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Liu Y, Bie F, Bai G, Huai Q, Li Y, Chen X, Zhou B, Gao S. Prognostic model based on B cell marker genes for NSCLC patients under neoadjuvant immunotherapy by integrated analysis of single-cell and bulk RNA-sequencing data. Clin Transl Oncol 2024; 26:2025-2036. [PMID: 38563846 DOI: 10.1007/s12094-024-03428-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 02/25/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Neoadjuvant immunotherapy has evolved as an effective option to treat non-small cell lung cancer (NSCLC). B cells play essential roles in the immune system as well as cancer progression. However, the repertoire of B cells and its association with clinical outcomes remains unclear in NSCLC patients receiving neoadjuvant immunotherapy. METHODS Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data for LUAD samples were accessed from the TCGA and GEO databases. LUAD-related B cell marker genes were confirmed based on comprehensive analysis of scRNA-seq data. We then constructed the B cell marker gene signature (BCMGS) and validated it. In addition, we evaluated the association of BCGMS with tumor immune microenvironment (TIME) characteristics. Furthermore, we validated the efficacy of BCGMS in a cohort of NSCLC patients receiving neoadjuvant immunotherapy. RESULTS A BCMGS was constructed based on the TCGA cohort and further validated in three independent GSE cohorts. In addition, the BCMGS was proven to be significantly associated with TIME characteristics. Moreover, a relatively higher risk score indicated poor clinical outcomes and a worse immune response among NSCLC patients receiving neoadjuvant immunotherapy. CONCLUSIONS We constructed an 18-gene prognostic signature derived from B cell marker genes based on scRNA-seq data, which had the potential to predict the prognosis and immune response of NSCLC patients receiving neoadjuvant immunotherapy.
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Affiliation(s)
- Yang Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fenglong Bie
- Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Guangyu Bai
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qilin Huai
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuan Li
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaowei Chen
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bolun Zhou
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shugeng Gao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Gao J, Nan Y, Liu G, Zhao S, Xiong H, Wang Y, Jin F. Nomogram for Predicting Efficacy and Prognosis After Chemotherapy for Advanced NSCLC. THE CLINICAL RESPIRATORY JOURNAL 2024; 18:e13815. [PMID: 39118382 PMCID: PMC11310410 DOI: 10.1111/crj.13815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 07/04/2024] [Accepted: 07/10/2024] [Indexed: 08/10/2024]
Abstract
PURPOSE One major issue is the therapeutic effect following chemotherapy for non-small cell lung cancer (NSCLC). Although numerous risk factors have been identified and novel therapies have been developed, improving patient overall survival (OS) remains a crucial postoperative issue. This study aimed to develop a nomogram for accurately predicting the OS of patients with Stage III-IV NSCLC treated with chemotherapy. METHODS The Department of Respiration at Tangdu Hospital, Air Force Medical University, prospectively collected data on 321 patients between January 2018 and December 2023. A week before treatment, the platelet-to-lymphocyte ratio (PLR), the neutrophil-to-lymphocyte ratio (NLR), and seven autoantibodies were measured using Youden's index, which was obtained using the ROC curve. The formula was used to compute the values of PLR and NLR. After using multifactor Cox regression analysis to identify risk factors, a nomogram was produced regarding the therapeutic effect following chemotherapy. The performance of the nomogram was assessed using a bootstrapped-concordance index and calibration plots. RESULT It was determined that NLR, sex-determining region Y-box 2 (SOX2), adenosine triphosphate binding RNA deconjugase 4-5 (GBU4-5), and MAGE family member A1 (MAGEA1) were significantly associated factors that could be combined to accurately predict the therapeutic effect following chemotherapy. Utilizing these risk indicators, we were able to develop a nomogram that predicted the patients' survival at 1, 3, and 5 years. At 3 years, the area under the curve representing the expected survival probability was 0.762 (95% confidence interval 0.66-0.87). With a bootstrapped-concordance index of 0.762, the nomogram demonstrated good calibration. CONCLUSIONS Our nomogram proved to be a valuable instrument in accurately predicting the overall survival of patients.
