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Wu L, Wei D, Chen W, Wu C, Lu Z, Li S, Liu W. Comprehensive Potential of Artificial Intelligence for Predicting PD-L1 Expression and EGFR Mutations in Lung Cancer: A Systematic Review and Meta-Analysis. J Comput Assist Tomogr 2024:00004728-990000000-00348. [PMID: 39143665 DOI: 10.1097/rct.0000000000001644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
OBJECTIVE To evaluate the methodological quality and the predictive performance of artificial intelligence (AI) for predicting programmed death ligand 1 (PD-L1) expression and epidermal growth factor receptors (EGFR) mutations in lung cancer (LC) based on systematic review and meta-analysis. METHODS AI studies based on PET/CT, CT, PET, and immunohistochemistry (IHC)-whole-slide image (WSI) were included to predict PD-L1 expression or EGFR mutations in LC. The modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to evaluate the methodological quality. A comprehensive meta-analysis was conducted to analyze the overall area under the curve (AUC). The Cochrane diagnostic test and I2 statistics were used to assess the heterogeneity of the meta-analysis. RESULTS A total of 45 AI studies were included, of which 10 were used to predict PD-L1 expression and 35 were used to predict EGFR mutations. Based on the analysis using the QUADAS-2 tool, 37 studies achieved a high-quality score of 7. In the meta-analysis of PD-L1 expression levels, the overall AUCs for PET/CT, CT, and IHC-WSI were 0.80 (95% confidence interval [CI], 0.77-0.84), 0.74 (95% CI, 0.69-0.77), and 0.95 (95% CI, 0.93-0.97), respectively. For EGFR mutation status, the overall AUCs for PET/CT, CT, and PET were 0.85 (95% CI, 0.81-0.88), 0.83 (95% CI, 0.80-0.86), and 0.75 (95% CI, 0.71-0.79), respectively. The Cochrane Diagnostic Test revealed an I2 value exceeding 50%, indicating substantial heterogeneity in the PD-L1 and EGFR meta-analyses. When AI was combined with clinicopathological features, the enhancement in predicting PD-L1 expression was not substantial, whereas the prediction of EGFR mutations showed improvement compared to the CT and PET models, albeit not significantly so compared to the PET/CT models. CONCLUSIONS The overall performance of AI in predicting PD-L1 expression and EGFR mutations in LC has promising clinical implications.
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Affiliation(s)
- Linyong Wu
- From the Department of Medical Ultrasound, Maoming People's Hospital, Maoming
| | - Dayou Wei
- From the Department of Medical Ultrasound, Maoming People's Hospital, Maoming
| | - Wubiao Chen
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong Province, PR China
| | - Chaojun Wu
- From the Department of Medical Ultrasound, Maoming People's Hospital, Maoming
| | - Zhendong Lu
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong Province, PR China
| | - Songhua Li
- From the Department of Medical Ultrasound, Maoming People's Hospital, Maoming
| | - Wenci Liu
- Radiology Imaging Center, The Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong Province, PR China
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Wang Y, Sun D, Zhang J, Kong Y, Morelli JN, Wen D, Wu G, Li X. Multi-sequence MRI-based radiomics: An objective method to diagnose early-stage osteonecrosis of the femoral head. Eur J Radiol 2024; 177:111563. [PMID: 38897051 DOI: 10.1016/j.ejrad.2024.111563] [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: 03/14/2024] [Revised: 06/03/2024] [Accepted: 06/10/2024] [Indexed: 06/21/2024]
Abstract
OBJECTIVES This study investigated the use of radiomics for diagnosing early-stage osteonecrosis of the femoral head (ONFH) by extracting features from multiple MRI sequences and constructing predictive models. MATERIALS AND METHODS We conducted a retrospective review, collected MR images of early-stage ONFH (102 from institution A and 20 from institution B) and healthy femoral heads (102 from institution A and 20 from institution B) from two institutions. We extracted radiomics features, handled batch effects using Combat, and normalized features using z-score. We employed the Least absolute shrinkage and selection operator (LASSO) algorithm, along with Max-Relevance and Min-Redundancy (mRMR), to select optimal features for constructing radiomics models based on single, double, and multi-sequence MRI data. We evaluated performance using receiver operating characteristic (ROC) and precision-recall (PR) curves, and compared area under curve of ROC (AUC-ROC) values with the DeLong test. Additionally, we studied the diagnostic performance of the multi-sequence radiomics model and radiologists, compared the diagnostic outcomes of the model and radiologists using the Fisher exact test. RESULTS We studied 122 early-stage ONFH and 122 normal femoral heads. The multi-sequence model exhibited the best diagnostic performance among all models (AUC-ROC, PR-AUC for training set: 0.96, 0.961; validation set: 0.96, 0.97; test set: 0.94, 0.94), and it outperformed three resident radiologists on the external testing group with an accuracy of 87.5 %, sensitivity of 85.00 %, and specificity of 90.00 % (p < 0.01), highlighting the robustness of our findings. CONCLUSIONS Our study underscored the novelty of the multi-sequence radiomics model in diagnosing early-stage ONFH. By leveraging features extracted from multiple imaging sequences, this approach demonstrated high efficacy, indicating its potential to advance early diagnosis for ONFH. These findings provided important guidance for enhancing early diagnosis of ONFH through radiomics methods, offering new avenues and possibilities for clinical practice and patient care.
