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Cai W, Wu S, Lin Z, Ming X, Yang X, Yang M, Chen X. Hypoxia-induced BAP1 enhances erastin-induced ferroptosis in nasopharyngeal carcinoma by stabilizing H2A. Cancer Cell Int 2024; 24:307. [PMID: 39245739 DOI: 10.1186/s12935-024-03494-z] [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: 06/26/2023] [Accepted: 08/30/2024] [Indexed: 09/10/2024] Open
Abstract
BACKGROUND Hypoxia plays an important role in the chemotherapy resistance of nasopharyngeal carcinoma (NPC). Ferroptosis is a newly discovered form of programmed cell death and ferroptosis inducers showed promising therapeutic effects in some cancers. However, the sensibility of NPC cells to ferroptosis under the hypoxic microenvironment is still unclear, and this study was designed to clarify it. METHODS NPC cells, treated with erastin, were placed in a normoxia or hypoxic environment (5% CO2, 94% N2 and 1% O2) at 37℃for 24 h. After exposed to hypoxia, ferroptosis-associated phenotypes were detected by CCK8, MDA, GSH, lipid ROS and Fe. The gene expression profiles of head and neck squamous cell carcinoma (HNSCC) tissues were downloaded from the TCGA database to screen construction molecule. BAP1 was screened out and its functions on erastin-induced ferroptosis in NPC cells were detected by knockdown of BAP1. Luciferase reporter assay and co-IP experiment were performed to explore the molecular mechanism. Finally, the tumour xenograft model was applied to further verify these results in vivo. RESULTS CCK8 assay showed that IC50 of NPC cells treated with erastin under hypoxia was significantly lower than that under normoxia. Hypoxia significantly increased the levels of lipid ROS and MDA, and decreased GSH content induced by erastin. A prognostic risk model for HNSCC with six ferroptosis-related genes was constructed and validated based on TCGA database. BAP1 was significantly up-regulated under hypoxia, and luciferase reporter assay showed that HIF-1α was an upstream transcription regulator of BAP1. Knockdown of BAP1 in NPC cells significantly increased the IC50 value of erastin under hypoxia and significantly ameliorated erastin-induced ferroptosis under hypoxia in aspect of lipid ROS, MDA content and GSH. Co-IP results showed that BAP1 mediated deubiquitination of H2A and decreased SLC7A11 expression. Finally, knockdown of BAP1 reduced sensitivity to erastin-induced ferroptosis in a tumour xenograft model. And the level of H2A was significantly decreased in xenograft tumors of BAP1 knockdown cells. CONCLUSION Hypoxia-induced BAP1 enhances erastin-induced ferroptosis in NPC by stabilizing H2A. Ferroptosis inducers targeting BAP1 may be an effective way to improve chemotherapy resistance in NPC, especially in the hypoxic microenvironment.
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Affiliation(s)
- Weisong Cai
- Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Sa Wu
- Department of Gynaecology II, Maternal and Child Health Hospital of Hubei Province, TongjiMedical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zehua Lin
- Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiaoping Ming
- Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Xiuping Yang
- Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Minlan Yang
- Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Xiong Chen
- Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
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Wang CK, Wang TW, Lu CF, Wu YT, Hua MW. Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis. Diagnostics (Basel) 2024; 14:924. [PMID: 38732337 PMCID: PMC11082984 DOI: 10.3390/diagnostics14090924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/12/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.
