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Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects. Int J Med Inform 2024; 188:105464. [PMID: 38728812 DOI: 10.1016/j.ijmedinf.2024.105464] [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: 10/15/2023] [Revised: 04/20/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction. OBJECTIVES This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges. METHODS Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)and Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability. CONCLUSION Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Dang LH, Hung SH, Le NTN, Chuang WK, Wu JY, Huang TC, Le NQK. Enhancing Nasopharyngeal Carcinoma Survival Prediction: Integrating Pre- and Post-Treatment MRI Radiomics with Clinical Data. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01109-7. [PMID: 38689151 DOI: 10.1007/s10278-024-01109-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/26/2024] [Accepted: 04/02/2024] [Indexed: 05/02/2024]
Abstract
Recurrences are frequent in nasopharyngeal carcinoma (NPC) despite high remission rates with treatment, leading to considerable morbidity. This study aimed to develop a prediction model for NPC survival by harnessing both pre- and post-treatment magnetic resonance imaging (MRI) radiomics in conjunction with clinical data, focusing on 3-year progression-free survival (PFS) as the primary outcome. Our comprehensive approach involved retrospective clinical and MRI data collection of 276 eligible NPC patients from three independent hospitals (180 in the training cohort, 46 in the validation cohort, and 50 in the external cohort) who underwent MRI scans twice, once within 2 months prior to treatment and once within 10 months after treatment. From the contrast-enhanced T1-weighted images before and after treatment, 3404 radiomics features were extracted. These features were not only derived from the primary lesion but also from the adjacent lymph nodes surrounding the tumor. We conducted appropriate feature selection pipelines, followed by Cox proportional hazards models for survival analysis. Model evaluation was performed using receiver operating characteristic (ROC) analysis, the Kaplan-Meier method, and nomogram construction. Our study unveiled several crucial predictors of NPC survival, notably highlighting the synergistic combination of pre- and post-treatment data in both clinical and radiomics assessments. Our prediction model demonstrated robust performance, with an accuracy of AUCs of 0.66 (95% CI: 0.536-0.779) in the training cohort, 0.717 (95% CI: 0.536-0.883) in the testing cohort, and 0.827 (95% CI: 0.684-0.948) in validation cohort in prognosticating patient outcomes. Our study presented a novel and effective prediction model for NPC survival, leveraging both pre- and post-treatment clinical data in conjunction with MRI features. Its constructed nomogram provides potentially significant implications for NPC research, offering clinicians a valuable tool for individualized treatment planning and patient counseling.
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Affiliation(s)
- Luong Huu Dang
- Department of Otolaryngology, Faculty of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Shih-Han Hung
- Department of Otolaryngology, School of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Otolaryngology, Wan Fang Hospital, Taipei, Taiwan
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Nhi Thao Ngoc Le
- International Ph.D. Program in Biomedical Engineering, College of Biomedical Engineering, Taipei, Taiwan
| | - Wei-Kai Chuang
- Department of Radiation Oncology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jeng-You Wu
- Department of Radiation Oncology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Ting-Chieh Huang
- Department of Otolaryngology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
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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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Chen Z, Wang Z, Liu S, Zhang S, Zhou Y, Zhang R, Yang W. Nomograms based on multiparametric MRI radiomics integrated with clinical-radiological features for predicting the response to induction chemotherapy in nasopharyngeal carcinoma. Eur J Radiol 2024; 175:111438. [PMID: 38613869 DOI: 10.1016/j.ejrad.2024.111438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 01/27/2024] [Accepted: 03/20/2024] [Indexed: 04/15/2024]
Abstract
OBJECTIVE To establish nomograms integrating multiparametric MRI radiomics with clinical-radiological features to identify the responders and non-responders to induction chemotherapy (ICT) in nasopharyngeal carcinoma (NPC). METHODS We retrospectively analyzed the clinical and MRI data of 168 NPC patients between December 2015 and April 2022. We used 3D-Slicer to segment the regions of interest (ROIs) and the "Pyradiomic" package to extract radiomics features. We applied the least absolute shrinkage and selection operator regression to select radiomics features. We developed clinical-only, radiomics-only, and the combined clinical-radiomics nomograms using logistic regression analysis. The receiver operating characteristic curves, DeLong test, calibration, and decision curves were used to assess the discriminative performance of the models. The model was internally validated using 10-fold cross-validation. RESULTS A total of 14 optimal features were finally selected to develop a radiomic signature, with an AUC of 0.891 (95 % CI, 0.825-0.946) in the training cohort and 0.837 (95 % CI, 0.723-0.932) in the testing cohort. The nomogram based on the Rad-Score and clinical-radiological factors for evaluating tumor response to ICT yielded an AUC of 0.926 (95 % CI, 0.875-0.965) and 0.901 (95 % CI, 0.815-0.979) in the two cohorts, respectively. Decision curves demonstrated that the combined clinical-radiomics nomograms were clinically useful. CONCLUSION Nomograms integrating multiparametric MRI-based radiomics and clinical-radiological features could non-invasively discriminate ICT responders from non-responders in NPC patients.
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Affiliation(s)
- Zhiqiang Chen
- Department of Radiology, the First Affiliated Hospital of Hainan Medical University, Haikou, Hainan 570102, China; Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China.
| | - Zhuo Wang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Shili Liu
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Shaoru Zhang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Yunshu Zhou
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Ruodi Zhang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Wenjun Yang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medicine and Life Sciences, Hainan Medical University, Haikou, Hainan 571199, China.
