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Dong H, Xi Y, Liu K, Chen L, Li Y, Pan X, Zhang X, Ye X, Ding Z. A Radiological-Radiomics model for differentiation between minimally invasive adenocarcinoma and invasive adenocarcinoma less than or equal to 3 cm: A two-center retrospective study. Eur J Radiol 2024; 176:111532. [PMID: 38820952 DOI: 10.1016/j.ejrad.2024.111532] [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: 01/08/2024] [Revised: 05/14/2024] [Accepted: 05/24/2024] [Indexed: 06/02/2024]
Abstract
OBJECTIVE To develop a Radiological-Radiomics (R-R) combined model for differentiation between minimal invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) of lung adenocarcinoma (LUAD) and evaluate its predictive performance. METHODS The clinical, pathological, and imaging data of a total of 509 patients (522 lesions) with LUAD diagnosed by surgical pathology from 2 medical centres were retrospectively collected, with 392 patients (402 lesions) from center 1 trained and validated using a five-fold cross-validation method, and 117 patients (120 lesions) from center 2 serving as an independent external test set. The least absolute shrinkage and selection operator (LASSO) method was utilized to filter features. Logistic regression was used to construct three models for predicting IA, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve curves (ROCs) were plotted, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS The R-R model for IA prediction achieved an AUC of 0.918 (95 % CI: 0.889-0.947), a sensitivity of 80.3 %, a specificity of 88.2 %, and an accuracy of 82.1 % in the training set. In the validation set, this model exhibited an AUC of 0.906 (95 % CI: 0.842-0.970), a sensitivity of 79.9 %, a specificity of 88.1 %, and an accuracy of 81.8 %. In the external test set, the AUC was 0.894 (95 % CI: 0.824-0.964), a sensitivity of 84.8 %, a specificity of 78.6 %, and an accuracy of 83.3 %. CONCLUSION The R-R model showed excellent diagnostic performance in differentiating MIA and IA, which can provide a certain reference for clinical diagnosis and surgical treatment plans.
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Affiliation(s)
- Hao Dong
- Department of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China
| | - Yuzhen Xi
- Department of Radiology, 903rd Hospital of PLA, Hangzhou, China
| | - Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yang Li
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xianpan Pan
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xingwei Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - XiaoDan Ye
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, China; Department of Cancer Center, Zhongshan Hospital, Fudan University, China.
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, China.
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Fan B, Fan B, Sun N, Zou H, Gu X. A radiomics model to predict γδ T-cell abundance and overall survival in head and neck squamous cell carcinoma. FASEB J 2024; 38:e23529. [PMID: 38441524 DOI: 10.1096/fj.202301353rr] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 02/01/2024] [Accepted: 02/19/2024] [Indexed: 03/07/2024]
Abstract
γδ T cells are becoming increasingly popular because of their attractive potential for antitumor immunotherapy. However, the role and assessment of γδ T cells in head and neck squamous cell carcinoma (HNSCC) are not well understood. We aimed to explore the prognostic value of γδ T cell and predict its abundance using a radiomics model. Computer tomography images with corresponding gene expression data and clinicopathological data were obtained from online databases. After outlining the volumes of interest manually, the radiomic features were screened using maximum melevance minimum redundancy and recursive feature elimination algorithms. A radiomics model was developed to predict γδ T-cell abundance using gradient boosting machine. Kaplan-Meier survival curves and univariate and multivariate Cox regression analyses were used for the survival analysis. In this study, we confirmed that γδ T-cell abundance was an independent predictor of favorable overall survival (OS) in patients with HNSCC. Moreover, a radiomics model was built to predict the γδ T-cell abundance level (the areas under the operating characteristic curves of 0.847 and 0.798 in the training and validation sets, respectively). The calibration and decision curves analysis demonstrated the fitness of the model. The high radiomic score was an independent protective factor for OS. Our results indicated that γδ T-cell abundance was a promising prognostic predictor in HNSCC, and the radiomics model could discriminate its abundance levels and predict OS. The noninvasive radiomics model provided a potentially powerful prediction tool to aid clinical judgment and antitumor immunotherapy.
