7–36 months). The patients were randomly assigned to the training set (N = 140) and validation set (N = 46). By comparison, we found that the overall performance of the automatic tumor detection model based on CE-T1WI was the best (6). The performance of automatic detection for primary tumor (GTV) and lymph node gross tumor volume (GTVln) based on the CE-T1WI model was better than that of models based on T1WI and T2WI (AP@0.5 is 59.6 and 55.6). The prediction model based on CE-T1WI for MSOM prediction achieved the best overall performance, and it obtained the largest AUC value (AUC = 0.733) in the validation set. The precision, recall, precision, and AUC of the prediction model based on CE-T1WI are 0.727, 0.533, 0.730, and 0.733 (95% CI 0.557–0.909), respectively. When clinical data were added to the deep learning prediction model, a better performance of the model could be obtained; the AUC of the integrated model based on T2WI, T1WI, and CE-T1WI were 0.719, 0.738, and 0.775, respectively. By comparing the 3-year survival of high-risk and low-risk patients based on the fusion model, we found that the 3-year DMFS of low and high MSOM risk patients were 95% and 11.4%, respectively (p < 0.001).ConclusionThe intelligent prediction model based on magnetic resonance imaging alone or combined with clinical data achieves excellent performance in automatic tumor detection and MSOM prediction for NPC patients and is worthy of clinical application.
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Affiliation(s)
- Yecai Huang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China
| | - Yuxin Zhu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Qiang Yang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China
| | - Yangkun Luo
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhang
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Xuegang Yang
- Department of Interventional Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yazhou Ren
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- *Correspondence: Yazhou Ren, ; Jinyi Lang, ; Guohui Xu,
| | - Jinyi Lang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- *Correspondence: Yazhou Ren, ; Jinyi Lang, ; Guohui Xu,
| | - Guohui Xu
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Interventional Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- *Correspondence: Yazhou Ren, ; Jinyi Lang, ; Guohui Xu,
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Li WZ, Wu G, Li TS, Dai GM, Liao YT, Yang QY, Chen F, Huang WY. Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma. Front Oncol 2022; 12:955866. [PMID: 36338711 PMCID: PMC9627984 DOI: 10.3389/fonc.2022.955866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 09/05/2022] [Indexed: 08/30/2023] Open
Abstract
To establish a multidimensional nomogram model for predicting progression-free survival (PFS) and risk stratification in patients with advanced nasopharyngeal carcinoma (NPC). This retrospective cross-sectional study included 156 patients with advanced NPC who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Radiomic features were extracted from the efflux rate constant (Ktrans ) and extracellular extravascular volume (Ve ) mapping derived from DCE-MRI. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied for feature selection. The Radscore was constructed using the selected features with their respective weights in the LASSO Cox regression analysis. A nomogram model combining the Radscore and clinical factors was built using multivariate Cox regression analysis. The C-index was used to assess the discrimination power of the Radscore and nomogram. The Kaplan-Meier method was used for survival analysis. Of the 360 radiomic features, 28 were selected (7, 6, and 15 features extracted from Ktrans , Ve, and Ktrans +Ve images, respectively). The combined Radscore k trans +Ve (C-index, 0.703, 95% confidence interval [CI]: 0.571-0.836) showed higher efficacy in predicting the prognosis of advanced NPC than Radscore k trans (C-index, 0.693; 95% CI, 0.560-0.826) and Radscore Ve (C-index, 0.614; 95% CI, 0.481-0.746) did. Multivariable Cox regression analysis revealed clinical stage, T stage, and treatment with nimotuzumab as risk factors for PFS. The nomogram established by Radscore k trans +Ve and risk factors (C-index, 0.732; 95% CI: 0.599-0.864) was better than Radscore k trans +Ve in predicting PFS in patients with advanced NPC. A lower Radscore k trans +Ve (HR 3.5584, 95% CI 2.1341-5.933), lower clinical stage (hazard ratio [HR] 1.5982, 95% CI 0.5262-4.854), lower T stage (HR 1.4365, 95% CI 0.6745-3.060), and nimotuzumab (NTZ) treatment (HR 0.7879, 95% CI 0.4899-1.267) were associated with longer PFS. Kaplan-Meier analysis showed a lower PFS in the high-risk group than in the low-risk group (p<0.0001). The nomogram based on combined pretreatment DCE-MRI radiomics features, NTZ, and clinicopathological risk factors may be considered as a noninvasive imaging marker for predicting individual PFS in patients with advanced NPC.
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Affiliation(s)
- Wen-zhu Li
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Tian-sheng Li
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Gan-mian Dai
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Yu-ting Liao
- Department of Pharmaceutical Diagnostics, GE Healthcare, Guangzhou, China
| | - Qian-yu Yang
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Wei-yuan Huang
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
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Zhang S, Li Y, Xin S, Yang L, Jiang M, Xin Y, Wang Y, Yang J, Lu J. Insight into LncRNA- and CircRNA-Mediated CeRNAs: Regulatory Network and Implications in Nasopharyngeal Carcinoma—A Narrative Literature Review. Cancers (Basel) 2022; 14:4564. [PMID: 36230487 PMCID: PMC9559536 DOI: 10.3390/cancers14194564] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/16/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is a kind of head-and-neck malignant tumor, and distant metastasis treatment resistance is the leading cause of patient death. In-depth understanding of NPC progression and treatment failure remains to be explored. Long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) are noncoding RNAs that play key regulatory role in shaping tumor cell activities. Recent studies have revealed that lncRNA and circRNA function as competitive endogenous RNAs (ceRNAs) by regulating the posttranscriptional expression of genes as miRNA baits. The imbalanced ceRNA networks derived from lncRNA/circRNA-miRNA-mRNA interaction are widely found to contribute to NPC development. Herein, we summarize typical examples of lncRNA/circRNA-associated ceRNAs in recent years, which involved the potential molecular mechanisms in the regulation of proliferation, apoptosis, treatment resistance and metastasis of NPC, and discuss their potential clinical significance in the prognosis and treatment of NPC. Interpreting the involvement of ceRNAs networks will provide new insight into the pathogenesis and treatment strategies of NPC. However, ceRNA regulatory mechanism has some limitations currently. Screening the most effective ceRNA targets and the clinical application of ceRNA still has many challenges.
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