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Gu B, Meng M, Xu M, Feng DD, Bi L, Kim J, Song S. Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma. Eur J Nucl Med Mol Imaging 2023; 50:3996-4009. [PMID: 37596343 PMCID: PMC10611876 DOI: 10.1007/s00259-023-06399-7] [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: 06/01/2023] [Accepted: 08/11/2023] [Indexed: 08/20/2023]
Abstract
PURPOSE Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients. METHODS A total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [18F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The DeepMTS-derived segmentation masks were leveraged to extract handcrafted radiomics features, which were also used for prognostic prediction (AutoRadio-Score). Finally, we developed a multi-task deep learning-based radiomic (MTDLR) nomogram by integrating DeepMTS-Score, AutoRadio-Score, and clinical data. Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the discriminative ability of the proposed MTDLR nomogram. For patient stratification, the PFS rates of high- and low-risk patients were calculated using Kaplan-Meier method and compared with the observed PFS probability. RESULTS Our MTDLR nomogram achieved C-index of 0.818 (95% confidence interval (CI): 0.785-0.851), 0.752 (95% CI: 0.638-0.865), and 0.717 (95% CI: 0.641-0.793) and area under curve (AUC) of 0.859 (95% CI: 0.822-0.895), 0.769 (95% CI: 0.642-0.896), and 0.730 (95% CI: 0.634-0.826) in the training, internal validation, and external validation cohorts, which showed a statistically significant improvement over conventional radiomic nomograms. Our nomogram also divided patients into significantly different high- and low-risk groups. CONCLUSION Our study demonstrated that MTDLR nomogram can perform reliable and accurate prognostic prediction in LA-NPC patients, and also enabled better patient stratification, which could facilitate personalized treatment planning.
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Affiliation(s)
- Bingxin Gu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
- Center for Biomedical Imaging, Fudan University, Shanghai, People's Republic of China
- Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, People's Republic of China
- Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Fudan University, Shanghai, People's Republic of China
| | - Mingyuan Meng
- School of Computer Science, the University of Sydney, Sydney, Australia
| | - Mingzhen Xu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
- Center for Biomedical Imaging, Fudan University, Shanghai, People's Republic of China
- Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, People's Republic of China
- Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Fudan University, Shanghai, People's Republic of China
| | - David Dagan Feng
- School of Computer Science, the University of Sydney, Sydney, Australia
| | - Lei Bi
- Institute of Translational Medicine, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jinman Kim
- School of Computer Science, the University of Sydney, Sydney, Australia
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
- Center for Biomedical Imaging, Fudan University, Shanghai, People's Republic of China.
- Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, People's Republic of China.
- Key Laboratory of Nuclear Physics and Ion-Beam Application (MOE), Fudan University, Shanghai, People's Republic of China.
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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Corti A, De Cecco L, Cavalieri S, Lenoci D, Pistore F, Calareso G, Mattavelli D, de Graaf P, Leemans CR, Brakenhoff RH, Ravanelli M, Poli T, Licitra L, Corino V, Mainardi L. MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures. Biomark Res 2023; 11:69. [PMID: 37455307 DOI: 10.1186/s40364-023-00494-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/03/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND . At present, the prognostic prediction in advanced oral cavity squamous cell carcinoma (OCSCC) is based on the tumor-node-metastasis (TNM) staging system, and the most used imaging modality in these patients is magnetic resonance image (MRI). With the aim to improve the prediction, we developed an MRI-based radiomic signature as a prognostic marker for overall survival (OS) in OCSCC patients and compared it with published gene expression signatures for prognosis of OS in head and neck cancer patients, replicated herein on our OCSCC dataset. METHODS For each patient, 1072 radiomic features were extracted from T1 and T2-weighted MRI (T1w and T2w). Features selection was performed, and an optimal set of five of them was used to fit a Cox proportional hazard regression model for OS. The radiomic signature was developed on a multi-centric locally advanced OCSCC retrospective dataset (n = 123) and validated on a prospective cohort (n = 108). RESULTS The performance of the signature was evaluated in terms of C-index (0.68 (IQR 0.66-0.70)), hazard ratio (HR 2.64 (95% CI 1.62-4.31)), and high/low risk group stratification (log-rank p < 0.001, Kaplan-Meier curves). When tested on a multi-centric prospective cohort (n = 108), the signature had a C-index of 0.62 (IQR 0.58-0.64) and outperformed the clinical and pathologic TNM stage and six out of seven gene expression prognostic signatures. In addition, the significant difference of the radiomic signature between stages III and IVa/b in patients receiving surgery suggests a potential association of MRI features with the pathologic stage. CONCLUSIONS Overall, the present study suggests that MRI signatures, containing non-invasive and cost-effective remarkable information, could be exploited as prognostic tools.