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Affiliation(s)
- Chih-Keng Wang
- School of Medicine, College of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Taichung Veterans General Hospital, Taichung 407219, Taiwan
| | - Ting-Wei Wang
- School of Medicine, College of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan
| | - Chia-Fung Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan;
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan
| | - Man-Wei Hua
- Department of Otolaryngology-Head and Neck Surgery, Taichung Veterans General Hospital, Taichung 407219, Taiwan
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Zeng F, Ye Z, Zhou Q. CT-based peritumoral radiomics nomogram on prediction of response and survival to induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma. J Cancer Res Clin Oncol 2024; 150:50. [PMID: 38286865 PMCID: PMC10824876 DOI: 10.1007/s00432-023-05590-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/22/2023] [Indexed: 01/31/2024]
Abstract
PURPOSE The study aims to harness the value of radiomics models combining intratumoral and peritumoral features obtained from pretreatment CT to predict treatment response as well as the survival of LA-NPC(locoregionally advanced nasopharyngeal carcinoma) patients receiving multiple types of induction chemotherapies, including immunotherapy and targeted therapy. METHODS 276 LA-NPC patients (221 in the training and 55 in the testing cohort) were retrospectively enrolled. Various statistical analyses and feature selection techniques were applied to identify the most relevant radiomics features. Multiple machine learning models were trained and compared to build signatures for the intratumoral and each peritumoral region, along with a clinical signature. The performance of each model was evaluated using different metrics. Subsequently, a nomogram model was constructed by combining the best-performing radiomics and clinical models. RESULTS In the testing cohort, the nomogram model exhibited an AUC of 0.816, outperforming the other models. The nomogram model's calibration curve showed good agreement between predicted and observed outcomes in both the training and testing sets. When predicting survival, the model's concordance index (C-index) was 0.888 in the training cohort and 0.899 in the testing cohort, indicating its robust predictive ability. CONCLUSION In conclusion, the combined nomogram model, incorporating radiomics and clinical features, outperformed other models in predicting treatment response and survival outcomes for LA-NPC patients receiving induction chemotherapies. These findings highlight the potential clinical utility of the model, suggesting its value in individualized treatment planning and decision-making.
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Affiliation(s)
- Fanyuan Zeng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Zhuomiao Ye
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Translational Medicine Research Center (TMRC), School of Medicine, Chongqing University, Shapingba, Chongqing, 400044, China
| | - Qin Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
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Wu Q, Chang Y, Yang C, Liu H, Chen F, Dong H, Chen C, Luo Q. Adjuvant chemotherapy or no adjuvant chemotherapy? A prediction model for the risk stratification of recurrence or metastasis of nasopharyngeal carcinoma combining MRI radiomics with clinical factors. PLoS One 2023; 18:e0287031. [PMID: 37751422 PMCID: PMC10522047 DOI: 10.1371/journal.pone.0287031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 05/28/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND Dose adjuvant chemotherapy (AC) should be offered in nasopharyngeal carcinoma (NPC) patients? Different guidelines provided the different recommendations. METHODS In this retrospective study, a total of 140 patients were enrolled and followed for 3 years, with 24 clinical features being collected. The imaging features on the enhanced-MRI sequence were extracted by using PyRadiomics platform. The pearson correlation coefficient and the random forest was used to filter the features associated with recurrence or metastasis. A clinical-radiomics model (CRM) was constructed by the Cox multivariable analysis in training cohort, and was validated in validation cohort. All patients were divided into high- and low-risk groups through the median Rad-score of the model. The Kaplan-Meier survival curves were used to compare the 3-year recurrence or metastasis free rate (RMFR) of patients with or without AC in high- and low-groups. RESULTS In total, 960 imaging features were extracted. A CRM was constructed from nine features (seven imaging features and two clinical factors). In the training cohort, the area under curve (AUC) of CRM for 3-year RMFR was 0.872 (P <0.001), and the sensitivity and specificity were 0.935 and 0.672, respectively; In the validation cohort, the AUC was 0.864 (P <0.001), and the sensitivity and specificity were 1.00 and 0.75, respectively. Kaplan-Meier curve showed that the 3-year RMFR and 3-year cancer specific survival (CSS) rate in the high-risk group were significantly lower than those in the low-risk group (P <0.001). In the high-risk group, patients who received AC had greater 3-year RMFR than those who did not receive AC (78.6% vs. 48.1%) (p = 0.03). CONCLUSION Considering increasing RMFR, a prediction model for NPC based on two clinical factors and seven imaging features suggested the AC needs to be added to patients in the high-risk group and not in the low-risk group.