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Affiliation(s)
- Yi Wang
- The Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Road, Wuhan 430030, Hubei Province, People's Republic of China
| | - Dong Sun
- The Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Road, Wuhan 430030, Hubei Province, People's Republic of China
| | - Jing Zhang
- The Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Road, Wuhan 430030, Hubei Province, People's Republic of China
| | - Yuefeng Kong
- Radiology Department, Wuhan Fourth Hospital, No. 473 Hanzheng Street, Wuhan 430030, Hubei Province, People's Republic of China
| | - John N Morelli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Donglin Wen
- The Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Road, Wuhan 430030, Hubei Province, People's Republic of China
| | - Gang Wu
- The Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Road, Wuhan 430030, Hubei Province, People's Republic of China.
| | - Xiaoming Li
- The Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Road, Wuhan 430030, Hubei Province, People's Republic of China.
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3
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Caii W, Wu X, Guo K, Chen Y, Shi Y, Chen J. Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients. Cancer Immunol Immunother 2024; 73:153. [PMID: 38833187 PMCID: PMC11150226 DOI: 10.1007/s00262-024-03724-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024]
Abstract
BACKGROUND The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunotherapy response, based on the integration of deep learning and habitat radiomics in patients with advanced non-small cell lung cancer (NSCLC). METHODS Independent patient cohorts from three medical centers were enrolled for training (n = 164) and test (n = 82). Habitat imaging radiomics features were derived from sub-regions clustered from individual's tumor by K-means method. The deep learning features were extracted based on 3D ResNet algorithm. Pearson correlation coefficient, T test and least absolute shrinkage and selection operator regression were used to select features. Support vector machine was applied to implement deep learning and habitat radiomics, respectively. Then, a combination model was developed integrating both sources of data. RESULTS The combination model obtained a strong well-performance, achieving area under receiver operating characteristics curve of 0.865 (95% CI 0.772-0.931). The model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use. CONCLUSION The integration of deep-leaning and habitat radiomics contributed to predicting response to immunotherapy in patients with NSCLC. The developed integration model may be used as potential tool for individual immunotherapy management.
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Affiliation(s)
- Weimin Caii
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, 325000, China
| | - Xiao Wu
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Kun Guo
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Yongxian Chen
- Department of Chest Cancer, Xiamen Second People's Hospital, Xiamen, 36100, China
| | - Yubo Shi
- Department of Pulmonary, Yueqing People's Hospital, Wenzhou, 325000, China
| | - Junkai Chen
- Department of Emergency, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, 325000, China.
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Singh S, Mohajer B, Wells SA, Garg T, Hanneman K, Takahashi T, AlDandan O, McBee MP, Jawahar A. Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research. Acad Radiol 2024; 31:2281-2291. [PMID: 38286723 DOI: 10.1016/j.acra.2024.01.024] [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: 10/30/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 01/31/2024]
Abstract
Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment. Radiogenomics is an emerging area in radiomics that investigates the association between imaging characteristics and gene expression profiles. There are an increasing number of research publications using different radiomics approaches without a clear consensus on which method works best. We aim to describe the workflow of radiomics along with a guide of what to expect when starting a radiomics-based research project.
