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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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Jayawickrama SM, Ranaweera PM, Pradeep RGGR, Jayasinghe YA, Senevirathna K, Hilmi AJ, Rajapakse RMG, Kanmodi KK, Jayasinghe RD. Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments. Cancer Rep (Hoboken) 2024; 7:e2045. [PMID: 38522008 PMCID: PMC10961052 DOI: 10.1002/cnr2.2045] [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: 08/28/2023] [Revised: 02/07/2024] [Accepted: 03/05/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND Precision healthcare has entered a new era because of the developments in personalized medicine, especially in the diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC). This paper explores the dynamic landscape of personalized medicine as applied to HNSCC, encompassing both current developments and future prospects. RECENT FINDINGS The integration of personalized medicine strategies into HNSCC diagnosis is driven by the utilization of genetic data and biomarkers. Epigenetic biomarkers, which reflect modifications to DNA that can influence gene expression, have emerged as valuable indicators for early detection and risk assessment. Treatment approaches within the personalized medicine framework are equally promising. Immunotherapy, gene silencing, and editing techniques, including RNA interference and CRISPR/Cas9, offer innovative means to modulate gene expression and correct genetic aberrations driving HNSCC. The integration of stem cell research with personalized medicine presents opportunities for tailored regenerative approaches. The synergy between personalized medicine and technological advancements is exemplified by artificial intelligence (AI) and machine learning (ML) applications. These tools empower clinicians to analyze vast datasets, predict patient responses, and optimize treatment strategies with unprecedented accuracy. CONCLUSION The developments and prospects of personalized medicine in HNSCC diagnosis and treatment offer a transformative approach to managing this complex malignancy. By harnessing genetic insights, biomarkers, immunotherapy, gene editing, stem cell therapies, and advanced technologies like AI and ML, personalized medicine holds the key to enhancing patient outcomes and ushering in a new era of precision oncology.
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Affiliation(s)
| | | | | | | | - Kalpani Senevirathna
- Centre for Research in Oral Cancer, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
| | | | | | - Kehinde Kazeem Kanmodi
- School of DentistryUniversity of RwandaKigaliRwanda
- Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
- Cephas Health Research Initiative IncIbadanNigeria
- School of Health and Life SciencesTeesside UniversityMiddlesbroughUK
| | - Ruwan Duminda Jayasinghe
- Centre for Research in Oral Cancer, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
- Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
- School of Health and Life SciencesTeesside UniversityMiddlesbroughUK
- Department of Oral Medicine and Periodontology, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
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Lin Y, Yang Z, Chen J, Li M, Cai Z, Wang X, Zhai T, Lin Z. A contrast-enhanced CT radiomics-based model to identify candidates for deintensified chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma patients. Eur Radiol 2024; 34:1302-1313. [PMID: 37594526 DOI: 10.1007/s00330-023-09987-1] [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: 11/16/2022] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 08/19/2023]
Abstract
OBJECTIVES To develop a contrast-enhanced CT (CECT) radiomics-based model to identify locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients who would benefit from deintensified chemoradiotherapy. METHODS LA-NPC patients who received low-dose concurrent cisplatin therapy (cumulative: 150 mg/m2), were randomly divided into training and validation groups. 107 radiomics features based on the primary nasopharyngeal tumor were extracted from each pre-treatment CECT scan. Through Cox regression analysis, a radiomics model and patients' corresponding radiomics scores were created with predictive independent radiomics features. T stage (T) and radiomics score (R) were compared as predictive factors. Combining the N stage (N), a clinical model (T + N), and a substitution model (R + N) were constructed. RESULTS Training and validation groups consisted of 66 and 33 patients, respectively. Three significant independent radiomics features (flatness, mean, and gray level non-uniformity in gray level dependence matrix (GLDM-GLN)) were found. The radiomics score showed better predictive ability than the T stage (concordance index (C-index): 0.67 vs. 0.61, AUC: 0.75 vs. 0.60). The R + N model had better predictive performance and more effective risk stratification than the T + N model (C-index: 0.77 vs. 0.68, AUC: 0.80 vs. 0.70). The R + N model identified a low-risk group as deintensified chemoradiotherapy candidates in which no patient developed progression within 3 years, with 5-year progression-free survival (PFS) and overall survival (OS) both 90.7% (hazard ratio (HR) = 4.132, p = 0.018). CONCLUSION Our radiomics-based model combining radiomics score and N stage can identify specific LA-NPC candidates for whom de-escalation therapy can be performed without compromising therapeutic efficacy. CLINICAL RELEVANCE STATEMENT Our study shows that the radiomics-based model (R + N) can accurately stratify patients into different risk groups, with satisfactory prognosis in the low-risk group when treated with low-dose concurrent chemotherapy, providing new options for individualized de-escalation strategies. KEY POINTS • A radiomics score, consisting of 3 predictive radiomics features (flatness, mean, and GLDM-GLN) integrated with the N stage, can identify specific LA-NPC populations for deintensified treatment. • In the selection of LA-NPC candidates for de-intensified treatment, radiomics score extracted from primary nasopharyngeal tumors based on CECT can be superior to traditional T stage classification as a predictor.
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Affiliation(s)
- Yinbing Lin
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Zhining Yang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Jiechen Chen
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Mei Li
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Zeman Cai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China
| | - Xiao Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China
- Shantou University Medical College, 22 Xinling Road, Shantou 515000, 515041, Guangdong, China
| | - Tiantian Zhai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China.
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China.
| | - Zhixiong Lin
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, 515000, Guangdong, China.
- Nasopharyngeal Carcinoma Research Center, Shantou University Medical College, Shantou University, 7 Raoping Road, Shantou, 515000, Guangdong, China.
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Liu H, Deng D, Zeng W, Huang Y, Zheng C, Li X, Li H, Xie C, He H, Xu G. AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality. Eur Radiol 2023; 33:7686-7696. [PMID: 37219618 PMCID: PMC10598173 DOI: 10.1007/s00330-023-09742-6] [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: 09/23/2022] [Revised: 03/21/2023] [Accepted: 04/14/2023] [Indexed: 05/24/2023]
Abstract
OBJECTIVE To compare examination time and image quality between artificial intelligence (AI)-assisted compressed sensing (ACS) technique and parallel imaging (PI) technique in MRI of patients with nasopharyngeal carcinoma (NPC). METHODS Sixty-six patients with pathologically confirmed NPC underwent nasopharynx and neck examination using a 3.0-T MRI system. Transverse T2-weighted fast spin-echo (FSE) sequence, transverse T1-weighted FSE sequence, post-contrast transverse T1-weighted FSE sequence, and post-contrast coronal T1-weighted FSE were obtained by both ACS and PI techniques, respectively. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and duration of scanning of both sets of images analyzed by ACS and PI techniques were compared. The images from the ACS and PI techniques were scored for lesion detection, margin sharpness of lesions, artifacts, and overall image quality using the 5-point Likert scale. RESULTS The examination time with ACS technique was significantly shorter than that with PI technique (p < 0.0001). The comparison of SNR and CNR showed that ACS technique was significantly superior with PI technique (p < 0.005). Qualitative image analysis showed that the scores of lesion detection, margin sharpness of lesions, artifacts, and overall image quality were higher in the ACS sequences than those in the PI sequences (p < 0.0001). Inter-observer agreement was evaluated for all qualitative indicators for each method, in which the results showed satisfactory-to-excellent agreement (p < 0.0001). CONCLUSION Compared with the PI technique, the ACS technique for MR examination of NPC can not only shorten scanning time but also improve image quality. CLINICAL RELEVANCE STATEMENT The artificial intelligence (AI)-assisted compressed sensing (ACS) technique shortens examination time for patients with nasopharyngeal carcinoma, while improving the image quality and examination success rate, which will benefit more patients. KEY POINTS • Compared with the parallel imaging (PI) technique, the artificial intelligence (AI)-assisted compressed sensing (ACS) technique not only reduced examination time, but also improved image quality. • Artificial intelligence (AI)-assisted compressed sensing (ACS) pulls the state-of-the-art deep learning technique into the reconstruction procedure and helps find an optimal balance of imaging speed and image quality.
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Affiliation(s)
- Haibin Liu
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Dele Deng
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Weilong Zeng
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Yingyi Huang
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Chunling Zheng
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Xinyang Li
- United Imaging Healthcare, Shanghai, People's Republic of China
| | - Hui Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Chuanmiao Xie
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Haoqiang He
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
| | - Guixiao Xu
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China.
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Bian X, Du S, Yue Z, Gao S, Zhao R, Huang G, Guo L, Peng C, Zhang L. Potential Antihuman Epidermal Growth Factor Receptor 2 Target Therapy Beneficiaries: The Role of MRI-Based Radiomics in Distinguishing Human Epidermal Growth Factor Receptor 2-Low Status of Breast Cancer. J Magn Reson Imaging 2023; 58:1603-1614. [PMID: 36763035 DOI: 10.1002/jmri.28628] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/21/2023] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Multiparametric MRI radiomics could distinguish human epidermal growth factor receptor 2 (HER2)-positive from HER2-negative breast cancers. However, its value for further distinguishing HER2-low from HER2-negative breast cancers has not been investigated. PURPOSE To investigate whether multiparametric MRI-based radiomics can distinguish HER2-positive from HER2-negative breast cancers (task 1) and HER2-low from HER2-negative breast cancers (task 2). STUDY TYPE Retrospective. POPULATION Task 1: 310 operable breast cancer patients from center 1 (97 HER2-positive and 213 HER2-negative); task 2: 213 HER2-negative patients (108 HER2-low and 105 HER2-zero); 59 patients from center 2 (16 HER2-positive, 27 HER2-low and 16 HER2-zero) for external validation. FIELD STRENGTH/SEQUENCE A 3.0 T/T1-weighted contrast-enhanced imaging (T1CE), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC). ASSESSMENT Patients in center 1 were assigned to a training and internal validation cohort at a 2:1 ratio. Intratumoral and peritumoral features were extracted from T1CE and ADC. After dimensionality reduction, the radiomics signatures (RS) of two tasks were developed using features from T1CE (RS-T1CE), ADC (RS-ADC) alone and T1CE + ADC combination (RS-Com). STATISTICAL TESTS Mann-Whitney U tests, the least absolute shrinkage and selection operator, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). RESULTS For task 1, RS-ADC yielded higher area under the ROC curve (AUC) in the training, internal, and external validation of 0.767/0.725/0.746 than RS-T1CE (AUC = 0.733/0.674/0.641). For task 2, RS-T1CE yielded higher AUC of 0.765/0.755/0.678 than RS-ADC (AUC = 0.706/0.608/0.630). For both of task 1 and task 2, RS-Com achieved the best performance with AUC of 0.793/0.778/0.760 and 0.820/0.776/0.711, respectively, and obtained higher clinical benefit in DCA compared with RS-T1CE and RS-ADC. The calibration curves of all RS demonstrated a good fitness. DATA CONCLUSION Multiparametric MRI radiomics could noninvasively and robustly distinguish HER2-positive from HER2-negative breast cancers and further distinguish HER2-low from HER2-negative breast cancers. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Xiaoqian Bian
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Siyao Du
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Zhibin Yue
- School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Si Gao
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Ruimeng Zhao
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Guoliang Huang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Liangcun Guo
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Can Peng
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Lina Zhang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
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Xu H, Lv W, Zhang H, Yuan Q, Wang Q, Wu Y, Lu L. Multimodality radiomics analysis based on [ 18F]FDG PET/CT imaging and multisequence MRI: application to nasopharyngeal carcinoma prognosis. Eur Radiol 2023; 33:6677-6688. [PMID: 37060444 DOI: 10.1007/s00330-023-09606-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 01/02/2023] [Accepted: 02/13/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVES To determine whether radiomics models developed from 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT combined with multisequence MRI could contribute to predicting the progression-free survival (PFS) of nasopharyngeal carcinoma (NPC) patients. METHODS One hundred thirty-two NPC patients who underwent both PET/CT and MRI scanning were retrospectively enrolled (88 vs. 44 for training vs. testing). For each modality/sequence (i.e., PET, CT, T1, T1C, and T2), 1906 radiomics features were extracted from the primary tumor volume. Univariate Cox model and correlation analysis were used for feature selection. A multivariate Cox model was used to establish radiomics signature. Prognostic performances of 5 individual modality models and 12 multimodality models (3 integrations × 4 fusion strategies) were assessed by the concordance index (C-index) and log-rank test. A clinical-radiomics nomogram was built to explore the clinical utilities of radiomics signature, which was evaluated by discrimination, calibration curve, and decision curve analysis (DCA). RESULTS The radiomics signatures of individual modalities showed limited prognostic efficacy with a C-index of 0.539-0.664 in the testing cohort. Different fusion strategies exhibited a slight difference in predictive performance. The PET/CT and MRI integrated model achieved the best performance with a C-index of 0.745 (95% CI, 0.619-0.865) in the testing cohort (log-rank test, p < 0.05). Clinical-radiomics nomogram further improved the prognosis, which also showed satisfactory discrimination, calibration, and net benefit. CONCLUSIONS Multimodality radiomics analysis by combining PET/CT with multisequence MRI could potentially improve the efficacy of PFS prediction for NPC patients. KEY POINTS • Individual modality radiomics models showed limited performance in prognosis evaluation for NPC patients. • Combined PET, CT and multisequence MRI radiomics signature could improve the prognostic efficacy. • Multilevel fusion strategies exhibit comparable performance but feature-level fusion deserves more attention.