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Affiliation(s)
- Anna Corti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Loris De Cecco
- Integrated Biology of Rare Tumors, Department of Research, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Deborah Lenoci
- Integrated Biology of Rare Tumors, Department of Research, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Federico Pistore
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
| | - Davide Mattavelli
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Pim de Graaf
- Amsterdam UMC location Vrije Universiteit, Radiology and Nuclear Medicine, de Boelelaan 1117, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - C René Leemans
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam UMC location Vrije Universiteit, Otolaryngology-Head and Neck Surgery, de Boelelaan 1117, Amsterdam, The Netherlands
| | - Ruud H Brakenhoff
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Amsterdam UMC location Vrije Universiteit, Otolaryngology-Head and Neck Surgery, de Boelelaan 1117, Amsterdam, The Netherlands
| | - Marco Ravanelli
- Unit of Radiology, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Tito Poli
- Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of Parma, Parma, Italy
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli studi di Milano, Milan, Italy
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Cardiotech Lab, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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9
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Lo Iacono F, Maragna R, Pontone G, Corino VDA. A robust radiomic-based machine learning approach to detect cardiac amyloidosis using cardiac computed tomography. FRONTIERS IN RADIOLOGY 2023; 3:1193046. [PMID: 37588665 PMCID: PMC10426499 DOI: 10.3389/fradi.2023.1193046] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/26/2023] [Indexed: 08/18/2023]
Abstract
Introduction Cardiac amyloidosis (CA) shares similar clinical and imaging characteristics (e.g., hypertrophic phenotype) with aortic stenosis (AS), but its prognosis is generally worse than severe AS alone. Recent studies suggest that the presence of CA is frequent (1 out of 8 patients) in patients with severe AS. The coexistence of the two diseases complicates the prognosis and therapeutic management of both conditions. Thus, there is an urgent need to standardize and optimize the diagnostic process of CA and AS. The aim of this study is to develop a robust and reliable radiomics-based pipeline to differentiate the two pathologies. Methods Thirty patients were included in the study, equally divided between CA and AS. For each patient, a cardiac computed tomography (CCT) was analyzed by extracting 107 radiomics features from the LV wall. Feature robustness was evaluated by means of geometrical transformations to the ROIs and intra-class correlation coefficient (ICC) computation. Various correlation thresholds (0.80, 0.85, 0.90, 0.95, 1), feature selection methods [p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA, sequential forwards selection] and machine learning classifiers (k-nearest neighbors, support vector machine, decision tree, logistic regression and gradient boosting) were assessed using a leave-one-out cross-validation. Data augmentation was performed using the synthetic minority oversampling technique. Finally, explainability analysis was performed by using the SHapley Additive exPlanations (SHAP) method. Results Ninety-two radiomic features were selected as robust and used in the further steps. Best performances of classification were obtained using a correlation threshold of 0.95, PCA (keeping 95% of the variance, corresponding to 9 PCs) and support vector machine classifier reaching an accuracy, sensitivity and specificity of 0.93. Four PCs were found to be mainly dependent on textural features, two on first-order statistics and three on shape and size features. Conclusion These preliminary results show that radiomics might be used as non-invasive tool able to differentiate CA from AS using clinical routine available images.
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Affiliation(s)
- Francesca Lo Iacono
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Riccardo Maragna
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Valentina D. A. Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
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10
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Zhao Q, Dong A, Cui C, Ou Q, Ruan G, Zhou J, Tian L, Liu L, Ma H, Li H. MRI-Based Metastatic Nodal Number and Associated Nomogram Improve Stratification of Nasopharyngeal Carcinoma Patients: Potential Indications for Individual Induction Chemotherapy. J Magn Reson Imaging 2023; 57:1790-1802. [PMID: 36169976 DOI: 10.1002/jmri.28435] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 09/05/2022] [Accepted: 09/08/2022] [Indexed: 12/08/2022] Open
Abstract
BACKGROUND Metastatic lymph nodal number (LNN) is associated with the survival of nasopharyngeal carcinoma (NPC); however, counting multiple nodes is cumbersome. PURPOSE To explore LNN threshold and evaluate its use in risk stratification and induction chemotherapy (IC) indication. STUDY TYPE Retrospective. POPULATION A total of 792 radiotherapy-treated NPC patients (N classification: N0 182, N1 438, N2 113, N3 59; training group: 396, validation group: 396; receiving IC: 390). FIELD STRENGTH/SEQUENCE T1-, T2- and postcontrast T1-weighted fast spin echo MRI at 1.5 or 3.0 T. ASSESSMENT Nomogram with (model B) or without (model A) LNN was constructed to evaluate the 5-year overall (OS), distant metastasis-free (DMFS), and progression-free survival (PFS) for the group as a whole and N1 stage subgroup. High- and low-risk groups were divided (above vs below LNN- or model B-threshold); their response to IC was evaluated among advanced patients in stage III/IV. STATISTICAL TESTS Maximally selected rank, univariate and multivariable Cox analysis identified the optimal LNN threshold and other variables. Harrell's concordance index (C-index) and 2-fold cross-validation evaluated discriminative ability of models. Matched-pair analysis compared survival outcomes of adding IC or not. A P value < 0.05 was considered statistically significant. RESULTS Median follow-up duration was 62.1 months. LNN ≥ 4 was independently associated with decreased 5-year DMFS, OS, and PFS in entire patients or N1 subgroup. Compared to model A, model B (adding LNN, LNN ≥ 4 vs <4) presented superior C-indexes in the training (0.755 vs 0.727) and validation groups (0.676 vs 0.642) for discriminating DMFS. High-risk patients benefited from IC with improved post-IC response and OS, but low-risk patients did not (P = 0.785 and 0.690, respectively). CONCLUSIONS LNN ≥ 4 is an independent risk stratification factor of worse survival in entire or N1 staging NPC patients. LNN ≥ 4 or the associated nomogram has potential to identify high-risk patients requiring IC. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: 4.
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Affiliation(s)
- Qin Zhao
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, People's Republic of China
| | - Annan Dong
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, People's Republic of China
| | - Chunyan Cui
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, People's Republic of China
| | - Qiaowen Ou
- Department of Clinical Nutrition, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong, People's Republic of China
| | - Guangying Ruan
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, People's Republic of China
| | - Jian Zhou
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, People's Republic of China
| | - Li Tian
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, People's Republic of China
| | - Lizhi Liu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, People's Republic of China
- Department of Radiology, The Third People's Hospital of Shenzhen, Shenzhen, Guangdong, People's Republic of China
| | - Huali Ma
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, People's Republic of China
| | - Haojiang Li
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, People's Republic of China
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11
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Zhang YP, Zhang XY, Cheng YT, Li B, Teng XZ, Zhang J, Lam S, Zhou T, Ma ZR, Sheng JB, Tam VCW, Lee SWY, Ge H, Cai J. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res 2023; 10:22. [PMID: 37189155 DOI: 10.1186/s40779-023-00458-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 05/17/2023] Open
Abstract
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.
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Affiliation(s)
- Yuan-Peng Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China
| | - Xin-Yun Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Yu-Ting Cheng
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Bing Li
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Xin-Zhi Teng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Saikit Lam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Ta Zhou
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Zong-Rui Ma
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jia-Bao Sheng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Victor C W Tam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Shara W Y Lee
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Hong Ge
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Jing Cai
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China.