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Affiliation(s)
- Shiva Singh
- Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland
| | - Bahram Mohajer
- Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland
| | - Shane A Wells
- Radiology, University of Michigan, Ann Arbor, Michigan
| | - Tushar Garg
- Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland
| | - Kate Hanneman
- Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | - Omran AlDandan
- Department of Radiology, Imam Abdulrahman Bin Faisal University, College of Medicine: Dammam, Eastern, Saudi Arabia
| | - Morgan P McBee
- Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Anugayathri Jawahar
- Radiology, Northwestern University-Feinberg School of Medicine, 800, Arkes Pavilion, 676 N St. Clair St, Chicago, IL 60611.
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5
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Ye G, Zhang C, Zhuang Y, Liu H, Song E, Li K, Liao Y. An advanced nomogram model using deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma. Transl Oncol 2024; 44:101922. [PMID: 38554572 PMCID: PMC10998193 DOI: 10.1016/j.tranon.2024.101922] [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: 09/12/2023] [Revised: 12/01/2023] [Accepted: 02/23/2024] [Indexed: 04/01/2024] Open
Abstract
PURPOSE To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing the occult lymph node metastasis (OLNM) status in clinical stage IA lung adenocarcinoma. METHODS A cohort of 473 cases of lung adenocarcinomas from two hospitals was included, with 404 cases allocated to the training cohort and 69 cases to the testing cohort. Clinical characteristics and semantic features were collected, and radiomics features were extracted from the computed tomography (CT) images. Additionally, deep transfer learning (DTL) features were generated using RseNet50. Predictive models were developed using the logistic regression (LR) machine learning algorithm. Moreover, gene analysis was conducted on RNA sequencing data from 14 patients to explore the underlying biological basis of deep learning radiomics scores. RESULT The training and testing cohorts achieved AUC values of 0.826 and 0.775 for the clinical model, 0.865 and 0.801 for the radiomics model, 0.927 and 0.885 for the DTL-radiomics model, and 0.928 and 0.898 for the nomogram model. The nomogram model demonstrated superiority over the clinical model. The decision curve analysis (DCA) revealed a net benefit in predicting OLNM for all models. The investigation into the biological basis of deep learning radiomics scores identified an association between high scores and pathways related to tumor proliferation and immune cell infiltration in the microenvironment. CONCLUSIONS The nomogram model, incorporating clinical-semantic features, radiomics, and DTL features, exhibited promising performance in predicting OLNM. It has the potential to provide valuable information for non-invasive lymph node staging and individualized therapeutic approaches.
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Affiliation(s)
- Guanchao Ye
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chi Zhang
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuzhou Zhuang
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Liu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Kuo Li
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Yongde Liao
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Balasundaram A, C Doss GP. Deciphering the Impact of Rare Missense Variants in EGFR-TKI-Resistant Non-Small-Cell Lung Cancer through Whole Exome Sequencing: A Computational Approach. ACS OMEGA 2024; 9:16288-16302. [PMID: 38617633 PMCID: PMC11007825 DOI: 10.1021/acsomega.3c10229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 04/16/2024]
Abstract
Targeted therapy revolutionizes the treatment of non-small-cell lung cancer (NSCLC), harboring molecular change. Epidermal growth factor receptor(EGFR) mutations play a crucial role in the development of NSCLC, serving as a pivotal factor in its pathogenesis. We elucidated the mechanisms of resistance and potential therapeutic strategies in NSCLC resistant to the EGFR-tyrosine kinase inhibitor (EGFR-TKI). This is achieved by identifying rare missense variants through whole exome sequencing (WES). The goal is to enhance our understanding, identify biomarkers, and lay the groundwork for targeted interventions, thereby offering hope for an improved NSCLC treatment landscape. We conducted WES analysis on 16 NSCLC samples with EGFR-TKI-resistant NSCLC obtained from SRA-NCBI (PRJEB50602) to reveal genomic profiles within the EGFR-TKI. Our findings showed that 48% of the variants were missense, and after filtering with the Ensembl variant effect predictor, 53 rare missense variants in 23 genes were identified as highly deleterious. Further examination using pathogenic tools like PredictSNP revealed 12 deleterious rare missense variants in 7 genes: ZNF717, PSPH, ESRRA, SEMA3G, PTPN7, CAVIN4, and MYBBP1A. Molecular dynamics simulation (MDS) suggested that the L385P variant alters the structural flexibility of ESRRA, potentially leading to unfolding of ERRα proteins. This could impact their function and alter ERRα expression. These insights from MDS enhance our understanding of the structural and dynamic consequences of the L385P ESRRA variant and provide valuable implications for subsequent therapeutic considerations and targeted interventions.