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Affiliation(s)
- Hui Xu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Provincial Key Laboratory of Medial Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Pazhou Lab, Guangzhou, 510330, China
| | - Wenbing Lv
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Provincial Key Laboratory of Medial Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Pazhou Lab, Guangzhou, 510330, China
| | - Hao Zhang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Qingyu Yuan
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Yuankui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- Guangdong Provincial Key Laboratory of Medial Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- Pazhou Lab, Guangzhou, 510330, China.
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8
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Feng L, Zhang S, Lu X, Yang X, Kan Y, Wang C, Zhang H, Wang W, Yang J. An Optimal Radiomics Nomogram Based on 18F-FDG PET/CT for Identifying Event-Free Survival in Pediatric Neuroblastoma. Acad Radiol 2023; 30:2309-2320. [PMID: 37393177 DOI: 10.1016/j.acra.2023.06.004] [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: 04/18/2023] [Revised: 05/13/2023] [Accepted: 06/02/2023] [Indexed: 07/03/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate whether the 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics features that combine tumor and bone marrow can more accurately identify event-free survival (EFS) in pediatric neuroblastoma. MATERIALS AND METHODS A total of 126 patients with neuroblastoma were retrospectively included and randomly divided into the training and validation cohorts (7:3 ratio). Radiomics features were extracted to develop a tumor- and bone marrow-based radiomics risk score (RRS). The Kaplan-Meier method was used to evaluate the effectiveness of RRS in EFS risk stratification. Univariate and multivariate Cox regression analyses were used to determine independent clinical risk factors and construct the clinical models. The conventional PET model was constructed based on conventional PET parameters, and the noninvasive combined model integrated the RRS and the noninvasive independent clinical risk factors. The performance of the models was evaluated using C-index, calibration curves, and decision curve analysis (DCA). RESULTS A total of 15 radiomics features were selected to build the RRS. According to Kaplan-Meier analysis, there was a significant difference in EFS between the low-risk and high-risk groups as defined by the value of RRS (P < .05). The noninvasive combined model combining RRS and the International Neuroblastoma Risk Group stage achieved the best prognostic prediction of EFS, with a C-index of 0.810 and 0.783 in the training and validation cohorts, respectively. The calibration curves and DCA indicated that the noninvasive combined model had good consistency and clinical utility. CONCLUSION The 18F-FDG PET/CT-based radiomics of neuroblastoma allows a reliable evaluation of EFS. The performance of the noninvasive combined model was superior to the clinical and conventional PET models.
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Affiliation(s)
- Lijuan Feng
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Shuxin Zhang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Xia Lu
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Xu Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Ying Kan
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Chao Wang
- SinoUnion Healthcare Inc., Beijing, China (C.W.)
| | - Hui Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China (H,Z,)
| | - Wei Wang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.)
| | - Jigang Yang
- Department of Nuclear Medicine, Beijing Friendship Hospital, Capital Medical University, 95 Yong An Road, Xi Cheng District, Beijing 100050, China (L.F., S.Z., X.L., X.Y., Y.K., W.W., J.Y.).
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Wang T, Hao J, Gao A, Zhang P, Wang H, Nie P, Jiang Y, Bi S, Liu S, Hao D. An MRI-Based Radiomics Nomogram to Assess Recurrence Risk in Sinonasal Malignant Tumors. J Magn Reson Imaging 2023; 58:520-531. [PMID: 36448476 DOI: 10.1002/jmri.28548] [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: 09/16/2022] [Revised: 11/19/2022] [Accepted: 11/21/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Sinonasal malignant tumors (SNMTs) have a high recurrence risk, which is responsible for the poor prognosis of patients. Assessing recurrence risk in SNMT patients is a current problem. PURPOSE To establish an MRI-based radiomics nomogram for assessing relapse risk in patients with SNMT. STUDY TYPE Retrospective. POPULATION A total of 143 patients with 68.5% females (development/validation set, 98/45 patients). FIELD STRENGTH/SEQUENCE A 1.5-T and 3-T, fat-suppressed fast spin echo (FSE) T2-weighted imaging (FS-T2WI), FSE T1-weighted imaging (T1WI), and FSE contrast-enhanced T1WI (T1WI + C). ASSESSMENT Three MRI sequences were used to manually delineate the region of interest. Three radiomics signatures (T1WI and FS-T2WI sequences, T1WI + C sequence, and three sequences combined) were built through dimensional reduction of high-dimensional features. The clinical model was built based on clinical and MRI features. The Ki-67-based and tumor-node-metastasis (TNM) model were established for comparison. The radiomics nomogram was built by combining the clinical model and best radiomics signature. The relapse-free survival analysis was used among 143 patients. STATISTICAL TESTS The intraclass/interclass correlation coefficients, univariate/multivariate Cox regression analysis, least absolute shrinkage and selection operator Cox regression algorithm, concordance index (C index), area under the curve (AUC), integrated Brier score (IBS), DeLong test, Kaplan-Meier curve, log-rank test, optimal cutoff values. A P value < 0.05 was considered statistically significant. RESULTS The T1 + C-based radiomics signature had best prognostic ability than the other two signatures (T1WI and FS-T2WI sequences, and three sequences combined). The radiomics nomogram had better prognostic ability and less error than the clinical model, Ki-67-based model, and TNM model (C index, 0.732; AUC, 0.765; IBS, 0.185 in the validation set). The cutoff values were 0.2 and 0.7 and then the cumulative risk rates were calculated. DATA CONCLUSION A radiomics nomogram for assessing relapse risk in patients with SNMT may provide better prognostic ability than the clinical model, Ki-67-based model, and TNM model. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 5.
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Affiliation(s)
- Tongyu Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jingwei Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Aixin Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Peng Zhang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Yan Jiang
- Department of Otolaryngology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shucheng Bi
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shunli Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Dapeng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
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10
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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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11
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Zhang B, Luo C, Zhang X, Hou J, Liu S, Gao M, Zhang L, Jin Z, Chen Q, Yu X, Zhang S. Integrative Scoring System for Survival Prediction in Patients With Locally Advanced Nasopharyngeal Carcinoma: A Retrospective Multicenter Study. JCO Clin Cancer Inform 2023; 7:e2200015. [PMID: 36877918 DOI: 10.1200/cci.22.00015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
PURPOSE Tumor stage is crucial for prognostic evaluation and therapeutic decisions in locally advanced nasopharyngeal carcinoma (NPC) but is imprecise. We aimed to propose a new prognostic system by integrating quantitative imaging features and clinical factors. MATERIALS AND METHODS This retrospective study included 1,319 patients with stage III-IVa NPC between April 1, 2010, and July 31, 2019, who underwent pretherapy magnetic resonance imaging (MRI) and received concurrent chemoradiotherapy with or without induction chemotherapy. The hand-crafted and deep-learned features were extracted from MRI for each patient. After feature selection, the clinical score, radiomic score, deep score, and integrative scores were constructed via Cox regression analysis. The scores were validated in two external cohorts. The predictive accuracy and discrimination were measured by the area under the curve (AUC) and risk group stratification. The end points were progression-free survival (PFS), overall survival (OS), and distant metastasis-free survival (DMFS). RESULTS Both radiomics and deep learning were complementary to clinical variables (age, T stage, and N stage; all P < .05). The clinical-deep score was superior or equivalent to clinical-radiomic score, whereas it was noninferior to clinical-radiomic-deep score (all P > .05). These findings were also verified in the evaluation of OS and DMFS. The clinical-deep score yielded an AUC of 0.713 (95% CI, 0.697 to 0.729) and 0.712 (95% CI, 0.693 to 0.731) in the two external validation cohorts for predicting PFS with good calibration. This scoring system could stratify patients into high- and low-risk groups with distinct survivals (all P < .05). CONCLUSION We established and validated a prognostic system integrating clinical data and deep learning to provide an individual prediction of survival for patients with locally advanced NPC, which might inform clinicians in treatment decision making.
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Affiliation(s)
- Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Chun Luo
- Department of Radiology, The First People's Hospital of Foshan, Foshan, Guangdong, China
| | - Xiao Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.,Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Artificial Intelligence and Clinical Innovation Research, Guangdong, Guangzhou, China
| | - Jing Hou
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Shuyi Liu
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Mingyong Gao
- Department of Radiology, The First People's Hospital of Foshan, Foshan, Guangdong, China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Zhe Jin
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Qiuying Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Xiaoping Yu
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
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Li Q, Yu Q, Gong B, Ning Y, Chen X, Gu J, Lv F, Peng J, Luo T. The Effect of Magnetic Resonance Imaging Based Radiomics Models in Discriminating stage I-II and III-IVa Nasopharyngeal Carcinoma. Diagnostics (Basel) 2023; 13:diagnostics13020300. [PMID: 36673110 PMCID: PMC9857437 DOI: 10.3390/diagnostics13020300] [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: 11/25/2022] [Revised: 12/28/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Nasopharyngeal carcinoma (NPC) is a common tumor in China. Accurate stages of NPC are crucial for treatment. We therefore aim to develop radiomics models for discriminating early-stage (I-II) and advanced-stage (III-IVa) NPC based on MR images. METHODS 329 NPC patients were enrolled and randomly divided into a training cohort (n = 229) and a validation cohort (n = 100). Features were extracted based on axial contrast-enhanced T1-weighted images (CE-T1WI), T1WI, and T2-weighted images (T2WI). Least absolute shrinkage and selection operator (LASSO) was used to build radiomics signatures. Seven radiomics models were constructed with logistic regression. The AUC value was used to assess classification performance. The DeLong test was used to compare the AUCs of different radiomics models and visual assessment. RESULTS Models A, B, C, D, E, F, and G were constructed with 13, 9, 7, 9, 10, 7, and 6 features, respectively. All radiomics models showed better classification performance than that of visual assessment. Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance (AUC: 0.847) in the training cohort. CE-T1WI showed the greatest significance for staging NPC. CONCLUSION Radiomics models can effectively distinguish early-stage from advanced-stage NPC patients, and Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance.
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13
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Zhang Q, Wu G, Yang Q, Dai G, Li T, Chen P, Li J, Huang W. Survival rate prediction of nasopharyngeal carcinoma patients based on MRI and gene expression using a deep neural network. Cancer Sci 2022; 114:1596-1605. [PMID: 36541519 PMCID: PMC10067413 DOI: 10.1111/cas.15704] [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: 07/13/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 12/24/2022] Open
Abstract
To achieve a better treatment regimen and follow-up assessment design for intensity-modulated radiotherapy (IMRT)-treated nasopharyngeal carcinoma (NPC) patients, an accurate progression-free survival (PFS) time prediction algorithm is needed. We propose developing a PFS prediction model of NPC patients after IMRT treatment using a deep learning method and comparing that with the traditional texture analysis method. One hundred and fifty-one NPC patients were included in this retrospective study. T1-weighted, proton density and dynamic contrast-enhanced magnetic resonance (MR) images were acquired. The expression level of five genes (HIF-1α, EGFR, PTEN, Ki-67, and VEGF) and infection of Epstein-Barr (EB) virus were tested. A residual network was trained to predict PFS from MR images. The output as well as patient characteristics were combined using a linear regression model to provide a final PFS prediction. The prediction accuracy was compared with that of the traditional texture analysis method. A regression model combining the deep learning output with HIF-1α expression and Epstein-Barr infection provides the best PFS prediction accuracy (Spearman correlation R2 = 0.53; Harrell's C-index = 0.82; receiver operative curve [ROC] analysis area under the curve [AUC] = 0.88; log-rank test hazard ratio [HR] = 8.45), higher than a regression model combining texture analysis with HIF-1α expression (Spearman correlation R2 = 0.14; Harrell's C-index =0.68; ROC analysis AUC = 0.76; log-rank test HR = 2.85). The deep learning method does not require a manually drawn tumor region of interest. MR image processing using deep learning combined with patient characteristics can provide accurate PFS prediction for nasopharyngeal carcinoma patients and does not rely on specific kernels or tumor regions of interest, which is needed for the texture analysis method.
