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Malik A, Vishnoi K, Noronha V, Prabhash K. A nuanced review of neoadjuvant therapies in oral cancer. Expert Rev Anticancer Ther 2025; 25:499-515. [PMID: 40069995 DOI: 10.1080/14737140.2025.2478891] [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: 02/02/2025] [Accepted: 03/10/2025] [Indexed: 03/21/2025]
Abstract
INTRODUCTION Oral squamous cell carcinoma (OSCC) is a significant global health burden. The goals of neoadjuvant chemotherapy (NACT) are to shrink tumors allowing for more conservative surgeries, improve survival and potentially improve quality of life. AREAS COVERED This review explores the current evidence of utility of NACT in OSCC management. We have covered indications of NACT, types of regimens and their associated toxicities, perioperative challenges with regards to margins and complications. We have also reviewed the upcoming regimens for NACT. EXPERT OPINION NACT is a potential option for treating borderline resectable and unresectable OSCC. It holds promise for organ preservation (mandibular/tongue). With newer modalities like immunotherapy and targeted therapies, the preferred choice of regimen may evolve. Post NACT, intraoperative margins and choice of adjuvant therapy remain debatable.
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Affiliation(s)
- Akshat Malik
- Department of Surgical Oncology, Max Super specialty Hospital, New Delhi, India
| | - Kanishk Vishnoi
- Department of Surgical Oncology, Max Super specialty Hospital, New Delhi, India
| | - Vanita Noronha
- Department of Medical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Kumar Prabhash
- Department of Medical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
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Yang F, Peng W, Wei H, Li X, Yu X, Li L, Zhao Y, Xie L, Lin M, Zhang H. Pretreatment arterial spin labeling combined with radiological depth of invasion predicts treatment outcome in nonmetastatic NPC. Radiother Oncol 2025; 205:110765. [PMID: 39889968 DOI: 10.1016/j.radonc.2025.110765] [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: 07/03/2024] [Revised: 01/10/2025] [Accepted: 01/27/2025] [Indexed: 02/03/2025]
Abstract
BACKGROUND AND PURPOSE To investigate the value of arterial spin labeling (ASL) and radiological depth of invasion (rDOI) in predicting long-term treatment outcomes in nonmetastatic nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS A total of 113 patients with NPC were included and randomly divided into training (n = 81) and validation (n = 32) cohorts. Tumor blood flow (TBF) parameters derived from the ASL (TBFMean, TBFSD, and nTBF) were obtained. Two radiologists independently measured the rDOI in both axial (rDOI_a) and coronal (rDOI_c) planes, subsequently categorizing patients into low-risk and high-risk groups. Spearman analysis was used to explore the correlation. In the training cohort, ASL-based, ASL + rDOI, and TNM models were constructed using univariate and multivariate Cox regression analyses. Model performance, including calibration, robustness, discrimination, and clinical utility, was assessed in both training and validation cohorts. The net classification index (NRI) and integrated discrimination improvement (IDI) were calculated. RESULTS The median follow-up duration was 63.4 (56.2, 100.1) months. Disease progression and death occurred in 45 (39.8 %) and 32 (28.3 %) patients, respectively. TBFMean and rDOI were independent predictors of overall survival (OS) (hazard ratios [HR], 0.973 and 2.975, respectively) and progression-free survival (PFS) (HR, 0.985 and 2.207, respectively). rDOI_a and rDOI_c were significantly correlated with T stage (r = 0.538-0.738, P ≤ 0.002). The ASL + rDOI model demonstrated good robustness and clinical utility in both training and validation cohorts. Post-hoc subgroup analysis showed favorable results. CONCLUSION TBFMean and rDOI can predict survival outcomes in patients with NPC. Adding rDOI further improved the prediction performance.
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Affiliation(s)
- Fan Yang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China; Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University, Stanford University Medical Center, Stanford, California, USA.
| | - Wenjing Peng
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China
| | - Haoran Wei
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China
| | - Xiaolu Li
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China
| | - Xiaoduo Yu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China
| | - Lin Li
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China
| | - Yanfeng Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China
| | - Lizhi Xie
- MR Research China, GE Healthcare, China
| | - Meng Lin
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China.
| | - Hongmei Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021 China.
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Deng M, Lin Y, Yan L, Chen C, Fei Z, Ding J. A bibliometric analysis of nasopharyngeal carcinoma radiomics: trends and insights. Front Oncol 2025; 15:1506778. [PMID: 40201350 PMCID: PMC11975905 DOI: 10.3389/fonc.2025.1506778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2024] [Accepted: 03/10/2025] [Indexed: 04/10/2025] Open
Abstract
Background Nasopharyngeal carcinoma (NPC) is a malignant tumor characterized by distinct geographic and pathological features. Enhancing diagnostic accuracy and timeliness in NPC is crucial for clinical implications. Radiomics has demonstrated significant potential in the clinical management of NPC. Nonetheless, a paucity of bibliometric studies has systematically examined the existing literature in th is domain. The objective of this study was to assess the current landscape and project future trends in NPC research. Methods This study conducted a search on English-language literature concerning the application of radiomics within the field of nasopharyngeal carcinoma (NPC) research from January 2015 to July 1, 2024, utilizing the Web of Science Core Collection (WoSCC) database. Bibliometric and visual analyses were performed using VOSviewer and CiteSpace software on publications related to countries/regions, authors, journals, references, and keywords. Results A total of 311 documents were retrieved, yielding 229 eligible documents after screening, comprising 209 articles and 20 reviews. Annual publications showed an upward trend, while citations revealed a generally declining trend. Notably, China contributed the most publications (n=175). Tian Jie and Dong Di each published 13 papers, and Zhang B was the most frequently co-cited author. Frontiers in Oncology published the most articles (n=25), and the International Journal of Radiation Oncology Biology Physics had the highest citation count (n=331). Sun Yat-sen University led institutional publications (n=39). The radiomics research in NPC focuses on survival prediction, texture analysis, and distant metastasis, and may guide future research directions. Conclusion The application of radiomics in NRC is growing annually, as indicated by bibliometric analysis. Radiomics has enhanced the precision of preoperative diagnosis, prediction, and prognosis in NRC. Bibliometric findings offer insights into radiomics research trends. However, creating extensive NPC datasets and bridging the research-to-clinical gap pose significant challenges. Future research should focus on these areas to advance the development.
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Affiliation(s)
| | | | | | | | - Zhaodong Fei
- Department of Radiation Oncology, School of Oncology Clinical Medicine, Fujian Medical
University, Fujian Provincial Cancer Hospital, Fuzhou, Fujian, China
| | - Jianming Ding
- Department of Radiation Oncology, School of Oncology Clinical Medicine, Fujian Medical
University, Fujian Provincial Cancer Hospital, Fuzhou, Fujian, China
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Mercurio M, Denami F, Melissaridou D, Corona K, Cerciello S, Laganà D, Gasparini G, Minici R. Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review. Diagnostics (Basel) 2025; 15:776. [PMID: 40150118 PMCID: PMC11941175 DOI: 10.3390/diagnostics15060776] [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: 02/02/2025] [Revised: 03/14/2025] [Accepted: 03/17/2025] [Indexed: 03/29/2025] Open
Abstract
Magnetic resonance imaging (MRI) is routinely used to confirm the suspected diagnosis of anterior cruciate ligament (ACL) injury. Recently, many studies explored the role of artificial intelligence (AI) and deep learning (DL), a sub-category of AI, in the musculoskeletal field and medical imaging. The aim of this study was to review the current applications of DL models to detect ACL injury on MRI, thus providing an updated and critical synthesis of the existing literature and identifying emerging trends and challenges in the field. A total of 23 relevant articles were identified and included in the review. Articles originated from 10 countries, with China having the most contributions (n = 9), followed by the United State of America (n = 4). Throughout the article, we analyzed the concept of DL in ACL tears and provided examples of how these tools can impact clinical practice and patient care. DL models for MRI detection of ACL injury reported high values of accuracy, especially helpful for less experienced clinicians. Time efficiency was also demonstrated. Overall, the deep learning models have proven to be a valid resource, although still requiring technological developments for implementation in daily practice.
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Affiliation(s)
- Michele Mercurio
- Department of Orthopaedic and Trauma Surgery, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy; (M.M.); (G.G.)
- Research Center on Musculoskeletal Health, MusculoSkeletal Health@UMG, Magna Graecia University, 88100 Catanzaro, Italy
| | - Federica Denami
- Department of Orthopaedic and Trauma Surgery, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy; (M.M.); (G.G.)
| | - Dimitra Melissaridou
- 1st Department of Orthopaedic Surgery, National and Kapodistrian University of Athens, Attikon Hospital, 12462 Athens, Greece;
| | - Katia Corona
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Simone Cerciello
- School of Medicine, Saint Camillus University, 00131 Rome, Italy;
| | - Domenico Laganà
- Department of Experimental and Clinical Medicine, Magna Graecia University, 88100 Catanzaro, Italy;
- Radiology Unit, Department of Experimental and Clinical Medicine, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy;
| | - Giorgio Gasparini
- Department of Orthopaedic and Trauma Surgery, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy; (M.M.); (G.G.)
- Research Center on Musculoskeletal Health, MusculoSkeletal Health@UMG, Magna Graecia University, 88100 Catanzaro, Italy
| | - Roberto Minici
- Radiology Unit, Department of Experimental and Clinical Medicine, Magna Graecia University, “Renato Dulbecco” University Hospital, 88100 Catanzaro, Italy;
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Chen S, Dai J, Zhao J, Han S, Zhang X, Chang J, Jiang D, Zhang H, Wang P, Hu S. Synthetic MRI Combined With Clinicopathological Characteristics for Pretreatment Prediction of Chemoradiotherapy Response in Advanced Nasopharyngeal Carcinoma. Korean J Radiol 2025; 26:135-145. [PMID: 39898394 PMCID: PMC11794295 DOI: 10.3348/kjr.2024.0385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 10/18/2024] [Accepted: 12/05/2024] [Indexed: 02/04/2025] Open
Abstract
OBJECTIVE To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). MATERIALS AND METHODS Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiver-operating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. RESULTS The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein-Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. CONCLUSION SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.
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Affiliation(s)
- Siyu Chen
- Department of Intensive Medicine, Affiliated Hospital of Jiangnan University, Wuxi, China
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Jiankun Dai
- GE Healthcare, MR Research China, Beijing, China
| | - Jing Zhao
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Shuang Han
- Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiaojun Zhang
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Jun Chang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Donghui Jiang
- Department of Intensive Medicine, Affiliated Hospital of Jiangnan University, Wuxi, China
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Heng Zhang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China.
| | - Peng Wang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China.
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China
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Wang Y, Chen F, Ouyang Z, He S, Qin X, Liang X, Huang W, Wang R, Hu K. MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort study. Transl Oncol 2025; 52:102245. [PMID: 39662448 PMCID: PMC11697067 DOI: 10.1016/j.tranon.2024.102245] [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: 08/22/2024] [Revised: 11/20/2024] [Accepted: 12/07/2024] [Indexed: 12/13/2024] Open
Abstract
BACKGROUND An increasing number of nasopharyngeal carcinoma (NPC) patients benefit from immunotherapy with chemotherapy as an induction treatment. Currently, there isn't a reliable method to assess the efficacy of this regimen, which hinders informed decision-making for follow-up care. AIM To establish and evaluate a model for predicting the efficacy of programmed death-1 (PD-1) inhibitor combined with GP (gemcitabine and cisplatin) induction chemotherapy based on deep learning features (DLFs) and radiomic features. METHODS Ninety-nine patients diagnosed with advanced NPC were enrolled and randomly divided into training set and test set in a 7:3 ratio. From MRI scans, DLFs and conventional radiomic characteristics were recovered. The random forest algorithm was employed to identify the most valuable features. A prediction model was then created using these radiomic characteristics and DLFs to determine the effectiveness of PD-1 inhibitor combined with GP chemotherapy. The model's performance was assessed using Receiver Operating Characteristic (ROC) curve analysis, area under the curve (AUC), accuracy (ACC), and negative predictive value (NPV). RESULTS Twenty-one prediction models were constructed. The Tf_Radiomics+Resnet101 model, which combines radiomic features and DLFs, demonstrated the best performance. The model's AUC, ACC, and NPV values in the training and test sets were 0.936 (95%CI: 0.827-1.0), 0.9, and 0.923, respectively. CONCLUSION The Tf_Radiomics+Resnet101 model, based on MRI and Resnet101 deep learning, shows a high ability to predict the clinically complete response (cCR) efficacy of PD-1 inhibitor combined with GP in advanced NPC. This model can significantly enhance the treatment management of patients with advanced NPC.