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Affiliation(s)
- Qiaoyuan Wu
- The Public Experimental Center of Medicine, Department of Pathology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, P. R. China
| | - Yonghu Chang
- School of Medical Information Engineering of Zunyi Medical University, Zunyi Medical University, Zunyi, Guizhou, P. R. China
| | - Cheng Yang
- The Third Clinical Medical College of Ningxia Medical University, Yinchuan, Ningxia, P. R. China
| | - Heng Liu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, P. R. China
| | - Fang Chen
- The Public Experimental Center of Medicine, Department of Pathology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, P. R. China
| | - Hui Dong
- The Public Experimental Center of Medicine, Department of Pathology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, P. R. China
| | - Cheng Chen
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, P.R. China
| | - Qing Luo
- The Public Experimental Center of Medicine, Department of Pathology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, P. R. China
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Li M, Qin H, Yu X, Sun J, Xu X, You Y, Ma C, Yang L. Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map. Insights Imaging 2023; 14:125. [PMID: 37454355 DOI: 10.1186/s13244-023-01477-8] [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: 02/20/2023] [Accepted: 07/03/2023] [Indexed: 07/18/2023] Open
Abstract
OBJECTIVE To investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer. METHODS A retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance. RESULTS Gender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802). CONCLUSION The R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer. CRITICAL RELEVANCE STATEMENT The radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis.
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Affiliation(s)
- Min Li
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Hongtao Qin
- Department of Radiology and Nuclear Medicine, The First Hospital of Hebei Medical University, No. 89, Donggang Road, Shijiazhuang, 050031, Hebei Province, People's Republic of China
| | - Xianbo Yu
- Siemens Healthineers Ltd., 7, Wangjing Zhonghuan Nanlu, Beijing, 100102, People's Republic of China
| | - Junyi Sun
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Xiaosheng Xu
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Yang You
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Chongfei Ma
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Li Yang
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China.
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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: 3.0] [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: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 05/23/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND To establish and validate radiomic models combining intratumoral (Intra) and peritumoral (Peri) features obtained from pretreatment MRI for the prediction of treatment response of lymph node metastasis from nasopharyngeal cancer (NPC). METHODS One hundred forty-five NPC patients (102 in the training and 43 in the validation set) were retrospectively enrolled. Radiomic features were extracted from Intra and Peri regions on the metastatic cervical lymph node, and selected with the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was applied to build radiomic models. Sensitivity, specificity, accuracy, and the area under the curve (AUC) of receiver operating characteristics were employed to evaluate the predictive power of each model. RESULTS The AUCs of the radiomic model of Intra, Peri, Intra + Peri, and Clinical-radiomic were 0.910, 0.887, 0.934, and 0.941, respectively, in the training set and 0.737, 0.794, 0.774, and 0.783, respectively, in the validation set. There were no significant differences in prediction performance among the radiomic models in the training and validation sets (all P > 0.05). The calibration curve of the radiomic model of Peri demonstrated good agreement between prediction and observation in the training and validation sets. CONCLUSIONS The pretreatment MRI-based radiomics model may be useful in predicting the treatment response of metastatic lymph nodes of NPC. Besides, the generalization ability of the radiomic model of Peri was better than that of Intra and Intra + Peri.