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Affiliation(s)
- Qihao Zhang
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital, Hainan, China
| | - Qianyu Yang
- Department of Radiology, Hainan General Hospital, Hainan, China
| | - Ganmian Dai
- Department of Radiology, Hainan General Hospital, Hainan, China
| | - Tiansheng Li
- Department of Radiology, Hainan General Hospital, Hainan, China
| | - Pianpian Chen
- Department of Pathology, Hainan General Hospital, Hainan, China
| | - Jiao Li
- Department of Pathology, Hainan General Hospital, Hainan, China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital, Hainan, China
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14
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Kulanthaivelu R, Kohan A, Hinzpeter R, Liu ZA, Hope A, Huang SH, Waldron J, O’Sullivan B, Ortega C, Metser U, Veit-Haibach P. Prognostic value of PET/CT and MR-based baseline radiomics among patients with non-metastatic nasopharyngeal carcinoma. Front Oncol 2022; 12:952763. [PMID: 36353565 PMCID: PMC9638017 DOI: 10.3389/fonc.2022.952763] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 10/03/2022] [Indexed: 11/09/2023] Open
Abstract
PURPOSE Radiomics is an emerging imaging assessment technique that has shown promise in predicting survival among nasopharyngeal carcinoma (NPC) patients. Studies so far have focused on PET or MR-based radiomics independently. The aim of our study was to evaluate the prognostic value of clinical and radiomic parameters derived from both PET/CT and MR. METHODS Retrospective evaluation of 124 NPC patients with PET/CT and radiotherapy planning MR (RP-MR). Primary tumors were segmented using dedicated software (LIFEx version 6.1) from PET, CT, contrast-enhanced T1-weighted (T1-w), and T2-weighted (T2-w) MR sequences with 376 radiomic features extracted. Summary statistics describe patient, disease, and treatment characteristics. The Kaplan-Meier (KM) method estimates overall survival (OS) and progression-free survival (PFS). Clinical factors selected based on univariable analysis and the multivariable Cox model were subsequently constructed with radiomic features added. RESULTS The final models comparing clinical, clinical + RP-MR, clinical + PET/CT and clinical + RP-MR + PET/CT for OS and PFS demonstrated that combined radiomic signatures were significantly associated with improved survival prognostication (AUC 0.62 vs 0.81 vs 0.75 vs 0.86 at 21 months for PFS and 0.56 vs 0.85 vs 0.79 vs 0.96 at 24 months for OS). Clinical + RP-MR features initially outperform clinical + PET/CT for both OS and PFS (<18 months), and later in the clinical course for PFS (>42 months). CONCLUSION Our study demonstrated that PET/CT-based radiomic features may improve survival prognostication among NPC patients when combined with baseline clinical and MR-based radiomic features.
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Affiliation(s)
- Roshini Kulanthaivelu
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Andres Kohan
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Ricarda Hinzpeter
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Zhihui Amy Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Andrew Hope
- Department of Radiation Oncology, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Shao Hui Huang
- Department of Radiation Oncology, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - John Waldron
- Department of Radiation Oncology, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Brian O’Sullivan
- Department of Radiation Oncology, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Claudia Ortega
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Ur Metser
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
| | - Patrick Veit-Haibach
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON, Canada
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Teng X, Zhang J, Ma Z, Zhang Y, Lam S, Li W, Xiao H, Li T, Li B, Zhou T, Ren G, Lee FKH, Au KH, Lee VHF, Chang ATY, Cai J. Improving radiomic model reliability using robust features from perturbations for head-and-neck carcinoma. Front Oncol 2022; 12:974467. [PMID: 36313629 PMCID: PMC9614273 DOI: 10.3389/fonc.2022.974467] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/28/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundUsing high robust radiomic features in modeling is recommended, yet its impact on radiomic model is unclear. This study evaluated the radiomic model’s robustness and generalizability after screening out low-robust features before radiomic modeling. The results were validated with four datasets and two clinically relevant tasks.Materials and methodsA total of 1,419 head-and-neck cancer patients’ computed tomography images, gross tumor volume segmentation, and clinically relevant outcomes (distant metastasis and local-regional recurrence) were collected from four publicly available datasets. The perturbation method was implemented to simulate images, and the radiomic feature robustness was quantified using intra-class correlation of coefficient (ICC). Three radiomic models were built using all features (ICC > 0), good-robust features (ICC > 0.75), and excellent-robust features (ICC > 0.95), respectively. A filter-based feature selection and Ridge classification method were used to construct the radiomic models. Model performance was assessed with both robustness and generalizability. The robustness of the model was evaluated by the ICC, and the generalizability of the model was quantified by the train-test difference of Area Under the Receiver Operating Characteristic Curve (AUC).ResultsThe average model robustness ICC improved significantly from 0.65 to 0.78 (P< 0.0001) using good-robust features and to 0.91 (P< 0.0001) using excellent-robust features. Model generalizability also showed a substantial increase, as a closer gap between training and testing AUC was observed where the mean train-test AUC difference was reduced from 0.21 to 0.18 (P< 0.001) in good-robust features and to 0.12 (P< 0.0001) in excellent-robust features. Furthermore, good-robust features yielded the best average AUC in the unseen datasets of 0.58 (P< 0.001) over four datasets and clinical outcomes.ConclusionsIncluding robust only features in radiomic modeling significantly improves model robustness and generalizability in unseen datasets. Yet, the robustness of radiomic model has to be verified despite building with robust radiomic features, and tightly restricted feature robustness may prevent the optimal model performance in the unseen dataset as it may lower the discrimination power of the model.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Yuanpeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Saikit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Francis Kar-ho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China
| | - Kwok-hung Au
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China
| | - Victor Ho-fun Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Amy Tien Yee Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium and Hospital, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- *Correspondence: Jing Cai,
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Downregulated miR-150-5p in the Tissue of Nasopharyngeal Carcinoma. Genet Res (Camb) 2022; 2022:2485055. [PMID: 36118276 PMCID: PMC9467814 DOI: 10.1155/2022/2485055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 06/01/2022] [Accepted: 07/06/2022] [Indexed: 01/09/2023] Open
Abstract
The clinical significance and potential targets of miR-150-5p have not been elucidated in nasopharyngeal carcinoma (NPC). The pooled analysis based on 539 NPC samples and 75 non-NPC nasopharyngeal samples demonstrated that the expression of miR-150-5p was down-regulated in NPC, with the area under the curve being 0.89 and the standardized mean difference being -0.66. Subsequently, we further screened the differentially expressed genes (DEGs) of 14 datasets, including 312 NPC samples and 70 non-NPC nasopharyngeal samples. After the DEGs were narrowed down with the predicted targets from the miRWalk database, 1316 prospective target genes of miR-150-5p were identified. The enrichment analysis suggested that "pathways in cancer" was the most significant pathway. Finally, six hub genes of "pathways in cancer", including EGFR, TP53, HRAS, CCND1, CDH1, and FGF2, were screened out through the STRING database. In conclusion, the down-regulation of miR-150-5p modulates the tumorigenesis and progression of NPC.
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Peng Z, Wang Y, Fan R, Gao K, Xie S, Wang F, Zhang J, Zhang H, He Y, Xie Z, Jiang W. Treatment of Recurrent Nasopharyngeal Carcinoma: A Sequential Challenge. Cancers (Basel) 2022; 14:cancers14174111. [PMID: 36077648 PMCID: PMC9454547 DOI: 10.3390/cancers14174111] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/19/2022] [Accepted: 08/19/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary Recurrent nasopharyngeal carcinoma is one of the major causes of death among NPC patients. However, there are no international guidelines for the treatment of patients with recurrent NPC now. In this article, we summarize past publications on clinical research and mechanistic studies related to recurrent NPC, combined with the experience and lessons learned by our institutional multidisciplinary team in the treatment of recurrent NPC. We propose an objective protocol for the treatment of recurrent NPC. Abstract Recurrent nasopharyngeal carcinoma (NPC), which occurs in 10–20% of patients with primary NPC after the initial treatment modality of intensity-modulated radiation therapy (IMRT), is one of the major causes of death among NPC patients. Patients with recurrent disease without distant metastases still have a chance to be saved, but re-treatment often carries more serious toxicities or higher risks. For this group of patients, both otolaryngologists and oncologists are committed to developing more appropriate treatment regimens that can prolong patient survival and improve survival therapy. Currently, there are no international guidelines for the treatment of patients with recurrent NPC. In this article, we summarize past publications on clinical research and mechanistic studies related to recurrent NPC, combined with the experience and lessons learned by our institutional multidisciplinary team in the treatment of recurrent NPC. We propose an objective protocol for the treatment of recurrent NPC.
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Affiliation(s)
- Zhouying Peng
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ruohao Fan
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Kelei Gao
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Shumin Xie
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Fengjun Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Junyi Zhang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Hua Zhang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yuxiang He
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhihai Xie
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
| | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha 410008, China
- Correspondence:
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18
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Guo SS, Chen YZ, Liu LT, Liu RP, Liang YJ, Wen DX, Jin J, Tang LQ, Mai HQ, Chen QY. Prognostic significance of AKR1C4 and the advantage of combining EBV DNA to stratify patients at high risk of locoregional recurrence of nasopharyngeal carcinoma. BMC Cancer 2022; 22:880. [PMID: 35953777 PMCID: PMC9373296 DOI: 10.1186/s12885-022-09924-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 07/18/2022] [Indexed: 11/10/2022] Open
Abstract
Background Distinguishing patients at a greater risk of recurrence is essential for treating locoregional advanced nasopharyngeal carcinoma (NPC). This study aimed to explore the potential of aldo–keto reductase 1C4 (AKR1C4) in stratifying patients at high risk of locoregional relapse. Methods A total of 179 patients with locoregionally advanced NPC were grouped by different strategies; they were: (a) divided into two groups according to AKR1C4 expression level, and (b) classified into three clusters by integrating AKR1C4 and Epstein-Barr virus (EBV) DNA. The Kaplan–Meier method was used to calculate locoregional relapse-free survival (LRFS), overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS). The Cox proportional hazards model was used to determine potential prognostic factors, and a nomogram was generated to predict 3-year and 5-year LRFS. Results A significant difference in the 5-year LRFS was observed between the high and low AKR1C4 expression groups (83.3% vs. 92.7%, respectively; p = 0.009). After integrating AKR1C4 expression and EBV DNA, the LRFS (84.7%, 84.5%, 96.9%, p = 0.014) of high-, intermediate-, and low- AKR1C4 and EBV DNA was also significant. Multivariate analysis indicated that AKR1C4 expression (p = 0.006) was an independent prognostic factor for LRFS. The prognostic factors incorporated into the nomogram were AKR1C4 expression, T stage, and EBV DNA, and the concordance index of the nomogram for locoregional relapse was 0.718. Conclusions In conclusion, high AKR1C4 expression was associated with a high possibility of relapse in NPC patients, and integrating EBV DNA and AKR1C4 can stratify high-risk patients with locoregional recurrence. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09924-3.
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Affiliation(s)
- Shan-Shan Guo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China.,Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yan-Zhou Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China.,Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Li-Ting Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China.,Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Rong-Ping Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China.,Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yu-Jing Liang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China.,Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Dong-Xiang Wen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China.,Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Jing Jin
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China.,Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Lin-Quan Tang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China.,Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Hai-Qiang Mai
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China.,Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Qiu-Yan Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China. .,Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou, China.
