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Deng Q, Hou Y, Zhang X, Zan H. A systematic review of the predictive value of radiomics for nasopharyngeal carcinoma prognosis. Medicine (Baltimore) 2024; 103:e39302. [PMID: 39213210 PMCID: PMC11365615 DOI: 10.1097/md.0000000000039302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 05/18/2024] [Accepted: 07/23/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Radiomics has been widely used in the study of tumours, which has predictive and prognostic value in nasopharyngeal carcinoma (NPC). Therefore, we collected relevant literature to explore the role of current radiomics in predicting the prognosis of NPC. METHODS We performed a systematic literature review and meta-analysis in accordance with the preferred reporting items in the systematic evaluation and meta-analysis guidelines. We included papers on radiomics published before May 5, 2024, to evaluate the predictive ability of radiomics for the prognosis of NPC. The methodological quality of the included articles was evaluated using the radiomics quality score. The area under the curve (AUC), combined sensitivity and combined specificity were used to evaluate the ability of radiomics models to predict the prognosis of NPC. RESULTS A total of 20 studies met the inclusion criteria for the current systematic review, and 13 papers were included in the meta-analysis. The radiomics quality score ranged from 7 to 20 (maximum score: 36). The diagnostic test forest plots showed that the diagnostic OR of radiology was 11.04 (95% CI: 5.11-23.87), while the ORs for sensitivity and 1-specificity were 0.75 (95% CI: 0.73-0.78) and 0.74 (95% CI: 0.72-0.76), respectively. It cannot be determined whether the combined model was superior to the radiomics model for predicting the prognosis of NPC. It is unclear whether the fact that the radiomics model was composed of features extracted from MRI is due to CT. The AUC of PFS was larger than that of disease-free survival (P < .05). The overall AUC value is 0.8265. CONCLUSION This study summarized all the studies that examined the predictive value of radiomics for NPC prognosis. Based on the summarized AUC values, as well as sensitivity and 1-specificity, it can be concluded that radiomics has good performance in predicting the prognosis of NPC. Radiomics models have certain advantages in predicting the effectiveness of PFS compared to predicting disease-free survival. It cannot be determined whether the combination model is superior to the radiomics model in predicting NPC prognosis, nor can it be determined whether imaging methods have differences in predictive ability. The findings confirmed and provided further evidence supporting the effectiveness of radiomics for the prediction of cancer prognosis.
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Affiliation(s)
- Qicheng Deng
- Department of Otolaryngology, Public Health Clinical Center of Chengdu, Chengdu, Sichuan Province, China
| | - Yijun Hou
- The Third People’s Hospital of Mianyang City, Mianyang, Sichuan Province, China
| | - Xi Zhang
- Nanchong Central Hospital, Sichuan Province, Nanchong, China
| | - Hongyu Zan
- Department of Otolaryngology, Public Health Clinical Center of Chengdu, Chengdu, Sichuan Province, China
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Cheng J, Su W, Wang Y, Zhan Y, Wang Y, Yan S, Yuan Y, Chen L, Wei Z, Zhang S, Gao X, Tang Z. Magnetic resonance imaging based on radiomics for differentiating T1-category nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia: a multicenter study. Jpn J Radiol 2024; 42:709-719. [PMID: 38409300 DOI: 10.1007/s11604-024-01544-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 01/29/2024] [Indexed: 02/28/2024]
Abstract
PURPOSE To investigate the role of magnetic resonance imaging (MRI) based on radiomics using T2-weighted imaging fat suppression (T2WI-FS) and contrast enhanced T1-weighted imaging (CE-T1WI) sequences in differentiating T1-category nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPH). MATERIALS AND METHODS This study enrolled 614 patients (training dataset: n = 390, internal validation dataset: n = 98, and external validation dataset: n = 126) of T1-category NPC and NPH. Three feature selection methods were used, including analysis of variance, recursive feature elimination, and relief. The logistic regression classifier was performed to construct the radiomics signatures of T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to differentiate T1-category NPC from NPH. The performance of the optimal radiomics signature (T2WI-FS + CE-T1WI) was compared with those of three radiologists in the internal and external validation datasets. RESULTS Twelve, 15, and 15 radiomics features were selected from T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to develop the three radiomics signatures, respectively. The area under the curve (AUC) values for radiomics signatures of T2WI-FS + CE-T1WI and CE-T1WI were significantly higher than that of T2WI-FS (AUCs = 0.940, 0.935, and 0.905, respectively) for distinguishing T1-category NPC and NPH in the training dataset (Ps all < 0.05). In the internal and external validation datasets, the radiomics signatures based on T2WI-FS + CE-T1WI and CE-T1WI outperformed T2WI-FS with no significant difference (AUCs = 0.938, 0.925, and 0.874 for internal validation dataset and 0.932, 0.918, and 0.882 for external validation dataset; Ps > 0.05). The radiomics signature of T2WI-FS + CE-T1WI significantly performed better than three radiologists in the internal and external validation datasets. CONCLUSION The MRI-based radiomics signature is meaningful in differentiating T1-category NPC from NPH and potentially helps clinicians select suitable therapy strategies.
