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Hu H, Liu Y, Qiu C, Zhang L, Cui H, Gu J. LINC00894 inhibited neuron cellular apoptosis and regulated activating transcription factor 3 expression. Gene 2024; 927:148670. [PMID: 38857714 DOI: 10.1016/j.gene.2024.148670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 06/01/2024] [Accepted: 06/07/2024] [Indexed: 06/12/2024]
Abstract
LINC00894 may be associated with synaptic function, but its biology function in neural cells is still unknown. In this study, LINC00894 knockdown decreased the EdU incorporated into newly synthesized DNA and cell viability in MTT or CCK-8 assay in HEK-293T and BE(2)-M17 (M17) neuroblastoma cells. And LINC00894 knockdown increased cellular apoptosis in Annexin V-FITC staining, the expression of activated Caspase3 and the level of reactive oxygen species (ROS) both in HEK-293T and M17 cells. Moreover, LINC00894 also protected cells from hydrogen peroxide induced apoptosis in in vitro models. Utilizing RNA sequencing (RNA-seq) integrated with quantitative reverse transcription polymerase chain reaction (RT-qPCR) and immunoblot, we identified that LINC00894 affected activating transcription factor 3 (ATF3) expression in HEK-293T, M17, and SH-SY5Y neuroblastoma cells. Finally, we found that ectopic expression of ATF3 restored cell proliferation and inhibited cell apoptosis in LINC00894 downregulated M17 cells. While knockdown of ATF3 also significantly increased the cell viability inhibition and apoptosis promotion induced by LINC00894 knockdown in M17 cells. Our results from in vitro models revealed that LINC00894 could promote neuronal cell proliferation and inhibit cellular apoptosis by affecting ATF3 expression.
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Affiliation(s)
- Hanjing Hu
- Department of Biochemistry and Molecular Biology, School of Medicine, Key Laboratory of Neuroregeneration and Ministry of Education of Jiangsu, Co-innovation Center of Neuroregeneration, NMPA Key Laboratory for Research and Evaluation of Tissue Engineering Technology Products, Nantong University, Nantong, Jiangsu, China
| | - Yuxiao Liu
- Department of Biochemistry and Molecular Biology, School of Medicine, Key Laboratory of Neuroregeneration and Ministry of Education of Jiangsu, Co-innovation Center of Neuroregeneration, NMPA Key Laboratory for Research and Evaluation of Tissue Engineering Technology Products, Nantong University, Nantong, Jiangsu, China
| | - Cheng Qiu
- Department of Biochemistry and Molecular Biology, School of Medicine, Key Laboratory of Neuroregeneration and Ministry of Education of Jiangsu, Co-innovation Center of Neuroregeneration, NMPA Key Laboratory for Research and Evaluation of Tissue Engineering Technology Products, Nantong University, Nantong, Jiangsu, China
| | - Liti Zhang
- Department of Biochemistry and Molecular Biology, School of Medicine, Key Laboratory of Neuroregeneration and Ministry of Education of Jiangsu, Co-innovation Center of Neuroregeneration, NMPA Key Laboratory for Research and Evaluation of Tissue Engineering Technology Products, Nantong University, Nantong, Jiangsu, China
| | - Hengxiang Cui
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Jianlan Gu
- Department of Biochemistry and Molecular Biology, School of Medicine, Key Laboratory of Neuroregeneration and Ministry of Education of Jiangsu, Co-innovation Center of Neuroregeneration, NMPA Key Laboratory for Research and Evaluation of Tissue Engineering Technology Products, Nantong University, Nantong, Jiangsu, China.
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Zheng F, Zhang L, Chen H, Zang Y, Chen X, Li Y. Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences. JOURNAL OF RADIATION RESEARCH 2024; 65:350-359. [PMID: 38650477 PMCID: PMC11115443 DOI: 10.1093/jrr/rrae007] [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: 06/19/2023] [Revised: 08/12/2023] [Indexed: 04/25/2024]
Abstract
Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances. One kind was established using all MRI sequences (T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient), and the other kind was based on single MRI sequence as listed above. For the machine-learning model based on all sequences, a total of seven radiomic features were selected with the Maximum Relevance and Minimum Redundancy algorithm. The predictive accuracy was 0.993 and 0.750 in the training and validation sets, respectively, and the area under curves were 1.000 and 0.754 in the two sets, respectively. For the machine-learning model based on single sequence, the numbers of selected features were 8, 10, 10, 13, 9, 7 and 6 for T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient, respectively, with predictive accuracies of 0.797-1.000 and 0.583-0.694 in the training and validation sets, respectively, and the area under curves of 0.874-1.000 and 0.538-0.697 in the two sets, respectively. Specifically, T1-weighted image-based model performed best, while contrast enhancement-based model performed worst in the independent validation set. The machine-learning models based on seven different single MRI sequences performed differently in predicting O6-methylguanine-DNA methyltransferase status in glioblastoma, while the machine-learning model based on the combination of all sequences performed best.