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Affiliation(s)
- Jiaying Gao
- Department of Respiration, Tangdu HospitalAir Force Medical UniversityXi'anShaanxiChina
| | - Yandong Nan
- Department of Respiration, Tangdu HospitalAir Force Medical UniversityXi'anShaanxiChina
| | - Gang Liu
- Department of Respiration, Tangdu HospitalAir Force Medical UniversityXi'anShaanxiChina
| | - Shihong Zhao
- Department of Respiration, Tangdu HospitalAir Force Medical UniversityXi'anShaanxiChina
| | - Huanqing Xiong
- Department of Respiration, Tangdu HospitalAir Force Medical UniversityXi'anShaanxiChina
- Department of RespirationShaanxi University of Chinese MedicineXianyangShaanxiChina
| | - Yifeng Wang
- Department of Respiration, Tangdu HospitalAir Force Medical UniversityXi'anShaanxiChina
| | - Faguang Jin
- Department of Respiration, Tangdu HospitalAir Force Medical UniversityXi'anShaanxiChina
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Liu J, Sui C, Bian H, Li Y, Wang Z, Fu J, Qi L, Chen K, Xu W, Li X. Radiomics based on 18F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer. Front Oncol 2024; 14:1425837. [PMID: 39132503 PMCID: PMC11310012 DOI: 10.3389/fonc.2024.1425837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/09/2024] [Indexed: 08/13/2024] Open
Abstract
Purpose This study aimed to establish and evaluate the value of integrated models involving 18F-FDG PET/CT-based radiomics and clinicopathological information in the prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for non-small cell lung cancer (NSCLC). Methods A total of 106 eligible NSCLC patients were included in the study. After volume of interest (VOI) segmentation, 2,016 PET-based and 2,016 CT-based radiomic features were extracted. To select an optimal machine learning model, a total of 25 models were constructed based on five sets of machine learning classifiers combined with five sets of predictive feature resources, including PET-based alone radiomics, CT-based alone radiomics, PET/CT-based radiomics, clinicopathological features, and PET/CT-based radiomics integrated with clinicopathological features. Area under the curves (AUCs) of receiver operator characteristic (ROC) curves were used as the main outcome to assess the model performance. Results The hybrid PET/CT-derived radiomic model outperformed PET-alone and CT-alone radiomic models in the prediction of pCR to NAT. Moreover, addition of clinicopathological information further enhanced the predictive performance of PET/CT-derived radiomic model. Ultimately, the support vector machine (SVM)-based PET/CT radiomics combined clinicopathological information presented an optimal predictive efficacy with an AUC of 0.925 (95% CI 0.869-0.981) in the training cohort and an AUC of 0.863 (95% CI 0.740-0.985) in the test cohort. The developed nomogram involving radiomics and pathological type was suggested as a convenient tool to enable clinical application. Conclusions The 18F-FDG PET/CT-based SVM radiomics integrated with clinicopathological information was an optimal model to non-invasively predict pCR to NAC for NSCLC.
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Affiliation(s)
- Jianjing Liu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Chunxiao Sui
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Haiman Bian
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yue Li
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Lung Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ziyang Wang
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Jie Fu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Lisha Qi
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Kun Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Xiaofeng Li
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Wang JL, Tang LS, Zhong X, Wang Y, Feng YJ, Zhang Y, Liu JY. A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma. Front Immunol 2024; 15:1405146. [PMID: 38947338 PMCID: PMC11211602 DOI: 10.3389/fimmu.2024.1405146] [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: 03/22/2024] [Accepted: 05/29/2024] [Indexed: 07/02/2024] Open
Abstract
Background Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable treatment responses. The purpose of this study is to establish and validate a radiomics based on enhanced computed tomography (CT) and combined with clinical data to predict the major pathological response to NIT in ESCC patients. Methods This retrospective study included 82 ESCC patients who were randomly divided into the training group (n = 57) and the validation group (n = 25). Radiomic features were derived from the tumor region in enhanced CT images obtained before treatment. After feature reduction and screening, radiomics was established. Logistic regression analysis was conducted to select clinical variables. The predictive model integrating radiomics and clinical data was constructed and presented as a nomogram. Area under curve (AUC) was applied to evaluate the predictive ability of the models, and decision curve analysis (DCA) and calibration curves were performed to test the application of the models. Results One clinical data (radiotherapy) and 10 radiomic features were identified and applied for the predictive model. The radiomics integrated with clinical data could achieve excellent predictive performance, with AUC values of 0.93 (95% CI 0.87-0.99) and 0.85 (95% CI 0.69-1.00) in the training group and the validation group, respectively. DCA and calibration curves demonstrated a good clinical feasibility and utility of this model. Conclusion Enhanced CT image-based radiomics could predict the response of ESCC patients to NIT with high accuracy and robustness. The developed predictive model offers a valuable tool for assessing treatment efficacy prior to initiating therapy, thus providing individualized treatment regimens for patients.