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Affiliation(s)
- Jingfeng Cheng
- Department of Radiology, Eye & ENT Hospital of Fudan University, Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Wenzhe Su
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Yuzhe Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yang Zhan
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yin Wang
- Department of Radiology, Eye & ENT Hospital of Fudan University, Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Shuyu Yan
- Fudan University, Shanghai, 200032, China
| | - Yuan Yuan
- Fudan University, Shanghai, 200032, China
| | | | - Zixun Wei
- Fudan University, Shanghai, 200032, China
| | - Shengjian Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, 200233, China.
| | - Zuohua Tang
- Department of Radiology, Eye & ENT Hospital of Fudan University, Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, China.
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Wang CK, Wang TW, Lu CF, Wu YT, Hua MW. Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis. Diagnostics (Basel) 2024; 14:924. [PMID: 38732337 PMCID: PMC11082984 DOI: 10.3390/diagnostics14090924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/12/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.
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Affiliation(s)
- Chih-Keng Wang
- School of Medicine, College of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Ting-Wei Wang
- School of Medicine, College of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan
| | - Chia-Fung Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan;
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan
| | - Man-Wei Hua
- Department of Otolaryngology-Head and Neck Surgery, Taichung Veterans General Hospital, Taichung 407219, Taiwan
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Cao X, Wang X, Song J, Su Y, Wang L, Yin Y. Pretreatment multiparametric MRI radiomics-integrated clinical hematological biomarkers can predict early rapid metastasis in patients with nasopharyngeal carcinoma. BMC Cancer 2024; 24:435. [PMID: 38589858 PMCID: PMC11003025 DOI: 10.1186/s12885-024-12209-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 04/01/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND To establish and validate a predictive model combining pretreatment multiparametric MRI-based radiomic signatures and clinical characteristics for the risk evaluation of early rapid metastasis in nasopharyngeal carcinoma (NPC) patients. METHODS The cutoff time was used to randomly assign 219 consecutive patients who underwent chemoradiation treatment to the training group (n = 154) or the validation group (n = 65). Pretreatment multiparametric magnetic resonance (MR) images of individuals with NPC were employed to extract 428 radiomic features. LASSO regression analysis was used to select radiomic features related to early rapid metastasis and develop the Rad-score. Blood indicators were collected within 1 week of pretreatment. To identify independent risk variables for early rapid metastasis, univariate and multivariate logistic regression analyses were employed. Finally, multivariate logistic regression analysis was applied to construct a radiomics and clinical prediction nomogram that integrated radiomic features and clinical and blood inflammatory predictors. RESULTS The NLR, T classification and N classification were found to be independent risk indicators for early rapid metastasis by multivariate logistic regression analysis. Twelve features associated with early rapid metastasis were selected by LASSO regression analysis, and the Rad-score was calculated. The AUC of the Rad-score was 0.773. Finally, we constructed and validated a prediction model in combination with the NLR, T classification, N classification and Rad-score. The area under the curve (AUC) was 0.936 (95% confidence interval (95% CI): 0.901-0.971), and in the validation cohort, the AUC was 0.796 (95% CI: 0.686-0.905). CONCLUSIONS A predictive model that integrates the NLR, T classification, N classification and MR-based radiomics for distinguishing early rapid metastasis may serve as a clinical risk stratification tool for effectively guiding individual management.
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Affiliation(s)
- Xiujuan Cao
- Shandong University Cancer Center, Shandong University, Jinan, Shandong, China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Xiaowen Wang
- Shandong University Cancer Center, Shandong University, Jinan, Shandong, China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Jian Song
- Medical Imageology, Shandong Medical College, Jinan, China
| | - Ya Su
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan, Shandong, 250117, People's Republic of China
| | - Lizhen Wang
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan, Shandong, 250117, People's Republic of China
| | - Yong Yin
- Shandong University Cancer Center, Shandong University, Jinan, Shandong, China.
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jiyan Road 440, Jinan, Shandong, 250117, People's Republic of China.