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Affiliation(s)
- Binna Fan
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
- Department of Nursing, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Binting Fan
- Department of Nursing, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Na Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Huawei Zou
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiao Gu
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
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Zhang J, Zhao Y, Lu Y, Li P, Dang S, Li X, Yin B, Zhao L. Meningioma consistency assessment based on the fusion of deep learning features and radiomics features. Eur J Radiol 2024; 170:111250. [PMID: 38071910 DOI: 10.1016/j.ejrad.2023.111250] [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: 12/05/2022] [Revised: 09/27/2023] [Accepted: 11/30/2023] [Indexed: 01/16/2024]
Abstract
PURPOSE This study aims to combine deep learning features with radiomics features for the computer-assisted preoperative assessment of meningioma consistency. METHODS 202 patients with surgery and pathological diagnosis of meningiomas at our institution between December 2016 and December 2018 were retrospectively included in the study. The T2-fluid attenuated inversion recovery (T2-Flair) images were evaluated to classify meningioma as soft or hard by professional neurosurgeons based on Zada's consistency grading system. All the patients were split randomly into a training cohort (n = 162) and a testing cohort (n = 40). A convolutional neural network (CNN) model was proposed to extract deep learning features. These deep learning features were combined with radiomics features. After multiple feature selections, selected features were used to construct classification models using four classifiers. AUC was used to evaluate the performance of each classifier. A signature was further constructed by using the least absolute shrinkage and selection operator (LASSO). A nomogram based on the signature was created for predicting meningioma consistency. RESULTS The logistic regression classifier constructed using 17 radiomics features and 9 deep learning features provided the best performance with a precision of 0.855, a recall of 0.854, an F1-score of 0.852 and an AUC of 0.943 (95 % CI, 0.873-1.000) in the testing cohort. The C-index of the nomogram was 0.822 (95 % CI, 0.758-0.885) in the training cohort and 0.943 (95 % CI, 0.873-1.000) in the testing cohort with good calibration. Decision curve analysis further confirmed the clinical usefulness of the nomogram for predicting meningioma consistency. CONCLUSIONS The proposed method for assessing meningioma consistency based on the fusion of deep learning features and radiomics features is potentially clinically valuable. It can be used to assist physicians in the preoperative determination of tumor consistency.
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Affiliation(s)
- Jiatian Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230022, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Yajing Zhao
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China
| | - Yiping Lu
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China
| | - Peng Li
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Shijie Dang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China.
| | - Lingxiao Zhao
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
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Yasin P, Mardan M, Abliz D, Xu T, Keyoumu N, Aimaiti A, Cai X, Sheng W, Mamat M. The Potential of a CT-Based Machine Learning Radiomics Analysis to Differentiate Brucella and Pyogenic Spondylitis. J Inflamm Res 2023; 16:5585-5600. [PMID: 38034044 PMCID: PMC10683663 DOI: 10.2147/jir.s429593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
Background Pyogenic spondylitis (PS) and Brucella spondylitis (BS) are common spinal infections with similar manifestations, making their differentiation challenging. This study aimed to explore the potential of CT-based radiomics features combined with machine learning algorithms to differentiate PS from BS. Methods This retrospective study involved the collection of clinical and radiological information from 138 patients diagnosed with either PS or BS in our hospital between January 2017 and December 2022, based on histopathology examination and/or germ isolations. The region of interest (ROI) was defined by two radiologists using a 3D Slicer open-source platform, utilizing blind analysis of sagittal CT images against histopathological examination results. PyRadiomics, a Python package, was utilized to extract ROI features. Several methods were performed to reduce the dimensionality of the extracted features. Machine learning algorithms were trained and evaluated using techniques like the area under the receiver operating characteristic curve (AUC; confusion matrix-related metrics, calibration plot, and decision curve analysis to assess their ability to differentiate PS from BS. Additionally, permutation feature importance (PFI; local interpretable model-agnostic explanations (LIME; and Shapley additive explanation (SHAP) techniques were utilized to gain insights into the interpretabilities of the models that are otherwise considered opaque black-boxes. Results A total of 15 radiomics features were screened during the analysis. The AUC value and Brier score of best the model were 0.88 and 0.13, respectively. The calibration plot and decision curve analysis displayed higher clinical efficiency in the differential diagnosis. According to the interpretation results, the most impactful features on the model output were wavelet LHL small dependence low gray-level emphasis (GLDN). Conclusion The CT-based radiomics models that we developed have proven to be useful in reliably differentiating between PS and BS at an early stage and can provide a reliable explanation for the classification results.
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Affiliation(s)
- Parhat Yasin
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, People’s Republic of China
| | - Muradil Mardan
- School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China
| | - Dilxat Abliz
- Department of Orthopedic, The Eighth Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, People’s Republic of China
| | - Tao Xu
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, People’s Republic of China
| | - Nuerbiyan Keyoumu
- Department of Anesthesiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, People’s Republic of China
| | - Abasi Aimaiti
- Department of Anesthesiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, People’s Republic of China
| | - Xiaoyu Cai
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, People’s Republic of China
| | - Weibin Sheng
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, People’s Republic of China
| | - Mardan Mamat
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, People’s Republic of China
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Kang W, Qiu X, Luo Y, Luo J, Liu Y, Xi J, Li X, Yang Z. Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis. J Transl Med 2023; 21:598. [PMID: 37674169 PMCID: PMC10481579 DOI: 10.1186/s12967-023-04437-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/12/2023] [Indexed: 09/08/2023] Open
Abstract
The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.