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Affiliation(s)
- Jia-Ling Wang
- Department of Biotherapy, Cancer Center, West China Hospital of Sichuan University, Chengdu, China
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Lian-Sha Tang
- Department of Biotherapy, Cancer Center, West China Hospital of Sichuan University, Chengdu, China
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Xia Zhong
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Wang
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Yu-Jie Feng
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Yun Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ji-Yan Liu
- Department of Biotherapy, Cancer Center, West China Hospital of Sichuan University, Chengdu, China
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Ye G, Wu G, Zhang C, Wang M, Liu H, Song E, Zhuang Y, Li K, Qi Y, Liao Y. CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer. Front Immunol 2024; 15:1414954. [PMID: 38933281 PMCID: PMC11199789 DOI: 10.3389/fimmu.2024.1414954] [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: 04/09/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Objectives To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image. Methods This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Results In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686. Conclusion The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.
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Affiliation(s)
- Guanchao Ye
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Guangyao Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chunyang Zhang
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Mingliang Wang
- Department of Thoracic Surgery, Henan Provincial People’s Hospital, Zhengzhou University, Zhengzhou, China
| | - Hong Liu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yuzhou Zhuang
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Kuo Li
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Qi
- Department of Thoracic Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Yongde Liao
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Caii W, Wu X, Guo K, Chen Y, Shi Y, Chen J. Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients. Cancer Immunol Immunother 2024; 73:153. [PMID: 38833187 PMCID: PMC11150226 DOI: 10.1007/s00262-024-03724-3] [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: 03/19/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunotherapy response, based on the integration of deep learning and habitat radiomics in patients with advanced non-small cell lung cancer (NSCLC). METHODS Independent patient cohorts from three medical centers were enrolled for training (n = 164) and test (n = 82). Habitat imaging radiomics features were derived from sub-regions clustered from individual's tumor by K-means method. The deep learning features were extracted based on 3D ResNet algorithm. Pearson correlation coefficient, T test and least absolute shrinkage and selection operator regression were used to select features. Support vector machine was applied to implement deep learning and habitat radiomics, respectively. Then, a combination model was developed integrating both sources of data. RESULTS The combination model obtained a strong well-performance, achieving area under receiver operating characteristics curve of 0.865 (95% CI 0.772-0.931). The model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use. CONCLUSION The integration of deep-leaning and habitat radiomics contributed to predicting response to immunotherapy in patients with NSCLC. The developed integration model may be used as potential tool for individual immunotherapy management.
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Affiliation(s)
- Weimin Caii
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, 325000, China
| | - Xiao Wu
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Kun Guo
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Yongxian Chen
- Department of Chest Cancer, Xiamen Second People's Hospital, Xiamen, 36100, China
| | - Yubo Shi
- Department of Pulmonary, Yueqing People's Hospital, Wenzhou, 325000, China
| | - Junkai Chen
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, 325000, China.