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Affiliation(s)
- Ambritha Balasundaram
- Laboratory of Integrative
Genomics, Department of Integrative Biology, School of BioSciences
and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India
| | - George Priya C Doss
- Laboratory of Integrative
Genomics, Department of Integrative Biology, School of BioSciences
and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India
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Qu W, Chen C, Cai C, Gong M, Luo Q, Song Y, Yang M, Shi M. Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study. Front Immunol 2024; 15:1327779. [PMID: 38596674 PMCID: PMC11003263 DOI: 10.3389/fimmu.2024.1327779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
Neoadjuvant chemoimmunotherapy has revolutionized the therapeutic strategy for non-small cell lung cancer (NSCLC), and identifying candidates likely responding to this advanced treatment is of important clinical significance. The current multi-institutional study aims to develop a deep learning model to predict pathologic complete response (pCR) to neoadjuvant immunotherapy in NSCLC based on computed tomography (CT) imaging and further prob the biologic foundation of the proposed deep learning signature. A total of 248 participants administrated with neoadjuvant immunotherapy followed by surgery for NSCLC at Ruijin Hospital, Ningbo Hwamei Hospital, and Affiliated Hospital of Zunyi Medical University from January 2019 to September 2023 were enrolled. The imaging data within 2 weeks prior to neoadjuvant chemoimmunotherapy were retrospectively extracted. Patients from Ruijin Hospital were grouped as the training set (n = 104) and the validation set (n = 69) at the 6:4 ratio, and other participants from Ningbo Hwamei Hospital and Affiliated Hospital of Zunyi Medical University served as an external cohort (n = 75). For the entire population, pCR was obtained in 29.4% (n = 73) of cases. The areas under the curve (AUCs) of our deep learning signature for pCR prediction were 0.775 (95% confidence interval [CI]: 0.649 - 0.901) and 0.743 (95% CI: 0.618 - 0.869) in the validation set and the external cohort, significantly superior than 0.579 (95% CI: 0.468 - 0.689) and 0.569 (95% CI: 0.454 - 0.683) of the clinical model. Furthermore, higher deep learning scores correlated to the upregulation for pathways of cell metabolism and more antitumor immune infiltration in microenvironment. Our developed deep learning model is capable of predicting pCR to neoadjuvant chemoimmunotherapy in patients with NSCLC.
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Affiliation(s)
- Wendong Qu
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Cheng Chen
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Chuang Cai
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Ming Gong
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Qian Luo
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Yongxiang Song
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Minglei Yang
- Department of Thoracic Surgery, Ningbo Hwamei Hospital, Chinese Academy of Sciences, Zhejiang, China
| | - Min Shi
- Department of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Hospital of Civil Aviation Administration of China, Shanghai, China
- Department of Oncology, Wuxi Branch of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Wuxi, China
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Hu Y, Geng Y, Wang H, Chen H, Wang Z, Fu L, Huang B, Jiang W. Improved Prediction of Epidermal Growth Factor Receptor Status by Combined Radiomics of Primary Nonsmall-Cell Lung Cancer and Distant Metastasis. J Comput Assist Tomogr 2024:00004728-990000000-00298. [PMID: 38498926 DOI: 10.1097/rct.0000000000001591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