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19
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Liu K, Qiu Q, Qin Y, Chen T, Zhang D, Huang L, Yin Y, Wang R. Radiomics Nomogram Based on Multiple-Sequence Magnetic Resonance Imaging Predicts Long-Term Survival in Patients Diagnosed With Nasopharyngeal Carcinoma. Front Oncol 2022; 12:852348. [PMID: 35463366 PMCID: PMC9021720 DOI: 10.3389/fonc.2022.852348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/04/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose Although the tumor–node–metastasis staging system is widely used for survival analysis of nasopharyngeal carcinoma (NPC), tumor heterogeneity limits its utility. In this study, we aimed to develop and validate a radiomics model, based on multiple-sequence magnetic resonance imaging (MRI), to estimate the probability of overall survival in patients diagnosed with NPC. Methods Multiple-sequence MRIs, including T1-weighted, T1 contrast, and T2-weighted imaging, were collected from patients diagnosed with NPC. Radiomics features were extracted from the contoured gross tumor volume of three sequences from each patient using the least absolute shrinkage and selection operator with the Cox regression model. The optimal Rad score was determined using 12 of the 851 radiomics features derived from the multiple-sequence MRI and its discrimination power was compared in the training and validation cohorts. For better prediction performance, an optimal nomogram (radiomics nomogram-MS) that incorporated the optimal Rad score and clinical risk factors was developed, and a calibration curve and a decision curve were used to further evaluate the optimized discrimination power. Results A total of 504 patients diagnosed with NPC were included in this study. The optimal Rad score was significantly correlated with overall survival in both the training [C-index: 0.731, 95% confidence interval (CI): 0.709–0.753] and validation cohorts (C-index: 0.807, 95% CI: 0.782–0.832). Compared with the nomogram developed with only single-sequence MRI, the radiomics nomogram-MS had a higher discrimination power in both the training (C-index: 0.827, 95% CI: 0.809–0.845) and validation cohorts (C-index: 0.836, 95% CI: 0.815–0.857). Analysis of the calibration and decision curves confirmed the effectiveness and utility of the optimal radiomics nomogram-MS. Conclusions The radiomics nomogram model that incorporates multiple-sequence MRI and clinical factors may be a useful tool for the early assessment of the long-term prognosis of patients diagnosed with NPC.
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Affiliation(s)
- Kai Liu
- Department of Head and Neck Comprehensive Radiotherapy, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China
| | - Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yonghui Qin
- Department of Head and Neck Comprehensive Radiotherapy, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China
| | - Ting Chen
- Department of Head and Neck Comprehensive Radiotherapy, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China
| | - Diangang Zhang
- Department of Head and Neck Comprehensive Radiotherapy, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China
| | - Li Huang
- Department of Head and Neck Comprehensive Radiotherapy, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ruozheng Wang
- Department of Head and Neck Comprehensive Radiotherapy, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China
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20
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Fang ZY, Li KZ, Yang M, Che YR, Luo LP, Wu ZF, Gao MQ, Wu C, Luo C, Lai X, Zhang YY, Wang M, Xu Z, Li SM, Liu JK, Zhou P, Wang WD. Integration of MRI-Based Radiomics Features, Clinicopathological Characteristics, and Blood Parameters: A Nomogram Model for Predicting Clinical Outcome in Nasopharyngeal Carcinoma. Front Oncol 2022; 12:815952. [PMID: 35311119 PMCID: PMC8924617 DOI: 10.3389/fonc.2022.815952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/08/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose This study aimed to develop a nomogram model based on multiparametric magnetic resonance imaging (MRI) radiomics features, clinicopathological characteristics, and blood parameters to predict the progression-free survival (PFS) of patients with nasopharyngeal carcinoma (NPC). Methods A total of 462 patients with pathologically confirmed nonkeratinizing NPC treated at Sichuan Cancer Hospital were recruited from 2015 to 2019 and divided into training and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomics feature dimension reduction and screening in the training cohort. Rad-score, age, sex, smoking and drinking habits, Ki-67, monocytes, monocyte ratio, and mean corpuscular volume were incorporated into a multivariate Cox proportional risk regression model to build a multifactorial nomogram. The concordance index (C-index) and decision curve analysis (DCA) were applied to estimate its efficacy. Results Nine significant features associated with PFS were selected by LASSO and used to calculate the rad-score of each patient. The rad-score was verified as an independent prognostic factor for PFS in NPC. The survival analysis showed that those with lower rad-scores had longer PFS in both cohorts (p < 0.05). Compared with the tumor–node–metastasis staging system, the multifactorial nomogram had higher C-indexes (training cohorts: 0.819 vs. 0.610; validation cohorts: 0.820 vs. 0.602). Moreover, the DCA curve showed that this model could better predict progression within 50% threshold probability. Conclusion A nomogram that combined MRI-based radiomics with clinicopathological characteristics and blood parameters improved the ability to predict progression in patients with NPC.
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Affiliation(s)
- Zeng-Yi Fang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, China.,Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China
| | - Ke-Zhen Li
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Man Yang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu-Rou Che
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Li-Ping Luo
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Zi-Fei Wu
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Ming-Quan Gao
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Chuan Wu
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China
| | - Xin Lai
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China
| | - Yi-Yao Zhang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Mei Wang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhu Xu
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Si-Ming Li
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jie-Ke Liu
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei-Dong Wang
- Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China.,Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, China.,Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China.,School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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21
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Duan W, Xiong B, Tian T, Zou X, He Z, Zhang L. Radiomics in Nasopharyngeal Carcinoma. CLINICAL MEDICINE INSIGHTS: ONCOLOGY 2022; 16:11795549221079186. [PMID: 35237090 PMCID: PMC8883403 DOI: 10.1177/11795549221079186] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 01/20/2022] [Indexed: 12/24/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common head and neck malignancies, and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy alone, concurrent chemoradiotherapy, and induction chemotherapy combined with concurrent chemoradiotherapy can be used according to different grades. Treatment options and prognoses vary greatly depending on the grade of disease in the patients. Accurate grading and risk assessment are required. Recently, radiomics has combined a large amount of invisible high-dimensional information extracted from computed tomography, magnetic resonance imaging, or positron emission tomography with powerful computing capabilities of machine-learning algorithms, providing the possibility to achieve an accurate diagnosis and individualized treatment for cancer patients. As an effective tumor biomarker of NPC, the radiomic signature has been widely used in grading, differential diagnosis, prediction of prognosis, evaluation of treatment response, and early identification of therapeutic complications. The process of radiomic research includes image segmentation, feature extraction, feature selection, model establishment, and evaluation. Many open-source or commercial tools can be used to achieve these procedures. The development of machine-learning algorithms provides more possibilities for radiomics research. This review aimed to summarize the application of radiomics in NPC and introduce the basic process of radiomics research.
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Affiliation(s)
- Wenyue Duan
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Bingdi Xiong
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Ting Tian
- Department of Clinical Medicine, Chengdu Medical College, Chengdu, People's Republic of China
| | - Xinyun Zou
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Zhennan He
- Department of Clinical Medicine, Chengdu Medical College, Chengdu, People's Republic of China
| | - Ling Zhang
- Department of Oncology, People's Liberation Army The General Hospital of Western Theater Command, Chengdu, People's Republic of China
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22
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Li G, Li L, Li Y, Qian Z, Wu F, He Y, Jiang H, Li R, Wang D, Zhai Y, Wang Z, Jiang T, Zhang J, Zhang W. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas. Brain 2022; 145:1151-1161. [PMID: 35136934 PMCID: PMC9050568 DOI: 10.1093/brain/awab340] [Citation(s) in RCA: 79] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/15/2021] [Accepted: 08/18/2021] [Indexed: 01/08/2023] Open
Abstract
Preoperative MRI is one of the most important clinical results for the diagnosis and treatment of glioma patients. The objective of this study was to construct a stable and validatable preoperative T2-weighted MRI-based radiomics model for predicting the survival of gliomas. A total of 652 glioma patients across three independent cohorts were covered in this study including their preoperative T2-weighted MRI images, RNA-seq and clinical data. Radiomic features (1731) were extracted from preoperative T2-weighted MRI images of 167 gliomas (discovery cohort) collected from Beijing Tiantan Hospital and then used to develop a radiomics prediction model through a machine learning-based method. The performance of the radiomics prediction model was validated in two independent cohorts including 261 gliomas from the The Cancer Genomae Atlas database (external validation cohort) and 224 gliomas collected in the prospective study from Beijing Tiantan Hospital (prospective validation cohort). RNA-seq data of gliomas from discovery and external validation cohorts were applied to establish the relationship between biological function and the key radiomics features, which were further validated by single-cell sequencing and immunohistochemical staining. The 14 radiomic features-based prediction model was constructed from preoperative T2-weighted MRI images in the discovery cohort, and showed highly robust predictive power for overall survival of gliomas in external and prospective validation cohorts. The radiomic features in the prediction model were associated with immune response, especially tumour macrophage infiltration. The preoperative T2-weighted MRI radiomics prediction model can stably predict the survival of glioma patients and assist in preoperatively assessing the extent of macrophage infiltration in glioma tumours.
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Affiliation(s)
- Guanzhang Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Lin Li
- Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Yiming Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Zenghui Qian
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Fan Wu
- Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
| | - Yufei He
- Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Haoyu Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Renpeng Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Di Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - You Zhai
- Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
| | - Zhiliang Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Tao Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.,Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China.,Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing 100070, China.,China National Clinical Research Center for Neurological Diseases, Beijing 100070, China.,Chinese Glioma Genome Atlas Network and Asian Glioma Genome Atlas Network, Beijing, China
| | - Jing Zhang
- Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Engineering Medicine, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Wei Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.,Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China.,China National Clinical Research Center for Neurological Diseases, Beijing 100070, China.,Chinese Glioma Genome Atlas Network and Asian Glioma Genome Atlas Network, Beijing, China
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23
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Lam SK, Zhang J, Zhang YP, Li B, Ni RY, Zhou T, Peng T, Cheung ALY, Chau TC, Lee FKH, Yip CWY, Au KH, Lee VHF, Chang ATY, Chan LWC, Cai J. A Multi-Center Study of CT-Based Neck Nodal Radiomics for Predicting an Adaptive Radiotherapy Trigger of Ill-Fitted Thermoplastic Masks in Patients with Nasopharyngeal Carcinoma. Life (Basel) 2022; 12:life12020241. [PMID: 35207528 PMCID: PMC8876942 DOI: 10.3390/life12020241] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/02/2021] [Accepted: 12/11/2021] [Indexed: 11/30/2022] Open
Abstract
Significant lymph node shrinkage is common in patients with nasopharyngeal carcinoma (NPC) throughout radiotherapy (RT) treatment, causing ill-fitted thermoplastic masks (IfTMs). To deal with this, an ad hoc adaptive radiotherapy (ART) may be required to ensure accurate and safe radiation delivery and to maintain treatment efficacy. Presently, the entire procedure for evaluating an eligible ART candidate is time-consuming, resource-demanding, and highly inefficient. In the artificial intelligence paradigm, the pre-treatment identification of NPC patients at risk for IfTMs has become greatly demanding for achieving efficient ART eligibility screening, while no relevant studies have been reported. Hence, we aimed to investigate the capability of computed tomography (CT)-based neck nodal radiomics for predicting IfTM-triggered ART events in NPC patients via a multi-center setting. Contrast-enhanced CT and the clinical data of 124 and 58 NPC patients from Queen Elizabeth Hospital (QEH) and Queen Mary Hospital (QMH), respectively, were retrospectively analyzed. Radiomic (R), clinical (C), and combined (RC) models were developed using the ridge algorithm in the QEH cohort and evaluated in the QMH cohort using the median area under the receiver operating characteristics curve (AUC). Delong’s test was employed for model comparison. Model performance was further assessed on 1000 replicates in both cohorts separately via bootstrapping. The R model yielded the highest “corrected” AUC of 0.784 (BCa 95%CI: 0.673–0.859) and 0.723 (BCa 95%CI: 0.534–0.859) in the QEH and QMH cohort following bootstrapping, respectively. Delong’s test indicated that the R model performed significantly better than the C model in the QMH cohort (p < 0.0001), while demonstrating no significant difference compared to the RC model (p = 0.5773). To conclude, CT-based neck nodal radiomics was capable of predicting IfTM-triggered ART events in NPC patients in this multi-center study, outperforming the traditional clinical model. The findings of this study provide valuable insights for future study into developing an effective screening strategy for ART eligibility in NPC patients in the long run, ultimately alleviating the workload of clinical practitioners, streamlining ART procedural efficiency in clinics, and achieving personalized RT for NPC patients in the future.