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Affiliation(s)
- Hao Xu
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Ai Wang
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Chi Zhang
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
| | - Jieke Liu
- Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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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: 2.0] [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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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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10
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Nie T, Liu D, Ai S, He Y, Yang M, Chen J, Yuan Z, Liu Y. A radiomics nomogram analysis based on CT images and clinical features for preoperative Lauren classification in gastric cancer. Jpn J Radiol 2022; 41:401-408. [PMID: 36370327 DOI: 10.1007/s11604-022-01360-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 11/01/2022] [Indexed: 11/14/2022]
Abstract
PURPOSE To develop a combined radiomics nomogram based on computed tomography (CT) images and clinical features to preoperatively distinguish Lauren's diffuse-type gastric cancer (GC) from intestinal-type GC. METHODS Ninety-five patients with Lauren's intestinal or diffuse-type GC confirmed by postoperative pathology had their preoperative clinical information and dynamic contrast CT images retrospectively analyzed and were subdivided into training and test groups in a 7:3 ratio. To select the optimal features and construct the radiomic signatures, we extracted, filtered, and minimized the radiomic features from arterial phase (AP) and venous phase (VP) CT images. We constructed four models (clinical model, AP radiomics model, VP radiomics model, and radiomics-clinical model) to assess and compare their predictive performance between the intestinal- and diffuse-type GC. Receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), and the DeLong test were used for assessment and comparison. In this study, radiomic nomograms integrating combined radiomic signatures and clinical characteristics were developed. RESULTS Compared to the AP radiomics model, the VP radiomics model had better performance, with an AUC of 0.832 (95% confidence interval [CI], 0.735, 0.929) in the training cohort and 0.760 (95% CI 0.580, 0.940) in the test cohort. Among the combined models that assessed Lauren's type GC, the model including age and VP radiomics showed the best performance, with an AUC of 0.849 (95% CI 0.758, 0.940) in the training cohort and 0.793 (95% CI 0.629, 0.957) in the test cohort. CONCLUSIONS Nomogram incorporating radiomic signatures and clinical features effectively differentiated Lauren's diffuse-type from intestinal-type GC.
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Affiliation(s)
- Tingting Nie
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China
| | - Dan Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China
| | - Shuangquan Ai
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China
| | - Yaoyao He
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China
| | - Miao Yang
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China
| | - Jun Chen
- GE Healthiness, Shanghai, 200126, People's Republic of China
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China.
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China.
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11
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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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12
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Duan W, Xiong B, Tian T, Zou X, He Z, Zhang L. Radiomics in Nasopharyngeal Carcinoma. CLINICAL MEDICINE INSIGHTS: ONCOLOGY 2022; 16:11795549221079186. [PMID: 35237090 PMCID: PMC8883403 DOI: 10.1177/11795549221079186] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 01/20/2022] [Indexed: 12/24/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common head and neck malignancies, and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy alone, concurrent chemoradiotherapy, and induction chemotherapy combined with concurrent chemoradiotherapy can be used according to different grades. Treatment options and prognoses vary greatly depending on the grade of disease in the patients. Accurate grading and risk assessment are required. Recently, radiomics has combined a large amount of invisible high-dimensional information extracted from computed tomography, magnetic resonance imaging, or positron emission tomography with powerful computing capabilities of machine-learning algorithms, providing the possibility to achieve an accurate diagnosis and individualized treatment for cancer patients. As an effective tumor biomarker of NPC, the radiomic signature has been widely used in grading, differential diagnosis, prediction of prognosis, evaluation of treatment response, and early identification of therapeutic complications. The process of radiomic research includes image segmentation, feature extraction, feature selection, model establishment, and evaluation. Many open-source or commercial tools can be used to achieve these procedures. The development of machine-learning algorithms provides more possibilities for radiomics research. This review aimed to summarize the application of radiomics in NPC and introduce the basic process of radiomics research.
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Affiliation(s)
- Wenyue Duan
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Bingdi Xiong
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Ting Tian
- Department of Clinical Medicine, Chengdu Medical College, Chengdu, People's Republic of China
| | - Xinyun Zou
- College of Medicine, Southwest Jiaotong University, Chengdu, People's Republic of China
| | - Zhennan He
- Department of Clinical Medicine, Chengdu Medical College, Chengdu, People's Republic of China