OBJECTIVES This study aimed to investigate radiomics based on primary nonsmall-cell lung cancer (NSCLC) and distant metastases to predict epidermal growth factor receptor (EGFR) mutation status. METHODS A total of 290 patients (mean age, 58.21 ± 9.28) diagnosed with brain (BM, n = 150) or spinal bone metastasis (SM, n = 140) from primary NSCLC were enrolled as a primary cohort. An external validation cohort, consisting of 69 patients (mean age, 59.87 ± 7.23; BM, n = 36; SM, n = 33), was enrolled from another center. Thoracic computed tomography-based features were extracted from the primary tumor and peritumoral area and selected using the least absolute shrinkage and selection operator regression to build a radiomic signature (RS-primary). Contrast-enhanced magnetic resonance imaging-based features were calculated and selected from the BM and SM to build RS-BM and RS-SM, respectively. The RS-BM-Com and RS-SM-Com were developed by integrating the most important features from the primary tumor, BM, and SM. RESULTS Six computed tomography-based features showed high association with EGFR mutation status: 3 from intratumoral and 3 from peritumoral areas. By combination of features from primary tumor and metastases, the developed RS-BM-Com and RS-SM-Com performed well with areas under curve in the training (RS-BM-Com vs RS-BM, 0.936 vs 0.885, P = 0.177; RS-SM-Com vs RS-SM, 0.929 vs 0.843, P = 0.003), internal validation (RS-BM-Com vs RS-BM, 0.920 vs 0.858, P = 0.492; RS-SM-Com vs RS-SM, 0.896 vs 0.859, P = 0.379), and external validation (RS-BM-Com vs RS-BM, 0.882 vs 0.805, P = 0.263; RS-SM-Com vs RS-SM, 0.865 vs 0.816, P = 0.312) cohorts. CONCLUSIONS This study indicates that the accuracy of detecting EGFR mutations significantly enhanced in the presence of metastases in primary NSCLC. The established radiomic signatures from this approach may be useful as new predictors for patients with distant metastases.
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Affiliation(s)
- Yue Hu
- From the Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing
| | - Yikang Geng
- School of Intelligent Medicine, China Medical University, Liaoning
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Liaoning
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang
| | - Zekun Wang
- Department of Medical Iconography, Liaoning Cancer Hospital & Institute, Liaoning
| | - Langyuan Fu
- School of Intelligent Medicine, China Medical University, Liaoning
| | - Bo Huang
- Department of Pathology, Liaoning Cancer Hospital and Institute, Liaoning
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Liaoning Cancer Hospital and Institute, Liaoning, People's Republic of China
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Gui Y, Zhang J. Research Progress of Artificial Intelligence in the Grading and Classification of Meningiomas. Acad Radiol 2024:S1076-6332(24)00073-4. [PMID: 38413314 DOI: 10.1016/j.acra.2024.02.003] [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: 12/02/2023] [Revised: 02/02/2024] [Accepted: 02/02/2024] [Indexed: 02/29/2024]
Abstract
A meningioma is a common primary central nervous system tumor. The histological features of meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis. Therefore, early diagnosis, grading, and typing of meningiomas are crucial for developing comprehensive and individualized diagnosis and treatment plans. The advancement of artificial intelligence (AI) in medical imaging, particularly radiomics and deep learning (DL), has contributed to the increasing research on meningioma grading and classification. These techniques are fast and accurate, involve fully automated learning, are non-invasive and objective, enable the efficient and non-invasive prediction of meningioma grades and classifications, and provide valuable assistance in clinical treatment and prognosis. This article provides a summary and analysis of the research progress in radiomics and DL for meningioma grading and classification. It also highlights the existing research findings, limitations, and suggestions for future improvement, aiming to facilitate the future application of AI in the diagnosis and treatment of meningioma.
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Affiliation(s)
- Yuan Gui
- Department of Radiology, the fifth affiliated hospital of zunyi medical university, zhufengdadao No.1439, Doumen District, Zhuhai, China
| | - Jing Zhang
- Department of Radiology, the fifth affiliated hospital of zunyi medical university, zhufengdadao No.1439, Doumen District, Zhuhai, China.