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Affiliation(s)
- Fei Zheng
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
- Department of Radiology, Peking University People’s Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, P. R. China
| | - Lingling Zhang
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Hongyan Chen
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Yuying Zang
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Xuzhu Chen
- Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
| | - Yiming Li
- Department of Neurosurgery, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China
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Zhong X, Peng J, Shu Z, Song Q, Li D. Prediction of p53 mutation status in rectal cancer patients based on magnetic resonance imaging-based nomogram: a study of machine learning. Cancer Imaging 2023; 23:88. [PMID: 37723592 PMCID: PMC10507842 DOI: 10.1186/s40644-023-00607-1] [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: 03/27/2023] [Accepted: 09/05/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND The current study aimed to construct and validate a magnetic resonance imaging (MRI)-based radiomics nomogram to predict tumor protein p53 gene status in rectal cancer patients using machine learning. METHODS Clinical and imaging data from 300 rectal cancer patients who underwent radical resections were included in this study, and a total of 166 patients with p53 mutations according to pathology reports were included in these patients. These patients were allocated to the training (n = 210) or validation (n = 90) cohorts (7:3 ratio) according to the examination time. Using the training data set, the radiomic features of primary tumor lesions from T2-weighted images (T2WI) of each patient were analyzed by dimensionality reduction. Multivariate logistic regression was used to screen predictive features, which were combined with a radiomics model to construct a nomogram to predict p53 gene status. The accuracy and reliability of the nomograms were assessed in both training and validation data sets using receiver operating characteristic (ROC) curves. RESULTS Using the radiomics model with the training and validation cohorts, the diagnostic efficacies were 0.828 and 0.795, the sensitivities were 0.825 and 0.891, and the specificities were 0.722 and 0.659, respectively. Using the nomogram with the training and validation data sets, the diagnostic efficacies were 0.86 and 0.847, the sensitivities were 0.758 and 0.869, and the specificities were 0.833 and 0.75, respectively. CONCLUSIONS The radiomics nomogram based on machine learning was able to predict p53 gene status and facilitate preoperative molecular-based pathological diagnoses.
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Affiliation(s)
- Xia Zhong
- The First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhenyu Shu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qiaowei Song
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Dongxue Li
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
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Du N, Shu W, Li K, Deng Y, Xu X, Ye Y, Tang F, Mao R, Lin G, Li S, Fang X. An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma. J Transl Med 2023; 21:119. [PMID: 36774480 PMCID: PMC9922464 DOI: 10.1186/s12967-023-03950-w] [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: 08/01/2022] [Accepted: 02/01/2023] [Indexed: 02/13/2023] Open
Abstract
BACKGROUND AND PURPOSE Ki-67 labeling index (LI) is an important indicator of tumor cell proliferation in glioma, which can only be obtained by postoperative biopsy at present. This study aimed to explore the correlation between Ki-67 LI and apparent diffusion coefficient (ADC) parameters and to predict the level of Ki-67 LI noninvasively before surgery by multiple MRI characteristics. METHODS Preoperative MRI data of 166 patients with pathologically confirmed glioma in our hospital from 2016 to 2020 were retrospectively analyzed. The cut-off point of Ki-67 LI for glioma grading was defined. The differences in MRI characteristics were compared between the low and high Ki-67 LI groups. The receiver operating characteristic (ROC) curve was used to estimate the accuracy of each ADC parameter in predicting the Ki-67 level, and finally a multivariate logistic regression model was constructed based on the results of ROC analysis. RESULTS ADCmin, ADCmean, rADCmin, rADCmean and Ki-67 LI showed a negative correlation (r = - 0.478, r = - 0.369, r = - 0.488, r = - 0.388, all P < 0.001). The Ki-67 LI of low-grade gliomas (LGGs) was different from that of high-grade gliomas (HGGs), and the cut-off point of Ki-67 LI for distinguishing LGGs from HGGs was 9.5%, with an area under the ROC curve (AUROC) of 0.962 (95%CI 0.933-0.990). The ADC parameters in the high Ki-67 group were significantly lower than those in the low Ki-67 group (all P < 0.05). The peritumoral edema (PTE) of gliomas in the high Ki-67 LI group was higher than that in the low Ki-67 LI group (P < 0.05). The AUROC of Ki-67 LI level assessed by the multivariate logistic regression model was 0.800 (95%CI 0.721-0.879). CONCLUSIONS There was a negative correlation between ADC parameters and Ki-67 LI, and the multivariate logistic regression model combined with peritumoral edema and ADC parameters could improve the prediction ability of Ki-67 LI.