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Yang J, Yang C, Feng J, Zhu F, Zhao Z. Predicting Microwave Ablation Early Efficacy in Pulmonary Malignancies via Δ Radiomics Models. J Comput Assist Tomogr 2024:00004728-990000000-00314. [PMID: 38657155 DOI: 10.1097/rct.0000000000001611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
OBJECTIVE This study aimed to explore the value of preoperative and postoperative computed tomography (CT)-based radiomic signatures and Δ radiomic signatures for evaluating the early efficacy of microwave ablation (MWA) for pulmonary malignancies. METHODS In total, 115 patients with pulmonary malignancies who underwent MWA treatment were categorized into response and nonresponse groups according to relevant guidelines and consensus. Quantitative image features of the largest pulmonary malignancies were extracted from CT noncontrast scan images preoperatively (time point 0, TP0) and immediately postoperatively (time point 1, TP1). Critical features were selected from TP0 and TP1 and as Δ radiomics signatures for building radiomics models. In addition, a combined radiomics model (C-RO) was developed by integrating radiomics parameters with clinical risk factors. Prediction performance was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). RESULTS The radiomics model using Δ features outperformed the radiomics model using TP0 and TP1 features, with training and validation AUCs of 0.892, 0.808, and 0.787, and 0.705, 0.825, and 0.778, respectively. By combining the TP0, TP1, and Δ features, the logistic regression model exhibited the best performance, with training and validation AUCs of 0.945 and 0.744, respectively. The DCA confirmed the clinical utility of the Δ radiomics model. CONCLUSIONS A combined prediction model, including TP0, TP1, and Δ radiometric features, can be used to evaluate the early efficacy of MWA in pulmonary malignancies.
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Affiliation(s)
- Jing Yang
- From the School of Medicine, Shaoxing University
| | - Chen Yang
- Department of Radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing
| | - Jianju Feng
- Department of Radiology, Zhuji People's Hospital, Zhuji, Zhejiang, China
| | - Fandong Zhu
- Department of Radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing
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Yang Y, Yi Y, Wang Z, Li S, Zhang B, Sang Z, Zhang L, Cao Q, Li B. A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma. BMC Cancer 2024; 24:460. [PMID: 38609892 PMCID: PMC11015586 DOI: 10.1186/s12885-024-12239-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 04/09/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND To predict pathological complete response (pCR) in patients receiving neoadjuvant immunochemotherapy (nICT) for esophageal squamous cell carcinoma (ESCC), we explored the factors that influence pCR after nICT and established a combined nomogram model. METHODS We retrospectively included 164 ESCC patients treated with nICT. The radiomics signature and hematology model were constructed utilizing least absolute shrinkage and selection operator (LASSO) regression, and the radiomics score (radScore) and hematology score (hemScore) were determined for each patient. Using the radScore, hemScore, and independent influencing factors obtained through univariate and multivariate analyses, a combined nomogram was established. The consistency and prediction ability of the nomogram were assessed utilizing calibration curve and the area under the receiver operating factor curve (AUC), and the clinical benefits were assessed utilizing decision curve analysis (DCA). RESULTS We constructed three predictive models.The AUC values of the radiomics signature and hematology model reached 0.874 (95% CI: 0.819-0.928) and 0.772 (95% CI: 0.699-0.845), respectively. Tumor length, cN stage, the radScore, and the hemScore were found to be independent factors influencing pCR according to univariate and multivariate analyses (P < 0.05). A combined nomogram was constructed from these factors, and AUC reached 0.934 (95% CI: 0.896-0.972). DCA demonstrated that the clinical benefits brought by the nomogram for patients across an extensive range were greater than those of other individual models. CONCLUSIONS By combining CT radiomics, hematological factors, and clinicopathological characteristics before treatment, we developed a nomogram model that effectively predicted whether ESCC patients would achieve pCR after nICT, thus identifying patients who are sensitive to nICT and assisting in clinical treatment decision-making.