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Affiliation(s)
- Yiru Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China
| | - Fuli Chen
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China
| | - Zhechen Ouyang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China
| | - Siyi He
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China
| | - Xinling Qin
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China
| | - Xian Liang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China
| | - Weimei Huang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China
| | - Rensheng Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China.
| | - Kai Hu
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China; Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning 530021, Guangxi, China; Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning 530021, Guangxi, China; State Key Laboratory of Targeting Oncology, Guangxi Medical University, Nanning 530021, Guangxi, China.
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Tseng HC, Shen CY, Kao PF, Chuang CY, Yan DY, Liao YH, Lu XP, Sheu TJ, Shen WC. Prediction of Persistent Tumor Status in Nasopharyngeal Carcinoma Post-Radiotherapy-Related Treatment: A Machine Learning Approach. Cancers (Basel) 2024; 17:96. [PMID: 39796725 PMCID: PMC11720740 DOI: 10.3390/cancers17010096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2024] [Revised: 12/20/2024] [Accepted: 12/27/2024] [Indexed: 01/13/2025] Open
Abstract
Background/Objectives: The duration of the response to radiotherapy-related treatment is a critical prognostic indicator for patients with nasopharyngeal carcinoma (NPC). Persistent tumor status, including residual tumor presence and early recurrence, is associated with poorer survival outcomes. To address this, we developed a prediction model to identify patients at a high risk of persistent tumor status prior to initiating treatment. Methods: This retrospective study included 104 patients with NPC receiving radiotherapy-related treatment who had completed a 3-year follow-up period; 29 were classified into the persistent tumor status group and 75 into the disease-free group. Radiomic features were extracted from pretreatment positron emission tomography (PET) images and used to construct a prediction model by employing machine learning algorithms. The model's diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC), whereas SHapley Additive exPlanations (SHAP) analysis was conducted to determine the contribution of individual features to the model. Results: The prediction model developed using the AdaBoost algorithm and validated through five-fold cross-validation achieved the highest AUC of 0.934. Its sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 89.66%, 86.67%, 72.22%, 95.59%, and 87.5%, respectively. SHAP analysis revealed that the feature of high dependence low metabolic uptake emphasis50 had the greatest impact on model predictions. Furthermore, patients classified as disease-free exhibited markedly higher overall survival rates compared with those with persistent tumor status. Conclusions: In conclusion, the proposed prediction model efficiently identified patients with NPC at a high risk of persistent tumor status by using radiomic features extracted from pretreatment PET images.
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Affiliation(s)
- Hsien-Chun Tseng
- Department of Radiation Oncology, Chung Shan Medical University Hospital, Taichung 40201, Taiwan;
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan; (C.-Y.S.); (P.-F.K.); (C.-Y.C.)
| | - Chao-Yu Shen
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan; (C.-Y.S.); (P.-F.K.); (C.-Y.C.)
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
| | - Pan-Fu Kao
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan; (C.-Y.S.); (P.-F.K.); (C.-Y.C.)
- Department of Nuclear Medicine, Chung Shan Medical University Hospital, Taichung 40201, Taiwan;
| | - Chun-Yi Chuang
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan; (C.-Y.S.); (P.-F.K.); (C.-Y.C.)
- Department of Otolaryngology, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
| | - Da-Yi Yan
- Artificial Intelligence Center, Chung Shan Medical University Hospital, Taichung 40201, Taiwan; (D.-Y.Y.); (Y.-H.L.); (T.-J.S.)
| | - Yi-Han Liao
- Artificial Intelligence Center, Chung Shan Medical University Hospital, Taichung 40201, Taiwan; (D.-Y.Y.); (Y.-H.L.); (T.-J.S.)
| | - Xuan-Ping Lu
- Department of Nuclear Medicine, Chung Shan Medical University Hospital, Taichung 40201, Taiwan;
| | - Ting-Jung Sheu
- Artificial Intelligence Center, Chung Shan Medical University Hospital, Taichung 40201, Taiwan; (D.-Y.Y.); (Y.-H.L.); (T.-J.S.)
| | - Wei-Chih Shen
- Artificial Intelligence Center, Chung Shan Medical University Hospital, Taichung 40201, Taiwan; (D.-Y.Y.); (Y.-H.L.); (T.-J.S.)
- Department of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan
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Zhu Y, Zheng D, Xu S, Chen J, Wen L, Zhang Z, Ruan H. Intratumoral habitat radiomics based on magnetic resonance imaging for preoperative prediction treatment response to neoadjuvant chemotherapy in nasopharyngeal carcinoma. Jpn J Radiol 2024; 42:1413-1424. [PMID: 39162780 DOI: 10.1007/s11604-024-01639-8] [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: 06/06/2024] [Accepted: 07/27/2024] [Indexed: 08/21/2024]
Abstract
PURPOSE The aim of this study is to determine intratumoral habitat regions from multi-sequences magnetic resonance imaging (MRI) and to assess the value of those regions for prediction of patient response to neoadjuvant chemotherapy (NAC) in nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS Two hundred and ninety seven patients with NPC were enrolled. Multi-sequences MRI data were used to outline three-dimensional volumes of interest (VOI) of the whole tumor. The original imaging data were divided into two groups, which were resampled to an isotropic resolution of 1 × 1 × 1 mm3 (group_1mm) and 3 × 3 × 3 mm3 (group_3mm). Nineteen radiomics features were computed for each voxel of three sequences in group_3mm, within the tumor region to extract local information. Then, k-means clustering was implemented to segment the whole tumor regions in two groups. After radiomics features were extracted and dimension reduction, habitat models were built using Multi-Layer Perceptron (MLP) algorithm. RESULTS Only T stage was included as the clinical model. The habitat3mm model, which included 10 radiomics features, achieved AUCs of 0.752 and 0.724 in the training and validation cohorts, respectively. Given the slightly better outcome of habitat3mm model, nomogram was developed in combination with habitat3mm model and T stage with the AUC of 0.749 and 0.738 in the training and validation cohorts. The decision curve analysis provides further evidence of the nomogram's clinical practicality. CONCLUSIONS A nomogram based on intratumoral habitat predicts the efficacy of NAC in NPC patients, offering the potential to improve both the treatment plan and patient outcomes.
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Affiliation(s)
- Yuemin Zhu
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Dechun Zheng
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China.
| | - Shugui Xu
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Jianwei Chen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Liting Wen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Zhichao Zhang
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
| | - Huiping Ruan
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420, Fuma Road, Jin'an District, Fuzhou, 350014, Fujian, People's Republic of China
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Broggi G, Maniaci A, Lentini M, Palicelli A, Zanelli M, Zizzo M, Koufopoulos N, Salzano S, Mazzucchelli M, Caltabiano R. Artificial Intelligence in Head and Neck Cancer Diagnosis: A Comprehensive Review with Emphasis on Radiomics, Histopathological, and Molecular Applications. Cancers (Basel) 2024; 16:3623. [PMID: 39518063 PMCID: PMC11545333 DOI: 10.3390/cancers16213623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2024] [Revised: 10/20/2024] [Accepted: 10/25/2024] [Indexed: 11/16/2024] Open
Abstract
The present review discusses the transformative role of AI in the diagnosis and management of head and neck cancers (HNCs). Methods: It explores how AI technologies, including ML, DL, and CNNs, are applied in various diagnostic tasks, such as medical imaging, molecular profiling, and predictive modeling. Results: This review highlights AI's ability to improve diagnostic accuracy and efficiency, particularly in analyzing medical images like CT, MRI, and PET scans, where AI sometimes outperforms human radiologists. This paper also emphasizes AI's application in histopathology, where algorithms assist in whole-slide image (WSI) analysis, tumor-infiltrating lymphocytes (TILs) quantification, and tumor segmentation. AI shows promise in identifying subtle or rare histopathological patterns and enhancing the precision of tumor grading and treatment planning. Furthermore, the integration of AI with molecular and genomic data aids in mutation analysis, prognosis, and personalized treatment strategies. Conclusions: Despite these advancements, the review identifies challenges in AI adoption, such as data standardization and model interpretability, and calls for further research to fully integrate AI into clinical practice for improved patient outcomes.
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Affiliation(s)
- Giuseppe Broggi
- Department of Medical and Surgical Sciences and Advanced Technologies “G.F. Ingrassia”, Anatomic Pathology, University of Catania, 95123 Catania, Italy; (G.B.); (S.S.); (M.M.); (R.C.)
| | - Antonino Maniaci
- Department of Medicine and Surgery, University of Enna Kore, 94100 Enna, Italy; (A.M.); (M.L.)
- ASP Ragusa-Hospital Giovanni Paolo II, 97100 Ragusa, Italy
| | - Mario Lentini
- Department of Medicine and Surgery, University of Enna Kore, 94100 Enna, Italy; (A.M.); (M.L.)
- ASP Ragusa-Hospital Giovanni Paolo II, 97100 Ragusa, Italy
| | - Andrea Palicelli
- Pathology Unit, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy;
| | - Magda Zanelli
- Pathology Unit, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy;
| | - Maurizio Zizzo
- Surgical Oncology Unit, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy;
| | - Nektarios Koufopoulos
- Second Department of Pathology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, 15772 Athens, Greece;
| | - Serena Salzano
- Department of Medical and Surgical Sciences and Advanced Technologies “G.F. Ingrassia”, Anatomic Pathology, University of Catania, 95123 Catania, Italy; (G.B.); (S.S.); (M.M.); (R.C.)
| | - Manuel Mazzucchelli
- Department of Medical and Surgical Sciences and Advanced Technologies “G.F. Ingrassia”, Anatomic Pathology, University of Catania, 95123 Catania, Italy; (G.B.); (S.S.); (M.M.); (R.C.)
| | - Rosario Caltabiano
- Department of Medical and Surgical Sciences and Advanced Technologies “G.F. Ingrassia”, Anatomic Pathology, University of Catania, 95123 Catania, Italy; (G.B.); (S.S.); (M.M.); (R.C.)
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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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Chakrabarty N, Mahajan A. Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review. Clin Oncol (R Coll Radiol) 2024; 36:498-513. [PMID: 37806795 DOI: 10.1016/j.clon.2023.09.013] [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: 05/03/2023] [Revised: 08/09/2023] [Accepted: 09/21/2023] [Indexed: 10/10/2023]
Abstract
The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.
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Affiliation(s)
- N Chakrabarty
- Department of Radiodiagnosis, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Parel, Mumbai, Maharashtra, India.
| | - A Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK.
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Zhao W, Xu H, Zhao R, Zhou S, Mei S, Wang Z, Zhao F, Xiao T, Huang F, Qiu W, Tang J, Liu Q. MRI-based Radiomics Model for Preoperative Prediction of Lateral Pelvic Lymph Node Metastasis in Locally Advanced Rectal Cancer. Acad Radiol 2024; 31:2753-2772. [PMID: 37643928 DOI: 10.1016/j.acra.2023.07.016] [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: 06/06/2023] [Revised: 07/19/2023] [Accepted: 07/19/2023] [Indexed: 08/31/2023]
Abstract
RATIONALE AND OBJECTIVES To develop a magnetic resonance imaging (MRI)-based radiomics model for preoperative prediction of lateral pelvic lymph node (LPLN) metastasis (LPLNM) in patients with locally advanced rectal cancer MATERIALS AND METHODS: We retrospectively enrolled 263 patients with rectal cancer who underwent total mesorectal excision and LPLN dissection. Radiomics features from the primary lesion and LPLNs on baseline MRI images were utilized to construct a radiomics model, and their radiomics scores were combined to develop a radiomics scoring system. A clinical prediction model was developed using logistic regression. A hybrid predicting model was created through multivariable logistic regression analysis, integrating the radiomics score with significant clinical risk factors (baseline Carcinoembryonic Antigen (CEA), clinical circumferential resection margin status, and the short axis diameter of LPLN). This hybrid model was presented with a hybrid clinical-radiomics nomogram, and its calibration, discrimination, and clinical usefulness were assessed. RESULTS A total of 148 patients were included in the analysis and randomly divided into a training cohort (n = 104) and an independent internal testing cohort (n = 44). The hybrid clinical-radiomics model exhibited the highest discrimination, with an area under the receiver operating characteristic (AUC) of 0.843 [95% confidence interval (CI), 0.706-0.968] in the testing cohort compared to the clinical model [AUC (95% CI) = 0.772 (0.589-0.856)] and radiomics model [AUC (95% CI) = 0.731 (0.613-0.849)]. The hybrid prediction model also demonstrated good calibration, and decision curve analysis confirmed its clinical usefulness. CONCLUSION This study developed a hybrid MRI-based radiomics model that incorporates a combination of radiomics score and significant clinical risk factors. The proposed model holds promise for individualized preoperative prediction of LPLNM in patients with locally advanced rectal cancer. DATA AVAILABILITY STATEMENT The data presented in this study are available on request from the corresponding author.