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Affiliation(s)
- Ningfang Du
- grid.8547.e0000 0001 0125 2443Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China
| | - Weiquan Shu
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Kefeng Li
- grid.266100.30000 0001 2107 4242School of Medicine, University of California, San Diego, CA USA ,Faculty of Health Sciences and Sports, Macao Polytechnic University, Macao SAR, China
| | - Yao Deng
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Xinxin Xu
- grid.8547.e0000 0001 0125 2443Clinical Research Center for Gerontology, Huadong Hospital, Fudan University, Shanghai, China
| | - Yao Ye
- grid.8547.e0000 0001 0125 2443Department of Pathology, Huadong Hospital, Fudan University, Shanghai, China
| | - Feng Tang
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Renling Mao
- grid.8547.e0000 0001 0125 2443Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
| | - Shihong Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
| | - Xuhao Fang
- Department of Neurosurgery, Huadong Hospital, Fudan University, Shanghai, China.
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Ouyang ZQ, He SN, Zeng YZ, Zhu Y, Ling BB, Sun XJ, Gu HY, He B, Han D, Lu Y. Contrast enhanced magnetic resonance imaging-based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study. Quant Imaging Med Surg 2023; 13:1100-1114. [PMID: 36819280 PMCID: PMC9929424 DOI: 10.21037/qims-22-689] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 12/05/2022] [Indexed: 01/12/2023]
Abstract
Background The aim of this study was to develop and validate a radiomics nomogram for preoperative prediction of Ki-67 proliferative index (Ki-67 PI) expression in patients with meningioma. Methods A total of 280 patients from 2 independent hospital centers were enrolled. Patients from center I were randomly divided into a training cohort of 168 patients and a test cohort of 72 patients, and 40 patients from center II served as an external validation cohort. Interoperator reproducibility test, Z-score standardization, analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were used to select radiomics features, which were extracted from contrast-enhanced T1-weighted imaging (CE-T1WI) imaging. The radiomics signature for predicting Ki-67 PI expression was developed and validated using 4 classifiers including logistic regression (LR), decision tree (DT), support vector machine (SVM), and adaptive boost (AdaBoost). Finally, combined radiological characteristics with radiomics signature were used to establish the nomogram to predict the risk of high Ki-67 PI expression in patients with meningioma. Results Fourteen radiomics features were used to construct the radiomics signature. The radiomics nomogram that incorporated the radiomics signature and radiological characteristics showed excellent discrimination in the training, test, and validation cohorts with areas under the curve of 0.817 (95% CI: 0.753-0.881), 0.822 (95% CI: 0.727-0.916), and 0.845 (95% CI: 0.708-0.982), respectively. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation. Conclusions The proposed contrast enhanced magnetic resonance imaging (MRI)-based radiomics nomogram could be an effective tool to predict the risk of Ki-67 high expression in patients with meningioma.
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Affiliation(s)
- Zhi-Qiang Ouyang
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, China;,Department of Radiology, Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shao-Nan He
- Department of Medical Imaging, First People's Hospital of Yunnan Province, Kunming, China
| | - Yi-Zhen Zeng
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yun Zhu
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Bing-Bing Ling
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xue-Jin Sun
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - He-Yi Gu
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Bo He
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Dan Han
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yi Lu
- Department of Medical Imaging, Laboratory of Brain Function, First Affiliated Hospital of Kunming Medical University, Kunming, China
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Li B, Li H, Zhang L, Ren T, Meng J. Expression analysis of human glioma susceptibility gene and P53 in human glioma and its clinical significance based on bioinformatics. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:53. [PMID: 36819578 PMCID: PMC9929792 DOI: 10.21037/atm-22-5646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/07/2022] [Indexed: 01/18/2023]
Abstract
Background The exact mechanism of glioblastoma multiforme (GBM) remains unclear. This study was to clarify the expression of P53 in glioma and its molecular mechanism, and to explore the possibility of P53 as a potential therapeutic target of glioma and its clinical application value, so as to provide a new theoretical basis for the treatment of glioma. Methods Firstly, a dataset was established to analyze the expression of P53 in different stages of glioma and its relationship with prognosis by using The Cancer Genome Atlas (TCGA) database, RNA-seq data, and survival data of glioma and normal control samples in gene expression profiling and interactive analysis (GEPIA). The genes co-expressed with P53 were screened out, their differential expression between glioma and normal control group was analyzed, and their functions were analyzed by enrichment analysis. The TGGA database was used for data verification and analysis. The correlation between P53 expression and clinicopathological parameters was analyzed. Kaplan-Meier survival analysis was used to analyze the relationship between P53 expression and overall survival (OS) and progression-free survival (PFS) of glioma patients, and Cox regression analysis was used to analyze the independent factors affecting OS and PFS of glioma patients. Results The results of TCGA data analysis were as follows: The expression level of P53 was different from that of different stages of glioma, namely, the expression level of P53 between grade II and grade III, grade III and grade IV, and grade II and grade IV were significantly different (P<0.05). The results of P53 gene-related survival analysis showed that KNL1 high expression and low expression were significantly different in OS, and the high expression group was associated with poor prognosis (P<0.05). Conclusions The P53 expression can be an effective biological indicator of poor prognosis of glioma.