| | - Ling Zhang
- Department of Oncology, People's Liberation Army The General Hospital of Western Theater Command, Chengdu, People's Republic of China
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13
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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: 8.5] [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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14
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Lam SK, Zhang J, Zhang YP, Li B, Ni RY, Zhou T, Peng T, Cheung ALY, Chau TC, Lee FKH, Yip CWY, Au KH, Lee VHF, Chang ATY, Chan LWC, Cai J. A Multi-Center Study of CT-Based Neck Nodal Radiomics for Predicting an Adaptive Radiotherapy Trigger of Ill-Fitted Thermoplastic Masks in Patients with Nasopharyngeal Carcinoma. Life (Basel) 2022; 12:life12020241. [PMID: 35207528 PMCID: PMC8876942 DOI: 10.3390/life12020241] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/02/2021] [Accepted: 12/11/2021] [Indexed: 11/30/2022] Open
Abstract
Significant lymph node shrinkage is common in patients with nasopharyngeal carcinoma (NPC) throughout radiotherapy (RT) treatment, causing ill-fitted thermoplastic masks (IfTMs). To deal with this, an ad hoc adaptive radiotherapy (ART) may be required to ensure accurate and safe radiation delivery and to maintain treatment efficacy. Presently, the entire procedure for evaluating an eligible ART candidate is time-consuming, resource-demanding, and highly inefficient. In the artificial intelligence paradigm, the pre-treatment identification of NPC patients at risk for IfTMs has become greatly demanding for achieving efficient ART eligibility screening, while no relevant studies have been reported. Hence, we aimed to investigate the capability of computed tomography (CT)-based neck nodal radiomics for predicting IfTM-triggered ART events in NPC patients via a multi-center setting. Contrast-enhanced CT and the clinical data of 124 and 58 NPC patients from Queen Elizabeth Hospital (QEH) and Queen Mary Hospital (QMH), respectively, were retrospectively analyzed. Radiomic (R), clinical (C), and combined (RC) models were developed using the ridge algorithm in the QEH cohort and evaluated in the QMH cohort using the median area under the receiver operating characteristics curve (AUC). Delong’s test was employed for model comparison. Model performance was further assessed on 1000 replicates in both cohorts separately via bootstrapping. The R model yielded the highest “corrected” AUC of 0.784 (BCa 95%CI: 0.673–0.859) and 0.723 (BCa 95%CI: 0.534–0.859) in the QEH and QMH cohort following bootstrapping, respectively. Delong’s test indicated that the R model performed significantly better than the C model in the QMH cohort (p < 0.0001), while demonstrating no significant difference compared to the RC model (p = 0.5773). To conclude, CT-based neck nodal radiomics was capable of predicting IfTM-triggered ART events in NPC patients in this multi-center study, outperforming the traditional clinical model. The findings of this study provide valuable insights for future study into developing an effective screening strategy for ART eligibility in NPC patients in the long run, ultimately alleviating the workload of clinical practitioners, streamlining ART procedural efficiency in clinics, and achieving personalized RT for NPC patients in the future.
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Affiliation(s)
- Sai-Kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Yuan-Peng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Rui-Yan Ni
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Tao Peng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Andy Lai-Yin Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China
| | - Tin-Ching Chau
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China; (T.-C.C.); (V.H.-F.L.)
| | - Francis Kar-Ho Lee
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China; (F.K.-H.L.); (C.W.-Y.Y.); (K.-H.A.)
| | - Celia Wai-Yi Yip
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China; (F.K.-H.L.); (C.W.-Y.Y.); (K.-H.A.)
| | - Kwok-Hung Au
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China; (F.K.-H.L.); (C.W.-Y.Y.); (K.-H.A.)
| | - Victor Ho-Fun Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China; (T.-C.C.); (V.H.-F.L.)
| | - Amy Tien-Yee Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Hong Kong, China;
| | - Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (S.-K.L.); (J.Z.); (Y.-P.Z.); (B.L.); (R.-Y.N.); (T.Z.); (T.P.); (A.L.-Y.C.); (L.W.-C.C.)
- Correspondence:
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15
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Zhang YM, Gong GZ, Qiu QT, Han YW, Lu HM, Yin Y. Radiomics for Diagnosis and Radiotherapy of Nasopharyngeal Carcinoma. Front Oncol 2022; 11:767134. [PMID: 35070971 PMCID: PMC8766636 DOI: 10.3389/fonc.2021.767134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is a malignant tumor of the head and neck. The primary clinical manifestations are nasal congestion, blood-stained nasal discharge, headache, and hearing loss. It occurs frequently in Southeast Asia, North Africa, and especially in southern China. Radiotherapy is the main treatment, and currently, imaging examinations used for the diagnosis, treatment, and prognosis of NPC include computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)-CT, and PET-MRI. These methods play an important role in target delineation, radiotherapy planning design, dose evaluation, and outcome prediction. However, the anatomical and metabolic information obtained at the macro level of images may not meet the increasing accuracy required for radiotherapy. As a technology used for mining deep image information, radiomics can provide further information for the diagnosis and treatment of NPC and promote individualized precision radiotherapy in the future. This paper reviews the application of radiomics in the diagnosis and treatment of nasopharyngeal carcinoma.