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Affiliation(s)
- Sai-Kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Yuan-Peng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Rui-Yan Ni
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Tao Peng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Andy Lai-Yin Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China
| | - Tin-Ching Chau
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China; (T.-C.C.); (V.H.-F.L.)
| | - Francis Kar-Ho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China; (F.K.-H.L.); (C.W.-Y.Y.); (K.-H.A.)
| | - Celia Wai-Yi Yip
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China; (F.K.-H.L.); (C.W.-Y.Y.); (K.-H.A.)
| | - Kwok-Hung Au
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China; (F.K.-H.L.); (C.W.-Y.Y.); (K.-H.A.)
| | - Victor Ho-Fun Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China; (T.-C.C.); (V.H.-F.L.)
| | - Amy Tien-Yee Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Hong Kong, China;
| | - Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
- Correspondence:
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Bao D, Liu Z, Geng Y, Li L, Xu H, Zhang Y, Hu L, Zhao X, Zhao Y, Luo D. Baseline MRI-based radiomics model assisted predicting disease progression in nasopharyngeal carcinoma patients with complete response after treatment. Cancer Imaging 2022; 22:10. [PMID: 35090572 PMCID: PMC8800208 DOI: 10.1186/s40644-022-00448-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 12/31/2021] [Indexed: 12/04/2022] Open
Abstract
Background Accurate pretreatment prediction for disease progression of nasopharyngeal carcinoma is key to intensify therapeutic strategies to high-risk individuals. Our aim was to evaluate the value of baseline MRI-based radiomics machine-learning models in predicting the disease progression in nasopharyngeal carcinoma patients who achieved complete response after treatment. Methods In this retrospective study, 171 patients with pathologically confirmed nasopharyngeal carcinoma were included. Using hold-out cross validation scheme (7:3), relevant radiomic features were selected with the least absolute shrinkage and selection operator method based on baseline T2-weighted fat suppression and contrast-enhanced T1-weighted images in the training cohort. After Pearson’s correlation analysis of selected radiomic features, multivariate logistic regression analysis was applied to radiomic features and clinical characteristics selection. Logistic regression analysis and support vector machine classifier were utilized to build the predictive model respectively. The predictive accuracy of the model was evaluated by ROC analysis along with sensitivity, specificity and AUC calculated in the validation cohort. Results A prediction model using logistic regression analysis comprising 4 radiomics features (HGLZE_T2H, HGLZE_T1, LDLGLE_T1, and GLNU_T1) and 5 clinical features (histology, T stage, N stage, smoking history, and age) showed the best performance with an AUC of 0.75 in the training cohort (95% CI: 0.66–0.83) and 0.77 in the validation cohort (95% CI: 0.64–0.90). The nine independent impact factors were entered into the nomogram. The calibration curves for probability of 3-year disease progression showed good agreement. The features of this prediction model showed satisfactory clinical utility with decision curve analysis. Conclusions A radiomics model derived from pretreatment MR showed good performance for predicting disease progression in nasopharyngeal carcinoma and may help to improve clinical decision making. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-022-00448-4.
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Magnetic Resonance Imaging-Based Radiomics for the Prediction of Progression-Free Survival in Patients with Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022; 14:cancers14030653. [PMID: 35158921 PMCID: PMC8833585 DOI: 10.3390/cancers14030653] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/21/2022] [Accepted: 01/26/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary More than 70% of patients with nasopharyngeal carcinoma (NPC) present with a locoregionally advanced state. Although the initial staging of NPC is primarily based on TNM staging, there is currently no well-established prognostic marker for NPC. Recently, radiomics has received considerable research attention as a potential prognostic biomarker for NPC. The aim of this systematic review and meta-analysis was to comprehensively evaluate the prognostic value of pretreatment magnetic resonance imaging (MRI)-based radiomics for NPC. The analyzed radiomic models demonstrated modest prognostic values, with a pooled mean estimated Harrell’s concordance index (C index) of 0.762. The prognostic models developed using more than eight radiomic features had significantly higher C-indices than those developed using fewer features. Our findings provide evidence that MRI-based radiomics may have a modest prognostic role in the treatment of NPC. However, more consistent study protocols are needed to verify the generalizability of radiomics. Abstract Advanced non-metastatic nasopharyngeal carcinoma (NPC) has variable treatment outcomes. However, there are no prognostic biomarkers for identifying high-risk patients with NPC. The aim of this systematic review and meta-analysis was to comprehensively assess the prognostic value of magnetic resonance imaging (MRI)-based radiomics for untreated NPC. The PubMed-Medline and EMBASE databases were searched for relevant articles published up to 12 August 2021. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist was used to determine the qualities of the selected studies. Random-effects modeling was used to calculate the pooled estimates of Harrell’s concordance index (C-index) for progression-free survival (PFS). Between-study heterogeneity was evaluated using Higgins’ inconsistency index (I2). Among the studies reported in the 57 articles screened, 10 with 3458 patients were eligible for qualitative and quantitative data syntheses. The mean adherence rate to the TRIPOD checklist was 68.6 ± 7.1%. The pooled estimate of the C-index was 0.762 (95% confidence interval, 0.687–0.837). Substantial between-study heterogeneity was observed (I2 = 89.2%). Overall, MRI-based radiomics shows good prognostic performance in predicting the PFS of patients with untreated NPC. However, more consistent and robust study protocols are necessary to validate the prognostic role of radiomics for NPC.
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Kang L, Niu Y, Huang R, Lin SY, Tang Q, Chen A, Fan Y, Lang J, Yin G, Zhang P. Predictive Value of a Combined Model Based on Pre-Treatment and Mid-Treatment MRI-Radiomics for Disease Progression or Death in Locally Advanced Nasopharyngeal Carcinoma. Front Oncol 2021; 11:774455. [PMID: 34950584 PMCID: PMC8688844 DOI: 10.3389/fonc.2021.774455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/04/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose A combined model was established based on the MRI-radiomics of pre- and mid-treatment to assess the risk of disease progression or death in locally advanced nasopharyngeal carcinoma. Materials and Methods A total of 243 patients were analyzed. We extracted 10,400 radiomics features from the primary nasopharyngeal tumors and largest metastatic lymph nodes on the axial contrast-enhanced T1 weighted and T2 weighted in pre- and mid-treatment MRI, respectively. We used the SMOTE algorithm, center and scale and box-cox, Pearson correlation coefficient, and LASSO regression to construct the pre- and mid-treatment MRI-radiomics prediction model, respectively, and the risk scores named P score and M score were calculated. Finally, univariate and multivariate analyses were used for P score, M score, and clinical data to build the combined model and grouped the patients into two risk levels, namely, high and low. Result A combined model of pre- and mid-treatment MRI-radiomics successfully categorized patients into high- and low-risk groups. The log-rank test showed that the high- and low-risk groups had good prognostic performance in PFS (P<0.0001, HR: 19.71, 95% CI: 12.77–30.41), which was better than TNM stage (P=0.004, HR:1.913, 95% CI:1.250–2.926), and also had an excellent predictive effect in LRFS, DMFS, and OS. Conclusion Risk grouping of LA-NPC using a combined model of pre- and mid-treatment MRI-radiomics can better predict disease progression or death.
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Affiliation(s)
- Le Kang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China.,Department of Hematology and Oncology, Anyue County People's Hospital, Ziyang, China.,Graduate School, Chengdu Medical College, Chengdu, China
| | - Yulin Niu
- Department of Transplantation Surgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Rui Huang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
| | - Stefan Yujie Lin
- University of Southern California, Viterbi School of Engineering Applied Data Science, Los Angeles, CA, United States
| | - Qianlong Tang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China.,Graduate School, Chengdu Medical College, Chengdu, China
| | - Ailin Chen
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China.,Graduate School, Chengdu Medical College, Chengdu, China
| | - Yixin Fan
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China.,Graduate School, Chengdu Medical College, Chengdu, China
| | - Jinyi Lang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
| | - Gang Yin
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
| | - Peng Zhang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
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Kong J, Zhu S, Shi G, Liu Z, Zhang J, Ren J. Prediction of Locoregional Recurrence-Free Survival of Oesophageal Squamous Cell Carcinoma After Chemoradiotherapy Based on an Enhanced CT-Based Radiomics Model. Front Oncol 2021; 11:739933. [PMID: 34631575 PMCID: PMC8499696 DOI: 10.3389/fonc.2021.739933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/06/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND AND PURPOSE Chemoradiotherapy is the standard treatment for moderate and advanced oesophageal cancer. The aim of this study was to establish a predictive model based on enhanced computed tomography examination, and to evaluate its clinical value for detecting locoregional recurrence-free survival (LRFS) in cases of oesophageal squamous cell carcinoma after radiotherapy. MATERIALS AND METHODS In total, 218 patients with pathologically diagnosed oesophageal squamous cell carcinoma who received radical chemoradiotherapy from July 2016 to December 2017 were collected in this study. Patients were randomly divided into either a training group (n=153) or a validation group (n=65) in a 7:3 ratio. Clinical patient information was then recorded. The enhanced computed tomography scan images of the patients were imported into 3D-slicer software (version 4.8.1), and the radiomic features were extracted by the Python programme package. In the training group, the dimensionality reduction of the radiomic features was implemented by Lasso regression, and then a radiological label, the model of predicting LRFS, was established and evaluated. To achieve a better prediction performance, the radiological label was combined with clinical risk factor information to construct a radiomics nomogram. A receiver operating characteristic curve was used to evaluate the efficacy of different models. Calibration curves were used to assess the consistency between the predicted and observed recurrence risk, and the Hosmer-Lemeshow method was used to test model fitness. The C-index evaluated the discriminating ability of the prediction model. Decision curve analysis was used to determine the clinical value of the constructed prediction model. RESULTS Of the 218 patients followed up in this study, 44 patients (28.8%) in the training group and 21 patients (32.3%) in the validation group experienced recurrence. There was no difference in LRFS between the two groups (χ2 = 0.525, P=0.405). Lasso regression was used in the training group to select six significant radiomic features. The radiological label established using these six features had a satisfactory prediction performance. The C-index was 0.716 (95% CI: 0.645-0.787) in the training group and 0.718 (95% CI: 0.612-0.825) in the validation group. The radiomics nomogram, which included the radiological label and clinical risk factors, achieved a better prediction than the radiological label alone. The C-index was 0.742 (95% CI: 0.674-0.810) in the training group and 0.715 (95% CI: 0.609-0.820) in the validation group. The results of the calibration curve and decision curve analyses indicated that the radiomics nomogram was superior in predicting LRFS of oesophageal carcinoma after radiotherapy. CONCLUSIONS A radiological label was successfully established to predict the LRFS of oesophageal squamous cell carcinoma after radiotherapy. The radiomics nomogram was complementary to the clinical prognostic features and could improve the prediction of the LRFS after radiotherapy for oesophageal cancer.
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Affiliation(s)
- Jie Kong
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Shuchai Zhu
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Gaofeng Shi
- Department of Computed Tomography and Magnetic Resonance, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zhikun Liu
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jun Zhang
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jialiang Ren
- Pharmaceutical Diagnosis, GE Healthcare, Beijing, China
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Alfieri S, Romanò R, Bologna M, Calareso G, Corino V, Mirabile A, Ferri A, Bellanti L, Poli T, Marcantoni A, Grosso E, Tarsitano A, Battaglia S, Blengio F, De Martino I, Valerini S, Vecchio S, Richetti A, Deantonio L, Martucci F, Grammatica A, Ravanelli M, Ibrahim T, Caruso D, Locati LD, Orlandi E, Bossi P, Mainardi L, Licitra LF. Prognostic role of pre-treatment magnetic resonance imaging (MRI)-based radiomic analysis in effectively cured head and neck squamous cell carcinoma (HNSCC) patients. Acta Oncol 2021; 60:1192-1200. [PMID: 34038324 DOI: 10.1080/0284186x.2021.1924401] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To identify and validate baseline magnetic resonance imaging (b-MRI) radiomic features (RFs) as predictors of disease outcomes in effectively cured head and neck squamous cell carcinoma (HNSCC) patients. MATERIALS AND METHODS Training set (TS) and validation set (VS) were retrieved from preexisting datasets (HETeCo and BD2Decide trials, respectively). Only patients with both pre- and post-contrast enhancement T1 and T2-weighted b-MRI and at least 2 years of follow-up (FUP) were selected. The combination of the best extracted RFs was used to classify low risk (LR) vs. high risk (HR) of disease recurrence. Sensitivity, specificity, and area under the curve (AUC) of the radiomic model were computed on both TS and VS. Overall survival (OS) and 5-year disease-free survival (DFS) Kaplan-Meier (KM) curves were compared for LR vs. HR. The radiomic-based risk class was used in a multivariate Cox model, including well-established clinical prognostic factors (TNM, sub-site, human papillomavirus [HPV]). RESULTS In total, 57 patients of TS and 137 of VS were included. Three RFs were selected for the signature. Sensitivity of recurrence risk classifier was 0.82 and 0.77, specificity 0.78 and 0.81, AUC 0.83 and 0.78 for TS and VS, respectively. VS KM curves for LR vs. HR groups significantly differed both for 5-year DFS (p<.0001) and OS (p=.0004). A combined model of RFs plus TNM improved prognostic performance as compared to TNM alone, both for VS 5-year DFS (C-index: 0.76 vs. 0.60) and OS (C-index: 0.74 vs. 0.64). CONCLUSIONS Radiomics of b-MRI can help to predict recurrence and survival outcomes in HNSCC.