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Affiliation(s)
- Baiyu Li
- Department of Neurology Care Ward, Gansu Provincial Hospital, Lanzhou, China
| | - Hang Li
- Department of Geriatrics, Chengdu Eighth People's Hospital (Geriatric Hospital of Chengdu Medical College), Chengdu, China
| | - Linghui Zhang
- Department of Internal Medicine, Department of Clinical Medicine, Shijiazhuang Medical College, Shijiazhuang, China
| | - Taowen Ren
- Department of Neurology Care Ward, Gansu Provincial Hospital, Lanzhou, China
| | - Jie Meng
- Department of Psychiatry, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
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An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach. Eur J Radiol 2023; 158:110639. [PMID: 36463703 DOI: 10.1016/j.ejrad.2022.110639] [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: 06/10/2022] [Revised: 10/05/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND The histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of tumor cells are major factors in the diagnosis, prognosis, and treatment management of patients. Many existing studies primarily focused on the classification of two classes of brain tumors and the Ki-67LI of gliomas. This study aimed to develop a preoperative non-invasive radiomics pipeline based on multiparametric-MRI to classify-three types of brain tumors, glioblastoma (GBM), metastasis (MET) and primary central nervous system lymphoma (PCNSL), and to predict their corresponding Ki-67LI. METHODS In this retrospective study, 153 patients with malignant brain tumors were involved. The radiomics features were extracted from three types of MRI (T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI)) with three masks (tumor core, edema, and whole tumor masks) and selected by a combination of Pearson correlation coefficient (CORR), LASSO, and Max-Relevance and Min-Redundancy (mRMR) filters. The performance of six classifiers was compared and the top three performing classifiers were used to construct the ensemble learning model (ELM). The proposed ELM was evaluated in the training dataset (108 patients) by 5-fold cross-validation and in the test dataset (45 patients) by hold-out. The accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-Score, and the area under the receiver operating characteristic curve (AUC) indicators evaluated the performance of the models. RESULTS The best feature sets and ELM with the optimal performance were selected to construct the tri-categorized brain tumor aided diagnosis model (training dataset AUC: 0.96 (95% CI: 0.93, 0.99); test dataset AUC: 0.93) and Ki-67LI prediction model (training dataset AUC: 0.96 (95% CI: 0.94, 0.98); test dataset AUC: 0.91). The CE-T1WI was the best single modality for all classifiers. Meanwhile, the whole tumor was the most vital mask for the tumor classification and the tumor core was the most vital mask for the Ki-67LI prediction. CONCLUSION The developed radiomics models led to the precise preoperative classification of GBM, MET, and PCNSL and the prediction of Ki-67LI, which could be utilized in clinical practice for the treatment planning for brain tumors.
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Piao S, Luo X, Bao Y, Hu B, Liu X, Zhu Y, Yang L, Geng D, Li Y. An MRI-based joint model of radiomics and spatial distribution differentiates autoimmune encephalitis from low-grade diffuse astrocytoma. Front Neurol 2022; 13:998279. [PMID: 36408523 PMCID: PMC9669344 DOI: 10.3389/fneur.2022.998279] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/12/2022] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND The differential diagnosis between autoimmune encephalitis and low-grade diffuse astrocytoma remains challenging. We aim to develop a quantitative model integrating radiomics and spatial distribution features derived from MRI for discriminating these two conditions. METHODS In our study, we included 188 patients with confirmed autoimmune encephalitis (n = 81) and WHO grade II diffuse astrocytoma (n = 107). Patients with autoimmune encephalitis (AE, n = 59) and WHO grade II diffuse astrocytoma (AS, n = 79) were divided into training and test sets, using stratified sampling according to MRI scanners. We further included an independent validation set (22 patients with AE and 28 patients with AS). Hyperintensity fluid-attenuated inversion recovery (FLAIR) lesions were segmented for each subject. Ten radiomics and eight spatial distribution features were selected via the least absolute shrinkage and selection operator (LASSO), and joint models were constructed by logistic regression for disease classification. Model performance was measured in the test set using the area under the receiver operating characteristic (ROC) curve (AUC). The discrimination performance of the joint model was compared with neuroradiologists. RESULTS The joint model achieved better performance (AUC 0.957/0.908, accuracy 0.914/0.840 for test and independent validation sets, respectively) than the radiomics and spatial distribution models. The joint model achieved lower performance than a senior neuroradiologist (AUC 0.917/0.875) but higher performance than a junior neuroradiologist (AUC 0.692/0.745) in the test and independent validation sets. CONCLUSION The joint model of radiomics and spatial distribution from a single FLAIR could effectively classify AE and AS, providing clinical decision support for the differential diagnosis between the two conditions.