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Affiliation(s)
- Yu-Mei Zhang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Guan-Zhong Gong
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Qing-Tao Qiu
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yun-Wei Han
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - He-Ming Lu
- Department of Radiotherapy, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yong Yin
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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16
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Liao H, Chen X, Lu S, Jin G, Pei W, Li Y, Wei Y, Huang X, Wang C, Liang X, Bao H, Liu L, Su D. MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma. J Magn Reson Imaging 2021; 56:547-559. [PMID: 34970824 DOI: 10.1002/jmri.28047] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Pretreatment individualized assessment of tumor response to induction chemotherapy (ICT) is a need in locoregionally advanced nasopharyngeal carcinoma (LANPC). Imaging method plays vital role in tumor response assessment. However, powerful imaging method for ICT response prediction in LANPC is insufficient. PURPOSE To establish a robust model for predicting response to ICT in LANPC by comparing the performance of back propagation neural network (BPNN) model with logistic regression model. STUDY TYPE Retrospective. POPULATION A total of 286 LANPC patients were assigned to training (N = 200, 43.8 ± 10.9 years, 152 male) and testing (N = 86, 43.5 ± 11.3 years, 57 male) cohorts. FIELD STRENGTH/SEQUENCE T2 -weighted imaging, contrast enhanced-T1 -weighted imaging using fast spin echo sequences at 1.5 T scanner. ASSESSMENT Predictive clinical factors were selected by univariate and multivariate logistic models. Radiomic features were screened by interclass correlation coefficient, single-factor analysis, and the least absolute shrinkage selection operator (LASSO). Four models based on clinical factors (Modelclinic ), radiomics features (Modelradiomics ), and clinical factors + radiomics signatures using logistic (Modelcombined ), and BPNN (ModelBPNN ) methods were established, and model performances were compared. STATISTICAL TESTS Student's t-test, Mann-Whitney U-test, and Chi-square test or Fisher's exact test were used for comparison analysis. The performance of models was assessed by area under the receiver operating characteristic (ROC) curve (AUC) and Delong test. P < 0.05 was considered statistical significance. RESULTS Three significant clinical factors: Epstein-Barr virus-DNA (odds ratio [OR] = 1.748; 95% confidence interval [CI], 0.969-3.171), sex (OR = 2.883; 95% CI, 1.364-6.745), and T stage (OR = 1.853; 95% CI, 1.201-3.052) were identified via univariate and multivariate logistic models. Twenty-four radiomics features were associated with treatment response. ModelBPNN demonstrated the highest performance among Modelcombined , Modelradiomics , and Modelclinic (AUC of training cohort: 0.917 vs. 0.808 vs. 0.795 vs. 0.707; testing cohort: 0.897 vs. 0.755 vs. 0.698 vs. 0.695). CONCLUSION A machine-learning approach using BPNN showed better ability than logistic regression model to predict tumor response to ICT in LANPC. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Hai Liao
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Xiaobo Chen
- Department of Radiology, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.,Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Shaolu Lu
- Department of Radiology, Wuzhou Red Cross Hospital, Wuzhou, Guangxi, China
| | - Guanqiao Jin
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Wei Pei
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Ye Li
- Department of Radiotherapy, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Yunyun Wei
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Xia Huang
- Department of Radiology, Wuzhou Red Cross Hospital, Wuzhou, Guangxi, China
| | - Chenghuan Wang
- Department of Radiology, Wuzhou Red Cross Hospital, Wuzhou, Guangxi, China
| | - Xueli Liang
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Huayan Bao
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Lidong Liu
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Danke Su
- Department of Medical Imaging Center, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
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Jansen JFA, Drenthen GS. Editorial for "MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma". J Magn Reson Imaging 2021; 56:560-561. [PMID: 34962010 DOI: 10.1002/jmri.28049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 12/16/2021] [Indexed: 11/10/2022] Open
Affiliation(s)
- Jacobus F A Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Gerhard S Drenthen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands
