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Zhu Y, Wu M. Noninvasive radiomic analysis of enhanced CT predicts CTLA4 expression and prognosis in head and neck squamous cell carcinoma. Sci Rep 2023; 13:16782. [PMID: 37798374 PMCID: PMC10556051 DOI: 10.1038/s41598-023-43582-0] [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: 05/14/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023] Open
Abstract
Developing a radiomic model to predict CTLA4 expression levels and assessing its prognostic accuracy for patients. Medical imaging data were sourced from the TCIA database, while transcriptome sequencing data were derived from the TCGA database. We utilized a linear kernel SVM algorithm to develop a radiomic model for predicting CTLA4 gene expression. We then assessed the model's clinical relevance using survival and Cox regression analyses. Performance evaluations of the model were illustrated through ROC, PR, calibration, and decision curves. (1) Bioinformatics analysis: Kaplan-Meier curves indicated that increased CTLA4 expression correlates with enhanced overall survival (OS) (p < 0.001). Both univariate and multivariate analyses revealed that high CTLA4 expression served as a protective factor for OS (HR = 0.562, 95% CI 0.427-0.741, p < 0.001). (2) Radiomics evaluation: the ROC curve demonstrated that the AUC for the SVM radiomics model was 0.766 in the training set and 0.742 in the validation set. The calibration curve affirmed that the model's prediction probability for high gene expression aligns with the actual outcomes. Furthermore, decision curve analysis (DCA) indicated that our model boasts robust clinical applicability. CTLA4 expression level serves as an independent prognostic factor for HNSCCs. Using enhanced CT images, the SVM radiomic model effectively predicts CTLA4 expression levels. As a result, this model offers strong prognostic insights for HNSCCs, guiding precise diagnosis, treatment, and assisting in clinical decision-making.
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Affiliation(s)
- Yeping Zhu
- Nanjing University of Chinese Medicine, No. 282, Hanzhong Road, Nanjing, Jiangsu, China
| | - Mianhua Wu
- Nanjing University of Chinese Medicine, No. 282, Hanzhong Road, Nanjing, Jiangsu, China.
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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Xv Y, Lv F, Guo H, Liu Z, Luo D, Liu J, Gou X, He W, Xiao M, Zheng Y. A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma. Front Oncol 2021; 11:712554. [PMID: 34926241 PMCID: PMC8677659 DOI: 10.3389/fonc.2021.712554] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 11/02/2021] [Indexed: 11/29/2022] Open
Abstract
Objective This study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs). Methods 370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Results Using multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness. Conclusion The CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.
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Affiliation(s)
- Yingjie Xv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Haoming Guo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhaojun Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Di Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin Gou
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weiyang He
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yineng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Chen H, Lin F, Zhang J, Lv X, Zhou J, Li ZC, Chen Y. Deep Learning Radiomics to Predict PTEN Mutation Status From Magnetic Resonance Imaging in Patients With Glioma. Front Oncol 2021; 11:734433. [PMID: 34671557 PMCID: PMC8521070 DOI: 10.3389/fonc.2021.734433] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/09/2021] [Indexed: 11/30/2022] Open
Abstract
Objectives Phosphatase and tensin homolog (PTEN) mutation is an indicator of poor prognosis of low-grade and high-grade glioma. This study built a reliable model from multi-parametric magnetic resonance imaging (MRI) for predicting the PTEN mutation status in patients with glioma. Methods In this study, a total of 244 patients with glioma were retrospectively collected from our center (n = 77) and The Cancer Imaging Archive (n = 167). All patients were randomly divided into a training set (n = 170) and a validation set (n = 74). Three models were built from preoperative MRI for predicting PTEN status, including a radiomics model, a convolutional neural network (CNN) model, and an integrated model based on both radiomics and CNN features. The performance of each model was evaluated by accuracy and the area under the receiver operating characteristic curve (AUC). Results The CNN model achieved an AUC of 0.84 and an accuracy of 0.81, which performed better than did the radiomics model, with an AUC of 0.83 and an accuracy of 0.66. Combining radiomics with CNN will further benefit the predictive performance (accuracy = 0.86, AUC = 0.91). Conclusions The combination of both the CNN and radiomics features achieved significantly higher performance in predicting the mutation status of PTEN in patients with glioma than did the radiomics or the CNN model alone.