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Affiliation(s)
- Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yifang Bao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Bin Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xueling Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yuqi Zhu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Liqin Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
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Resveratrol Enhances the Radiosensitivity by Inducing DNA Damage and Antitumor Immunity in a Glioblastoma Rat Model under 3 T MRI Monitoring. JOURNAL OF ONCOLOGY 2022; 2022:9672773. [PMID: 36276282 PMCID: PMC9584721 DOI: 10.1155/2022/9672773] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 09/21/2022] [Indexed: 11/28/2022]
Abstract
Glioblastoma (GBM) is the most common intracranial tumor with characteristic of malignancy. Resveratrol, a natural originated polyphenolic compound, has been reported to act as a potential radiosensitizer in cancer therapy. Magnetic resonance imaging (MRI) is the first choice for the diagnosis, pathological grading, and efficacy evaluation of GBM. In this study, MRI was applied to observe whether resveratrol could intensify the anti-GBM tumor effect by enhancing antitumor immunity during radiotherapy. We established an intracranial C6 GBM model in SD rats, treated with radiation and resveratrol. The increased body weight, the inhibition on mortality, and tumor volume in radiated- GBM rats were further enhanced by resveratrol addition, while the pathological damage of brain was alleviated. The modulation of radiation on inflammation, cell cycle, and apoptosis was strengthened by resveratrol; and Ki-67, PD-L1, and cell cycle- and apoptosis-related protein expressions were also improved by cotreatment. Besides, cotreatment attenuated DNA damage and induced G0/G1-phase cell arrest of GBM rats, accompanied with the changed expression of ATM-AKT-STAT3 pathway-related proteins. Moreover, the percentages of CD3+CD8+T cells and IFN-γ+CD8+T cells were enhanced, while (CD4+CD25+Foxp3)/CD4+T cells were decreased by radiation or resveratrol, which was strengthened by cotreatment. The modulation effect of cotreatment on CD3, Foxp3, and IFN-γ levels was also stronger than radiation or resveratrol alone. To conclude, resveratrol enhanced the effect of radiotherapy by inducing DNA damage and antitumor immunity in the intracranial C6 GBM.
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He Y, Liang T, Chen Z, Mo S, Liao Y, Gao Q, Huang K, Peng T, Zhou W, Han C. Recurrence of Early Hepatocellular Carcinoma after Surgery May Be Related to Intestinal Oxidative Stress and the Development of a Predictive Model. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:7261786. [PMID: 36238647 PMCID: PMC9553367 DOI: 10.1155/2022/7261786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/07/2022] [Accepted: 09/12/2022] [Indexed: 11/27/2022]
Abstract
Background Early stage hepatocellular carcinoma (HCC) has a high recurrence rate after surgery and lacks reliable predictive tools. We explored the potential of combining enhanced CT with gut microbiome to develop a predictive model for recurrence after early HCC surgery. Methods A total of 112 patients with early HCC who underwent hepatectomy from September 2018 to December 2020 were included in this study, and the machine learning method was divided into a training group (N = 71) and a test group (N = 41) with the observed endpoint of recurrence-free survival (RFS). Features were extracted from the arterial and portal phases of enhanced computed tomography (CT) images and gut microbiome, and features with minimum absolute contraction and selection operator regression were created, and the extracted features were scored to create a preoperative prediction model by using the multivariate Cox regression analysis with risk stratification analysis. Results In the study cohort, the model constructed by combining radiological and gut flora features provided good predictive performance (C index, 0.811 (0.650-0.972)). The combined radiology and gut flora-based model constructed risk strata with high, intermediate, or low risk of recurrence and different characteristics of recurrent tumor imaging and gut flora. Recurrence of early stage hepatocellular carcinoma may be associated with oxidative stress in the intestinal flora. Conclusions This study successfully constructs a risk model integrating enhanced CT and gut microbiome characteristics that can be used for the risk of postoperative recurrence in patients with early HCC. In addition, intestinal flora associated with HCC recurrence may be involved in oxidative stress.