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Affiliation(s)
- Wendi Kang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiang Qiu
- Obstetrics and Gynecology Hospital of, Fudan University, Shanghai, 200011, China
| | - Yingen Luo
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Jianwei Luo
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410013, Hunan, China
| | - 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
| | - Junqing Xi
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiao Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Zhengqiang Yang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China.
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Gozzi F, Bertolini M, Gentile P, Verzellesi L, Trojani V, De Simone L, Bolletta E, Mastrofilippo V, Farnetti E, Nicoli D, Croci S, Belloni L, Zerbini A, Adani C, De Maria M, Kosmarikou A, Vecchi M, Invernizzi A, Ilariucci F, Zanelli M, Iori M, Cimino L. Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma. Diagnostics (Basel) 2023; 13:2451. [PMID: 37510195 PMCID: PMC10378347 DOI: 10.3390/diagnostics13142451] [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: 05/24/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Anterior segment optical coherence tomography (AS-OCT) allows the explore not only the anterior chamber but also the front part of the vitreous cavity. Our cross-sectional single-centre study investigated whether AS-OCT can distinguish between vitreous involvement due to vitreoretinal lymphoma (VRL) and vitritis in uveitis. We studied AS-OCT images from 28 patients (11 with biopsy-proven VRL and 17 with differential diagnosis uveitis) using publicly available radiomics software written in MATLAB. Patients were divided into two balanced groups: training and testing. Overall, 3260/3705 (88%) AS-OCT images met our defined quality criteria, making them eligible for analysis. We studied five different sets of grey-level samplings (16, 32, 64, 128, and 256 levels), finding that 128 grey levels performed the best. We selected the five most effective radiomic features ranked by the ability to predict the class (VRL or uveitis). We built a classification model using the xgboost python function; through our model, 87% of eyes were correctly diagnosed as VRL or uveitis, regardless of exam technique or lens status. Areas under the receiver operating characteristic curves (AUC) in the 128 grey-level model were 0.95 [CI 0.94, 0.96] and 0.84 for training and testing datasets, respectively. This preliminary retrospective study highlights how AS-OCT can support ophthalmologists when there is clinical suspicion of VRL.
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Affiliation(s)
- Fabrizio Gozzi
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Marco Bertolini
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Pietro Gentile
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
- Clinical and Experimental Medicine Ph.D. Program, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Laura Verzellesi
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Valeria Trojani
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Luca De Simone
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Elena Bolletta
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | | | - Enrico Farnetti
- Molecular Pathology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Davide Nicoli
- Molecular Pathology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Stefania Croci
- Clinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Lucia Belloni
- Clinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Alessandro Zerbini
- Clinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Chantal Adani
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Michele De Maria
- Ophthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Areti Kosmarikou
- Ophthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Marco Vecchi
- Ophthalmology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Alessandro Invernizzi
- Eye Clinic, Luigi Sacco Hospital, Department of Biomedical and Clinical Science, University of Milan, 20157 Milan, Italy
- Faculty of Health and Medicine, Save Sight Institute, University of Sydney, Sydney, NSW 2000, Australia
| | | | - Magda Zanelli
- Surgical Oncology Unit, Azienda USL-IRCCS di Reggio Emilia, 42123 Reggio Emilia, Italy
| | - Mauro Iori
- Medical Physics Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
| | - Luca Cimino
- Ocular Immunology Unit, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy
- Department of Surgery, Medicine, Dentistry and Morphological Sciences, with Interest in Transplants, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia, 41124 Modena, Italy