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Affiliation(s)
- Yu Yang
- Shandong Medical Imaging and Radiotherapy Engineering Center (SMIREC), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yan Yi
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhongtang Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Shanshan Li
- Department of Oncology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Bin Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zheng Sang
- Shandong Medical Imaging and Radiotherapy Engineering Center (SMIREC), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Lili Zhang
- Shandong Medical Imaging and Radiotherapy Engineering Center (SMIREC), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Qiang Cao
- Shandong Medical Imaging and Radiotherapy Engineering Center (SMIREC), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
| | - Baosheng Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
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Huang D, Lin C, Jiang Y, Xin E, Xu F, Gan Y, Xu R, Wang F, Zhang H, Lou K, Shi L, Hu H. Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy. Front Oncol 2024; 14:1348678. [PMID: 38585004 PMCID: PMC10996281 DOI: 10.3389/fonc.2024.1348678] [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: 12/03/2023] [Accepted: 03/06/2024] [Indexed: 04/09/2024] Open
Abstract
Objective To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy. Methods A total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort. Radiomics features of intratumoral (T) and peritumoral regions (P1 = 0-5mm, P2 = 5-10mm, and P3 = 10-15mm) were extracted from pre-treatment CT. Intra- and inter- class correlation coefficients and least absolute shrinkage and selection operator were used to feature selection. Four single ROI models mentioned above and a combined radiomics (CR: T+P1+P2+P3) model were established by using machine learning algorithms. Clinical factors were selected to construct the combined radiomics-clinical (CRC) model, which was validated in the external center ZCH (n=43). The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis. Results Histopathological type was the only independent clinical risk factor. The model CR with eight selected radiomics features demonstrated a good predictive performance in the internal validation (AUC=0.810) and significantly improved than the model T (AUC=0.810 vs 0.619, p<0.05). The model CRC yielded the best predictive capability (AUC=0.814) and obtained satisfactory performance in the independent external test set (AUC=0.768, 95% CI: 0.62-0.91). Conclusion We established a CRC model that incorporates intratumoral and peritumoral features and histopathological type, providing an effective approach for selecting NSCLC patients suitable for neoadjuvant immunochemotherapy.
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Affiliation(s)
- Dingpin Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chen Lin
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Yangyang Jiang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Enhui Xin
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yi Gan
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Rui Xu
- DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China
- DUT-RU Co-Research Center of Advanced Information Computing Technology (ICT) for Active Life, Dalian University of Technology, Dalian, Liaoning, China
| | - Fang Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haiping Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kaihua Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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10
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Luo X, Zheng R, Zhang J, He J, Luo W, Jiang Z, Li Q. CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis. Front Oncol 2024; 14:1329801. [PMID: 38384802 PMCID: PMC10879429 DOI: 10.3389/fonc.2024.1329801] [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: 10/29/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
Background Radiomics, an emerging field, presents a promising avenue for the accurate prediction of biomarkers in different solid cancers. Lung cancer remains a significant global health challenge, contributing substantially to cancer-related mortality. Accurate assessment of Ki-67, a marker reflecting cellular proliferation, is crucial for evaluating tumor aggressiveness and treatment responsiveness, particularly in non-small cell lung cancer (NSCLC). Methods A systematic review and meta-analysis conducted following the preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guidelines. Two authors independently conducted a literature search until September 23, 2023, in PubMed, Embase, and Web of Science. The focus was on identifying radiomics studies that predict Ki-67 expression in lung cancer. We evaluated quality using both Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. For statistical analysis in the meta-analysis, we used STATA 14.2 to assess sensitivity, specificity, heterogeneity, and diagnostic values. Results Ten retrospective studies were pooled in the meta-analysis. The findings demonstrated that the use of computed tomography (CT) scan-based radiomics for predicting Ki-67 expression in lung cancer exhibited encouraging diagnostic performance. Pooled sensitivity, specificity, and area under the curve (AUC) in training cohorts were 0.78, 0.81, and 0.85, respectively. In validation cohorts, these values were 0.78, 0.70, and 0.81. Quality assessment using QUADAS-2 and RQS indicated generally acceptable study quality. Heterogeneity in training cohorts, attributed to factors like contrast-enhanced CT scans and specific Ki-67 thresholds, was observed. Notably, publication bias was detected in the training cohort, indicating that positive results are more likely to be published than non-significant or negative results. Thus, journals are encouraged to publish negative results as well. Conclusion In summary, CT-based radiomics exhibit promise in predicting Ki-67 expression in lung cancer. While the results suggest potential clinical utility, additional research efforts should concentrate on enhancing diagnostic accuracy. This could pave the way for the integration of radiomics methods as a less invasive alternative to current procedures like biopsy and surgery in the assessment of Ki-67 expression.