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12
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Bologna M, Corino V, Cavalieri S, Calareso G, Gazzani SE, Poli T, Ravanelli M, Mattavelli D, de Graaf P, Nauta I, Scheckenbach K, Licitra L, Mainardi L. Prognostic radiomic signature for head and neck cancer: development and validation on a multi-centric MRI dataset. Radiother Oncol 2023; 183:109638. [PMID: 37004837 DOI: 10.1016/j.radonc.2023.109638] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 03/10/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023]
Abstract
BACKGROUND AND PURPOSE Prognosis in locally advanced head and neck cancer (HNC) is currently based on TNM staging system and tumor subsite. However, quantitative imaging features (i.e., radiomic features) from magnetic resonance imaging (MRI) may provide additional prognostic info. The aim of this work is to develop and validate an MRI-based prognostic radiomic signature for locally advanced HNC. MATERIALS AND METHODS Radiomic features were extracted from T1- and T2-weighted MRI (T1w and T2w) using the segmentation of the primary tumor as mask. In total 1072 features (536 per image type) were extracted for each tumor. A retrospective multi-centric dataset (n=285) was used for features selection and model training. The selected features were used to fit a Cox proportional hazard regression model for overall survival (OS) that outputs the radiomic signature. The signature was then validated on a prospective multi-centric dataset (n=234). Prognostic performance for OS and disease-free survival (DFS) was evaluated using C-index. Additional prognostic value of the radiomic signature was explored. RESULTS The radiomic signature had C-index=0.64 for OS and C-index=0.60 for DFS in the validation set. The addition of the radiomic signature to other clinical features (TNM staging and tumor subsite) increased prognostic ability for both OS (HPV- C-index 0.63 to 0.65; HPV+ C-index 0.75 to 0.80) and DFS (HPV- C-index 0.58 to 0.61; HPV+ C-index 0.64 to 0.65). CONCLUSION An MRI-based prognostic radiomic signature was developed and prospectively validated. Such signature can successfully integrate clinical factors in both HPV+ and HPV- tumors. Grant support: European Union Horizon 2020 Framework Programme, Grant/Award, Number: 689715.
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Affiliation(s)
- Marco Bologna
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy.
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Stefano Cavalieri
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Silvia Eleonora Gazzani
- Unit of Diagnostic and Interventional Radiology, Department of Surgical Sciences, University of Parma, Parma, Italy
| | - Tito Poli
- Unit of Maxillo-Facial Surgery, Department of Biomedical, Biotechnological and Translational Sciences (S.Bi.Bi.T.), University of Parma, Parma, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Davide Mattavelli
- Department of Otorhinolaryngology Head and Neck Surgery, Spedali Civili di Brescia and University of Brescia, Brescia, Italy
| | - Pim de Graaf
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Irene Nauta
- Department of Otolaryngology/Head and Neck Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, The Netherlands
| | - Kathrin Scheckenbach
- Department of Otolaryngology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano and Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan. Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
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13
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Li S, Wan X, Deng YQ, Hua HL, Li SL, Chen XX, Zeng ML, Zha Y, Tao ZZ. Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued. Cancer Imaging 2023; 23:14. [PMID: 36759889 PMCID: PMC9912633 DOI: 10.1186/s40644-023-00530-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 02/01/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC. METHODS A total of 381 NPC patients who were divided into high- and low-risk groups according to progression-free survival were retrospectively included. Deeplab v3 and U-Net were trained to build segmentation models for the automatic segmentation of the tumor and suspicious lymph nodes. Five datasets were constructed by expanding 5, 10, 20, 40, and 60 pixels outward from the edge of the automatically segmented region. Inception-Resnet-V2, ECA-ResNet50t, EfficientNet-B3, and EfficientNet-B0 were trained with the original, segmented, and the five new constructed datasets to establish the classification models. The receiver operating characteristic curve was used to evaluate the performance of each model. RESULTS The Dice coefficients of Deeplab v3 and U-Net were 0.741(95%CI:0.722-0.760) and 0.737(95%CI:0.720-0.754), respectively. The average areas under the curve (aAUCs) of deep learning models for classification trained with the original and segmented images and with images expanded by 5, 10, 20, 40, and 60 pixels were 0.717 ± 0.043, 0.739 ± 0.016, 0.760 ± 0.010, 0.768 ± 0.018, 0.802 ± 0.013, 0.782 ± 0.039, and 0.753 ± 0.014, respectively. The models trained with the images expanded by 20 pixels obtained the best performance. CONCLUSIONS The peritumoral region NPC contains information related to prognosis, and the incorporation of this region could improve the performance of deep learning models for prognosis prediction.
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Affiliation(s)
- Song Li
- grid.89957.3a0000 0000 9255 8984Department of Otorhinolaryngology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029 China ,grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Xia Wan
- grid.510937.9Department of Otolaryngology-Head & Neck Surgery, Ezhou Central Hospital, No. 9 Wenxing Road, Ezhou, 436000 P.R. China
| | - Yu-Qin Deng
- grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Hong-Li Hua
- grid.412632.00000 0004 1758 2270Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Sheng-Lan Li
- grid.412632.00000 0004 1758 2270Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Xi-Xiang Chen
- grid.412632.00000 0004 1758 2270Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060 P.R. China
| | - Man-Li Zeng
- grid.510937.9Department of Otolaryngology-Head & Neck Surgery, Ezhou Central Hospital, No. 9 Wenxing Road, Ezhou, 436000 P.R. China
| | - Yunfei Zha
- Department of Radiology, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei, 430060, P.R. China.
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei, 430060, P.R. China.