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Xie Y, Wang M, Xia H, Sun H, Yuan Y, Jia J, Chen L. Development and validation of a CECT-based radiomics model for predicting IL1B expression and prognosis of head and neck squamous cell carcinoma. Front Oncol 2023; 13:1121485. [PMID: 36969073 PMCID: PMC10036854 DOI: 10.3389/fonc.2023.1121485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 02/23/2023] [Indexed: 03/12/2023] Open
Abstract
IntroductionIt is necessary to explore a noninvasive method to stratify head and neck squamous cell carcinoma (HNSCC)’s prognosis and to seek new indicators for individualized precision treatment. As a vital inflammatory cytokine, IL1B might drive a new tumor subtype that could be reflected in overall survival (OS) and predicted using the radiomics method.MethodsA total of 139 patients with RNA-Seq data from The Cancer Genome Atlas (TCGA) and matched CECT data from The Cancer Image Archive (TCIA) were included in the analysis. The prognostic value of IL1B expression in patients with HNSCC was analyzed using Kaplan-Meier analysis, Cox regression analysis and subgroup analysis. Furthermore, the molecular function of IL1B on HNSCC was explored using function enrichment and immunocytes infiltration analyses. Radiomic features were extracted with PyRadiomics and processed using max-relevance minredundancy, recursive feature elimination, and gradient boosting machine algorithm to construct aradiomics model for predicting IL1B expression. The area under the receiver operating characteristic curve (AUC), calibration curve, precision recall (PR) curve, and decision curve analysis (DCA) curve were used to examine the performance of the model.ResultsIncreased IL1B expression in patients with HNSCC indicated a poor prognosis (hazard ratio [HR] = 1.56, P = 0.003) and was harmful in patients who underwent radiotherapy (HR = 1.87, P = 0.007) or chemotherapy (HR = 2.514, P < 0.001). Shape_Sphericity, glszm_SmallAreaEmphasis, and firstorder_Kurtosis were included in the radiomics model (AUC: training cohort, 0.861; validation cohort, 0.703). The calibration curves, PR curves and DCA showed good diagnostic effect of the model. The rad-score was close related to IL1B (P = 4.490*10-9), and shared the same corelated trend to EMT-related genes with IL1B. A higher rad-score was associated with worse overall survival (P = 0.041).DiscussionThe CECT-based radiomics model provides preoperative IL1B expression predictionand offers non-invasive instructions for the prognosis and individualized treatment of patients withHNSCC.
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Affiliation(s)
- Yang Xie
- The State Key Laboratory Breeding Base of Basic Science of Stomatology, Hubei Province and Key Laboratory of Oral Biomedicine (Hubei-MOST and KLOBM), School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Min Wang
- Hubei-MOST and KLOBM, Department of Oral Implantology, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Haibin Xia
- Hubei-MOST and KLOBM, Department of Oral Implantology, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Huifang Sun
- The State Key Laboratory Breeding Base of Basic Science of Stomatology, Hubei Province and Key Laboratory of Oral Biomedicine (Hubei-MOST and KLOBM), School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Yi Yuan
- Department of Oral Radiology, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Jun Jia
- Department of Oral Maxillofacial-Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Liangwen Chen
- The State Key Laboratory Breeding Base of Basic Science of Stomatology, Hubei Province and Key Laboratory of Oral Biomedicine (Hubei-MOST and KLOBM), School and Hospital of Stomatology, Wuhan University, Wuhan, China
- *Correspondence: Liangwen Chen,
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Xue C, Zhou Q, Xi H, Zhou J. Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment. Diagn Interv Imaging 2023; 104:113-122. [PMID: 36283933 DOI: 10.1016/j.diii.2022.10.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 12/24/2022]
Abstract
With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.
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Affiliation(s)
- Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Huaze Xi
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China.
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Dercle L, McGale J, Sun S, Marabelle A, Yeh R, Deutsch E, Mokrane FZ, Farwell M, Ammari S, Schoder H, Zhao B, Schwartz LH. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J Immunother Cancer 2022; 10:jitc-2022-005292. [PMID: 36180071 PMCID: PMC9528623 DOI: 10.1136/jitc-2022-005292] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2022] [Indexed: 11/04/2022] Open
Abstract
Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology’s role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57–180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10–16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.