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Affiliation(s)
- Xinmin Luo
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Renying Zheng
- Department of Oncology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Jiao Zhang
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Juan He
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Wei Luo
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Zhi Jiang
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Qiang Li
- Department of Radiology, Yuechi County Traditional Chinese Medicine Hospital in Sichuan Province, Guang’an, Sichuan, China
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11
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Zerunian M, Nacci I, Caruso D, Polici M, Masci B, De Santis D, Mercantini P, Arrivi G, Mazzuca F, Paolantonio P, Pilozzi E, Vecchione A, Tarallo M, Fiori E, Iannicelli E, Laghi A. Is CT Radiomics Superior to Morphological Evaluation for pN0 Characterization? A Pilot Study in Colon Cancer. Cancers (Basel) 2024; 16:660. [PMID: 38339411 PMCID: PMC10854865 DOI: 10.3390/cancers16030660] [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: 11/24/2023] [Revised: 01/02/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
The aim of this study was to compare CT radiomics and morphological features when assessing benign lymph nodes (LNs) in colon cancer (CC). This retrospective study included 100 CC patients (test cohort) who underwent a preoperative CT examination and were diagnosed as pN0 after surgery. Regional LNs were scored with a morphological Likert scale (NODE-SCORE) and divided into two groups: low likelihood (LLM: 0-2 points) and high likelihood (HLM: 3-7 points) of malignancy. The T-test and the Mann-Whitney test were used to compare 107 radiomic features extracted from the two groups. Radiomic features were also extracted from primary lesions (PLs), and the receiver operating characteristic (ROC) was used to test a LN/PL ratio when assessing the LN's status identified with radiomics and with the NODE-SCORE. An amount of 337 LNs were divided into 167 with LLM and 170 with HLM. Radiomics showed 15/107 features, with a significant difference (p < 0.02) between the two groups. The comparison of selected features between 81 PLs and the corresponding LNs showed all significant differences (p < 0.0001). According to the LN/PL ratio, the selected features recognized a higher number of LNs than the NODE-SCORE (p < 0.001). On validation of the cohort of 20 patients (10 pN0, 10 pN2), significant ROC curves were obtained for LN/PL busyness (AUC = 0.91; 0.69-0.99; 95% C.I.; and p < 0.001) and for LN/PL dependence entropy (AUC = 0.76; 0.52-0.92; 95% C.I.; and p = 0.03). The radiomics ratio between CC and LNs is more accurate for noninvasively discriminating benign LNs compared to CT morphological features.
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Affiliation(s)
- Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
- Ph.D. School in Translational Medicine and Oncology, Department of Medical and Surgical Sciences and Translational Medicine, Faculty of Medicine and Psychology, Sapienza University of Rome, Via Giorgio Nicola Papanicolau–ang. Via di Grottarossa 1035, 00189 Rome, Italy
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
| | - Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
- Ph.D. School in Translational Medicine and Oncology, Department of Medical and Surgical Sciences and Translational Medicine, Faculty of Medicine and Psychology, Sapienza University of Rome, Via Giorgio Nicola Papanicolau–ang. Via di Grottarossa 1035, 00189 Rome, Italy
| | - Benedetta Masci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
| | - Domenico De Santis
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
| | - Paolo Mercantini
- Surgery Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy;
| | - Giulia Arrivi
- Oncology Unit, Department of Clinical and Molecular Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (G.A.); (F.M.)
| | - Federica Mazzuca
- Oncology Unit, Department of Clinical and Molecular Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (G.A.); (F.M.)
| | - Pasquale Paolantonio
- Department of Radiology, San Giovanni Addolorata Hospital Complex, Via dell’Amba Aradam 8, 00184 Rome, Italy;
| | - Emanuela Pilozzi
- Pathology Unit, Department of Clinical and Molecular Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (E.P.); (A.V.)
| | - Andrea Vecchione
- Pathology Unit, Department of Clinical and Molecular Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (E.P.); (A.V.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, Via Giovanni Maria Lancisi 2, 00161 Rome, Italy; (M.T.); (E.F.)
| | - Enrico Fiori
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, Via Giovanni Maria Lancisi 2, 00161 Rome, Italy; (M.T.); (E.F.)