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Yang Y, Wang M, Qiu K, Wang Y, Ma X. Computed tomography-based deep-learning prediction of induction chemotherapy treatment response in locally advanced nasopharyngeal carcinoma. Strahlenther Onkol 2021; 198:183-193. [PMID: 34817635 DOI: 10.1007/s00066-021-01874-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/07/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND Deep learning methods have great potential to predict treatment response. The objective of this study was to evaluate and validate the predictive performance of the computed tomography (CT)-based model using deep learning features for identification of responders and nonresponders to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS All eligible patients were included retrospectively between January 2012 and December 2018, and assigned to the training (n = 208) or the testing cohort (n = 89). We extracted deep learning features of six pretrained convolutional neural networks (CNNs) via transfer learning method, and handcrafted radiomics features manually. Support vector machine (SVM) was adopted as the classifier. All predictive models were evaluated using the area under the receiver operating characteristics curve (AUC), by which an optimal model was selected. We also built clinical and clinical-radiological models for comparison. RESULTS The model with features extracted from ResNet50 (RN-SVM) had optimal performance among all models with features extracted from pretrained CNNs with an AUC of 0.811, accuracy of 68.54%, sensitivity of 61.54%, specificity of 87.50%, positive predictive value (PPV) of 93.02%, and negative predictive value (NPV) of 45.65% in the testing cohort. The handcrafted radiomics model was slightly inferior to the RN-SVM model with an AUC of 0.663 and accuracy of 60.67% in the testing cohort. All the imaging-derived models had better predictive performance than the clinical model. CONCLUSION The noninvasive deep learning method could provide efficient prediction of treatment response to IC in locally advanced NPC and might be a practicable approach in therapeutic strategy decision-making.
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Affiliation(s)
- Yuhan Yang
- West China School of Medicine, Sichuan University, No. 17 People's South Road, 610041, Chengdu, Sichuan, China
| | - Manni Wang
- West China Hospital, Sichuan University, Guoxue Road 37, 610041, Chengdu, China
| | - Ke Qiu
- West China School of Medicine, Sichuan University, No. 17 People's South Road, 610041, Chengdu, Sichuan, China
| | - Yixi Wang
- West China School of Medicine, Sichuan University, No. 17 People's South Road, 610041, Chengdu, Sichuan, China
| | - Xuelei Ma
- Department of Biotherapy and Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Guoxue Road 37, 610041, Chengdu, China.
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Zhu L, Ouyang T, Xiong Y, Ba L, Li Q, Qiu M, Zou Z, Peng G. Prognostic Value of Plasma Epstein-Barr Virus DNA Levels Pre- and Post-Neoadjuvant Chemotherapy in Patients With Nasopharyngeal Carcinoma. Front Oncol 2021; 11:714433. [PMID: 34707987 PMCID: PMC8543894 DOI: 10.3389/fonc.2021.714433] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 08/09/2021] [Indexed: 01/14/2023] Open
Abstract
Background In this study, we evaluated the prognostic value of the plasma levels of Epstein-Barr virus (EBV) DNA in patients with nasopharyngeal carcinoma (NPC) at different treatment stages. Methods We retrospectively analyzed the Data of 206 patients with NPC. Pre-neoadjuvant chemotherapy (pre-NACT), post-NACT, post-radiotherapy, and post-treatment plasma EBV DNA levels were used to establish prognostic nomograms. The concordance index (C-index) and calibration curves were used to compare the prognostic accuracy of the nomograms. The results were confirmed in a validation cohort consisting of patients who were tested for EBV DNA levels at all four stages of treatment. The Kaplan-Meier method was used to calculate the progression-free survival (PFS) and overall survival (OS). Survival differences were calculated using the log-rank test. Results EBV DNA-positive patients had worse 3-year PFS and 5-year OS than EBV DNA-negative patients; this was true for pre-NACT (PFS: 82.7% vs. 57.3%, P < 0.001; OS: 90.9% vs. 68.7%, P = 0.08) and post-NACT (PFS: 85.0% vs. 50.6%, P < 0.001; OS: 91.7% vs. 65.7%; P = 0.001) EBV DNA levels but not for post-radiotherapy (PFS: 72.2% vs. 60.9%, P = 0.192; OS: 73.1% vs. 77.2%, P = 0.472) or post-treatment (PFS: 77.3% vs. 59.2%, P = 0.063; OS: 77.5% vs. 79.7%, P = 0.644) levels. Nomograms combining pre-NACT and post-NACT EBV DNA levels had a superior prognostic ability than those of post-radiotherapy and post-treatment EBV DNA levels. Conclusion Pre-NACT EBV DNA levels combined with post-NACT EBV DNA levels can more reliably predict survival outcomes in patients with NPC.