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14
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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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15
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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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Krajnc D, Spielvogel CP, Grahovac M, Ecsedi B, Rasul S, Poetsch N, Traub-Weidinger T, Haug AR, Ritter Z, Alizadeh H, Hacker M, Beyer T, Papp L. Automated data preparation for in vivo tumor characterization with machine learning. Front Oncol 2022; 12:1017911. [PMID: 36303841 PMCID: PMC9595446 DOI: 10.3389/fonc.2022.1017911] [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: 08/12/2022] [Accepted: 09/23/2022] [Indexed: 11/23/2022] Open
Abstract
Background This study proposes machine learning-driven data preparation (MLDP) for optimal data preparation (DP) prior to building prediction models for cancer cohorts. Methods A collection of well-established DP methods were incorporated for building the DP pipelines for various clinical cohorts prior to machine learning. Evolutionary algorithm principles combined with hyperparameter optimization were employed to iteratively select the best fitting subset of data preparation algorithms for the given dataset. The proposed method was validated for glioma and prostate single center cohorts by 100-fold Monte Carlo (MC) cross-validation scheme with 80-20% training-validation split ratio. In addition, a dual-center diffuse large B-cell lymphoma (DLBCL) cohort was utilized with Center 1 as training and Center 2 as independent validation datasets to predict cohort-specific clinical endpoints. Five machine learning (ML) classifiers were employed for building prediction models across all analyzed cohorts. Predictive performance was estimated by confusion matrix analytics over the validation sets of each cohort. The performance of each model with and without MLDP, as well as with manually-defined DP were compared in each of the four cohorts. Results Sixteen of twenty established predictive models demonstrated area under the receiver operator characteristics curve (AUC) performance increase utilizing the MLDP. The MLDP resulted in the highest performance increase for random forest (RF) (+0.16 AUC) and support vector machine (SVM) (+0.13 AUC) model schemes for predicting 36-months survival in the glioma cohort. Single center cohorts resulted in complex (6-7 DP steps) DP pipelines, with a high occurrence of outlier detection, feature selection and synthetic majority oversampling technique (SMOTE). In contrast, the optimal DP pipeline for the dual-center DLBCL cohort only included outlier detection and SMOTE DP steps. Conclusions This study demonstrates that data preparation prior to ML prediction model building in cancer cohorts shall be ML-driven itself, yielding optimal prediction models in both single and multi-centric settings.
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Affiliation(s)
- Denis Krajnc
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Clemens P. Spielvogel
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria
| | - Marko Grahovac
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Boglarka Ecsedi
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Sazan Rasul
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Nina Poetsch
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Alexander R. Haug
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, Vienna, Austria
| | - Zsombor Ritter
- Department of Medical Imaging, University of Pécs, Medical School, Pécs, Hungary
| | - Hussain Alizadeh
- 1st Department of Internal Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
- *Correspondence: Thomas Beyer,
| | - Laszlo Papp
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
- Applied Quantum Computing group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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17
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Jiang T, Tan Y, Nan S, Wang F, Chen W, Wei Y, Liu T, Qin W, Lu F, Jiang F, Jiang H. Radiomics based on pretreatment MRI for predicting distant metastasis of nasopharyngeal carcinoma: A preliminary study. Front Oncol 2022; 12:975881. [PMID: 36016603 PMCID: PMC9396739 DOI: 10.3389/fonc.2022.975881] [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: 06/22/2022] [Accepted: 07/18/2022] [Indexed: 12/24/2022] Open
Abstract
Objective To explore the feasibility of predicting distant metastasis (DM) of nasopharyngeal carcinoma (NPC) patients based on MRI radiomics model. Methods A total of 146 patients with NPC pathologically confirmed, who did not exhibit DM before treatment, were retrospectively reviewed and followed up for at least one year to analyze the DM risk of the disease. The MRI images of these patients including T2WI and CE-T1WI sequences were extracted. The cases were randomly divided into training group (n=116) and validation group (n=30). The images were filtered before radiomics feature extraction. The least absolute shrinkage and selection operator (LASSO) regression was used to develop the dimension of texture parameters and the logistic regression was used to construct the prediction model. The ROC curve and calibration curve were used to evaluate the predictive performance of the model, and the area under curve (AUC), accuracy, sensitivity, and specificity were calculated. Results 72 patients had DM and 74 patients had no DM. The AUC, accuracy, sensitivity and specificity of the model were 0. 80 (95% CI: 0.72~0. 88), 75.0%, 76.8%, 73.3%. and0.70 (95% CI: 0.51~0.90), 66.7%, 72.7%, 63.2% in training group and validation group, respectively. Conclusion The radiomics model based on logistic regression algorithm has application potential for evaluating the DM risk of patients with NPC.
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Affiliation(s)
- Tingting Jiang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yalan Tan
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Shuaimin Nan
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Fang Wang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Wujie Chen
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yuguo Wei
- Precision Health Institution, General Electric (GE) Healthcare, Hangzhou, China
| | - Tongxin Liu
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Weifeng Qin
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Fangxiao Lu
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Feng Jiang
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
| | - Haitao Jiang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- *Correspondence: Haitao Jiang,
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18
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Pei W, Wang C, Liao H, Chen X, Wei Y, Huang X, Liang X, Bao H, Su D, Jin G. MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma. BMC Cancer 2022; 22:739. [PMID: 35794590 PMCID: PMC9261049 DOI: 10.1186/s12885-022-09832-6] [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: 12/06/2021] [Accepted: 06/27/2022] [Indexed: 12/08/2022] Open
Abstract
Background The present study aimed to explore the application value of random survival forest (RSF) model and Cox model in predicting the progression-free survival (PFS) among patients with locoregionally advanced nasopharyngeal carcinoma (LANPC) after induction chemotherapy plus concurrent chemoradiotherapy (IC + CCRT). Methods Eligible LANPC patients underwent magnetic resonance imaging (MRI) scan before treatment were subjected to radiomics feature extraction. Radiomics and clinical features of patients in the training cohort were subjected to RSF analysis to predict PFS and were tested in the testing cohort. The performance of an RSF model with clinical and radiologic predictors was assessed with the area under the receiver operating characteristic (ROC) curve (AUC) and Delong test and compared with Cox models based on clinical and radiologic parameters. Further, the Kaplan-Meier method was used for risk stratification of patients. Results A total of 294 LANPC patients (206 in the training cohort; 88 in the testing cohort) were enrolled and underwent magnetic resonance imaging (MRI) scans before treatment. The AUC value of the clinical Cox model, radiomics Cox model, clinical + radiomics Cox model, and clinical + radiomics RSF model in predicting 3- and 5-year PFS for LANPC patients was [0.545 vs 0.648 vs 0.648 vs 0.899 (training cohort), and 0.566 vs 0.736 vs 0.730 vs 0.861 (testing cohort); 0.556 vs 0.604 vs 0.611 vs 0.897 (training cohort), and 0.591 vs 0.661 vs 0.676 vs 0.847 (testing cohort), respectively]. Delong test showed that the RSF model and the other three Cox models were statistically significant, and the RSF model markedly improved prediction performance (P < 0.001). Additionally, the PFS of the high-risk group was lower than that of the low-risk group in the RSF model (P < 0.001), while comparable in the Cox model (P > 0.05). Conclusion The RSF model may be a potential tool for prognostic prediction and risk stratification of LANPC patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09832-6.