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Affiliation(s)
- Yongfei He
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Tianyi Liang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Zijun Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Shutian Mo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Yuan Liao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Qiang Gao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Ketuan Huang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Tao Peng
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Weijie Zhou
- Deputy Chief Technician of Laboratory, Baise People's Hospital, Baise, Guangxi Zhuang Autonomous Region, China
| | - Chuangye Han
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
- Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, Guangxi Zhuang Autonomous Region, China
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11
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Jiang X, Jia H, Zhang Z, Wei C, Wang C, Dong J. The Feasibility of Combining ADC Value With Texture Analysis of T 2WI, DWI and CE-T 1WI to Preoperatively Predict the Expression Levels of Ki-67 and p53 of Endometrial Carcinoma. Front Oncol 2022; 11:805545. [PMID: 35127515 PMCID: PMC8811460 DOI: 10.3389/fonc.2021.805545] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 12/29/2021] [Indexed: 01/13/2023] Open
Abstract
PURPOSE To evaluate the feasibility of apparent diffusion coefficient (ADC) value combined with texture analysis (TA) in preoperatively predicting the expression levels of Ki-67 and p53 in endometrial carcinoma (EC) patients. METHODS Clinical, pathological and MRI findings of 110 EC patients were analyzed retrospectively. The expression levels of Ki-67 and p53 in EC tissues were detected by immunohistochemistry. ADC value was calculated, and three-dimensional (3D) texture features were measured on T2-weighted images (T2WI), diffusion-weighted images (DWI), and contrast-enhanced T1-weighted images (CE-T1WI). The univariate and multivariate logistic regression and cross-validations were used for the selection of texture features. The receiver operating characteristic (ROC) curve was performed to estimate the diagnostic efficiency of prediction model by the area under the curve (AUC) in the training and validation cohorts. RESULTS Significant differences of the ADC values were found in predicting Ki-67 and p53 (P=0.039, P=0.007). The AUC of the ADC value in predicting the expression levels of Ki-67 and p53 were 0.698, 0.853 and 0.626, 0.702 in the training and validation cohorts. The AUC of the TA model based on T2WI, DWI, CE-T1WI, and ADC value combined with T2WI + DWI + CE-T1WI in the training and validation cohorts for predicting the expression of Ki-67 were 0.741, 0.765, 0.733, 0.922 and 0.688, 0.691, 0.651, 0.938, respectively, and for predicting the expression of p53 were 0.763, 0.805, 0.781, 0.901 and 0.796, 0.713, 0.657, 0.922, respectively. CONCLUSION ADC values combined with TA are beneficial for predicting the expression levels of Ki-67 and p53 in EC patients before surgery, and they provide higher auxiliary diagnostic values for clinical application.
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Affiliation(s)
- Xueyan Jiang
- Department of Radiology, Bengbu Medical College, Bengbu, China
| | - Haodong Jia
- Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Zhongyuan Zhang
- Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Chao Wei
- Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Chuanbin Wang
- Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Jiangning Dong
- Department of Radiology, Bengbu Medical College, Bengbu, China.,Department of Radiology, The First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
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12
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Wei RL, Wei XT. Advanced Diagnosis of Glioma by Using Emerging Magnetic Resonance Sequences. Front Oncol 2021; 11:694498. [PMID: 34422648 PMCID: PMC8374052 DOI: 10.3389/fonc.2021.694498] [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: 04/13/2021] [Accepted: 07/19/2021] [Indexed: 12/15/2022] Open
Abstract
Glioma, the most common primary brain tumor in adults, can be difficult to discern radiologically from other brain lesions, which affects surgical planning and follow-up treatment. Recent advances in MRI demonstrate that preoperative diagnosis of glioma has stepped into molecular and algorithm-assisted levels. Specifically, the histology-based glioma classification is composed of multiple different molecular subtypes with distinct behavior, prognosis, and response to therapy, and now each aspect can be assessed by corresponding emerging MR sequences like amide proton transfer-weighted MRI, inflow-based vascular-space-occupancy MRI, and radiomics algorithm. As a result of this novel progress, the clinical practice of glioma has been updated. Accurate diagnosis of glioma at the molecular level can be achieved ahead of the operation to formulate a thorough plan including surgery radical level, shortened length of stay, flexible follow-up plan, timely therapy response feedback, and eventually benefit patients individually.