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Affiliation(s)
- Lisheng Zhu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Ouyang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Xiong
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li Ba
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiuting Li
- Division of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mengjun Qiu
- Division of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhenwei Zou
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Gang Peng
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics (Basel) 2021; 11:1523. [PMID: 34573865 PMCID: PMC8465998 DOI: 10.3390/diagnostics11091523] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 08/10/2021] [Accepted: 08/19/2021] [Indexed: 12/23/2022] Open
Abstract
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.
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Affiliation(s)
- Song Li
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Yu-Qin Deng
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Zhi-Ling Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China;
| | - Hong-Li Hua
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
| | - Ze-Zhang Tao
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan 430060, China; (S.L.); (Y.-Q.D.); (H.-L.H.)
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21
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Jiang YT, Chen KH, Yang J, Liang ZG, Qu S, Li L, Zhu XD. Establishment of a Prognostic Nomogram for Patients With Locoregionally Advanced Nasopharyngeal Carcinoma Incorporating TNM Stage, Post-Induction Chemotherapy Tumor Volume and Epstein-Barr Virus DNA Load. Front Oncol 2021; 11:683475. [PMID: 34222003 PMCID: PMC8242239 DOI: 10.3389/fonc.2021.683475] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 05/27/2021] [Indexed: 12/08/2022] Open
Abstract
Objectives To establish and validate an effective nomogram to predict clinical outcomes for patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC). Materials and Methods The clinicopathological parameters and follow-up information of 402 locoregionally advanced NPC patients (training cohort, n = 302; validation cohort, n = 100) were retrospectively enrolled. The nomogram was built with the important prognostic variables identified by Cox regression analysis. Overall survival (OS) and progression-free survival (PFS) were the primary and secondary endpoints, respectively. The predictive power and clinical utility of the nomogram were assessed using the Harrell concordance index (C-index), calibration curve, and decision curve analysis. We compared the eighth staging system model with the nomogram to analyze whether the model could improve the accuracy of prognosis Results Epstein–Barr virus (EBV) DNA load, the gross tumor volume (GTVnx), and cervical lymph node tumor volume (GTVnd) after induction chemotherapy were the independent predictors of OS and PFS. The calibration curves indicated superb agreement between the nomogram-predicted probabilities and observed actual probabilities of survival. The C-index and area under the receiver operator characteristic curve (AUC) of the nomogram integrating these significant factors and N stage, and TNM stage were higher than those of the eighth TNM system alone. In addition, the decision curve analyses demonstrated the clinical value and higher overall net benefit of the nomogram. High-risk groups identified by the nomogram had significantly poorer OS and PFS than the low-risk group (p < 0.05). Conclusions The multidimensional nomogram incorporating TNM stage, EBV DNA load, and tumor volume after induction chemotherapy led to a more precise prognostic prediction and could be helpful for stratifying risk and guiding treatment decisions in locoregionally advanced NPC patients who have undergone induction chemotherapy and concurrent chemoradiation.
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Affiliation(s)
- Yu-Ting Jiang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Kai-Hua Chen
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Jie Yang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Zhong-Guo Liang
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Song Qu
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Ling Li
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Xiao-Dong Zhu
- Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, China.,Department of Oncology, Affiliated Wuming Hospital of Guangxi Medical University, Nanning, China
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