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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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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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23
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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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24
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Li Y, Liu Y, Yin P, Hao C, Sun C, Chen L, Wang S, Hong N. MRI-Based Bone Marrow Radiomics Nomogram for Prediction of Overall Survival in Patients With Multiple Myeloma. Front Oncol 2021; 11:709813. [PMID: 34926240 PMCID: PMC8671997 DOI: 10.3389/fonc.2021.709813] [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: 05/14/2021] [Accepted: 11/12/2021] [Indexed: 01/19/2023] Open
Abstract
Purpose To develop and validate a radiomics nomogram for predicting overall survival (OS) in multiple myeloma (MM) patients. Material and Methods A total of 121 MM patients was enrolled and divided into training (n=84) and validation (n=37) sets. The radiomics signature was established by the selected radiomics features from lumbar MRI. The radiomics signature and clinical risk factors were integrated in multivariate Cox regression model for constructing radiomics nomogram to predict MM OS. The predictive ability and accuracy of the nomogram were evaluated by the index of concordance (C-index) and calibration curves, and compared with other four models including the clinical model, radiomics signature model, the Durie-Salmon staging system (D-S) and the International Staging System (ISS). The potential association between the radiomics signature and progression-free survival (PFS) was also explored. Results The radiomics signature, 1q21 gain, del (17p), and β2-MG≥5.5 mg/L showed significant association with MM OS. The predictive ability of radiomics nomogram was better than the clinical model, radiomics signature model, the D-S and the ISS (C-index: 0.793 vs. 0.733 vs. 0.742 vs. 0.554 vs. 0.671 in training set, and 0.812 vs. 0.799 vs.0.717 vs. 0.512 vs. 0.761 in validation set). The radiomics signature lacked the predictive ability for PFS (log-rank P=0.001 in training set and log-rank P=0.103 in validation set), whereas the 1-, 2- and 3-year PFS rates all showed significant difference between the high and low risk groups (P ≤ 0.05). Conclusion The MRI-based bone marrow radiomics may be an additional useful tool for MM OS prediction.
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Affiliation(s)
- Yang Li
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Yang Liu
- Peking University Institute of Hematology, Peking University People's Hospital, Beijing, China.,Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Peking University, Beijing, China.,Collaborative Innovation Center of Hematology, Peking University, Beijing, China
| | - Ping Yin
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Chuanxi Hao
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Chao Sun
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Lei Chen
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Sicong Wang
- Pharmaceutical Diagnostics, GE Healthcare, Shanghai, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
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25
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Khodabakhshi Z, Amini M, Mostafaei S, Haddadi Avval A, Nazari M, Oveisi M, Shiri I, Zaidi H. Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information. J Digit Imaging 2021. [PMID: 34382117 DOI: 10.1007/s10278-021-00500-y/figures/5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2023] Open
Abstract
The aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients' overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.
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Affiliation(s)
- Zahra Khodabakhshi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Mehdi Amini
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Shayan Mostafaei
- Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
- Epidemiology and Biostatistics Unit, Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mostafa Nazari
- Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine , Kings College London, London, UK
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, Geneva University, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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26
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Gao Y, Mao Y, Lu S, Tan L, Li G, Chen J, Huang D, Zhang X, Qiu Y, Liu Y. Magnetic resonance imaging-based radiogenomics analysis for predicting prognosis and gene expression profile in advanced nasopharyngeal carcinoma. Head Neck 2021; 43:3730-3742. [PMID: 34516714 DOI: 10.1002/hed.26867] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 07/25/2021] [Accepted: 08/31/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND To establish a radiomics nomogram for survival prediction and determine if genomic data were related to radiomics signature in advanced nasopharyngeal carcinoma (NPC). METHODS Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1WI) in 316 patients. A progression-free survival (PFS) nomogram was developed and validated by the combination of the radiomics signature and clinicopathologic factors. Whole transcriptomics sequencing was performed in pretreatment tumor samples; correlation of gene expression and radiomics signature was further investigated. RESULTS A 24-feature-combined radiomics signature was highly correlated with PFS; its integration with clinical predictors showed good prediction performance in the training and the validation cohort (C-index: 0.80 and 0.73). A significant correlation was observed between certain gene expression and Rad-score, especially the mRNA expression of CDKL2, PLIN5, and SPAG1. CONCLUSION As a noninvasive method, the MRI-based radiomics signature might enable the pretreatment prediction of prognosis and gene expressions profile in advanced NPC.