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea University Hospital, Via di Grottarossa 1035–1039, 00189 Rome, Italy; (M.Z.); (I.N.); (M.P.); (B.M.); (D.D.S.); (E.I.); (A.L.)
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12
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Huang Z, Li L, Gong Z, Tang L. Construction and Validation of a Nomogram to Predict the Postoperative Venous Thromboembolism Risk in Patients with HGSOC. Clin Appl Thromb Hemost 2024; 30:10760296241255958. [PMID: 38767088 PMCID: PMC11107311 DOI: 10.1177/10760296241255958] [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: 01/23/2024] [Revised: 04/20/2024] [Accepted: 05/02/2024] [Indexed: 05/22/2024] Open
Abstract
Venous thromboembolism (VTE) is a common complication in patients with high-grade serous ovarian cancer (HGSOC) after surgery. This study aims to establish a comprehensive risk assessment model to better identify the potential risk of postoperative VTE in HGSOC. Clinical data from 587 HGSOC patients who underwent surgical treatment were retrospectively collected. Univariate and multivariate logistic regression analyses were performed to identify independent factors influencing the occurrence of postoperative VTE in HGSOC. A nomogram model was constructed in the training set and further validated in the verification set. Logistic regression identified age (odds ratio [OR] = 1.063, P = .002), tumor size (OR = 3.815, P < .001), postoperative transfusion (OR = 5.646, P = .001), and postoperative D-dimer (OR = 1.246, P = .003) as independent risk factors for postoperative VTE in HGSOC patients. A nomogram was constructed using these factors. The receiver operating characteristic curve showed an area under the curve (AUC) of 0.840 (95% confidence interval [CI]: 0.782, 0.898) in the training set and 0.793 (95% CI: 0.704, 0.882) in the validation set. The calibration curve demonstrated a good consistency between model predictions and actual results. The decision curve analysis indicated the model benefits at a threshold probability of less than 70%. A nomogram predicting postoperative VTE in HGSOC was established and validated. This model will assist clinicians in the early identification of high-risk patients, enabling the implementation of appropriate preventive measures.
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Affiliation(s)
- Zhen Huang
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ling Li
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhengxin Gong
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Liangdan Tang
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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13
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Wang J, Chen Z, Chen J. Diagnostic value of MRI radiomics in differentiating high‑grade glioma from low‑grade glioma: A meta‑analysis. Oncol Lett 2023; 26:436. [PMID: 37664663 PMCID: PMC10472021 DOI: 10.3892/ol.2023.14023] [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: 11/30/2022] [Accepted: 07/13/2023] [Indexed: 09/05/2023] Open
Abstract
No clear conclusions have yet been reached regarding the accuracy of magnetic resonance imaging (MRI) radiomics in distinguishing high-grade glioma (HGG) from low-grade glioma (LGG). In the present study, a meta-analysis was conducted to determine the diagnostic value of MRI radiomics in differentiating between HGG and LGG, in order to guide their clinical diagnosis. PubMed, Embase and the Cochrane Library databases were searched up to November 2022. The search included studies in which true positive, false positive, true negative and false negative values for the differentiation of HGG from LGG were reported or could be calculated by retrograde extrapolation. Duplicate publications, research without full text, studies with incomplete information or unextractable data, animal studies, reviews and systematic reviews were excluded. STATA 15.1 was used to analyze the data. The meta-analysis included 15 studies, which comprised a total of 1,124 patients, of which 701 had HGG and 423 had LGG. The pooled sensitivity and specificity of the studies overall were 0.92 (95% CI: 0.89-0.95) and 0.89 (95% CI: 0.85-0.92), respectively. The positive and negative likelihood ratios of the studies overall were 7.89 (95% CI: 6.01-10.37) and 0.09 (95% CI: 0.07-0.12), respectively. The pooled diagnostic odds ratio of the studies was 85.20 (95% CI: 54.52-133.14). The area under the summary receiver operating characteristic curve was 0.91. These findings indicate that radiomics may be an accurate tool for the differentiation of glioma grades. However, further research is needed to verify the most appropriate of these technologies.
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Affiliation(s)
- Jiefang Wang
- Department of Radiology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Zhichao Chen
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Jieyun Chen
- Department of Radiology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
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