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Affiliation(s)
- Laurent Dercle
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Jeremy McGale
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Shawn Sun
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Aurelien Marabelle
- Therapeutic Innovation and Early Trials, Gustave Roussy, Villejuif, Île-de-France, France
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Eric Deutsch
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France
| | | | - Michael Farwell
- Division of Nuclear Medicine and Molecular Imaging, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Samy Ammari
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France.,Radiology, Institut de Cancérologie Paris Nord, Sarcelles, France
| | - Heiko Schoder
- Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Binsheng Zhao
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Lawrence H Schwartz
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
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10
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Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study. Cancers (Basel) 2022; 14:cancers14153656. [PMID: 35954318 PMCID: PMC9367613 DOI: 10.3390/cancers14153656] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/23/2022] [Accepted: 07/25/2022] [Indexed: 02/04/2023] Open
Abstract
The tumour immune microenvironment influences the efficacy of immune checkpoint inhibitors. Within this microenvironment are CD8-expressing tumour-infiltrating lymphocytes (CD8+ TILs), which are an important mediator and marker of anti-tumour response. In practice, the assessment of CD8+ TILs via tissue sampling involves logistical challenges. Radiomics, the high-throughput extraction of features from medical images, may offer a novel and non-invasive alternative. We performed a systematic review of the available literature reporting radiomic signatures associated with CD8+ TILs. We also aimed to evaluate the methodological quality of the identified studies using the Radiomics Quality Score (RQS) tool, and the risk of bias and applicability with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Articles were searched from inception until 31 December 2021, in three electronic databases, and screened against eligibility criteria. Twenty-seven articles were included. A wide variety of cancers have been studied. The reported radiomic signatures were heterogeneous, with very limited reproducibility between studies of the same cancer group. The overall quality of studies was found to be less than desirable (mean RQS = 33.3%), indicating a need for technical maturation. Some potential avenues for further investigation are also discussed.
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11
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Lin Y, Dai P, Lin Q, Chen J. A Predictive Nomogram for Atypical Meningioma Based on Preoperative MRI and Routine Blood Tests. World Neurosurg 2022; 163:e610-e616. [DOI: 10.1016/j.wneu.2022.04.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 11/30/2022]
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12
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Liu X, Sun C, Long M, Yang Y, Lin P, Xia S, Shen W. Computed tomography-based radiomics signature as a pretreatment predictor of progression-free survival in locally advanced hypopharyngeal carcinoma with a different response to induction chemotherapy. Eur Arch Otorhinolaryngol 2022; 279:3551-3562. [PMID: 35212776 DOI: 10.1007/s00405-022-07306-w] [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: 12/12/2021] [Accepted: 02/07/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE To establish and validate a radiomics signature for stratifying the risk of progression-free survival (PFS) in patients with locally advanced hypopharyngeal carcinoma (LAHC) undergoing induction chemotherapy (IC). METHODS We extracted radiomics features from baseline contrast-enhanced computed tomography (CECT) images. We enrolled 112 LAHC patients (78 in the training cohort and 34 in the validation cohort). We used cox regression model and random survival forests variable hunting (RSFVH) algorithm for feature selection and radiomics signature building. The radiomics signature was established in the training cohort and tested in the validation cohort. We used the Kaplan-Meier analysis and log-rank test to evaluate the ability of radiomics signature in PFS risk stratification among patients with different IC responses and constructed a radiomics nomogram to predict individual PFS risk. RESULTS The radiomics signature performed well in stratifying patients into highrisk and low-risk groups of progression in both the training and validation cohorts. The radiomics nomogram showed good discriminative ability for predicting PFS. Survival outcome analysis of subsets indicated that the radiomics signature performed well in stratifying the risk of PFS in patients with LAHC with different IC responses. CONCLUSIONS The radiomics signature was a pretreatment predictor for PFS in patients with LAHC who exhibited different responses to IC.
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Affiliation(s)
- Xiaobin Liu
- Department of Radiology, First Central Clinical College, Tianjin Medical University, Qixiangtai Road No. 22, Heping District, Tianjin, 300070, China
- Department of Radiology, Tianjin Medical Imaging Institute, Tianjin First Central Hospital, School of Medicine, Nankai University, Fukang Road No. 24, Nankai District, Tianjin, 300192, China
| | - Chuanqi Sun
- Department of Biomedical Engineering, Guangzhou Medical University, Xinzao Road No. 1, Panyu District, Guangzhou, 511436, China
| | - Miaomiao Long
- Department of Radiology, Tianjin Medical Imaging Institute, Tianjin First Central Hospital, School of Medicine, Nankai University, Fukang Road No. 24, Nankai District, Tianjin, 300192, China
| | - Yining Yang
- Department of Radiotherapy, Tianjin First Central Hospital, School of Medicine, Nankai University, Fukang Road No. 24, Nankai District, Tianjin, 300192, China
| | - Peng Lin
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, School of Medicine, Nankai University, Fukang Road No. 24, Nankai District, Tianjin, 300192, China
| | - Shuang Xia
- Department of Radiology, Tianjin Medical Imaging Institute, Tianjin First Central Hospital, School of Medicine, Nankai University, Fukang Road No. 24, Nankai District, Tianjin, 300192, China
| | - Wen Shen
- Department of Radiology, Tianjin Medical Imaging Institute, Tianjin First Central Hospital, School of Medicine, Nankai University, Fukang Road No. 24, Nankai District, Tianjin, 300192, China.
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