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Affiliation(s)
- Wei Zhao
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Hui Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China (H.X.)
| | - Rui Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (R.Z.)
| | - Sicheng Zhou
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Shiwen Mei
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Zhijie Wang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Fuqiang Zhao
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Tixian Xiao
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Fei Huang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Wenlong Qiu
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Jianqiang Tang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.)
| | - Qian Liu
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China (W.Z., S.Z., S.M., Z.W., F.Z., T.X., F.H., W.Q., J.T., Q.L.).
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Li Z, Li C, Li L, Yang D, Wang S, Song J, Jiang M, Kang M. Quantitative parameter analysis of pretreatment dual-energy computed tomography in nasopharyngeal carcinoma cervical lymph node characteristics and prediction of radiotherapy sensitivity. Radiat Oncol 2024; 19:81. [PMID: 38918834 PMCID: PMC11200824 DOI: 10.1186/s13014-024-02468-9] [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: 11/25/2023] [Accepted: 06/10/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Treatment efficacy may differ among patients with nasopharyngeal carcinoma (NPC) at similar tumor-node-metastasis stages. Moreover, end-of-treatment tumor regression is a reliable indicator of treatment sensitivity. This study aimed to investigate whether quantitative dual-energy computed tomography (DECT) parameters could predict sensitivity to neck-lymph node radiotherapy in patients with NPC. METHODS Overall, 388 lymph nodes were collected from 98 patients with NPC who underwent pretreatment DECT. The patients were divided into complete response (CR) and partial response (PR) groups. Clinical characteristics and quantitative DECT parameters were compared between the groups, and the optimal predictive ability of each parameter was determined using receiver operating characteristic (ROC) analysis. A nomogram prediction model was constructed and validated using univariate and binary logistic regression. RESULTS DECT parameters were higher in the CR group than in the PR group. The iodine concentration (IC), normalized IC, Mix-0.6, spectral Hounsfield unit curve slope, effective atomic number, and virtual monoenergetic images were significantly different between the groups. The area under the ROC curve of the DECT parameters was 0.73-0.77. Based on the binary logistic regression, a column chart was constructed using 10 predictive factors, including age, sex, N stage, maximum lymph node diameter, arterial phase NIC, venous phase NIC, λHU and spectral Hounsfield units at 70 keV. The area under the ROC curve value of the constructed model was 0.813, with a sensitivity and specificity of 85.6% and 81.3%, respectively. CONCLUSION Quantitative DECT parameters could effectively predict the sensitivity of NPC to radiotherapy. Therefore, DECT parameters and NPC clinical features can be combined to construct a nomogram with high predictive power and used as a clinical analytical tool.
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Affiliation(s)
- Zhiru Li
- Department of Oncology, Sichuan Provincial People's Hospital·Qionglai Medical Center Hospital, Chengdu, Sichuan, People's Republic of China
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, No. 6, Shuangyong Road, Nanning, Guangxi, Guangxi, 530021, People's Republic of China
- Guangxi Tumor Radiation Therapy Clinical Medical Research Center, Nanning, Guangxi, People's Republic of China
| | - Chao Li
- Department of Obstetrics and Gynecology, Sichuan Provincial People's Hospital·Qionglai Medical Center Hospital, Chengdu, Sichuan, People's Republic of China
| | - Liyan Li
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Dong Yang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, No. 6, Shuangyong Road, Nanning, Guangxi, Guangxi, 530021, People's Republic of China
- Guangxi Tumor Radiation Therapy Clinical Medical Research Center, Nanning, Guangxi, People's Republic of China
| | - Shuangyue Wang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, No. 6, Shuangyong Road, Nanning, Guangxi, Guangxi, 530021, People's Republic of China
- Guangxi Tumor Radiation Therapy Clinical Medical Research Center, Nanning, Guangxi, People's Republic of China
| | - Junmei Song
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, No. 6, Shuangyong Road, Nanning, Guangxi, Guangxi, 530021, People's Republic of China
- Guangxi Tumor Radiation Therapy Clinical Medical Research Center, Nanning, Guangxi, People's Republic of China
| | - Muliang Jiang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
| | - Min Kang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, No. 6, Shuangyong Road, Nanning, Guangxi, Guangxi, 530021, People's Republic of China.
- Guangxi Tumor Radiation Therapy Clinical Medical Research Center, Nanning, Guangxi, People's Republic of China.
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Sim Y, Sohn B, Kim S, Kim HR, Hong MH, Kim J, Lee SK, Lim SM. Radiomics and PD-L1 expression predict immunotherapy benefits in patients with head and neck squamous cell carcinoma. Future Oncol 2024; 20:2869-2878. [PMID: 38861311 PMCID: PMC11572203 DOI: 10.1080/14796694.2024.2342226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 04/09/2024] [Indexed: 06/12/2024] Open
Abstract
Aim: To evaluate the performance of MRI-derived radiomic risk score (RRS) and PD-L1 expression to predict overall survival (OS) and progression-free survival (PFS) of patients with recurrent head and neck squamous cell carcinoma receiving nivolumab therapy.Materials & methods: Three hundred forty radiomic features from pretreatment MRI were used to construct the RRS. The integrated area under the receiver operating characteristic curve (iAUC) was calculated to evaluate the performance of the RRS and PD-L1.Results: The RRS showed iAUCs of 0.69 and 0.57 for OS and PFS, respectively. PD-L1 expression showed iAUCs of 0.61 and 0.62 for OS and PFS, respectively.Conclusion: RRS and PD-L1 potentially predict the OS and PFS of patients with recurrent head and neck squamous cell carcinoma receiving nivolumab therapy.
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Affiliation(s)
- Yongsik Sim
- Department of Radiology & Research Institute of Radiological Science & Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Beomseok Sohn
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sooyon Kim
- Department of Statistics & Data Science, Yonsei University, Seoul, Korea
| | - Hye Ryun Kim
- Division of Medical Oncology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Min Hee Hong
- Division of Medical Oncology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Jinna Kim
- Department of Radiology & Research Institute of Radiological Science & Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Seung-Koo Lee
- Department of Radiology & Research Institute of Radiological Science & Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sun Min Lim
- Division of Medical Oncology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
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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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Cao J, Li Q, Zhang H, Wu Y, Wang X, Ding S, Chen S, Xu S, Duan G, Qiu D, Sun J, Shi J, Liu S. Radiomics model based on MRI to differentiate spinal multiple myeloma from metastases: A two-center study. J Bone Oncol 2024; 45:100599. [PMID: 38601920 PMCID: PMC11004638 DOI: 10.1016/j.jbo.2024.100599] [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: 07/02/2023] [Revised: 12/19/2023] [Accepted: 01/09/2024] [Indexed: 04/12/2024] Open
Abstract
Purpose Spinal multiple myeloma (MM) and metastases are two common cancer types with similar imaging characteristics, for which differential diagnosis is needed to ensure precision therapy. The aim of this study is to establish radiomics models for effective differentiation between them. Methods Enrolled in this study were 263 patients from two medical institutions, including 127 with spinal MM and 136 with spinal metastases. Of them, 210 patients from institution I were used as the internal training cohort and 53 patients from Institution II were used as the external validation cohort. Contrast-enhanced T1-weighted imaging (CET1) and T2-weighted imaging (T2WI) sequences were collected and reviewed. Based on the 1037 radiomics features extracted from both CET1 and T2WI images, Logistic Regression (LR), AdaBoost (AB), Support Vector Machines (SVM), Random Forest (RF), and multiple kernel learning based SVM (MKL-SVM) were constructed. Hyper-parameters were tuned by five-fold cross-validation. The diagnostic efficiency among different radiomics models was compared by accuracy (ACC), sensitivity (SEN), specificity (SPE), area under the ROC curve (AUC), YI, positive predictive value (PPV), negative predictive value (NPY), and F1-score. Results Based on single-sequence, the RF model outperformed all other models. All models based on T2WI images performed better than those based on CET1. The efficiency of all models was boosted by incorporating CET1 and T2WI sequences, and the MKL-SVM model achieved the best performance with ACC, AUC, and F1-score of 0.862, 0.870, and 0.874, respectively. Conclusions The radiomics models constructed based on MRI achieved satisfactory diagnostic performance for differentiation of spinal MM and metastases, demonstrating broad application prospects for individualized diagnosis and treatment.
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Affiliation(s)
- Jiashi Cao
- Department of Orthopedics, Navy Medical Center, the Navy Medical University, No. 338 Huaihai West Road, Shanghai 200052, China
| | - Qiong Li
- Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center/Cancer Hospital, No. 651 Dongfeng East Road, Guangzhou 510060, China
| | - Huili Zhang
- School of Communication and Information Engineering, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Yanyan Wu
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Xiang Wang
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Saisai Ding
- School of Communication and Information Engineering, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Song Chen
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Shaochun Xu
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Guangwen Duan
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Defu Qiu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jiuyi Sun
- Department of Orthopedics, Navy Medical Center, the Navy Medical University, No. 338 Huaihai West Road, Shanghai 200052, China
| | - Jun Shi
- School of Communication and Information Engineering, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai 200444, China
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital of the Navy Medical University, No. 415 Fengyang Road, Shanghai 200003, 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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Jayawickrama SM, Ranaweera PM, Pradeep RGGR, Jayasinghe YA, Senevirathna K, Hilmi AJ, Rajapakse RMG, Kanmodi KK, Jayasinghe RD. Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments. Cancer Rep (Hoboken) 2024; 7:e2045. [PMID: 38522008 PMCID: PMC10961052 DOI: 10.1002/cnr2.2045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 02/07/2024] [Accepted: 03/05/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND Precision healthcare has entered a new era because of the developments in personalized medicine, especially in the diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC). This paper explores the dynamic landscape of personalized medicine as applied to HNSCC, encompassing both current developments and future prospects. RECENT FINDINGS The integration of personalized medicine strategies into HNSCC diagnosis is driven by the utilization of genetic data and biomarkers. Epigenetic biomarkers, which reflect modifications to DNA that can influence gene expression, have emerged as valuable indicators for early detection and risk assessment. Treatment approaches within the personalized medicine framework are equally promising. Immunotherapy, gene silencing, and editing techniques, including RNA interference and CRISPR/Cas9, offer innovative means to modulate gene expression and correct genetic aberrations driving HNSCC. The integration of stem cell research with personalized medicine presents opportunities for tailored regenerative approaches. The synergy between personalized medicine and technological advancements is exemplified by artificial intelligence (AI) and machine learning (ML) applications. These tools empower clinicians to analyze vast datasets, predict patient responses, and optimize treatment strategies with unprecedented accuracy. CONCLUSION The developments and prospects of personalized medicine in HNSCC diagnosis and treatment offer a transformative approach to managing this complex malignancy. By harnessing genetic insights, biomarkers, immunotherapy, gene editing, stem cell therapies, and advanced technologies like AI and ML, personalized medicine holds the key to enhancing patient outcomes and ushering in a new era of precision oncology.