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Affiliation(s)
- Ruo-Lun Wei
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin-Ting Wei
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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13
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Jafari SH, Rabiei N, Taghizadieh M, Mirazimi SMA, Kowsari H, Farzin MA, Razaghi Bahabadi Z, Rezaei S, Mohammadi AH, Alirezaei Z, Dashti F, Nejati M. Joint application of biochemical markers and imaging techniques in the accurate and early detection of glioblastoma. Pathol Res Pract 2021; 224:153528. [PMID: 34171601 DOI: 10.1016/j.prp.2021.153528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/09/2021] [Accepted: 06/14/2021] [Indexed: 11/28/2022]
Abstract
Glioblastoma is a primary brain tumor with the most metastatic effect in adults. Despite the wide range of multidimensional treatments, tumor heterogeneity is one of the main causes of tumor spread and gives great complexity to diagnostic and therapeutic methods. Therefore, featuring noble noninvasive prognostic methods that are focused on glioblastoma heterogeneity is perceived as an urgent need. Imaging neuro-oncological biomarkers including MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status, tumor grade along with other tumor characteristics and demographic features (e.g., age) are commonly referred to during diagnostic, therapeutic and prognostic processes. Therefore, the use of new noninvasive prognostic methods focused on glioblastoma heterogeneity is considered an urgent need. Some neuronal biomarkers, including the promoter methylation status of the promoter MGMT, the characteristics and grade of the tumor, along with the patient's demographics (such as age and sex) are involved in diagnosis, treatment, and prognosis. Among the wide array of imaging techniques, magnetic resonance imaging combined with the more physiologically detailed technique of H-magnetic resonance spectroscopy can be useful in diagnosing neurological cancer patients. In addition, intracranial tumor qualitative analysis and sometimes tumor biopsies help in accurate diagnosis. This review summarizes the evidence for biochemical biomarkers being a reliable biomarker in the early detection and disease management in GBM. Moreover, we highlight the correlation between Imaging techniques and biochemical biomarkers and ask whether they can be combined.
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Affiliation(s)
- Seyed Hamed Jafari
- Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Nikta Rabiei
- School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Taghizadieh
- Department of Pathology, School of Medicine, Center for Women's Health Research Zahra, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Sayad Mohammad Ali Mirazimi
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
| | - Hamed Kowsari
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
| | - Mohammad Amin Farzin
- Department of Laboratory Medicine, School of Allied Medical Sciences, Kashan University of Medical Sciences, Kashan, Iran
| | - Zahra Razaghi Bahabadi
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran
| | - Samaneh Rezaei
- Department of Medical Biotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amir Hossein Mohammadi
- Research Center for Biochemistry and Nutrition in Metabolic Diseases, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran
| | - Zahra Alirezaei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Paramedical School, Bushehr University of Medical Sciences, Bushehr, Iran.
| | - Fatemeh Dashti
- School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; Student Research Committee, Kashan University of Medical Sciences, Kashan, Iran.
| | - Majid Nejati
- Anatomical Sciences Research Center, Institute for Basic Sciences, Kashan University of Medical Sciences, Kashan, Iran.
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14
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van Kempen EJ, Post M, Mannil M, Kusters B, ter Laan M, Meijer FJA, Henssen DJHA. Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis. Cancers (Basel) 2021; 13:cancers13112606. [PMID: 34073309 PMCID: PMC8198025 DOI: 10.3390/cancers13112606] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Glioma prognosis and treatment are based on histopathological characteristics and molecular profile. Following the World Health Organization (WHO) guidelines (2016), the most important molecular diagnostic markers include IDH1/2-genotype and 1p/19q codeletion status, although more recent publications also include ARTX genotype and TERT- and MGMT promoter methylation. Machine learning algorithms (MLAs), however, were described to successfully determine these molecular characteristics non-invasively by using magnetic resonance imaging (MRI) data. The aim of this review and meta-analysis was to define the diagnostic accuracy of MLAs with regard to these different molecular markers. We found high accuracies of MLAs to predict each individual molecular marker, with IDH1/2-genotype being the most investigated and the most accurate. Radiogenomics could therefore be a promising tool for discriminating genetically determined gliomas in a non-invasive fashion. Although encouraging results are presented here, large-scale, prospective trials with external validation groups are warranted. Abstract Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made reliably before surgery, without biopsy. Machine learning algorithms (MLAs) could play a role in achieving this by enabling glioma characterization on magnetic resonance imaging (MRI) data without invasive tissue sampling. The aim of this study is to provide a performance evaluation and meta-analysis of various MLAs for glioma characterization. Systematic literature search and meta-analysis were performed on the aggregated data, after which subgroup analyses for several target conditions were conducted. This study is registered with PROSPERO, CRD42020191033. We identified 724 studies; 60 and 17 studies were eligible to be included in the systematic review and meta-analysis, respectively. Meta-analysis showed excellent accuracy for all subgroups, with the classification of 1p/19q codeletion status scoring significantly poorer than other subgroups (AUC: 0.748, p = 0.132). There was considerable heterogeneity among some of the included studies. Although promising results were found with regard to the ability of MLA-tools to be used for the non-invasive classification of gliomas, large-scale, prospective trials with external validation are warranted in the future.