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Affiliation(s)
- Yan Gao
- Department of Otolaryngology - Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China.,Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, China.,Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, China
| | - Yitao Mao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Shanhong Lu
- Department of Otolaryngology - Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China.,Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, China.,Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, China
| | - Lei Tan
- College of Computer and Information Engineering, Hunan University of Technology and Business, Changsha, China
| | - Guo Li
- Department of Otolaryngology - Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China.,Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, China.,Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, China
| | - Juan Chen
- Department of Otolaryngology - Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China.,Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, China.,Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, China
| | - Donghai Huang
- Department of Otolaryngology - Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China.,Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, China.,Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, China
| | - Xin Zhang
- Department of Otolaryngology - Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China.,Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, China.,Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, China
| | - Yuanzheng Qiu
- Department of Otolaryngology - Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China.,Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, China.,Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
| | - Yong Liu
- Department of Otolaryngology - Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, China.,Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, China.,Clinical Research Center for Pharyngolaryngeal Diseases and Voice Disorders in Hunan Province, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
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27
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Sakai K. [2. Radiomics of MRI]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:866-875. [PMID: 34421076 DOI: 10.6009/jjrt.2021_jsrt_77.8.866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Koji Sakai
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
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28
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López F, Mäkitie A, de Bree R, Franchi A, de Graaf P, Hernández-Prera JC, Strojan P, Zidar N, Strojan Fležar M, Rodrigo JP, Rinaldo A, Centeno BA, Ferlito A. Qualitative and Quantitative Diagnosis in Head and Neck Cancer. Diagnostics (Basel) 2021; 11:diagnostics11091526. [PMID: 34573868 PMCID: PMC8466857 DOI: 10.3390/diagnostics11091526] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/14/2021] [Accepted: 08/20/2021] [Indexed: 12/11/2022] Open
Abstract
The diagnosis is the art of determining the nature of a disease, and an accurate diagnosis is the true cornerstone on which rational treatment should be built. Within the workflow in the management of head and neck tumours, there are different types of diagnosis. The purpose of this work is to point out the differences and the aims of the different types of diagnoses and to highlight their importance in the management of patients with head and neck tumours. Qualitative diagnosis is performed by a pathologist and is essential in determining the management and can provide guidance on prognosis. The evolution of immunohistochemistry and molecular biology techniques has made it possible to obtain more precise diagnoses and to identify prognostic markers and precision factors. Quantitative diagnosis is made by the radiologist and consists of identifying a mass lesion and the estimation of the tumour volume and extent using imaging techniques, such as CT, MRI, and PET. The distinction between the two types of diagnosis is clear, as the methodology is different. The accurate establishment of both diagnoses plays an essential role in treatment planning. Getting the right diagnosis is a key aspect of health care, and it provides an explanation of a patient’s health problem and informs subsequent decision. Deep learning and radiomics approaches hold promise for improving diagnosis.
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Affiliation(s)
- Fernando López
- Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain;
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), University of Oviedo CIBERONC-ISCIII, 33011 Oviedo, Spain
- Correspondence:
| | - Antti Mäkitie
- Department of Otorhinolaryngology–Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland;
| | - Remco de Bree
- Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, 3584CX Utrecht, The Netherlands;
| | - Alessandro Franchi
- Department of Translational Research, School of Medicine, University of Pisa, 56124 Pisa, Italy;
| | - Pim de Graaf
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 Amsterdam, The Netherlands;
| | | | - Primoz Strojan
- Department of Radiation Oncology, Institute of Oncology, 1000 Ljubljana, Slovenia;
| | - Nina Zidar
- Department of Head and Neck Pathology, Faculty of Medicine, Institute of Pathology, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Margareta Strojan Fležar
- Department of Cytopathology, Faculty of Medicine, Institute of Pathology, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Juan P. Rodrigo
- Department of Otorhinolaryngology, Head and Neck Surgery, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain;
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), University of Oviedo CIBERONC-ISCIII, 33011 Oviedo, Spain
| | | | - Barbara A. Centeno
- Department of Pathology, Moffitt Cancer Center, Tampa, FL 33612, USA; (J.C.H.-P.); (B.A.C.)
| | - Alfio Ferlito
- Coordinator of the International Head and Neck Scientific Group, 35100 Padua, Italy;
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29
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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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30
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Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information. J Digit Imaging 2021; 34:1086-1098. [PMID: 34382117 PMCID: PMC8554934 DOI: 10.1007/s10278-021-00500-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/28/2021] [Accepted: 07/22/2021] [Indexed: 01/06/2023] Open
Abstract
The aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients’ overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.
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31
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Kim MJ, Choi Y, Sung YE, Lee YS, Kim YS, Ahn KJ, Kim MS. Early risk-assessment of patients with nasopharyngeal carcinoma: the added prognostic value of MR-based radiomics. Transl Oncol 2021; 14:101180. [PMID: 34274801 PMCID: PMC8319024 DOI: 10.1016/j.tranon.2021.101180] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/04/2021] [Accepted: 07/13/2021] [Indexed: 11/29/2022] Open
Abstract
The current study extracted radiomics—a large quantitative data of imaging features—from magnetic resonance images of patients with nasopharyngeal carcinoma. The survival model fitted with radiomic features showed good prognostic performance in predicting the progression-free survival of patients with nasopharyngeal carcinoma (integrated area under the curve, 0.71; 95% confidence interval, 0.71–0.72). Addition of radiomics to clinical survival model improved the prognostication of progression-free survival in patients diagnosed with nasopharyngeal carcinoma (integrated area under the curve from 0.76 to 0.81, p<0.001).