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Affiliation(s)
| | | | | | | | - Kalpani Senevirathna
- Centre for Research in Oral Cancer, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
| | | | | | - Kehinde Kazeem Kanmodi
- School of DentistryUniversity of RwandaKigaliRwanda
- Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
- Cephas Health Research Initiative IncIbadanNigeria
- School of Health and Life SciencesTeesside UniversityMiddlesbroughUK
| | - Ruwan Duminda Jayasinghe
- Centre for Research in Oral Cancer, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
- Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
- School of Health and Life SciencesTeesside UniversityMiddlesbroughUK
- Department of Oral Medicine and Periodontology, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
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20
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Cheng Q, Lin H, Zhao J, Lu X, Wang Q. Application of machine learning-based multi-sequence MRI radiomics in diagnosing anterior cruciate ligament tears. J Orthop Surg Res 2024; 19:99. [PMID: 38297322 PMCID: PMC10829177 DOI: 10.1186/s13018-024-04602-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 01/28/2024] [Indexed: 02/02/2024] Open
Abstract
OBJECTIVE To compare the diagnostic power among various machine learning algorithms utilizing multi-sequence magnetic resonance imaging (MRI) radiomics in detecting anterior cruciate ligament (ACL) tears. Additionally, this research aimed to create and validate the optimal diagnostic model. METHODS In this retrospective analysis, 526 patients were included, comprising 178 individuals with ACL tears and 348 with a normal ACL. Radiomics features were derived from multi-sequence MRI scans, encompassing T1-weighted imaging and proton density (PD)-weighted imaging. The process of selecting the most reliable radiomics features involved using interclass correlation coefficient (ICC) testing, t tests, and the least absolute shrinkage and selection operator (LASSO) technique. After the feature selection process, five machine learning classifiers were created. These classifiers comprised logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP). A thorough performance evaluation was carried out, utilizing diverse metrics like the area under the receiver operating characteristic curve (ROC), specificity, accuracy, sensitivity positive predictive value, and negative predictive value. The classifier exhibiting the best performance was chosen. Subsequently, three models were developed: the PD model, the T1 model, and the combined model, all based on the optimal classifier. The diagnostic performance of these models was assessed by employing AUC values, calibration curves, and decision curve analysis. RESULTS Out of 2032 features, 48 features were selected. The SVM-based multi-sequence radiomics outperformed all others, achieving AUC values of 0.973 and 0.927, sensitivities of 0.933 and 0.857, and specificities of 0.930 and 0.829, in the training and validation cohorts, respectively. CONCLUSION The multi-sequence MRI radiomics model, which is based on machine learning, exhibits exceptional performance in diagnosing ACL tears. It provides valuable insights crucial for the diagnosis and treatment of knee joint injuries, serving as an accurate and objective supplementary diagnostic tool for clinical practitioners.
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Affiliation(s)
- Qi Cheng
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Haoran Lin
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Jie Zhao
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Xiao Lu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China
| | - Qiang Wang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, Wuhu, 241001, Anhui, People's Republic of China.
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21
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Mao Y, Jiang L, Wang JL, Chen FQ, Zhang WP, Liu ZX, Li C. Radiomic nomogram for discriminating parotid pleomorphic adenoma from parotid adenolymphoma based on grayscale ultrasonography. Front Oncol 2024; 13:1268789. [PMID: 38273852 PMCID: PMC10808803 DOI: 10.3389/fonc.2023.1268789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
Objectives To differentiate parotid pleomorphic adenoma (PA) from adenolymphoma (AL) using radiomics of grayscale ultrasonography in combination with clinical features. Methods This retrospective study aimed to analyze the clinical and radiographic characteristics of 162 cases from December 2019 to March 2023. The study population consisted of a training cohort of 113 patients and a validation cohort of 49 patients. Grayscale ultrasonography was processed using ITP-Snap software and Python to delineate regions of interest (ROIs) and extract radiomic features. Univariate analysis, Spearman's correlation, greedy recursive elimination strategy, and least absolute shrinkage and selection operator (LASSO) correlation were employed to select relevant radiographic features. Subsequently, eight machine learning methods (LR, SVM, KNN, RandomForest, ExtraTrees, XGBoost, LightGBM, and MLP) were employed to build a quantitative radiomic model using the selected features. A radiomic nomogram was developed through the utilization of multivariate logistic regression analysis, integrating both clinical and radiomic data. The accuracy of the nomogram was assessed using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test. Results To differentiate PA from AL, the radiomic model using SVM showed optimal discriminatory ability (accuracy = 0.929 and 0.857, sensitivity = 0.946 and 0.800, specificity = 0.921 and 0.897, positive predictive value = 0.854 and 0.842, and negative predictive value = 0.972 and 0.867 in the training and validation cohorts, respectively). A nomogram incorporating rad-Signature and clinical features achieved an area under the ROC curve (AUC) of 0.983 (95% confidence interval [CI]: 0.965-1) and 0.910 (95% CI: 0.830-0.990) in the training and validation cohorts, respectively. Decision curve analysis showed that the nomogram and radiomic model outperformed the clinical-factor model in terms of clinical usefulness. Conclusion A nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.
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Affiliation(s)
- Yi Mao
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - LiPing Jiang
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Jing-Ling Wang
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Fang-Qun Chen
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Wie-Ping Zhang
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Zhi-Xing Liu
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
- Department of Ultrasound, GanJiang New District Peoples Hospital, Nanchang, Jiangxi, China
| | - Chen Li
- Department of Ultrasound, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
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22
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Ding SX, Sun YF, Meng H, Wang JN, Xue LY, Gao BL, Yin XP. Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer. Sci Rep 2023; 13:22052. [PMID: 38086918 PMCID: PMC10716186 DOI: 10.1038/s41598-023-49540-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 12/09/2023] [Indexed: 12/18/2023] Open
Abstract
To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.
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Affiliation(s)
- Si-Xuan Ding
- Department of Radiology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, No. 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, People's Republic of China
| | - Yu-Feng Sun
- College of Quality and Technical Supervision, Hebei University, No. 180, Wu Si East Road, Baoding City, 071000, Hebei Province, People's Republic of China
| | - Huan Meng
- Department of Radiology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, No. 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, People's Republic of China
| | - Jia-Ning Wang
- Department of Radiology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, No. 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, People's Republic of China
| | - Lin-Yan Xue
- College of Quality and Technical Supervision, Hebei University, No. 180, Wu Si East Road, Baoding City, 071000, Hebei Province, People's Republic of China.
| | - Bu-Lang Gao
- Department of Radiology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, No. 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, People's Republic of China
| | - Xiao-Ping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, No. 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, People's Republic of China.
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23
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Li H, Huang W, Wang S, Balasubramanian PS, Wu G, Fang M, Xie X, Zhang J, Dong D, Tian J, Chen F. Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma. Vis Comput Ind Biomed Art 2023; 6:23. [PMID: 38036750 PMCID: PMC10689317 DOI: 10.1186/s42492-023-00149-0] [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: 06/29/2023] [Accepted: 10/27/2023] [Indexed: 12/02/2023] Open
Abstract
Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model's ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.
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Affiliation(s)
- Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, 570311, China
| | - Siwen Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | | | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, 570311, China
| | - Mengjie Fang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Xuebin Xie
- Department of Radiology, Kiang Wu Hospital, Santo António, Macao, 999078, China
| | - Jie Zhang
- Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Zhuhai, Guangdong, 519000, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China.
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China.
- Zhuhai Precision Medical Center, Zhuhai People's Hospital, Zhuhai, Guangdong, 519000, China.
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, 570311, China.
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24
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Yang Q, Huang H, Zhang G, Weng N, Ou Z, Sun M, Luo H, Zhou X, Gao Y, Wu X. Contrast-enhanced CT-based radiomic analysis for determining the response to anti-programmed death-1 therapy in esophageal squamous cell carcinoma patients: A pilot study. Thorac Cancer 2023; 14:3266-3274. [PMID: 37743537 PMCID: PMC10665784 DOI: 10.1111/1759-7714.15117] [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: 07/21/2023] [Revised: 09/06/2023] [Accepted: 09/07/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND In view of the fact that radiomics features have been reported as predictors of immunotherapy to various cancers, this study aimed to develop a prediction model to determine the response to anti-programmed death-1 (anti-PD-1) therapy in esophageal squamous cell carcinoma (ESCC) patients from contrast-enhanced CT (CECT) radiomics features. METHODS Radiomic analysis of images was performed retrospectively for image samples before and after anti-PD-1 treatment, and efficacy analysis was performed for the results of two different time node evaluations. A total of 68 image samples were included in this study. Quantitative radiomic features were extracted from the images, and the least absolute shrinkage and selection operator method was applied to select radiomic features. After obtaining selected features, three classification models were used to establish a radiomics model to predict the ESCC status and efficacy of therapy. A cross-validation strategy utilizing three folds was employed to train and test the model. Performance evaluation of the model was done using the area under the curve (AUC) of receiver operating characteristic, sensitivity, specificity, and precision metric. RESULTS Wavelet and area of gray level change (log-sigma) were the most significant radiomic features for predicting therapy efficacy. Fifteen radiomic features from the whole tumor and peritumoral regions were selected and comprised of the fusion radiomics score. A radiomics classification was developed with AUC of 0.82 and 0.884 in the before and after-therapy cohorts, respectively. CONCLUSIONS The combined model incorporating radiomic features and clinical CECT predictors helps to predict the response to anti-PD-1therapy in patients with ESCC.
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Affiliation(s)
- Qinzhu Yang
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
| | - Haofan Huang
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
- Department of Biomedical EngineeringHong Kong Polytechnic UniversityHong Kong SARChina
| | - Guizhi Zhang
- Department of RadiologyThe Eighth Affiliated Hospital of Sun Yat‐sen UniversityShenzhenChina
| | - Nuoqing Weng
- Department of Gastrointestinal Surgery, The Eighth Affiliated HospitalSun Yat‐sen UniversityShenzhenChina
| | - Zhenkai Ou
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
| | - Meili Sun
- Department of RadiologySun Yat‐sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhouChina
| | - Huixing Luo
- Department of Gastrointestinal Surgery, The Eighth Affiliated HospitalSun Yat‐sen UniversityShenzhenChina
| | - Xuhui Zhou
- Department of RadiologyThe Eighth Affiliated Hospital of Sun Yat‐sen UniversityShenzhenChina
| | - Yi Gao
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
- Shenzhen Key Laboratory of Precision Medicine for Hematological MalignanciesShenzhenChina
- Marshall Laboratory of Biomedical EngineeringShenzhenChina
| | - Xiaobin Wu
- Department of Gastrointestinal Surgery, The Eighth Affiliated HospitalSun Yat‐sen UniversityShenzhenChina
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25
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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: 22] [Impact Index Per Article: 11.0] [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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Jha AK, Mithun S, Sherkhane UB, Dwivedi P, Puts S, Osong B, Traverso A, Purandare N, Wee L, Rangarajan V, Dekker A. Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:569-582. [PMID: 37720353 PMCID: PMC10501896 DOI: 10.37349/etat.2023.00153] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/20/2023] [Indexed: 09/19/2023] Open
Abstract
Cancer is a fatal disease and the second most cause of death worldwide. Treatment of cancer is a complex process and requires a multi-modality-based approach. Cancer detection and treatment starts with screening/diagnosis and continues till the patient is alive. Screening/diagnosis of the disease is the beginning of cancer management and continued with the staging of the disease, planning and delivery of treatment, treatment monitoring, and ongoing monitoring and follow-up. Imaging plays an important role in all stages of cancer management. Conventional oncology practice considers that all patients are similar in a disease type, whereas biomarkers subgroup the patients in a disease type which leads to the development of precision oncology. The utilization of the radiomic process has facilitated the advancement of diverse imaging biomarkers that find application in precision oncology. The role of imaging biomarkers and artificial intelligence (AI) in oncology has been investigated by many researchers in the past. The existing literature is suggestive of the increasing role of imaging biomarkers and AI in oncology. However, the stability of radiomic features has also been questioned. The radiomic community has recognized that the instability of radiomic features poses a danger to the global generalization of radiomic-based prediction models. In order to establish radiomic-based imaging biomarkers in oncology, the robustness of radiomic features needs to be established on a priority basis. This is because radiomic models developed in one institution frequently perform poorly in other institutions, most likely due to radiomic feature instability. To generalize radiomic-based prediction models in oncology, a number of initiatives, including Quantitative Imaging Network (QIN), Quantitative Imaging Biomarkers Alliance (QIBA), and Image Biomarker Standardisation Initiative (IBSI), have been launched to stabilize the radiomic features.