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Affiliation(s)
- Salvatore Alfieri
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Rebecca Romanò
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Marco Bologna
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Giuseppina Calareso
- Radiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Aurora Mirabile
- Department of Oncology, Division of Experimental Medicine, IRCCS San Raffaele Hospital, Milan, Italy
| | - Andrea Ferri
- Department of Surgery, Maxillo-Facial Surgery Division, University Hospital of Parma, Parma, Italy
| | - Luca Bellanti
- Department of Surgery, Maxillo-Facial Surgery Division, University Hospital of Parma, Parma, Italy
| | - Tito Poli
- Department of Biomedical, Biotechnological and Translational Sciences (S.Bi.Bi.T.), Unit of Maxillo-Facial Surgery, University of Parma, Parma, Italy
| | | | - Enrica Grosso
- Division of Head and Neck Surgery, Istituto Europeo di Oncologia (IEO), Milan, Italy
| | - Achille Tarsitano
- Department of Biomedical and Neuromotor Sciences, Maxillofacial Surgery Unit, S. Orsola-Malpighi Hospital, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Salvatore Battaglia
- Department of Biomedical and Neuromotor Sciences, Maxillofacial Surgery Unit, S. Orsola-Malpighi Hospital, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Fulvia Blengio
- Medical Oncology Department, AO SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Iolanda De Martino
- Medical Oncology Department, AO SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Sara Valerini
- Neuroscience Head and Neck Department, Otolaryngology Unit, Azienda Ospedaliero Universitaria di Modena, Modena, Italy
| | - Stefania Vecchio
- Medical Oncology 2, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Antonella Richetti
- Radiation Oncology Clinic Oncology, Institute of Southern Switzerland (IOSI), Bellinzona-Lugano, Switzerland
| | - Letizia Deantonio
- Radiation Oncology Clinic Oncology, Institute of Southern Switzerland (IOSI), Bellinzona-Lugano, Switzerland
| | - Francesco Martucci
- Radiation Oncology Clinic Oncology, Institute of Southern Switzerland (IOSI), Bellinzona-Lugano, Switzerland
| | - Alberto Grammatica
- Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, Unit of Otorhinolaryngology-Head and Neck Surgery, ASST Spedali Civili di Brescia, University of Brescia, Brescia, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, Unit of Radiology, University of Brescia, Brescia, Italy
| | - Toni Ibrahim
- Osteoncology and Rare Tumors Center, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
| | - Damiano Caruso
- Department of Surgical and Medical Sciences and Translational Medicine, Sant'Andrea University Hospital, Sapienza University of Rome, Rome, Italy
| | - Laura Deborah Locati
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Ester Orlandi
- Radiotherapy Unit 2, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Paolo Bossi
- Medical Oncology, Department of Medical and Surgical Specialties, Radiological Sciences and Public, Health University of Brescia, ASST-Spedali Civili, Brescia, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Lisa F. Licitra
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
- University of Milan, Milan, Italy
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Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021; 11:1523. [PMID: 34573865 PMCID: PMC8465998 DOI: 10.3390/diagnostics11091523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/10/2021] [Accepted: 08/19/2021] [Indexed: 12/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.
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Affiliation(s)
- Song Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Yu-Qin Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Zhi-Ling Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Hong-Li Hua
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
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The Efficacy of Radiotherapy for Nasopharyngeal Carcinoma under Magnetic Resonance Imaging. CONTRAST MEDIA & MOLECULAR IMAGING 2021; 2021:8280479. [PMID: 34393679 PMCID: PMC8349285 DOI: 10.1155/2021/8280479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/12/2021] [Accepted: 07/22/2021] [Indexed: 11/21/2022]
Abstract
This study aimed to analyze the application value of diffusion tensor imaging (DTI) in the diagnosis of nasopharyngeal carcinoma (NC) radiotherapy. In this study, 102 patients with NC were selected as the experimental group (EG), and 58 healthy people examined in hospital were included in a control group (CG). All subjects were required to be examined with routine magnetic resonance imaging (MRI) and DTI before and after the treatment. The fractional anisotropy (FA) of the patients in EG before and after treatment and the CG were recorded. The apparent diffusion coefficients (ADC) of patients in the two groups were measured and recorded before and after the treatment. The recovery rate and adverse events of the patients in EG were observed and recorded after the treatment. The results showed that the FA values of the right cerebellum and left parietal lobe (LPL) of patients after treatment in the EG were much higher than those before treatment and the CG (P < 0.05); the FA values of the right temporal lobe (RTL), right occipital lobe (ROL), and right parietal lobe (RPL) after treatment in the EG were obviously lower than those before the treatment and the CG (P < 0.05); the complete remission rate (CRR) of the EG after treatment was greatly higher than the partial remission rate (PRR) and disease stability rate (DSR) (P < 0.05), and the objective remission rate (ORR) and disease control rate (DCR) were higher than 90%, respectively. The ADC value of the EG before treatment was (0.752 ± 0.021) × 10−3 mm2/s, which was visibly lower than that after treatment ((1.365 ± 0.058) × 10−3 mm2/s) and that in the CG ((1.856 ± 0.079)) × 10−3 mm2/s), showing statistically obvious differences (P < 0.05). The incidence of anemia, oral reactions, hypertension, and gastrointestinal reaction in the EG after treatment was 61.46%, 45.35%, 47.28%, and 39.67%, respectively. In short, the FA value of DTI parameter could clearly indicate the changes in brain area characteristics of NC patients before and after treatment. The RTL, ROL, and RPL of NC patients were damaged after radiotherapy, and the FA value decreased observably, which may be related to brain edema and demyelination changes. The damage of white matter microstructure in each brain area further affected the cognitive function of the patient.
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Spadarella G, Calareso G, Garanzini E, Ugga L, Cuocolo A, Cuocolo R. MRI based radiomics in nasopharyngeal cancer: Systematic review and perspectives using radiomic quality score (RQS) assessment. Eur J Radiol 2021; 140:109744. [PMID: 33962253 DOI: 10.1016/j.ejrad.2021.109744] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/23/2021] [Accepted: 04/27/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND MRI based radiomics has the potential to better define tumor biology compared to qualitative MRI assessment and support decisions in patients affected by nasopharyngeal carcinoma. Aim of this review was to systematically evaluate the methodological quality of studies using MRI- radiomics for nasopharyngeal cancer patient evaluation. METHODS A systematic search was performed in PUBMED, WEB OF SCIENCE and SCOPUS using "MRI, magnetic resonance imaging, radiomic, texture analysis, nasopharyngeal carcinoma, nasopharyngeal cancer" in all possible combinations. The methodological quality of study included ( = 24) was evaluated according to the RQS (Radiomic quality score). Subgroup, for journal type (imaging/clinical) and biomarker (prognostic/predictive), and correlation, between RQS and journal Impact Factor, analyses were performed. Mann-Whitney U test and Spearman's correlation were performed. P value < .05 were defined as statistically significant. RESULTS Overall, no studies reported a phantom study or a test re-test for assessing stability in image, biological correlation or open science data. Only 8% of them included external validation. Almost half of articles (45 %) performed multivariable analysis with non-radiomics features. Only 1 study was prospective (4%). The mean RQS was 7.5 ± 5.4. No significant differences were detected between articles published in clinical/imaging journal and between studies with a predictive or prognostic biomarker. No significant correlation was found between total RQS and Impact Factor of the year of publication (p always > 0.05). CONCLUSIONS Radiomic articles in nasopharyngeal cancer are mostly of low methodological quality. The greatest limitations are the lack of external validation, biological correlates, prospective design and open science.
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Affiliation(s)
- Gaia Spadarella
- Department of Translational Medical Sciences, University of Naples "Federico II", Naples, Italy.
| | - Giuseppina Calareso
- Department of Radiology, Fondazione IRCCS, Istituto Nazionale Dei Tumori, Milan, Italy
| | - Enrico Garanzini
- Department of Radiology, Fondazione IRCCS, Istituto Nazionale Dei Tumori, Milan, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Alberto Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", Naples, Italy; Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
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Wang XY, Zhu SY, Wu WJ, Li HJ, Li J, Lin XF, Li L, Liu LZ. Extent of paranasal sinus involvement and its prognostic value in nasopharyngeal carcinoma: Proposed modification in the current UICC/AJCC staging system. Radiother Oncol 2021; 160:221-227. [PMID: 33984350 DOI: 10.1016/j.radonc.2021.04.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/21/2021] [Accepted: 04/23/2021] [Indexed: 11/28/2022]
Abstract
PURPOSE This study aimed to evaluate the prognostic value of paranasal sinus involvement (PSI) in NPC and to explore its appropriate position in the current AJCC staging system. MATERIALS AND METHODS Pretreatment MRI of 1317 patients with NPC treated with intensity-modulated radiotherapy (IMRT) between January 2010, and January 2013, were reviewed retrospectively. Survival was compared between patients with PSI-slight (sinus bone wall erosion only) and PSI-severe (tumor penetrated into sinus cavity). Multivariable analysis was performed to identify the independent predictors of survival. RESULTS The study included 1317 patients (median age 46 years; range, 11-78 years). PSI-slight was present in 15.2% (200/1317) patients and PSI-severe in 20.0% (263/1317) patients. Overall survival (OS), distant metastasis-free survival (DMFS), loco-regional recurrence-free survival (LRFS), and progression-free survival (PFS) were significantly lower in patients with PSI-severe (all P < .05). In multivariable analysis, PSI-severe was an independent prognostic factor for OS, DMFS, LRFS, and PFS (all P < .05). 96 AJCC T3 category patients with PSI-severe were reclassified as T4 category. The revised T category had significantly better predictive value (higher C-index) than that the AJCC system for survival (OS, 0.661 vs. 0.652; DMFS, 0.655 vs. 0.650; and PFS, 0.625 vs. 0.625; P < .05 for all). CONCLUSION PSI-severe is an independent negative prognostic factor in nasopharyngeal carcinoma, which is recommended to be classified as T4 category in the 8th AJCC staging system for NPC.
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Affiliation(s)
- Xiao-Yi Wang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, China
| | - Si-Yu Zhu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, China
| | - Wei-Jie Wu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, China
| | - Hao-Jiang Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, China
| | - Jiao Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, China
| | - Xiao-Feng Lin
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, China
| | - Li Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, China.
| | - Li-Zhi Liu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, China.
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Wang Q, Zhang Y, Zhang E, Xing X, Chen Y, Su MY, Lang N. Prediction of the early recurrence in spinal giant cell tumor of bone using radiomics of preoperative CT: Long-term outcome of 62 consecutive patients. J Bone Oncol 2021; 27:100354. [PMID: 33850701 PMCID: PMC8039834 DOI: 10.1016/j.jbo.2021.100354] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 02/26/2021] [Accepted: 02/28/2021] [Indexed: 12/27/2022] Open
Abstract
Characteristics of 62 patients with spinal GCTB who underwent surgery. A prognostic classification model was built based on features selected by SVM. The combined histogram and texture features could predict recurrence of GCTB.
Objectives To determine if radiomics analysis based on preoperative computed tomography (CT) can predict early postoperative recurrence of giant cell tumor of bone (GCTB) in the spine. Methods In a retrospective review, 62 patients with pathologically confirmed spinal GCTB from March 2008 to February 2018, with a minimum follow-up of 24 months, were identified. The mean follow-up was 73.7 months (range, 28.7–152.1 months). The clinical information including age, gender, lesion location, multi-vertebral involvement, and surgical methods, were obtained. CT images acquired before the operation were retrieved for radiomics analysis. For each case, the tumor regions of interest (ROI) was manually outlined, and a total of 107 radiomics features were extracted. The features were selected via the sequential selection process by using the support vector machine (SVM), then used to construct classification models with Gaussian kernels. The differentiation between recurrence and non-recurrence groups was evaluated by ROC analysis, using 10-fold cross-validation. Results Of the 62 patients, 17 had recurrence with a recurrence rate of 27.4%. None of the clinical information was significantly different between the two groups. Patients receiving curettage had a higher recurrence rate (6/16 = 37.5%) compared to patients receiving TES (6/26 = 23.1%) or intralesional spondylectomy (5/20 = 25%). The final radiomics model was built using 10 selected features, which achieved an accuracy of 89% with AUC of 0.78. Conclusions The radiomics model developed based on pre-operative CT can achieve a high accuracy to predict the recurrence of spinal GCTB. Patients who have a high risk of early recurrence should be treated more aggressively to minimize recurrence.