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Chen J, Liu L, He Z, Su D, Liu C. CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:180-195. [PMID: 38343232 DOI: 10.1007/s10278-023-00903-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/12/2023] [Accepted: 08/10/2023] [Indexed: 03/02/2024]
Abstract
To explore the value of CT-based radiomics model in the differential diagnosis of benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early malignant ovarian tumors (eMOTs). The retrospective research was conducted with pathologically confirmed 258 ovarian tumor patients from January 2014 to February 2021. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). By providing a three-dimensional (3D) characterization of the volume of interest (VOI) at the maximum level of images, 4238 radiomic features were extracted from the VOI per patient. The Wilcoxon-Mann-Whitney (WMW) test, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the radiomics models. The test cohort was used to verify the generalization ability of the radiomics models. The receiver-operating characteristic (ROC) was used to evaluate diagnostic performance of radiomics model. Global and discrimination performance of five models was evaluated by average area under the ROC curve (AUC). The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro/macro average AUC, 0.98/0.99), which was then confirmed with by LOOCV (micro/macro average AUC, 0.89/0.88) and external validation (test cohort) (micro/macro average AUC, 0.81/0.79). Our proposed CT-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs.
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Affiliation(s)
- Jia Chen
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Medical Imaging, Guangxi Key Clinical Specialty, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Medical Imaging, Dominant Cultivation Discipline of Guangxi Medical, University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Lei Liu
- School of Computer Science and Engineering, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, Guangxi, People's Republic of China
| | - Ziying He
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Danke Su
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Medical Imaging, Guangxi Key Clinical Specialty, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Medical Imaging, Dominant Cultivation Discipline of Guangxi Medical, University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
| | - Chanzhen Liu
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
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11
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Wang TW, Chao HS, Chiu HY, Lu CF, Liao CY, Lee Y, Chen JR, Shiao TH, Chen YM, Wu YT. Radiomics of metastatic brain tumor as a predictive image biomarker of progression-free survival in patients with non-small-cell lung cancer with brain metastasis receiving tyrosine kinase inhibitors. Transl Oncol 2024; 39:101826. [PMID: 37984256 PMCID: PMC10689936 DOI: 10.1016/j.tranon.2023.101826] [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: 07/15/2023] [Revised: 10/18/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Epidermal growth factor receptor (EGFR)-targeted tyrosine kinase inhibitors (TKIs) are the first-line therapy for EGFR-mutant non-small-cell lung cancer (NSCLC). Early prediction of treatment failure in patients with brain metastases treated with EGFR-TKIs may help in making decisions for systemic drug therapy or local brain tumor control. This study examined the predictive power of the radiomics of both brain metastasis tumors and primary lung tumors. We propose a deep learning based CoxCC model based on quantitative brain magnetic resonance imaging (MRI), a prognostic index and clinical data; the model can be used to predict progression-free survival (PFS) after EGFR-TKI therapy in advanced EGFR-mutant NSCLC. METHODS This retrospective single-center study included 271 patients receiving first-line EGFR-TKI targeted therapy in 2018-2019. Among them, 72 patients who had brain metastases before receiving first-line EGFR-TKI treatment. Three radiomic features were extracted from pretreatment brain MRI images. A CoxCC model for the progression risk stratification of EGFR-TKI treatment was proposed on the basis of MRI radiomics, clinical features, and a prognostic index. We performed time-dependent PFS predictions to evaluate the performance of the CoxCC model. RESULTS The CoxCC model based on a prognostic index, clinical features, and radiomic features of brain metastasis exhibited higher performance than clinical features combined with indexes previously proposed for determining the prognosis of brain metastasis, including recursive partitioning analysis, diagnostic-specific graded prognostic assessment, graded prognostic assessment for lung cancer using molecular markers (lung-molGPA), and modified lung-molGPA, with c-index values of 0.75, 0.67, 0.66, 0.65, and 0.65, respectively. The model achieved areas under the curve of 0.88, 0.73, 0.92, and 0.90 for predicting PFS at 3, 6, 9 and 12 months, respectively. PFS significantly differed between the high- and low-risk groups (p < 0.001). CONCLUSIONS For patients with advanced-stage NSCLC with brain metastasis, MRI radiomics of brain metastases may predict PFS. The CoxCC model integrating brain metastasis radiomics, clinical features, and a prognostic index provided reliable multi-time-point PFS predictions for patients with advanced NSCLC and brain metastases receiving EGFR-TKI treatment.