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Affiliation(s)
- Ashish Kumar Jha
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Umeshkumar B. Sherkhane
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
| | - Pooj Dwivedi
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
- Department of Nuclear Medicine, Advance Center for Treatment, Research, Education in Cancer, Kharghar, Navi-Mumbai 410210, Maharashtra, India
| | - Senders Puts
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Biche Osong
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Nilendu Purandare
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine, Tata Memorial Hospital, Mumbai 400012, Maharashtra, India
- Homi Bhabha National Institute, BARC Training School Complex, Anushaktinagar, Mumbai 400094, Maharashtra, India
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands
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Wang A, Xu H, Zhang C, Ren J, Liu J, Zhou P. Radiomic analysis of MRI for prediction of response to induction chemotherapy in nasopharyngeal carcinoma patients. Clin Radiol 2023:S0009-9260(23)00223-4. [PMID: 37331848 DOI: 10.1016/j.crad.2023.05.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/03/2023] [Accepted: 05/23/2023] [Indexed: 06/20/2023]
Abstract
AIM To establish and validate radiomic models for response prediction to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC) using the radiomic features from pretreatment MRI. MATERIALS AND METHODS This retrospective analysis included 184 consecutive NPC patients, 132 in the primary cohort and 52 in the validation cohort. Radiomic features were derived from contrast-enhanced T1-weighted imaging (CE-T1) and T2-weighted imaging (T2-WI) for each subject. The radiomic features were then selected and combined with clinical characteristics to build radiomic models. The potential of the radiomic models was evaluated based on its discrimination and calibration. To measure the performance of these radiomic models in predicting the treatment response to IC in NPC, the area under the receiver operating characteristic curve (AUC), and sensitivity, specificity, and accuracy were used. RESULTS Four radiomic models were constructed in the present study including the radiomic signature of CE-T1, T2-WI, CE-T1 + T2-WI, and the radiomic nomogram of CE-T1. The radiomic signature of CE-T1 + T2-WI performed well in distinguishing response and non-response to IC in patients with NPC, which yielded an AUC of 0.940 (95% CI, 0.885-0.974), sensitivity of 83.1%, specificity of 91.8%, and accuracy of 87.1% in the primary cohort, and AUC of 0.952 (95% CI, 0.855-0.992), sensitivity of 74.2%, specificity of 95.2%, and accuracy of 82.7% in the validation cohort. CONCLUSION MRI-based radiomic models could be helpful for personalised risk stratification and treatment in NPC patients receiving IC.
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Affiliation(s)
- A Wang
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - H Xu
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - C Zhang
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - J Ren
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - J Liu
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
| | - P Zhou
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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Xu H, Wang A, Zhang C, Ren J, Zhou P, Liu J. Intra- and peritumoral MRI radiomics assisted in predicting radiochemotherapy response in metastatic cervical lymph nodes of nasopharyngeal cancer. BMC Med Imaging 2023; 23:66. [PMID: 37254101 DOI: 10.1186/s12880-023-01026-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 05/23/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND To establish and validate radiomic models combining intratumoral (Intra) and peritumoral (Peri) features obtained from pretreatment MRI for the prediction of treatment response of lymph node metastasis from nasopharyngeal cancer (NPC). METHODS One hundred forty-five NPC patients (102 in the training and 43 in the validation set) were retrospectively enrolled. Radiomic features were extracted from Intra and Peri regions on the metastatic cervical lymph node, and selected with the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was applied to build radiomic models. Sensitivity, specificity, accuracy, and the area under the curve (AUC) of receiver operating characteristics were employed to evaluate the predictive power of each model. RESULTS The AUCs of the radiomic model of Intra, Peri, Intra + Peri, and Clinical-radiomic were 0.910, 0.887, 0.934, and 0.941, respectively, in the training set and 0.737, 0.794, 0.774, and 0.783, respectively, in the validation set. There were no significant differences in prediction performance among the radiomic models in the training and validation sets (all P > 0.05). The calibration curve of the radiomic model of Peri demonstrated good agreement between prediction and observation in the training and validation sets. CONCLUSIONS The pretreatment MRI-based radiomics model may be useful in predicting the treatment response of metastatic lymph nodes of NPC. Besides, the generalization ability of the radiomic model of Peri was better than that of Intra and Intra + Peri.
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Affiliation(s)
- Hao Xu
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Ai Wang
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Chi Zhang
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
| | - Jieke Liu
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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Wu M, Xu W, Fei Y, Li Y, Yuan J, Qiu L, Zhang Y, Chen G, Cheng Y, Cao Y, Sun X, Zhou S. MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma. Front Oncol 2023; 13:1192953. [PMID: 37256173 PMCID: PMC10225671 DOI: 10.3389/fonc.2023.1192953] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 04/26/2023] [Indexed: 06/01/2023] Open
Abstract
Objective Tumor residue after concurrent chemoradiotherapy (CCRT) in nasopharyngeal carcinoma (NPC) patients often predicts poor prognosis. Thus, the objective of this retrospective study is to develop a nomogram that combines magnetic resonance (MRI) radiomics features and clinical features to predict the early response of locally advanced nasopharyngeal carcinoma (LA-NPC). Methods A total of 91 patients with LA-NPC were included in this study. Patients were randomly divided into training and validation cohorts at a ratio of 3:1. Univariate and multivariate analyses were performed on the clinical parameters of the patients to select clinical features to build a clinical model. In the training cohort, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used to select radiomics features for construction of a radiomics model. The logistic regression algorithm was then used to combine the clinical features with the radiomics features to construct the clinical radiomics nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical radiomics nomogram. Results Platelet lymphocyte ratio (PLR) and nasopharyngeal tumor volume were identified as independent predictors of early response in patients with locally advanced nasopharyngeal carcinoma. A total of 5502 radiomics features were extracted, from which 25 radiomics features were selected to construct the radiomics model. The clinical radiomics nomogram demonstrated the highest AUC in both the training and validation cohorts (training cohort 0.975 vs 0.973 vs 0.713; validation cohort 0.968 vs 0.952 vs 0.706). The calibration curve and DCA indicated good predictive performance for the nomogram. Conclusion A clinical radiomics nomogram, which combines clinical features with radiomics features based on MRI, can predict early tumor regression in patients with LA-NPC. The performance of the nomogram is superior to that of either the clinical model or radiomics model alone. Therefore, it can be used to identify patients without CR at an early stage and provide guidance for personalized therapy.
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Affiliation(s)
- Mengxing Wu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Weilin Xu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yinjiao Fei
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yurong Li
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jinling Yuan
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Lei Qiu
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yumeng Zhang
- Department of Radiation Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China
| | - Guanhua Chen
- Department of Radiotherapy, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Yu Cheng
- Department of Oncology, The Second Hospital of Nanjing, Nanjing, Jiangsu, China
| | - Yuandong Cao
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xinchen Sun
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Shu Zhou
- Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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Guo Y, Dai G, Xiong X, Wang X, Chen H, Zhou X, Huang W, Chen F. Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma. Transl Oncol 2023; 31:101648. [PMID: 36905870 PMCID: PMC10020114 DOI: 10.1016/j.tranon.2023.101648] [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: 09/20/2022] [Revised: 02/09/2023] [Accepted: 02/27/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Intravoxel incoherent motion (IVIM) plays an important role in predicting treatment responses in patient with nasopharyngeal carcinoma (NPC). The goal of this study was to develop and validate a radiomics nomogram based on IVIM parametric maps and clinical data for the prediction of treatment responses in NPC patients. METHODS Eighty patients with biopsy-proven NPC were enrolled in this study. Sixty-two patients had complete responses and 18 patients had incomplete responses to treatment. Each patient received a multiple b-value diffusion-weighted imaging (DWI) examination before treatment. Radiomics features were extracted from IVIM parametric maps derived from DWI image. Feature selection was performed by the least absolute shrinkage and selection operator method. Radiomics signature was generated by support vector machine based on the selected features. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) values were used to evaluate the diagnostic performance of radiomics signature. A radiomics nomogram was established by integrating the radiomics signature and clinical data. RESULTS The radiomics signature showed good prognostic performance to predict treatment response in both training (AUC = 0.906, P<0.001) and testing (AUC = 0.850, P<0.001) cohorts. The radiomic nomogram established by integrating the radiomic signature with clinical data significantly outperformed clinical data alone (C-index, 0.929 vs 0.724; P<0.0001). CONCLUSIONS The IVIM-based radiomics nomogram provided high prognostic ability to treatment responses in patients with NPC. The IVIM-based radiomics signature has the potential to be a new biomarker in prediction of the treatment responses and may affect treatment strategies in patients with NPC.
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Affiliation(s)
- Yihao Guo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou 570311, China
| | - Ganmian Dai
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou 570311, China
| | - Xiaoli Xiong
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou 570311, China
| | - Xiaoyi Wang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou 570311, China
| | - Huijuan Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou 570311, China
| | - Xiaoyue Zhou
- Siemens Healthineers Digital Technology (Shanghai) Co., Ltd., Shanghai 201306, China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou 570311, China.
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou 570311, China.
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Hao Y, Jiang H, Diao Z, Shi T, Liu L, Li H, Zhang W. MSU-Net: Multi-scale Sensitive U-Net based on pixel-edge-region level collaborative loss for nasopharyngeal MRI segmentation. Comput Biol Med 2023; 159:106956. [PMID: 37116241 DOI: 10.1016/j.compbiomed.2023.106956] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 11/29/2022] [Accepted: 04/16/2023] [Indexed: 04/30/2023]
Abstract
Radiotherapy is the traditional treatment of early nasopharyngeal carcinoma (NPC). Automatic accurate segmentation of risky lesions in the nasopharynx is crucial in radiotherapy. U-Net has been proved its effective medical image segmentation ability. However, the great difference in the structure and size of nasopharynx among different patients requires a network that pays more attention to multi-scale information. In this paper, we propose a multi-scale sensitive U-Net (MSU-Net) based on pixel-edge-region level collaborative loss (LCo-PER) for NPC segmentation task. A series of novel feature fusion modules based on spatial continuity and multi-scale semantic are proposed for extracting multi-level features while efficiently searching for all size lesions. A spatial continuity information extraction module (SCIEM) is proposed for effectively using the spatial continuity information of context slices to search small lesions. And a multi-scale semantic feature extraction module (MSFEM) is proposed for extracting features of different receptive fields. LCo-PER is proposed for the network training which makes network model could take into account the size of different lesions. The global Dice, Precision, Recall and IOU of the testing set are 84.50%, 97.48%, 84.33% and 82.41%, respectively. The results show that our method is better than the other state-of-the-art methods for NPC segmentation which obtain higher accuracy and effective segmentation performance.
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Affiliation(s)
| | - Huiyan Jiang
- Software College, Northeastern University, Shenyang 110819, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110819, China.
| | - Zhaoshuo Diao
- Software College, Northeastern University, Shenyang 110819, China
| | - Tianyu Shi
- Software College, Northeastern University, Shenyang 110819, 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, 651 Dongfeng Road East, Guangzhou, Guangdong 510060, 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, 651 Dongfeng Road East, Guangzhou, Guangdong 510060, China
| | - Weijing Zhang
- 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, 651 Dongfeng Road East, Guangzhou, Guangdong 510060, China
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Yang F, Wei H, Li X, Yu X, Zhao Y, Li L, Li Y, Xie L, Wang S, Lin M. Pretreatment synthetic magnetic resonance imaging predicts disease progression in nonmetastatic nasopharyngeal carcinoma after intensity modulation radiation therapy. Insights Imaging 2023; 14:59. [PMID: 37016104 PMCID: PMC10073373 DOI: 10.1186/s13244-023-01411-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 03/22/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND To investigate the potential of synthetic MRI (SyMRI) in the prognostic assessment of patients with nonmetastatic nasopharyngeal carcinoma (NPC), and the predictive value when combined with diffusion-weighted imaging (DWI) as well as clinical factors. METHODS Fifty-three NPC patients who underwent SyMRI were prospectively included. 10th Percentile, Mean, Kurtosis, and Skewness of T1, T2, and PD maps and ADC value were obtained from the primary tumor. Cox regression analysis was used for analyzing the association between SyMRI and DWI parameters and progression-free survival (PFS), and then age, sex, staging, and treatment as confounding factors were also included. C-index was obtained by bootstrap. Moreover, significant parameters were used to construct models in predicting 3-year disease progression. ROC curves and leave-one-out cross-validation were used to evaluate the performance and stability. RESULTS Disease progression occurred in 16 (30.2%) patients at a follow-up of 39.6 (3.5, 48.2) months. T1_Kurtosis, T1_Skewness, T2_10th, PD_Mean, and ADC were correlated with PFS, and T1_Kurtosis (HR: 1.093) and ADC (HR: 1.009) were independent predictors of PFS. The C-index of SyMRI and SyMRI + DWI + Clinic models was 0.687 and 0.779. Moreover, the SyMRI + DWI + Clinic model predicted 3-year disease progression better than DWI or Clinic model (p ≤ 0.008). Interestingly, there was no significant difference between the SyMRI model (AUC: 0.748) and SyMRI + DWI + Clinic model (AUC: 0.846, p = 0.092). CONCLUSION SyMRI combined with histogram analysis could predict disease progression in NPC patients, and SyMRI + DWI + Clinic model further improved the predictive performance.