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Key Words
- CT texture analysis
- CT, Computed Tomography
- DICOM, Digital Imaging and Communications in Medicine
- GCTB, Giant Cell Tumor of Bone
- GLCM, Gray Level Co-occurrence Matrix
- GLDM, Gray Level Dependence Matrix
- GLRLM, Gray Level Run Length Matrix
- GLSZM, Gray Level Size Zone Matrix
- Giant cell tumor of bone
- MRI, Magnetic Resonance Imaging
- NGTDM, Neighborhood Gray Tone Difference Matrix
- OPG, Osteoprotegerin
- PACS, Picture Archiving and Communication System
- Prognosis
- RANK, Receptor Activator of Nuclear factor Kappa-Β
- RANKL, Receptor Activator of Nuclear factor Kappa-Β Ligand
- ROC, Receiver Operating Characteristic
- ROI, Regions of Interest
- Radiomics
- SVM, Support Vector Machine
- Spine
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Affiliation(s)
- Qizheng Wang
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
| | - Yang Zhang
- 164 Irvine Hall, Center for Functional Onco-Imaging, University of California, Irvine, CA 92697-5020, USA.,Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Enlong Zhang
- Department of Radiology, Peking University International Hospital, Life Park Road No.1 Life Science Park of Zhong Guancun, Chang Ping District, Beijing 100191, China
| | - Xiaoying Xing
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
| | - Yongye Chen
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
| | - Min-Ying Su
- 164 Irvine Hall, Center for Functional Onco-Imaging, University of California, Irvine, CA 92697-5020, USA.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China
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Zhang QX, Zhuang LP, Lin ZY. Prognostic models for 1-year survival of NPC after radiotherapy in different ages. Eur Arch Otorhinolaryngol 2021; 278:4955-4965. [PMID: 33715019 DOI: 10.1007/s00405-021-06730-8] [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: 10/27/2020] [Accepted: 03/02/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE Previous studies have shown that approximately 10% of nasopharyngeal cancer (NPC) patients die within a year of disease onset, and that age is an independent predictor. However, no predictive model has been developed. We aimed to establish novel prognostic models to predict the 1-year cancer-specific survival (CSS) of young, middle-aged, and older patients with NPC after radiotherapy. METHODS The data of 2822 NPC patients who underwent radiotherapy between 2004 and 2015 were reviewed from the surveillance, epidemiology, and end results database. We divided them into young, middle-aged, and older people groups according to age (< 44 years, 45-59 years, and ≥ 60 years, respectively). Multivariate analyses were performed, and prognostic models were constructed. RESULTS Multivariate analyses indicated that age, ethnicity, histological subtype, T, and M stage were independent predictors of 1-year CSS in the older people group. In contrast, ethnicity and age were not found to have predictive value in the young and middle-aged groups, respectively. Accordingly, three prognostic models with excellent predictive values were established for the three groups (C-indices: 0.791 [95% CI 0.722-0.859], 0.763 [95% CI 0.721-0.806] and 0.723 [95% CI 0.683-0.763], respectively). These predictive values are higher than those of the eighth edition American joint committee cancer tumor-node-metastasis (TNM) classification system. CONCLUSION Three prognostic models for predicting the 1-year CSS of young, middle-aged, and older NPC patients after radiotherapy showed better predictive power than the TNM classification system. These models may guide treatment strategies and clinical decision-making in different cohorts.
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Affiliation(s)
- Qu-Xia Zhang
- Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, No. 420 Fu-ma Road, Fuzhou, 350014, China.
| | | | - Zhong-Yang Lin
- Department of Otolaryngology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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Peng Z, Wang Y, Wang Y, Jiang S, Fan R, Zhang H, Jiang W. Application of radiomics and machine learning in head and neck cancers. Int J Biol Sci 2021; 17:475-486. [PMID: 33613106 PMCID: PMC7893590 DOI: 10.7150/ijbs.55716] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/10/2020] [Indexed: 02/07/2023] Open
Abstract
With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.
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Affiliation(s)
| | | | | | | | | | | | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
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Zhang LL, Xu F, Song D, Huang MY, Huang YS, Deng QL, Li YY, Shao JY. Development of a Nomogram Model for Treatment of Nonmetastatic Nasopharyngeal Carcinoma. JAMA Netw Open 2020; 3:e2029882. [PMID: 33306119 PMCID: PMC7733160 DOI: 10.1001/jamanetworkopen.2020.29882] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 10/25/2020] [Indexed: 01/26/2023] Open
Abstract
IMPORTANCE Because of tumor heterogeneity, overall survival (OS) differs significantly among individuals with nasopharyngeal carcinoma (NPC), even among those with the same clinical stage. Relying solely on TNM staging to guide treatment remains imperfect. OBJECTIVES To establish a comprehensive nomogram to estimate individualized OS and to explore stratified treatment regimens for risk subgroups in nonmetastatic NPC. DESIGN, SETTING, AND PARTICIPANTS This cohort study included 8093 patients diagnosed with NPC at a single center in China from April 2009 to December 2015. The sample was split into a training cohort (5398 participants [66.7%]) and validation cohort (2695 [33.3%]). Data were analyzed in May 2020. EXPOSURES Age, T stage, N stage, Epstein-Barr virus (EBV) DNA level, serum lactate dehydrogenase (LDH) levels, and albumin (ALB) levels. MAIN OUTCOMES AND MEASURES The primary end point was OS. The nomogram for estimating OS was generated based on multivariate Cox proportional hazards regression. The performance of the nomogram was quantified using Harrell concordance index (C index), the area under the curve (AUC) of the receiver operating characteristic curve, and a calibration curve. OS rates were established using the Kaplan-Meier method, and intersubgroup differences were examined by the log-rank test. RESULTS Among the 8093 participants, 5688 (70.3%) were men, and the median age at diagnosis was 45 years (range, 7-85 years). Six variables (age, T stage, N stage, EBV DNA levels, LDH levels, and ALB levels) were identified through multivariate Cox regression and incorporated into a nomogram to estimate OS. The resulting nomogram showed excellent discriminative ability and significantly outperformed the eighth edition of the American Joint Committee on Cancer/Union for International Cancer Control TNM staging system for estimating OS (C index, 0.716 [95% CI, 0.698-0.734] vs 0.643 [95% CI, 0.624-0.661]; P < .001; AUC, 0.717 [95% CI, 0.698-0.737] vs 0.643 [95% CI, 0.623-0.662]; P < .001), and the calibration curves showed satisfactory agreement between the actual and nomogram-estimated OS rates. The validation cohort confirmed the results. Patients were stratified into 4 risk groups based on the 25th, 50th, and 75th percentile score values estimated from the nomogram. The 4 nomogram-defined risk groups demonstrated significantly different intergroup OS (3-year OS rates: risk group 1, 1328 of 1345 [98.7%]; risk group 2, 1289 of 1341 [96.1%]; risk group 3, 1222 of 1321 [92.5%]; risk group 4, 1173 of 1391 [84.3%]; P < .001). These risk groups were associated with the efficacy of different treatment regimens. For example, for risk group 4, induction chemotherapy with concurrent chemoradiotherapy was associated with a significantly better OS than concurrent chemoradiotherapy (log-rank P = .008) and intensity-modulated radiotherapy alone (log-rank P < .001). CONCLUSIONS AND RELEVANCE In this study, the proposed nomogram model enabled individualized prognostication of OS and could help to guide risk-adapted treatment for patients with nonmetastatic NPC.
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Affiliation(s)
- Lu-Lu Zhang
- Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Department of Molecular Diagnostics, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China
| | - Fei Xu
- Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Department of Molecular Diagnostics, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China
| | - Di Song
- Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Department of Molecular Diagnostics, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China
| | - Meng-Yao Huang
- Sun Yat-Sen University School of Mathematics, Guangzhou, People’s Republic of China
| | - Yong-Shi Huang
- Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Department of Molecular Diagnostics, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China
| | - Qi-Ling Deng
- Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Department of Molecular Diagnostics, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China
| | - Yi-Yang Li
- Department of Oncology, First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Jian-Yong Shao
- Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Department of Molecular Diagnostics, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People’s Republic of China
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Bologna M, Corino V, Calareso G, Tenconi C, Alfieri S, Iacovelli NA, Cavallo A, Cavalieri S, Locati L, Bossi P, Romanello DA, Ingargiola R, Rancati T, Pignoli E, Sdao S, Pecorilla M, Facchinetti N, Trama A, Licitra L, Mainardi L, Orlandi E. Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients. Cancers (Basel) 2020; 12:E2958. [PMID: 33066161 PMCID: PMC7601980 DOI: 10.3390/cancers12102958] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 02/06/2023] Open
Abstract
Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors. This study aims at developing a magnetic resonance imaging (MRI)-based radiomic signature as a prognostic marker for different clinical endpoints in NPC patients from non-endemic areas. A total 136 patients with advanced NPC and available MRI imaging (T1-weighted and T2-weighted) were selected. For each patient, 2144 radiomic features were extracted from the main tumor and largest lymph node. A multivariate Cox regression model was trained on a subset of features to obtain a radiomic signature for overall survival (OS), which was also applied for the prognosis of other clinical endpoints. Validation was performed using 10-fold cross-validation. The added prognostic value of the radiomic features to clinical features and volume was also evaluated. The radiomics-based signature had good prognostic power for OS and loco-regional recurrence-free survival (LRFS), with C-index of 0.68 and 0.72, respectively. In all the cases, the addition of radiomics to clinical features improved the prognostic performance. Radiomic features can provide independent prognostic information in NPC patients from non-endemic areas.
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Affiliation(s)
- Marco Bologna
- Department of Electronics, Information and Bioengineering (DEIB) Politecnico di Milano, 20133 Milan, Italy; (V.C.); (L.M.)
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering (DEIB) Politecnico di Milano, 20133 Milan, Italy; (V.C.); (L.M.)
| | - Giuseppina Calareso
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (G.C.); (S.S.)
| | - Chiara Tenconi
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, 20133 Milan, Italy; (C.T.); (L.L.)
- Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (D.A.R.); (R.I.); (E.P.)
| | - Salvatore Alfieri
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (S.A.); (S.C.); (L.L.)
| | - Nicola Alessandro Iacovelli
- Radiotherapy 2 Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (N.A.I.); (N.F.); (E.O.)
| | - Anna Cavallo
- Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (D.A.R.); (R.I.); (E.P.)
| | - Stefano Cavalieri
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (S.A.); (S.C.); (L.L.)
| | - Laura Locati
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (S.A.); (S.C.); (L.L.)
| | - Paolo Bossi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili, 25123 Brescia, Italy;
| | - Domenico Attilio Romanello
- Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (D.A.R.); (R.I.); (E.P.)
| | - Rossana Ingargiola
- Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (D.A.R.); (R.I.); (E.P.)
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy;
| | - Emanuele Pignoli
- Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (A.C.); (D.A.R.); (R.I.); (E.P.)
| | - Silvana Sdao
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (G.C.); (S.S.)
| | - Mattia Pecorilla
- Post-Graduate School in Radiodiagnostics, Università degli Studi di Milano, 20133 Milan, Italy;
| | - Nadia Facchinetti
- Radiotherapy 2 Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (N.A.I.); (N.F.); (E.O.)
| | - Annalisa Trama
- Research Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy;
| | - Lisa Licitra
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, 20133 Milan, Italy; (C.T.); (L.L.)
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (S.A.); (S.C.); (L.L.)
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering (DEIB) Politecnico di Milano, 20133 Milan, Italy; (V.C.); (L.M.)
| | - Ester Orlandi
- Radiotherapy 2 Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (N.A.I.); (N.F.); (E.O.)
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Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy. PLoS One 2020; 15:e0240043. [PMID: 33017440 PMCID: PMC7535039 DOI: 10.1371/journal.pone.0240043] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 09/18/2020] [Indexed: 01/28/2023] Open
Abstract
Background We hypothesized that spatial heterogeneity exists between recurrent and non-recurrent regions within a tumor. The aim of this study was to determine if there is a difference between radiomics features derived from recurrent versus non recurrent regions within the tumor based on pre-treatment MRI. Methods A total of 14 T4NxM0 NPC patients with histologically proven “in field” recurrence in the post nasal space following curative intent IMRT were included in this study. Pretreatment MRI were co-registered with MRI at the time of recurrence for the delineation of gross tumor volume at diagnosis(GTV) and at recurrence(GTVr). A total of 7 histogram features and 40 texture features were computed from the recurrent(GTVr) and non-recurrent region(GTV-GTVr). Paired t-tests and Wilcoxon signed-rank tests were carried out on the 47 quantified radiomics features. Results A total of 7 features were significantly different between recurrent and non-recurrent regions. Other than the variance from intensity-based histogram, the remaining six significant features were either from the gray-level size zone matrix (GLSZM) or the neighbourhood gray-tone difference matrix (NGTDM). Conclusions The radiomic features extracted from pre-treatment MRI can potentially reflect the difference between recurrent and non-recurrent regions within a tumor and has a potential role in pre-treatment identification of intra-tumoral radio-resistance for selective dose escalation.