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Ren CX, Xu GX, Dai DQ, Lin L, Sun Y, Liu QS. Cross-site prognosis prediction for nasopharyngeal carcinoma from incomplete multi-modal data. Med Image Anal 2024; 93:103103. [PMID: 38368752 DOI: 10.1016/j.media.2024.103103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/05/2023] [Accepted: 02/05/2024] [Indexed: 02/20/2024]
Abstract
Accurate prognosis prediction for nasopharyngeal carcinoma based on magnetic resonance (MR) images assists in the guidance of treatment intensity, thus reducing the risk of recurrence and death. To reduce repeated labor and sufficiently explore domain knowledge, aggregating labeled/annotated data from external sites enables us to train an intelligent model for a clinical site with unlabeled data. However, this task suffers from the challenges of incomplete multi-modal examination data fusion and image data heterogeneity among sites. This paper proposes a cross-site survival analysis method for prognosis prediction of nasopharyngeal carcinoma from domain adaptation viewpoint. Utilizing a Cox model as the basic framework, our method equips it with a cross-attention based multi-modal fusion regularization. This regularization model effectively fuses the multi-modal information from multi-parametric MR images and clinical features onto a domain-adaptive space, despite the absence of some modalities. To enhance the feature discrimination, we also extend the contrastive learning technique to censored data cases. Compared with the conventional approaches which directly deploy a trained survival model in a new site, our method achieves superior prognosis prediction performance in cross-site validation experiments. These results highlight the key role of cross-site adaptability of our method and support its value in clinical practice.
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Affiliation(s)
- Chuan-Xian Ren
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China.
| | - Geng-Xin Xu
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Dao-Qing Dai
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Li Lin
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Ying Sun
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
| | - Qing-Shan Liu
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Chen Z, Wang Z, Liu S, Zhang S, Zhou Y, Zhang R, Yang W. Nomograms based on multiparametric MRI radiomics integrated with clinical-radiological features for predicting the response to induction chemotherapy in nasopharyngeal carcinoma. Eur J Radiol 2024; 175:111438. [PMID: 38613869 DOI: 10.1016/j.ejrad.2024.111438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 01/27/2024] [Accepted: 03/20/2024] [Indexed: 04/15/2024]
Abstract
OBJECTIVE To establish nomograms integrating multiparametric MRI radiomics with clinical-radiological features to identify the responders and non-responders to induction chemotherapy (ICT) in nasopharyngeal carcinoma (NPC). METHODS We retrospectively analyzed the clinical and MRI data of 168 NPC patients between December 2015 and April 2022. We used 3D-Slicer to segment the regions of interest (ROIs) and the "Pyradiomic" package to extract radiomics features. We applied the least absolute shrinkage and selection operator regression to select radiomics features. We developed clinical-only, radiomics-only, and the combined clinical-radiomics nomograms using logistic regression analysis. The receiver operating characteristic curves, DeLong test, calibration, and decision curves were used to assess the discriminative performance of the models. The model was internally validated using 10-fold cross-validation. RESULTS A total of 14 optimal features were finally selected to develop a radiomic signature, with an AUC of 0.891 (95 % CI, 0.825-0.946) in the training cohort and 0.837 (95 % CI, 0.723-0.932) in the testing cohort. The nomogram based on the Rad-Score and clinical-radiological factors for evaluating tumor response to ICT yielded an AUC of 0.926 (95 % CI, 0.875-0.965) and 0.901 (95 % CI, 0.815-0.979) in the two cohorts, respectively. Decision curves demonstrated that the combined clinical-radiomics nomograms were clinically useful. CONCLUSION Nomograms integrating multiparametric MRI-based radiomics and clinical-radiological features could non-invasively discriminate ICT responders from non-responders in NPC patients.
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Affiliation(s)
- Zhiqiang Chen
- Department of Radiology, the First Affiliated Hospital of Hainan Medical University, Haikou, Hainan 570102, China; Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China.
| | - Zhuo Wang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Shili Liu
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Shaoru Zhang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Yunshu Zhou
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Ruodi Zhang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Wenjun Yang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medicine and Life Sciences, Hainan Medical University, Haikou, Hainan 571199, China.