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Affiliation(s)
- Fan Yang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Haoran Wei
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaolu Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaoduo Yu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yanfeng Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lin Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yujie Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lizhi Xie
- MR Research China, GE Healthcare, Beijing, China
| | - Sicong Wang
- MR Research China, GE Healthcare, Beijing, China
| | - Meng Lin
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Chen K, Hou L, Chen M, Li S, Shi Y, Raynor WY, Yang H. Predicting the Efficacy of SBRT for Lung Cancer with 18F-FDG PET/CT Radiogenomics. Life (Basel) 2023; 13:life13040884. [PMID: 37109413 PMCID: PMC10142286 DOI: 10.3390/life13040884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/18/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023] Open
Abstract
Purpose: to develop a radiogenomic model on the basis of 18F-FDG PET/CT radiomics and clinical-parameter EGFR for predicting PFS stratification in lung-cancer patients after SBRT treatment. Methods: A total of 123 patients with lung cancer who had undergone 18F-FDG PET/CT examination before SBRT from September 2014 to December 2021 were retrospectively analyzed. All patients’ PET/CT images were manually segmented, and the radiomic features were extracted. LASSO regression was used to select radiomic features. Logistic regression analysis was used to screen clinical features to establish the clinical EGFR model, and a radiogenomic model was constructed by combining radiomics and clinical EGFR. We used the receiver operating characteristic curve and calibration curve to assess the efficacy of the models. The decision curve and influence curve analysis were used to evaluate the clinical value of the models. The bootstrap method was used to validate the radiogenomic model, and the mean AUC was calculated to assess the model. Results: A total of 2042 radiomics features were extracted. Five radiomic features were related to the PFS stratification of lung-cancer patients with SBRT. T-stage and overall stages (TNM) were independent factors for predicting PFS stratification. AUCs under the ROC curve of the radiomics, clinical EGFR, and radiogenomic models were 0.84, 0.67, and 0.86, respectively. The calibration curve shows that the predicted value of the radiogenomic model was in good agreement with the actual value. The decision and influence curve showed that the model had high clinical application values. After Bootstrap validation, the mean AUC of the radiogenomic model was 0.850(95%CI 0.849–0.851). Conclusions: The radiogenomic model based on 18F-FDG PET/CT radiomics and clinical EGFR has good application value in predicting the PFS stratification of lung-cancer patients after SBRT treatment.
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Affiliation(s)
- Kuifei Chen
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Liqiao Hou
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Meng Chen
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Shuling Li
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
| | - Yangyang Shi
- Department of Radiation Oncology, University of Arizona, Tucson, AZ 85724, USA
| | - William Y. Raynor
- Department of Radiology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Haihua Yang
- Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 317000, China
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou 317000, China
- Correspondence: or
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Predicting survival after radiosurgery in patients with lung cancer brain metastases using deep learning of radiomics and EGFR status. Phys Eng Sci Med 2023; 46:585-596. [PMID: 36857023 DOI: 10.1007/s13246-023-01234-7] [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: 10/11/2022] [Accepted: 02/13/2023] [Indexed: 03/02/2023]
Abstract
The early prediction of overall survival (OS) in patients with lung cancer brain metastases (BMs) after Gamma Knife radiosurgery (GKRS) can facilitate patient management and outcome improvement. However, the disease progression is influenced by multiple factors, such as patient characteristics and treatment strategies, and hence satisfactory performance of OS prediction remains challenging. Accordingly, we proposed a deep learning approach based on comprehensive predictors, including clinical, imaging, and genetic information, to accomplish reliable and personalized OS prediction in patients with BMs after receiving GKRS. Overall 1793 radiomic features extracted from pre-GKRS magnetic resonance images (MRI), clinical information, and epidermal growth factor receptor (EGFR) mutation status were retrospectively collected from 237 BM patients who underwent GKRS. DeepSurv, a multi-layer perceptron model, with 4 different aggregation methods of radiomics was applied to predict personalized survival curves and survival status at 3, 6, 12, and 24 months. The model combining clinical features, EGFR status, and radiomics from the largest BM showed the best prediction performance with concordance index of 0.75 and achieved areas under the curve of 0.82, 0.80, 0.84, and 0.92 for predicting survival status at 3, 6, 12, and 24 months, respectively. The DeepSurv model showed a significant improvement (p < 0.001) in concordance index compared to the validated lung cancer BM prognostic molecular markers. Furthermore, the model provided a novel estimate of the risk-of-death period for patients. The personalized survival curves generated by the DeepSurv model effectively predicted the risk-of-death period which could facilitate personalized management of patients with lung cancer BMs.
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Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review. Cancers (Basel) 2023; 15:cancers15041174. [PMID: 36831517 PMCID: PMC9954362 DOI: 10.3390/cancers15041174] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/31/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.
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Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy. Cancers (Basel) 2023; 15:cancers15041105. [PMID: 36831445 PMCID: PMC9954441 DOI: 10.3390/cancers15041105] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/27/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023] Open
Abstract
This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann-Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038-0.063, AUC = 0.690-0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047-0.070, AUC = 0.699-0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028-0.074, AUC = 0.719-0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.
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Cai C, Lv W, Chi F, Zhang B, Zhu L, Yang G, Zhao S, Zhu Y, Han X, Dai Z, Wang X, Lu L. Prognostic generalization of multi-level CT-dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma. Med Phys 2023; 50:922-934. [PMID: 36317870 DOI: 10.1002/mp.16044] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 09/13/2022] [Accepted: 09/24/2022] [Indexed: 11/12/2022] Open
Abstract
PURPOSE To investigate the prognostic performance of multi-level computed tomography (CT)-dose fusion dosiomics at the image-, matrix-, and feature-levels from the gross tumor volume (GTV) at nasopharynx and the involved lymph node for nasopharyngeal carcinoma (NPC) patients. METHODS Two hundred and nineteen NPC patients (175 vs. 44 for training vs. internal validation) were used to train prediction model, and 32 NPC patients were used for external validation. We first extracted CT and dose information from intratumoral nasopharynx (GTV_nx) and lymph node (GTV_nd) regions. Then, the corresponding peritumoral regions (RING_3 mm and RING_5 mm) were also considered. Thus, the individual and combination of intratumoral and peritumoral regions were as follows: GTV_nx, GTV_nd, RING_3 mm_nx, RING_3 mm_nd, RING_5 mm_nx, RING_5 mm_nd, GTV_nxnd, RING_3 mm_nxnd, RING_5 mm_nxnd, GTV + RING_3 mm_nxnd, and GTV + RING_5 mm_nxnd. For each region, 11 models were built by combining five clinical parameters and 127 features from: (1) dose images alone; (2-7) fused dose and CT images via wavelet-based fusion using CT weights of 0.2, 0.4, 0.6, and 0.8, gradient transfer fusion, and guided-filtering-based fusion (GFF); (8) fused matrices (sumMat); (9-10) fused features derived via feature averaging (avgFea) and feature concatenation (conFea); and finally, (11) CT images alone. The concordance index (C-index) and Kaplan-Meier curves with log-rank test were used to assess model performance. RESULTS The fusion models' performance was better than single CT/dose model on both internal and external validation. Models that combined the information from both GTV_nx and GTV_nd regions outperformed the single region model. For internal validation, GTV + RING_3 mm_nxnd GFF model achieved the highest C-index both in recurrence-free survival (RFS) and metastasis-free survival (MFS) predictions (RFS: 0.822; MFS: 0.786). The highest C-index in external validation set was achieved by RING_3 mm_nxnd model (RFS: 0.762; MFS: 0.719). The GTV + RING_3 mm_nxnd GFF model is able to significantly separate patients into high-risk and low-risk groups compared to dose-only or CT-only models. CONCLUSION Fusion dosiomics model combining the primary tumor, the involved lymph node, and 3 mm peritumoral information outperformed single-modality models for different outcome predictions, which is helpful for clinical decision-making and the development of personalized treatment.
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Affiliation(s)
- Chunya Cai
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Wenbing Lv
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
| | - Feng Chi
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Bailin Zhang
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lin Zhu
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Geng Yang
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Shiwu Zhao
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yuanhu Zhu
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xu Han
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
| | - Zhenhui Dai
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Xuetao Wang
- Department of Radiotherapy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, China
- Pazhou Lab, Guangzhou, China
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Shah D, Gehani A, Mahajan A, Chakrabarty N. Advanced Techniques in Head and Neck Cancer Imaging: Guide to Precision Cancer Management. Crit Rev Oncog 2023; 28:45-62. [PMID: 37830215 DOI: 10.1615/critrevoncog.2023047799] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Precision treatment requires precision imaging. With the advent of various advanced techniques in head and neck cancer treatment, imaging has become an integral part of the multidisciplinary approach to head and neck cancer care from diagnosis to staging and also plays a vital role in response evaluation in various tumors. Conventional anatomic imaging (CT scan, MRI, ultrasound) remains basic and focuses on defining the anatomical extent of the disease and its spread. Accurate assessment of the biological behavior of tumors, including tumor cellularity, growth, and response evaluation, is evolving with recent advances in molecular, functional, and hybrid/multiplex imaging. Integration of these various advanced diagnostic imaging and nonimaging methods aids understanding of cancer pathophysiology and provides a more comprehensive evaluation in this era of precision treatment. Here we discuss the current status of various advanced imaging techniques and their applications in head and neck cancer imaging.
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Affiliation(s)
- Diva Shah
- Senior Consultant Radiologist, Department of Radiodiagnosis, HCG Cancer Centre, Ahmedabad, 380060, Gujarat, India
| | - Anisha Gehani
- Department of Radiology and Imaging Sciences, Tata Medical Centre, New Town, WB 700160, India
| | - Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, L7 8YA, United Kingdom
| | - Nivedita Chakrabarty
- Department of Radiodiagnosis, Tata Memorial Hospital, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), 400012, Mumbai, India
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Huang L, Yang Z, Kang M, Ren H, Jiang M, Tang C, Hu Y, Shen M, Lin H, Long L. Performance of Pretreatment MRI-Based Radiomics in Recombinant Human Endostatin Plus Concurrent Chemoradiotherapy Response Prediction in Nasopharyngeal Carcinoma: A Retrospective Study. Technol Cancer Res Treat 2023; 22:15330338231160619. [PMID: 37094106 PMCID: PMC10134146 DOI: 10.1177/15330338231160619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
PURPOSE To investigate the capability of an Magnetic resonance imaging (MRI) radiomics model based on pretreatment texture features in predicting the short-term efficacy of recombinant human endostatin (RHES) plus concurrent chemoradiotherapy (CCRT) for nasopharyngeal carcinoma (NPC). METHODS We retrospectively enrolled 65 patients newly diagnosed as having NPC and treated with RHES + CCRT. A total of 144 texture features were extracted from the MRI before RHES + CCRT treatment of all the NPC patients. The maximum relevance minimum redundancy (mRMR) method was used to remove redundant, irrelevant texture features, and calculate the Rad score of the primary tumor. Multivariable logistic regression was used to select the most predictive features subset, and prediction models were constructed. The performance of the 3 models in predicting the early response of RHES + CCRT for NPC was explored. RESULTS The diagnostic efficiency of combined model and radiomics model in distinguishing between the effective and the ineffective groups of patients was found to be moderate. The area under the ROC curve (AUC) of the combined model and radiomics model was 0.74 (95% confidence interval [CI]: 0.62-0.86) and 0.71 (95% CI: 0.58-0.84), respectively, with both being higher than the AUC of the clinics model (0.63, 95% CI: 0.49-0.78). Compared with the radiomics model, the combined model showed marginally improved diagnostic performance in predicting RHES + CCRT treatment response. The accuracy of combined model and radiomics model for RHES + CCRT response assessment in NPC were higher than those of the clinics model (0.723, 0.723 vs 0.677). CONCLUSION The pretreatment MRI-based radiomics may be a noninvasive and effective method for the prediction of RHES + CCRT early response in patients with NPC.