Objectives To assess the additive prognostic value of MR-based radiomics in predicting progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC) Methods Patients newly diagnosed with non-metastatic NPC between June 2006 and October 2019 were retrospectively included and randomly grouped into training and test cohorts (7:3 ratio). Radiomic features (n=213) were extracted from T2-weighted and contrast-enhanced T1-weighted MRI. The patients were staged according to the 8th edition of American Joint Committee on Cancer Staging Manual. The least absolute shrinkage and selection operator was used to select the relevant radiomic features. Univariate and multivariate Cox proportional hazards analyses were conducted for PFS, yielding three different survival models (clinical, stage, and radiomic). The integrated time-dependent area under the curve (iAUC) for PFS was calculated and compared among different combinations of survival models, and the analysis of variance was used to compare the survival models. The prognostic performance of all models was validated using a test set with integrated Brier scores. Results This study included 81 patients (training cohort=57; test cohort=24), and the mean PFS was 57.5 ± 43.6 months. In the training cohort, the prognostic performances of survival models improved significantly with the addition of radiomics to the clinical (iAUC, 0.72–0.80; p=0.04), stage (iAUC, 0.70–0.79; p=0.001), and combined models (iAUC, 0.76–0.81; p<0.001). In the test cohort, the radiomics and combined survival models were robustly validated for their ability to predict PFS. Conclusion Integration of MR-based radiomic features with clinical and stage variables improved the prediction PFS in patients diagnosed with NPC.
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Affiliation(s)
- Min-Jung Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yangsean Choi
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
| | - Yeoun Eun Sung
- Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Youn Soo Lee
- Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yeon-Sil Kim
- Department of Radiation Oncology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Kook-Jin Ahn
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Min-Sik Kim
- Department of Head and Neck Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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32
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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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33
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Zuo X, Meng P, Bao Y, Tao C, Wang Y, Liu X, Bu Y, Zhu J. Cell cycle dysregulation with overexpression of KIF2C/MCAK is a critical event in nasopharyngeal carcinoma. Genes Dis 2021; 10:212-227. [PMID: 37013060 PMCID: PMC10066047 DOI: 10.1016/j.gendis.2021.05.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/05/2021] [Accepted: 05/22/2021] [Indexed: 01/21/2023] Open
Abstract
Nasopharyngeal carcinoma (NPC) is a common malignant carcinoma of the head and neck, and the biological mechanisms underlying the pathogenesis of NPC remain not fully understood. In the present study, we systematically analyzed four independent NPC transcriptomic datasets and focused on identifying the critical molecular networks and novel key hub genes implicated in NPC. We found totally 170 common overlapping differentially expressed genes (DEGs) in the four NPC datasets. GO and KEGG pathway analysis revealed that cell cycle dysregulation is a critical event in NPC. Protein-protein interaction (PPI) network analysis identified a 15 hub-gene core network with overexpressed kinesin family member 2C (KIF2C) as a central regulator. Loss-of-function study demonstrated that knockdown of KIF2C significantly inhibited cell growth and cell motility, and delayed cell cycle progression, accompanied with dramatic mitotic defects in spindle formation in NPC cells. RNA-seq analysis revealed that KIF2C knockdown led to deregulation of various downstream genes. KIF2C could also regulate the AKT/mTOR pathways, and enhance paclitaxel sensitivity in NPC cells. Taken together, our results suggest that cell cycle dysregulation is a critical event during NPC pathogenesis and KIF2C is a novel key mitotic hub gene with therapeutic potential in NPC.
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Affiliation(s)
- Xiaofeng Zuo
- Department of Otolaryngology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing 400016, China
- Department of Biochemistry and Molecular Biology, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing 400016, China
| | - Peixin Meng
- Department of Biochemistry and Molecular Biology, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing 400016, China
| | - Yuxin Bao
- Department of Otolaryngology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing 400016, China
- Department of Biochemistry and Molecular Biology, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing 400016, China
| | - Chuntao Tao
- Department of Biochemistry and Molecular Biology, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing 400016, China
| | - Yitao Wang
- Department of Biochemistry and Molecular Biology, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing 400016, China
| | - Xianjun Liu
- Department of Biochemistry and Molecular Biology, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing 400016, China
| | - Youquan Bu
- Department of Biochemistry and Molecular Biology, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing 400016, China
- Corresponding author. Department of Biochemistry and Molecular Biology, Chongqing Medical University, 1# Yixueyuan Road, Yuzhong District, Chongqing 400016, China.
| | - Jiang Zhu
- Department of Otolaryngology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing 400016, China
- Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing 400016, China
- Corresponding author. Department of Otolaryngology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, 1# Yixueyuan Road, Yuzhong District, Chongqing 400016, China.
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Li Q, Dong F, Jiang B, Zhang M. Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics. Front Oncol 2021; 11:646267. [PMID: 34109112 PMCID: PMC8182051 DOI: 10.3389/fonc.2021.646267] [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: 12/25/2020] [Accepted: 04/26/2021] [Indexed: 01/01/2023] Open
Abstract
Objectives To explore the magnetic resonance imaging (MRI) characteristics of brain diffuse midline gliomas with the H3 K27M mutation (DMG-M) using radiomics. Materials and Methods Thirty patients with diffuse midline gliomas, including 16 with the H3 K27M mutant and 14 with wild type tumors, were retrospectively included in this study. A total of 272 radiomic features were initially extracted from MR images of each tumor. Principal component analysis, univariate analysis, and three other feature selection methods, including variance thresholding, recursive feature elimination, and the elastic net, were used to analyze the radiomic features. Based on the results, related visually accessible features of the tumors were further evaluated. Results Patients with DMG-M were younger than those with diffuse midline gliomas with H3 K27M wild (DMG-W) (median, 25.5 and 48 years old, respectively; p=0.005). Principal component analysis showed that there were obvious overlaps in the first two principal components for both DMG-M and DMG-W tumors. The feature selection results showed that few features from T2-weighted images (T2WI) were useful for differentiating DMG-M and DMG-W tumors. Thereafter, four visually accessible features related to T2WI were further extracted and analyzed. Among these features, only cystic formation showed a significant difference between the two types of tumors (OR=7.800, 95% CI 1.476-41.214, p=0.024). Conclusions DMGs with and without the H3 K27M mutation shared similar MRI characteristics. T2W sequences may be valuable, and cystic formation a useful MRI biomarker, for diagnosing brain DMG-M.
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Affiliation(s)
- Qian Li
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fei Dong
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Biao Jiang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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