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Tafuri B, De Blasi R, Nigro S, Logroscino G. Explainable machine learning radiomics model for Primary Progressive Aphasia classification. Front Syst Neurosci 2024; 18:1324437. [PMID: 38562661 PMCID: PMC10982515 DOI: 10.3389/fnsys.2024.1324437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Primary Progressive Aphasia (PPA) is a neurodegenerative disease characterized by linguistic impairment. The two main clinical subtypes are semantic (svPPA) and non-fluent/agrammatic (nfvPPA) variants. Diagnosing and classifying PPA patients represents a complex challenge that requires the integration of multimodal information, including clinical, biological, and radiological features. Structural neuroimaging can play a crucial role in aiding the differential diagnosis of PPA and constructing diagnostic support systems. Methods In this study, we conducted a white matter texture analysis on T1-weighted images, including 56 patients with PPA (31 svPPA and 25 nfvPPA), and 53 age- and sex-matched controls. We trained a tree-based algorithm over combined clinical/radiomics measures and used Shapley Additive Explanations (SHAP) model to extract the greater impactful measures in distinguishing svPPA and nfvPPA patients from controls and each other. Results Radiomics-integrated classification models demonstrated an accuracy of 95% in distinguishing svPPA patients from controls and of 93.7% in distinguishing svPPA from nfvPPA. An accuracy of 93.7% was observed in differentiating nfvPPA patients from controls. Moreover, Shapley values showed the strong involvement of the white matter near left entorhinal cortex in patients classification models. Discussion Our study provides new evidence for the usefulness of radiomics features in classifying patients with svPPA and nfvPPA, demonstrating the effectiveness of an explainable machine learning approach in extracting the most impactful features for assessing PPA.
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Affiliation(s)
- Benedetta Tafuri
- Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro at Pia Fondazione “Card. G. Panico”, Tricase, Italy
| | - Roberto De Blasi
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro at Pia Fondazione “Card. G. Panico”, Tricase, Italy
| | - Salvatore Nigro
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro at Pia Fondazione “Card. G. Panico”, Tricase, Italy
| | - Giancarlo Logroscino
- Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro at Pia Fondazione “Card. G. Panico”, Tricase, Italy
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Xu H, Wang A, Zhang C, Ren J, Zhou P, Liu J. Intra- and peritumoral MRI radiomics assisted in predicting radiochemotherapy response in metastatic cervical lymph nodes of nasopharyngeal cancer. BMC Med Imaging 2023; 23:66. [PMID: 37254101 DOI: 10.1186/s12880-023-01026-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 05/23/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND To establish and validate radiomic models combining intratumoral (Intra) and peritumoral (Peri) features obtained from pretreatment MRI for the prediction of treatment response of lymph node metastasis from nasopharyngeal cancer (NPC). METHODS One hundred forty-five NPC patients (102 in the training and 43 in the validation set) were retrospectively enrolled. Radiomic features were extracted from Intra and Peri regions on the metastatic cervical lymph node, and selected with the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was applied to build radiomic models. Sensitivity, specificity, accuracy, and the area under the curve (AUC) of receiver operating characteristics were employed to evaluate the predictive power of each model. RESULTS The AUCs of the radiomic model of Intra, Peri, Intra + Peri, and Clinical-radiomic were 0.910, 0.887, 0.934, and 0.941, respectively, in the training set and 0.737, 0.794, 0.774, and 0.783, respectively, in the validation set. There were no significant differences in prediction performance among the radiomic models in the training and validation sets (all P > 0.05). The calibration curve of the radiomic model of Peri demonstrated good agreement between prediction and observation in the training and validation sets. CONCLUSIONS The pretreatment MRI-based radiomics model may be useful in predicting the treatment response of metastatic lymph nodes of NPC. Besides, the generalization ability of the radiomic model of Peri was better than that of Intra and Intra + Peri.
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Affiliation(s)
- Hao Xu
- 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
| | - Ai Wang
- 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
| | - Chi Zhang
- 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
| | - 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
| | - Peng Zhou
- 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.
| | - Jieke Liu
- 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.