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Affiliation(s)
- Lixuan Huang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
| | - Zongxiang Yang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
| | - Min Kang
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Tumor Radiation Therapy Clinical Medical Research Center, Nanning, Guangxi Province, China
| | - Hao Ren
- Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
| | - Muliang Jiang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
| | - Cheng Tang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
| | - Yao Hu
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
| | - Mingjun Shen
- Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Tumor Radiation Therapy Clinical Medical Research Center, Nanning, Guangxi Province, China
| | - Huashan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha, China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Province, China
- Key Laboratory of Early Prevention and Treatment for Regional High-Frequency Tumor, Guangxi Medical University, Ministry of Education, Nanning, Guangxi Province, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi Province, China
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MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy. Sci Rep 2022; 12:18631. [PMID: 36329116 PMCID: PMC9633752 DOI: 10.1038/s41598-022-22826-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
Real-time magnetic resonance image guided stereotactic ablative radiotherapy (MRgSBRT) is used to treat abdominal tumors. Longitudinal data is generated from daily setup images. Our study aimed to identify delta radiomic texture features extracted from these images to predict for local control in patients with liver tumors treated with MRgSBRT. Retrospective analysis of an IRB-approved database identified patients treated with MRgSBRT for primary liver and secondary metastasis histologies. Daily low field strength (0.35 T) images were retrieved, and the gross tumor volume was identified on each image. Next, images' gray levels were equalized, and 39 s-order texture features were extracted. Delta-radiomics were calculated as the difference between feature values on the initial scan and after delivered biological effective doses (BED, α/β = 10) of 20 Gy and 40 Gy. Then, features were ranked by the Gini Index during training of a random forest model. Finally, the area under the receiver operating characteristic curve (AUC) was estimated using a bootstrapped logistic regression with the top two features. We identified 22 patients for analysis. The median dose delivered was 50 Gy in 5 fractions. The top two features identified after delivery of BED 20 Gy were gray level co-occurrence matrix features energy and gray level size zone matrix based large zone emphasis. The model generated an AUC = 0.9011 (0.752-1.0) during bootstrapped logistic regression. The same two features were selected after delivery of a BED 40 Gy, with an AUC = 0.716 (0.600-0.786). Delta-radiomic features after a single fraction of SBRT predicted local control in this exploratory cohort. If confirmed in larger studies, these features may identify patients with radioresistant disease and provide an opportunity for physicians to alter management much sooner than standard restaging after 3 months. Expansion of the patient database is warranted for further analysis of delta-radiomic features.
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Li WZ, Wu G, Li TS, Dai GM, Liao YT, Yang QY, Chen F, Huang WY. Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma. Front Oncol 2022; 12:955866. [PMID: 36338711 PMCID: PMC9627984 DOI: 10.3389/fonc.2022.955866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 09/05/2022] [Indexed: 08/30/2023] Open
Abstract
To establish a multidimensional nomogram model for predicting progression-free survival (PFS) and risk stratification in patients with advanced nasopharyngeal carcinoma (NPC). This retrospective cross-sectional study included 156 patients with advanced NPC who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Radiomic features were extracted from the efflux rate constant (Ktrans ) and extracellular extravascular volume (Ve ) mapping derived from DCE-MRI. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied for feature selection. The Radscore was constructed using the selected features with their respective weights in the LASSO Cox regression analysis. A nomogram model combining the Radscore and clinical factors was built using multivariate Cox regression analysis. The C-index was used to assess the discrimination power of the Radscore and nomogram. The Kaplan-Meier method was used for survival analysis. Of the 360 radiomic features, 28 were selected (7, 6, and 15 features extracted from Ktrans , Ve, and Ktrans +Ve images, respectively). The combined Radscore k trans +Ve (C-index, 0.703, 95% confidence interval [CI]: 0.571-0.836) showed higher efficacy in predicting the prognosis of advanced NPC than Radscore k trans (C-index, 0.693; 95% CI, 0.560-0.826) and Radscore Ve (C-index, 0.614; 95% CI, 0.481-0.746) did. Multivariable Cox regression analysis revealed clinical stage, T stage, and treatment with nimotuzumab as risk factors for PFS. The nomogram established by Radscore k trans +Ve and risk factors (C-index, 0.732; 95% CI: 0.599-0.864) was better than Radscore k trans +Ve in predicting PFS in patients with advanced NPC. A lower Radscore k trans +Ve (HR 3.5584, 95% CI 2.1341-5.933), lower clinical stage (hazard ratio [HR] 1.5982, 95% CI 0.5262-4.854), lower T stage (HR 1.4365, 95% CI 0.6745-3.060), and nimotuzumab (NTZ) treatment (HR 0.7879, 95% CI 0.4899-1.267) were associated with longer PFS. Kaplan-Meier analysis showed a lower PFS in the high-risk group than in the low-risk group (p<0.0001). The nomogram based on combined pretreatment DCE-MRI radiomics features, NTZ, and clinicopathological risk factors may be considered as a noninvasive imaging marker for predicting individual PFS in patients with advanced NPC.
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Affiliation(s)
- Wen-zhu Li
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Tian-sheng Li
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Gan-mian Dai
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Yu-ting Liao
- Department of Pharmaceutical Diagnostics, GE Healthcare, Guangzhou, China
| | - Qian-yu Yang
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Feng Chen
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Wei-yuan Huang
- Department of Radiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China
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Jiang S, Han L, Liang L, Long L. Development and validation of an MRI-based radiomic model for predicting overall survival in nasopharyngeal carcinoma patients with local residual tumors after intensity-modulated radiotherapy. BMC Med Imaging 2022; 22:174. [PMID: 36195860 PMCID: PMC9533536 DOI: 10.1186/s12880-022-00902-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To investigate the potential value of the pretreatment MRI-based radiomic model in predicting the overall survival (OS) of nasopharyngeal carcinoma (NPC) patients with local residual tumors after intensity-modulated radiotherapy (IMRT). METHODS A total of 218 consecutive nonmetastatic NPC patients with local residual tumors after IMRT [training cohort (n = 173) and validation cohort (n = 45)] were retrospectively included in this study. Clinical and MRI data were obtained. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features from pretreatment MRI. The clinical, radiomic, and combined models for predicting OS were constructed. The models' performances were evaluated using Harrell's concordance index (C-index), calibration curve, and decision curve analysis. RESULTS The C-index of the radiomic model was higher than that of the clinical model, with the C-index of 0.788 (95% CI 0.724-0.852) versus 0.672 (95% CI 0.599-0.745) in the training cohort and 0.753 (95% CI 0.604-0.902) versus 0.634 (95% CI 0.593-0.675) in the validation cohort. Calibration curves showed good agreement between the radiomic model-predicted probability of 2- and 3-year OS and the actual observed probability in the training and validation groups. Decision curve analysis showed that the radiomic model had higher clinical usefulness than the clinical model. The discrimination of the combined model improved significantly in the training cohort (P < 0.01) but not in the validation cohort, with the C-index of 0.834 and 0.734, respectively. The radiomic model divided patients into high- and low-risk groups with a significant difference in OS in both the training and validation cohorts. CONCLUSIONS Pretreatment MRI-based radiomic model may improve OS prediction in NPC patients with local residual tumors after IMRT and may assist in clinical decision-making.
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Affiliation(s)
- Shengping Jiang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, China
| | - Lin Han
- Department of Rehabilitation Medicine, The First People's Hospital of Yulin, No. 495 Jiaoyu Road, Yulin, 537000, China
| | - Leifeng Liang
- Department of Radiation Oncology, The First People's Hospital of Yulin, No. 495 Jiaoyu Road, Yulin, 537000, China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, China.
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China.
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Liu L, Pei W, Liao H, Wang Q, Gu D, Liu L, Su D, Jin G. A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma. Front Oncol 2022; 12:792535. [PMID: 35814380 PMCID: PMC9256909 DOI: 10.3389/fonc.2022.792535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThis paper aimed to establish and verify a radiomics model based on magnetic resonance imaging (MRI) for predicting the progression-free survival of nasopharyngeal carcinoma (NPC) after induction chemotherapy (IC).Materials and MethodsThis cohort consists of 288 patients with clinical pathologically confirmed NPC, which was collected from January 2015 to December 2018. All NPC patients were randomly divided into two cohorts: training (n=202) and validation (n=86). Radiomics features from the MRI images of NPC patients were extracted and selected before IC. The patients were classified into high- and low-risk groups according to the median of Radscores. The significant imaging features and clinical variables in the univariate analysis were constructed for progression-free survival (PFS) using the multivariate Cox regression model. A survival analysis was performed using Kaplan–Meier with log-rank test and then each model’s stratification ability was evaluated.ResultsEpstein–Barr virus (EBV) DNA before treatment was an independent predictor for PFS (p < 0.05). Based on the pyradiomic platform, we extracted 1,316 texture parameters in total. Finally, 16 texture features were used to build the model. The clinical radiomics-based model had good prediction capability for PFS, with a C-index of 0.827. The survival curve revealed that the PFS of the high-risk group was poorer than that of the low-risk group.ConclusionThis research presents a nomogram that merges the radiomics signature and the clinical feature of the plasma EBV DNA load, which may improve the ability of preoperative prediction of progression-free survival and facilitate individualization of treatment in NPC patients before IC.
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Affiliation(s)
- Lu Liu
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Wei Pei
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Hai Liao
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Qiang Wang
- Department of Anesthesiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Donglian Gu
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Lijuan Liu
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Danke Su
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Guanqiao Jin
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
- *Correspondence: Guanqiao Jin,
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Yang C, Jiang Z, Cheng T, Zhou R, Wang G, Jing D, Bo L, Huang P, Wang J, Zhang D, Jiang J, Wang X, Lu H, Zhang Z, Li D. Radiomics for Predicting Response of Neoadjuvant Chemotherapy in Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:893103. [PMID: 35600395 PMCID: PMC9121398 DOI: 10.3389/fonc.2022.893103] [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: 03/10/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose This study examined the methodological quality of radiomics to predict the effectiveness of neoadjuvant chemotherapy in nasopharyngeal carcinoma (NPC). We performed a meta-analysis of radiomics studies evaluating the bias risk and treatment response estimation. Methods Our study was conducted through a literature review as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We included radiomics-related papers, published prior to January 31, 2022, in our analysis to examine the effectiveness of neoadjuvant chemotherapy in NPC. The methodological quality was assessed using the radiomics quality score. The intra-class correlation coefficient (ICC) was employed to evaluate inter-reader reproducibility. The pooled area under the curve (AUC), pooled sensitivity, and pooled specificity were used to assess the ability of radiomics to predict response to neoadjuvant chemotherapy in NPC. Lastly, the Quality Assessment of Diagnostic Accuracy Studies technique was used to analyze the bias risk. Results A total of 12 studies were eligible for our systematic review, and 6 papers were included in our meta-analysis. The radiomics quality score was set from 7 to 21 (maximum score: 36). There was satisfactory ICC (ICC = 0.987, 95% CI: 0.957–0.996). The pooled sensitivity and specificity were 0.88 (95% CI: 0.71–0.95) and 0.82 (95% CI: 0.68–0.91), respectively. The overall AUC was 0.91 (95% CI: 0.88–0.93). Conclusion Prediction response of neoadjuvant chemotherapy in NPC using machine learning and radiomics is beneficial in improving standardization and methodological quality before applying it to clinical practice.