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Affiliation(s)
- Hongyu Chen
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Fuhua Lin
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jinming Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xiaofei Lv
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jian Zhou
- Department of Medical Imaging, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zhi-Cheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yinsheng Chen
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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Wu L, Lin P, Zhao Y, Li X, Yang H, He Y. Prediction of Genetic Alterations in Oncogenic Signaling Pathways in Squamous Cell Carcinoma of the Head and Neck: Radiogenomic Analysis Based on Computed Tomography Images. J Comput Assist Tomogr 2021; 45:932-940. [PMID: 34469904 PMCID: PMC8608003 DOI: 10.1097/rct.0000000000001213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE This study investigated the role of radiomics in evaluating the alterations of oncogenic signaling pathways in head and neck cancer. METHODS Radiomics features were extracted from 106 enhanced computed tomography images with head and neck squamous cell carcinoma. Support vector machine-recursive feature elimination was used for feature selection. Support vector machine algorithm was used to develop radiomics scores to predict genetic alterations in oncogenic signaling pathways. The performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS The alterations of the Cell Cycle, HIPPO, NOTCH, PI3K, RTK RAS, and TP53 signaling pathways were predicted by radiomics scores. The AUC values of the training cohort were 0.94, 0.91, 0.94, 0.93, 0.87, and 0.93, respectively. The AUC values of the validation cohort were all greater than 0.7. CONCLUSIONS Radiogenomics is a new method for noninvasive acquisition of tumor molecular information at the genetic level.
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Affiliation(s)
- Linyong Wu
- From the Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning
| | - Peng Lin
- From the Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning
| | - Yujia Zhao
- From the Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning
| | - Xin Li
- GE Healthcare, Shanghai, China
| | - Hong Yang
- From the Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning
| | - Yun He
- From the Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning
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Shivakumar N, Chandrashekar A, Handa AI, Lee R. Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review. Postgrad Med J 2021; 98:e20. [PMID: 33688072 DOI: 10.1136/postgradmedj-2020-139620] [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: 12/16/2020] [Revised: 02/08/2021] [Accepted: 02/20/2021] [Indexed: 11/16/2022]
Abstract
CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis. This review aims to provide an overview on the use of DL in CT image analysis in the diagnostic evaluation of metastatic disease. A total of 29 studies were included which could be grouped together into three areas of research: the use of deep learning on the detection of metastatic disease from CT imaging, characterisation of lesions on CT into metastasis and prediction of the presence or development of metastasis based on the primary tumour. In conclusion, DL in CT image analysis could have a potential role in evaluating metastatic disease; however, prospective clinical trials investigating its clinical value are required.
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Affiliation(s)
- Natesh Shivakumar
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Anirudh Chandrashekar
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Ashok Inderraj Handa
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Regent Lee
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, Oxfordshire, UK
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Wang X, Li BB. Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature. Front Genet 2021; 12:624820. [PMID: 33643386 PMCID: PMC7902873 DOI: 10.3389/fgene.2021.624820] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 01/07/2021] [Indexed: 12/24/2022] Open
Abstract
Head and neck tumors are the sixth most common neoplasms. Multiomics integrates multiple dimensions of clinical, pathologic, radiological, and biological data and has the potential for tumor diagnosis and analysis. Deep learning (DL), a type of artificial intelligence (AI), is applied in medical image analysis. Among the DL techniques, the convolution neural network (CNN) is used for image segmentation, detection, and classification and in computer-aided diagnosis. Here, we reviewed multiomics image analysis of head and neck tumors using CNN and other DL neural networks. We also evaluated its application in early tumor detection, classification, prognosis/metastasis prediction, and the signing out of the reports. Finally, we highlighted the challenges and potential of these techniques.
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Affiliation(s)
- Xi Wang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing, China
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences, Beijing, China
| | - Bin-bin Li
- Department of Oral Pathology, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing, China
- Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences, Beijing, China
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