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Cui Y, Yang W, Ren J, Li D, Du X, Zhang J, Yang X. Prognostic value of multiparametric MRI-based radiomics model: Potential role for chemotherapeutic benefits in locally advanced rectal cancer. Radiother Oncol 2020; 154:161-169. [PMID: 32976874 DOI: 10.1016/j.radonc.2020.09.039] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 08/29/2020] [Accepted: 09/17/2020] [Indexed: 12/29/2022]
Abstract
BACKGROUND AND PURPOSE We aimed to develop a radiomics model for the prediction of survival and chemotherapeutic benefits using pretreatment multiparameter MR images and clinicopathological features in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS 186 consecutive patients with LARC underwent feature extraction from the whole tumor on T2-weighted, contrast enhanced T1-weighted, and ADC images. Feature selection was based on feature stability and the Boruta algorithm. Radiomics signatures for predicting DFS (disease-free survival) were then generated using the selected features. Combining clinical risk factors, a radiomics nomogram was constructed using Cox proportional hazards regression model. The predictive performance was evaluated by Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis. RESULTS Four features were selected to construct the radiomics signature, significantly associated with DFS (P < 0.001). The radiomics nomogram, incorporating radiomics signature and two clinicopathological variables (pN and tumor differentiation), exhibited better prediction performance for DFS than the clinicopathological model, with C-index of 0.780 (95%CI, 0.718-0.843) and 0.803 (95%CI, 0.717-0.889) in the training and validation cohorts, respectively. The radiomics nomogram-defined high-risk group had a shorter DFS, DMFS, and OS than those in the low-risk group (all P < 0.05). Further analysis showed that patients with higher nomogram-defined score exhibited a favorable response to adjuvant chemotherapy (AC) while the low-risk could not. CONCLUSION This study demonstrated that the newly developed pretreatment multiparameter MRI-based radiomics model could serve as a powerful predictor of prognosis, and may act as a potential indicator for guiding AC in patients with LARC.
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Affiliation(s)
- Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Wenhui Yang
- Shanxi Bethune Hospital Cancer Center, Taiyuan, China
| | | | - Dandan Li
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Xiaosong Du
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Junjie Zhang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Xiaotang Yang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China.
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40
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How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Eur Radiol 2020; 31:2272-2280. [PMID: 32975661 DOI: 10.1007/s00330-020-07284-9] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 07/06/2020] [Accepted: 09/10/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVE Test a practical realignment approach to compensate the technical variability of MR radiomic features. METHODS T1 phantom images acquired on 2 scanners, FLAIR and contrast-enhanced T1-weighted (CE-T1w) images of 18 brain tumor patients scanned on both 1.5-T and 3-T scanners, and 36 T2-weighted (T2w) images of prostate cancer patients scanned in one of two centers were investigated. The ComBat procedure was used for harmonizing radiomic features. Differences in statistical distributions in feature values between 1.5- and 3-T images were tested before and after harmonization. The prostate studies were used to determine the impact of harmonization to distinguish between Gleason grades (GGs). RESULTS In the phantom data, 40 out of 42 radiomic feature values were significantly different between the 2 scanners before harmonization and none after. In white matter regions, the statistical distributions of features were significantly different (p < 0.05) between the 1.5- and 3-T images for 37 out of 42 features in both FLAIR and CE-T1w images. After harmonization, no statistically significant differences were observed. In brain tumors, 41 (FLAIR) or 36 (CE-T1w) out of 42 features were significantly different between the 1.5- and 3-T images without harmonization, against 1 (FLAIR) or none (CE-T1w) with harmonization. In prostate studies, 636 radiomic features were significantly different between GGs after harmonization against 461 before. The ability to distinguish between GGs using radiomic features was increased after harmonization. CONCLUSION ComBat harmonization efficiently removes inter-center technical inconsistencies in radiomic feature values and increases the sensitivity of studies using data from several scanners. KEY POINTS • Radiomic feature values obtained using different MR scanners or imaging protocols can be harmonized by combining off-the-shelf image standardization and feature realignment procedures. • Harmonized radiomic features enable one to pool data from different scanners and centers without a substantial loss of statistical power caused by intra- and inter-center variability. • The proposed realignment method is applicable to radiomic features from different MR sequences and tumor types and does not rely on any phantom acquisition.
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41
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A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:7562140. [PMID: 32908581 PMCID: PMC7474760 DOI: 10.1155/2020/7562140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 08/06/2020] [Accepted: 08/12/2020] [Indexed: 11/18/2022]
Abstract
Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC.
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42
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Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma. Mol Imaging Biol 2020; 22:1581-1591. [DOI: 10.1007/s11307-020-01507-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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43
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Shen H, Wang Y, Liu D, Lv R, Huang Y, Peng C, Jiang S, Wang Y, He Y, Lan X, Huang H, Sun J, Zhang J. Predicting Progression-Free Survival Using MRI-Based Radiomics for Patients With Nonmetastatic Nasopharyngeal Carcinoma. Front Oncol 2020; 10:618. [PMID: 32477932 PMCID: PMC7235342 DOI: 10.3389/fonc.2020.00618] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Accepted: 04/03/2020] [Indexed: 12/14/2022] Open
Abstract
Objectives: This study aimed to explore the predictive value of MRI-based radiomic model for progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC). Methods: A total of 327 nonmetastatic NPC patients [training cohort (n = 230) and validation cohort (n = 97)] were enrolled. The clinical and MRI data were collected. The least absolute shrinkage selection operator (LASSO) and recursive feature elimination (RFE) were used to select radiomic features. Five models [Model 1: clinical data, Model 2: overall stage, Model 3: radiomics, Model 4: radiomics + overall stage, Model 5: radiomics + overall stage + Epstein–Barr virus (EBV) DNA] were constructed. The prognostic performances of these models were evaluated by Harrell's concordance index (C-index). The Kaplan–Meier method was applied for the survival analysis. Results: Model 5 incorporating radiomics, overall stage, and EBV DNA yielded the highest C-indices for predicting PFS in comparison with Model 1, Model 2, Model 3, and Model 4 (training cohorts: 0.805 vs. 0.766 vs. 0.749 vs. 0.641 vs. 0.563, validation cohorts: 0.874 vs. 0.839 vs. 836 vs. 0.689 vs. 0.456). The survival curve showed that the high-risk group yielded a lower PFS than the low-risk group. Conclusions: The model incorporating radiomics, overall stage, and EBV DNA showed better performance for predicting PFS in nonmetastatic NPC patients.
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Affiliation(s)
- Hesong Shen
- Department of Radiology, Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China.,Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China
| | - Yu Wang
- Department of Radiology, Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China.,Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China
| | - Daihong Liu
- Department of Radiology, Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China.,Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China
| | - Rongfei Lv
- Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China
| | - Yuanying Huang
- Department of Oncology and Hematology, Chongqing General Hospital, Chongqing, China
| | - Chao Peng
- Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China
| | - Shixi Jiang
- Department of Radiology, Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China
| | - Ying Wang
- Department of Radiotherapy, Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China
| | - Yongpeng He
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China
| | - Xiaosong Lan
- Department of Radiology, Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China
| | - Hong Huang
- Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China
| | - Jianqing Sun
- Clinical Science, Philips Healthcare, Shanghai, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China.,Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing, China
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Holbrook MD, Blocker SJ, Mowery YM, Badea A, Qi Y, Xu ES, Kirsch DG, Johnson GA, Badea CT. MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice. Tomography 2020; 6:23-33. [PMID: 32280747 PMCID: PMC7138523 DOI: 10.18383/j.tom.2019.00021] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have developed quantitative imaging for application in the preclinical arm of a coclinical trial by using a genetically engineered mouse model of soft tissue sarcoma. Magnetic resonance imaging (MRI) images were acquired 1 day before and 1 week after radiation therapy. After the second MRI, the primary tumor was surgically removed by amputating the tumor-bearing hind limb, and mice were followed for up to 6 months. An automatic analysis pipeline was used for multicontrast MRI data using a convolutional neural network for tumor segmentation followed by radiomics analysis. We then calculated radiomics features for the tumor, the peritumoral area, and the 2 combined. The first radiomics analysis focused on features most indicative of radiation therapy effects; the second radiomics analysis looked for features that might predict primary tumor recurrence. The segmentation results indicated that Dice scores were similar when using multicontrast versus single T2-weighted data (0.863 vs 0.861). One week post RT, larger tumor volumes were measured, and radiomics analysis showed greater heterogeneity. In the tumor and peritumoral area, radiomics features were predictive of primary tumor recurrence (AUC: 0.79). We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies.
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Affiliation(s)
- M. D. Holbrook
- Departments of Radiology, Center for In Vivo Microscopy; and
| | - S. J. Blocker
- Departments of Radiology, Center for In Vivo Microscopy; and
| | - Y. M. Mowery
- Radiation Oncology, Duke University Medical Center, Durham, NC
| | - A. Badea
- Departments of Radiology, Center for In Vivo Microscopy; and
| | - Y. Qi
- Departments of Radiology, Center for In Vivo Microscopy; and
| | - E. S. Xu
- Radiation Oncology, Duke University Medical Center, Durham, NC
| | - D. G. Kirsch
- Radiation Oncology, Duke University Medical Center, Durham, NC
| | - G. A. Johnson
- Departments of Radiology, Center for In Vivo Microscopy; and
| | - C. T. Badea
- Departments of Radiology, Center for In Vivo Microscopy; and
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45
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Magnetic Resonance Imaging Texture Analysis Predicts Recurrence in Patients with Nasopharyngeal Carcinoma. Can Assoc Radiol J 2020; 70:394-402. [DOI: 10.1016/j.carj.2019.06.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 06/11/2019] [Accepted: 06/27/2019] [Indexed: 12/19/2022] Open
Abstract
Background The personalization of oncologic treatment using radiomic signatures is mounting in nasopharyngeal carcinoma (NPC). We ascertain the predictive ability of 3D volumetric magnetic resonance imaging (MRI) texture features on NPC disease recurrence. Methods A retrospective study of 58 patients with NPC undergoing primary curative-intent treatment was performed. Forty-two image texture features were extracted from pre-treatment MRI in addition to clinical factors. A multivariate logistic regression was used to model the texture features. A receiver operating characteristic curve on 100 bootstrap samples was used to maximize generalizability to out-of-sample data. A Cox proportional model was used to predict disease recurrence in the final model. Results A total of 58 patients were included in the study. MRI texture features predicted disease recurrence with an area under the curve (AUC), sensitivity, and specificity of 0.79, 0.73, and 0.71, respectively. Loco-regional recurrence was predicted with AUC, sensitivity, and specificity of 0.82, 0.73 and 0.74 respectively while prediction for distant metastasis had an AUC, sensitivity, and specificity of 0.92, 0.79 and 0.84, respectively. Texture features on MRI had a hazard ratio of 4.37 (95% confidence interval 1.72–20.2) for disease recurrence when adjusting for age, sex, smoking, and TNM staging. Conclusion Texture features on MRI are independent predictors of NPC recurrence in patients undergoing curative-intent treatment.
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Stieb S, Kiser K, van Dijk L, Livingstone NR, Elhalawani H, Elgohari B, McDonald B, Ventura J, Mohamed ASR, Fuller CD. Imaging for Response Assessment in Radiation Oncology: Current and Emerging Techniques. Hematol Oncol Clin North Am 2019; 34:293-306. [PMID: 31739950 DOI: 10.1016/j.hoc.2019.09.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Imaging in radiation oncology is essential for the evaluation of treatment response in tumors and organs at risk. This influences further treatment decisions and could possibly be used to adapt therapy. This review article focuses on the currently used imaging modalities for response assessment in radiation oncology and gives an overview of new and promising techniques within this field.
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Affiliation(s)
- Sonja Stieb
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Kendall Kiser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Lisanne van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Nadia Roxanne Livingstone
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Brigid McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Juan Ventura
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Abdallah Sherif Radwan Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
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