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Affiliation(s)
- Chao Yang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Zekun Jiang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Tingting Cheng
- Department of General Practice, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Rongrong Zhou
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Guangcan Wang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Di Jing
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Linlin Bo
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Pu Huang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Jianbo Wang
- Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Daizhou Zhang
- Shandong Provincial Key Laboratory of Mucosal and Transdermal Drug Delivery Technologies, Shandong Academy of Pharmaceutical Sciences, Jinan, China
| | - Jianwei Jiang
- Optical and Digital Image Processing Division, Qingdao NovelBeam Technology Co., Ltd., Qingdao, China
| | - Xing Wang
- Software Research and Development Center, Shangdong AccurDx Diagnosis of Biotech Co., Ltd., Jinan, China
| | - Hua Lu
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Zijian Zhang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
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45
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Jha AK, Mithun S, Purandare NC, Kumar R, Rangarajan V, Wee L, Dekker A. Radiomics: a quantitative imaging biomarker in precision oncology. Nucl Med Commun 2022; 43:483-493. [PMID: 35131965 DOI: 10.1097/mnm.0000000000001543] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Cancer treatment is heading towards precision medicine driven by genetic and biochemical markers. Various genetic and biochemical markers are utilized to render personalized treatment in cancer. In the last decade, noninvasive imaging biomarkers have also been developed to assist personalized decision support systems in oncology. The imaging biomarkers i.e., radiomics is being researched to develop specific digital phenotype of tumor in cancer. Radiomics is a process to extract high throughput data from medical images by using advanced mathematical and statistical algorithms. The radiomics process involves various steps i.e., image generation, segmentation of region of interest (e.g. a tumor), image preprocessing, radiomic feature extraction, feature analysis and selection and finally prediction model development. Radiomics process explores the heterogeneity, irregularity and size parameters of the tumor to calculate thousands of advanced features. Our study investigates the role of radiomics in precision oncology. Radiomics research has witnessed a rapid growth in the last decade with several studies published that show the potential of radiomics in diagnosis and treatment outcome prediction in oncology. Several radiomics based prediction models have been developed and reported in the literature to predict various prediction endpoints i.e., overall survival, progression-free survival and recurrence in various cancer i.e., brain tumor, head and neck cancer, lung cancer and several other cancer types. Radiomics based digital phenotypes have shown promising results in diagnosis and treatment outcome prediction in oncology. In the coming years, radiomics is going to play a significant role in precision oncology.
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Affiliation(s)
- Ashish Kumar Jha
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Sneha Mithun
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Nilendu C Purandare
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Rakesh Kumar
- Department of Nuclear Medicine, All India Institute of Medical Science, New Delhi, India
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital
- Homi Bhabha National Institute (HBNI), Deemed University, Mumbai
| | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands
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46
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Bologna M, Calareso G, Resteghini C, Sdao S, Montin E, Corino V, Mainardi L, Licitra L, Bossi P. Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer. NMR IN BIOMEDICINE 2022; 35:e4265. [PMID: 32009265 DOI: 10.1002/nbm.4265] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 01/13/2020] [Accepted: 01/15/2020] [Indexed: 06/10/2023]
Abstract
In this paper, several radiomics-based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs) are built and tested. Models were built as a combination of radiomic features extracted from three types of MRI images: T1-weighted images, T2-weighted images and apparent diffusion coefficient (ADC) maps. Fifty patients (aged 54 ± 12 years, 41 men) were included in this study. Patients were classified according to their response to IC (25 responders and 25 nonresponders). Not all types of images were acquired for all of the patients: 49 had T1-weighted images, 50 had T2-weighted images and 34 had ADC maps. Only in a subset of 33 patients were all three types of image acquired. Eighty-nine radiomic features were extracted from the MRI images. Dimensionality reduction was performed by using principal component analysis (PCA) and by selecting only the three main components. Different algorithms (trees ensemble, K-nearest neighbors, support vector machine, naïve Bayes) were used to classify the patients as either responders or nonresponders. Several radiomic models (either monomodality or multimodality obtained by a combination of T1-weighted, T2-weighted and ADC images) were developed and the performance was assessed through 100 iterations of train and test split. The area under the curve (AUC) of the models ranged from 0.56 to 0.78. Trees ensemble, support vector machine and naïve Bayes performed similarly, but in all cases ADC-based models performed better. Trees ensemble gave the highest AUC (0.78 for the T1-weighted+T2-weighted+ADC model) and was used for further analyses. For trees ensemble, the models based on ADC features performed better than those models that did not use those features (P < 0.02 for one-tail Hanley test, AUC range 0.68-0.78 vs 0.56-0.69) except the T1-weighted+ADC model (AUC 0.71 vs 0.69, nonsignificant differences). The results suggest the relevance of ADC-based radiomics for prediction of response to IC in SNCs.
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Affiliation(s)
- Marco Bologna
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Giuseppina Calareso
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Carlo Resteghini
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Silvana Sdao
- Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Eros Montin
- Center for Advanced Imaging Innovation and Research (CAI2R), and the Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Lisa Licitra
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
- Department of Oncology and Hemato-oncology, University of Milan, Milan, Italy
| | - Paolo Bossi
- Head and Neck Medical Oncology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
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Pham N, Ju C, Kong T, Mukherji SK. Artificial Intelligence in Head and Neck Imaging. Semin Ultrasound CT MR 2022; 43:170-175. [PMID: 35339257 DOI: 10.1053/j.sult.2022.02.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Artificial intelligence (AI) can be applied to head and neck imaging to augment image quality and various clinical tasks including segmentation of tumor volumes, tumor characterization, tumor prognostication and treatment response, and prediction of metastatic lymph node disease. Head and neck oncology care is well positioned for the application of AI since treatment is guided by a wealth of information derived from CT, MRI, and PET imaging data. AI-based methods can integrate complex imaging, histologic, molecular, and clinical data to model tumor biology and behavior, and potentially identify associations, far beyond what conventional qualitative imaging can provide alone.
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Affiliation(s)
- Nancy Pham
- Neuroradiology, Radiology Department, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA; Neuroradiology, Radiology Department, University of Illinois.
| | - Connie Ju
- Neuroradiology, Radiology Department, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA
| | - Tracie Kong
- Neuroradiology, Radiology Department, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA
| | - Suresh K Mukherji
- Neuroradiology, Radiology Department, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA
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Li HJ, Liu LZ, Huang Y, Jin YB, Chen XP, Luo W, Su JC, Chen K, Zhang J, Zhang GY. Establishment and Validation of a Novel MRI Radiomics Feature-Based Prognostic Model to Predict Distant Metastasis in Endemic Nasopharyngeal Carcinoma. Front Oncol 2022; 12:794975. [PMID: 35402262 PMCID: PMC8983880 DOI: 10.3389/fonc.2022.794975] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/23/2022] [Indexed: 01/12/2023] Open
Abstract
PurposeWe aimed to establish a prognostic model based on magnetic resonance imaging (MRI) radiomics features for individual distant metastasis risk prediction in patients with nasopharyngeal carcinoma (NPC).MethodsRegression analysis was applied to select radiomics features from T1-weighted (T1-w), contrast-enhanced T1-weighted (T1C-w), and T2-weighted (T2-w) MRI scans. All prognostic models were established using a primary cohort of 518 patients with NPC. The prognostic ability of the radiomics, clinical (based on clinical factors), and merged prognostic models (integrating clinical factors with radiomics) were identified using a concordance index (C-index). Models were tested using a validation cohort of 260 NPC patients. Distant metastasis-free survival (DMFS) were calculated by using the Kaplan-Meier method and compared by using the log-rank test.ResultsIn the primary cohort, seven radiomics prognostic models showed similar discrimination ability for DMFS to the clinical prognostic model (P=0.070-0.708), while seven merged prognostic models displayed better discrimination ability than the clinical prognostic model or corresponding radiomics prognostic models (all P<0.001). In the validation cohort, the C-indices of seven radiomics prognostic models (0.645-0.722) for DMFS prediction were higher than in the clinical prognostic model (0.552) (P=0.016 or <0.001) or in corresponding merged prognostic models (0.605-0.678) (P=0.297 to 0.857), with T1+T1C prognostic model (based on Radscore combinations of T1 and T1C Radiomics models) showing the highest C-index (0.722). In the decision curve analysis of the validation cohort for all prognostic models, the T1+T1C prognostic model displayed the best performance.ConclusionsRadiomics models, especially the T1+T1C prognostic model, provided better prognostic ability for DMFS in patients with NPC.
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Affiliation(s)
- Hao-Jiang Li
- Department of Radiology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Li-Zhi Liu
- Department of Radiology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ying Huang
- Department of Radiation Oncology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ya-Bin Jin
- Clinical Research Institute, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Xiang-Ping Chen
- Clinical Research Institute, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Wei Luo
- Clinical Research Institute, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Jian-Chun Su
- Department of Radiation Oncology, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Kai Chen
- Department of Radiation Oncology, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Jing Zhang
- Department of Radiation Oncology, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Guo-Yi Zhang
- Department of Radiation Oncology, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
- *Correspondence: Guo-Yi Zhang,
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49
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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: 22] [Impact Index Per Article: 7.3] [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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50
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Lam SK, Zhang Y, Zhang J, Li B, Sun JC, Liu CYT, Chou PH, Teng X, Ma ZR, Ni RY, Zhou T, Peng T, Xiao HN, Li T, Ren G, Cheung ALY, Lee FKH, Yip CWY, Au KH, Lee VHF, Chang ATY, Chan LWC, Cai J. Multi-Organ Omics-Based Prediction for Adaptive Radiation Therapy Eligibility in Nasopharyngeal Carcinoma Patients Undergoing Concurrent Chemoradiotherapy. Front Oncol 2022; 11:792024. [PMID: 35174068 PMCID: PMC8842229 DOI: 10.3389/fonc.2021.792024] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 12/01/2021] [Indexed: 12/18/2022] Open
Abstract
Purpose To investigate the role of different multi-organ omics-based prediction models for pre-treatment prediction of Adaptive Radiotherapy (ART) eligibility in patients with nasopharyngeal carcinoma (NPC). Methods and Materials Pre-treatment contrast-enhanced computed tomographic and magnetic resonance images, radiotherapy dose and contour data of 135 NPC patients treated at Hong Kong Queen Elizabeth Hospital were retrospectively analyzed for extraction of multi-omics features, namely Radiomics (R), Morphology (M), Dosiomics (D), and Contouromics (C), from a total of eight organ structures. During model development, patient cohort was divided into a training set and a hold-out test set in a ratio of 7 to 3 via 20 iterations. Four single-omics models (R, M, D, C) and four multi-omics models (RD, RC, RM, RMDC) were developed on the training data using Ridge and Multi-Kernel Learning (MKL) algorithm, respectively, under 10-fold cross validation, and evaluated on hold-out test data using average area under the receiver-operator-characteristics curve (AUC). The best-performing single-omics model was first determined by comparing the AUC distribution across the 20 iterations among the four single-omics models using two-sided student t-test, which was then retrained using MKL algorithm for a fair comparison with the four multi-omics models. Results The R model significantly outperformed all other three single-omics models (all p-value<0.0001), achieving an average AUC of 0.942 (95%CI: 0.938-0.946) and 0.918 (95%CI: 0.903-0.933) in training and hold-out test set, respectively. When trained with MKL, the R model (R_MKL) yielded an increased AUC of 0.984 (95%CI: 0.981-0.988) and 0.927 (95%CI: 0.905-0.948) in training and hold-out test set respectively, while demonstrating no significant difference as compared to all studied multi-omics models in the hold-out test sets. Intriguingly, Radiomic features accounted for the majority of the final selected features, ranging from 64% to 94%, in all the studied multi-omics models. Conclusions Among all the studied models, the Radiomic model was found to play a dominant role for ART eligibility in NPC patients, and Radiomic features accounted for the largest proportion of features in all the multi-omics models.
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Affiliation(s)
- Sai-Kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Yuanpeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jia-Chen Sun
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Carol Yee-Tung Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Pak-Hei Chou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Zong-Rui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Rui-Yan Ni
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Tao Peng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Hao-Nan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Andy Lai-Yin Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.,Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Francis Kar-Ho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China
| | - Celia Wai-Yi Yip
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China
| | - Kwok-Hung Au
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China
| | - Victor Ho-Fun Lee
- Department of Clinical Oncology, The University of Hong Kong5Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong SAR, China
| | - Amy Tien-Yee Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong SAR, China
| | - Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
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