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Affiliation(s)
- Evi J. van Kempen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Max Post
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Manoj Mannil
- Clinic of Radiology, University Hospital Münster, WWU University of Münster, 48149 Münster, Germany;
| | - Benno Kusters
- Department of Pathology, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Mark ter Laan
- Department of Neurosurgery, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands;
| | - Frederick J. A. Meijer
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
| | - Dylan J. H. A. Henssen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University, 6500HB Nijmegen, The Netherlands; (E.J.v.K.); (M.P.); (F.J.A.M.)
- Correspondence:
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15
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Chen M, Cao J, Hu J, Topatana W, Li S, Juengpanich S, Lin J, Tong C, Shen J, Zhang B, Wu J, Pocha C, Kudo M, Amedei A, Trevisani F, Sung PS, Zaydfudim VM, Kanda T, Cai X. Clinical-Radiomic Analysis for Pretreatment Prediction of Objective Response to First Transarterial Chemoembolization in Hepatocellular Carcinoma. Liver Cancer 2021; 10:38-51. [PMID: 33708638 PMCID: PMC7923935 DOI: 10.1159/000512028] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 09/23/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND The preoperative selection of patients with intermediate-stage hepatocellular carcinoma (HCC) who are likely to have an objective response to first transarterial chemoembolization (TACE) remains challenging. OBJECTIVE To develop and validate a clinical-radiomic model (CR model) for preoperatively predicting treatment response to first TACE in patients with intermediate-stage HCC. METHODS A total of 595 patients with intermediate-stage HCC were included in this retrospective study. A tumoral and peritumoral (10 mm) radiomic signature (TPR-signature) was constructed based on 3,404 radiomic features from 4 regions of interest. A predictive CR model based on TPR-signature and clinical factors was developed using multivariate logistic regression. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the model's performance. RESULTS The final CR model consisted of 5 independent predictors, including TPR-signature (p < 0.001), AFP (p = 0.004), Barcelona Clinic Liver Cancer System Stage B (BCLC B) subclassification (p = 0.01), tumor location (p = 0.039), and arterial hyperenhancement (p = 0.050). The internal and external validation results demonstrated the high-performance level of this model, with internal and external AUCs of 0.94 and 0.90, respectively. In addition, the predicted objective response via the CR model was associated with improved survival in the external validation cohort (hazard ratio: 2.43; 95% confidence interval: 1.60-3.69; p < 0.001). The predicted treatment response also allowed for significant discrimination between the Kaplan-Meier curves of each BCLC B subclassification. CONCLUSIONS The CR model had an excellent performance in predicting the first TACE response in patients with intermediate-stage HCC and could provide a robust predictive tool to assist with the selection of patients for TACE.
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Affiliation(s)
- Mingyu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China,Engineering Research Center of Cognitive Healthcare of Zhejiang Province, Hangzhou, China,Zhejiang University School of Medicine, Hangzhou, China
| | - Jiasheng Cao
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | - Jiahao Hu
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | - Win Topatana
- Zhejiang University School of Medicine, Hangzhou, China
| | - Shijie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | | | - Jian Lin
- General Surgery, Longyou People's Hospital, Quzhou, China
| | - Chenhao Tong
- General Surgery, Shaoxing People's Hospital, Shaoxing, China
| | - Jiliang Shen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | - Bin Zhang
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | - Jennifer Wu
- Perlmutter Cancer Center, NYU Langone Health, New York, New York, USA
| | - Christine Pocha
- Avera McKennnan Hospital and University Medical Center, Sanford School of Medicine, University of South Dakota, Sioux Falls, South Dakota, USA
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University School of Medicine, Osaka, Japan
| | - Amedeo Amedei
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Franco Trevisani
- Department of Medical and Surgical Sciences, Semeiotica Medica, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Pil Soo Sung
- Department of Internal Medicine, College of Medicine, Eunpyeong St. Mary's Hospital, Seoul, Republic of Korea
| | - Victor M. Zaydfudim
- Department of Surgery, Section of Hepatobiliary and Pancreatic Surgery, University of Virginia, Charlottesville, Virginia, USA
| | - Tatsuo Kanda
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Xiujun Cai
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China,Engineering Research Center of Cognitive Healthcare of Zhejiang Province, Hangzhou, China,Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, Hangzhou, China,*Xiujun Cai, Department of General Surgery, Sir Run-Run Shaw Hospital, No. 3 East Qingchun Road, Hangzhou 310016 (China),
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