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Sun MX, Zhao MJ, Zhao LH, Jiang HR, Duan YX, Li G. A nomogram model based on pre-treatment and post-treatment MR imaging radiomics signatures: application to predict progression-free survival for nasopharyngeal carcinoma. Radiat Oncol 2023; 18:67. [PMID: 37041545 PMCID: PMC10088158 DOI: 10.1186/s13014-023-02257-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 04/03/2023] [Indexed: 04/13/2023] Open
Abstract
BACKGROUND To establish a novel model using radiomics analysis of pre-treatment and post-treatment magnetic resonance (MR) images for prediction of progression-free survival in the patients with stage II-IVA nasopharyngeal carcinoma (NPC) in South China. METHODS One hundred and twenty NPC patients who underwent chemoradiotherapy were enrolled (80 in the training cohort and 40 in the validation cohort). Acquiring data and screening features were performed successively. Totally 1133 radiomics features were extracted from the T2-weight images before and after treatment. Least absolute shrinkage and selection operator regression, recursive feature elimination algorithm, random forest, and minimum-redundancy maximum-relevancy (mRMR) method were used for feature selection. Nomogram discrimination and calibration were evaluated. Harrell's concordance index (C-index) and receiver operating characteristic (ROC) analyses were applied to appraise the prognostic performance of nomograms. Survival curves were plotted using Kaplan-Meier method. RESULTS Integrating independent clinical predictors with pre-treatment and post-treatment radiomics signatures which were calculated in conformity with radiomics features, we established a clinical-and-radiomics nomogram by multivariable Cox regression. Nomogram consisting of 14 pre-treatment and 7 post-treatment selected features has been proved to yield a reliable predictive performance in both training and validation groups. The C-index of clinical-and-radiomics nomogram was 0.953 (all P < 0.05), which was higher than that of clinical (0.861) or radiomics nomograms alone (based on pre-treatment statistics: 0.942; based on post-treatment statistics: 0.944). Moreover, we received Rad-score of pre-treatment named RS1 and post-treatment named RS2 and all were used as independent predictors to divide patients into high-risk and low-risk groups. Kaplan-Meier analysis showed that lower RS1 (less than cutoff value, - 1.488) and RS2 (less than cutoff value, - 0.180) were easier to avoid disease progression (all P < 0.01). It showed clinical benefit with decision curve analysis. CONCLUSIONS MR-based radiomics measured the burden on primary tumor before treatment and the tumor regression after chemoradiotherapy, and was used to build a model to predict progression-free survival (PFS) in the stage II-IVA NPC patients. It can also help to distinguish high-risk patients from low-risk patients, thus guiding personalized treatment decisions effectively.
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Affiliation(s)
- Mi-Xue Sun
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Meng-Jing Zhao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Li-Hao Zhao
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Hao-Ran Jiang
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Yu-Xia Duan
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.
| | - Gang Li
- Department of Radiation Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.
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Yang F, Wei H, Li X, Yu X, Zhao Y, Li L, Li Y, Xie L, Wang S, Lin M. Pretreatment synthetic magnetic resonance imaging predicts disease progression in nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy. Insights Imaging 2023; 14:59. [PMID: 37016104 PMCID: PMC10073373 DOI: 10.1186/s13244-023-01411-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 03/22/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND To investigate the potential of synthetic MRI (SyMRI) in the prognostic assessment of patients with nonmetastatic nasopharyngeal carcinoma (NPC), and the predictive value when combined with diffusion-weighted imaging (DWI) as well as clinical factors. METHODS Fifty-three NPC patients who underwent SyMRI were prospectively included. 10th Percentile, Mean, Kurtosis, and Skewness of T1, T2, and PD maps and ADC value were obtained from the primary tumor. Cox regression analysis was used for analyzing the association between SyMRI and DWI parameters and progression-free survival (PFS), and then age, sex, staging, and treatment as confounding factors were also included. C-index was obtained by bootstrap. Moreover, significant parameters were used to construct models in predicting 3-year disease progression. ROC curves and leave-one-out cross-validation were used to evaluate the performance and stability. RESULTS Disease progression occurred in 16 (30.2%) patients at a follow-up of 39.6 (3.5, 48.2) months. T1_Kurtosis, T1_Skewness, T2_10th, PD_Mean, and ADC were correlated with PFS, and T1_Kurtosis (HR: 1.093) and ADC (HR: 1.009) were independent predictors of PFS. The C-index of SyMRI and SyMRI + DWI + Clinic models was 0.687 and 0.779. Moreover, the SyMRI + DWI + Clinic model predicted 3-year disease progression better than DWI or Clinic model (p ≤ 0.008). Interestingly, there was no significant difference between the SyMRI model (AUC: 0.748) and SyMRI + DWI + Clinic model (AUC: 0.846, p = 0.092). CONCLUSION SyMRI combined with histogram analysis could predict disease progression in NPC patients, and SyMRI + DWI + Clinic model further improved the predictive performance.
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Affiliation(s)
- Fan Yang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Haoran Wei
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaolu Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaoduo Yu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yanfeng Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lin Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yujie Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lizhi Xie
- MR Research China, GE Healthcare, Beijing, China
| | - Sicong Wang
- MR Research China, GE Healthcare, Beijing, China
| | - Meng Lin
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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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] [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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