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Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Hwa-Yen Chiu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen Lee
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jyun-Ru Chen
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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12
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Gong J, Fu F, Ma X, Wang T, Ma X, You C, Zhang Y, Peng W, Chen H, Gu Y. Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images. Phys Med Biol 2023; 68:245021. [PMID: 37972417 DOI: 10.1088/1361-6560/ad0d43] [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: 07/09/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Abstract
Objective.Epidermal growth factor receptor (EGFR) mutation genotyping plays a pivotal role in targeted therapy for non-small cell lung cancer (NSCLC). We aimed to develop a computed tomography (CT) image-based hybrid deep radiomics model to predict EGFR mutation status in NSCLC and investigate the correlations between deep image and quantitative radiomics features.Approach.First, we retrospectively enrolled 818 patients from our centre and 131 patients from The Cancer Imaging Archive database to establish a training cohort (N= 654), an independent internal validation cohort (N= 164) and an external validation cohort (N= 131). Second, to predict EGFR mutation status, we developed three CT image-based models, namely, a multi-task deep neural network (DNN), a radiomics model and a feature fusion model. Third, we proposed a hybrid loss function to train the DNN model. Finally, to evaluate the model performance, we computed the areas under the receiver operating characteristic curves (AUCs) and decision curve analysis curves of the models.Main results.For the two validation cohorts, the feature fusion model achieved AUC values of 0.86 ± 0.03 and 0.80 ± 0.05, which were significantly higher than those of the single-task DNN and radiomics models (allP< 0.05). There was no significant difference between the feature fusion and the multi-task DNN models (P> 0.8). The binary prediction scores showed excellent prognostic value in predicting disease-free survival (P= 0.02) and overall survival (P< 0.005) for validation cohort 2.Significance.The results demonstrate that (1) the feature fusion and multi-task DNN models achieve significantly higher performance than that of the conventional radiomics and single-task DNN models, (2) the feature fusion model can decode the imaging phenotypes representing NSCLC heterogeneity related to both EGFR mutation and patient NSCLC prognosis, and (3) high correlations exist between some deep image and radiomics features.
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Affiliation(s)
- Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 20003, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Fangqiu Fu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Thoracic Surgery and State key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, 200032, People's Republic of China
| | - Xiaowen Ma
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 20003, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Ting Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 20003, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xiangyi Ma
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Thoracic Surgery and State key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, 200032, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 20003, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Yang Zhang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Thoracic Surgery and State key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, 200032, People's Republic of China
| | - Weijun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 20003, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Haiquan Chen
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Department of Thoracic Surgery and State key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, 200032, People's Republic of China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 20003, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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13
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Wang M, Jiang H. PST-Radiomics: a PET/CT lymphoma classification method based on pseudo spatial-temporal radiomic features and structured atrous recurrent convolutional neural network. Phys Med Biol 2023; 68:235014. [PMID: 37956448 DOI: 10.1088/1361-6560/ad0c0f] [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: 07/23/2023] [Accepted: 11/13/2023] [Indexed: 11/15/2023]
Abstract
Objective.Existing radiomic methods tend to treat each isolated tumor as an inseparable whole, when extracting radiomic features. However, they may discard the critical intra-tumor metabolic heterogeneity (ITMH) information, that contributes to triggering tumor subtypes. To improve lymphoma classification performance, we propose a pseudo spatial-temporal radiomic method (PST-Radiomics) based on positron emission tomography computed tomography (PET/CT).Approach.Specifically, to enable exploitation of ITMH, we first present a multi-threshold gross tumor volume sequence (GTVS). Next, we extract 1D radiomic features based on PET images and each volume in GTVS and create a pseudo spatial-temporal feature sequence (PSTFS) tightly interwoven with ITMH. Then, we reshape PSTFS to create 2D pseudo spatial-temporal feature maps (PSTFM), of which the columns are elements of PSTFS. Finally, to learn from PSTFM in an end-to-end manner, we build a light-weighted pseudo spatial-temporal radiomic network (PSTR-Net), in which a structured atrous recurrent convolutional neural network serves as a PET branch to better exploit the strong local dependencies in PSTFM, and a residual convolutional neural network is used as a CT branch to exploit conventional radiomic features extracted from CT volumes.Main results.We validate PST-Radiomics based on a PET/CT lymphoma subtype classification task. Experimental results quantitatively demonstrate the superiority of PST-Radiomics, when compared to existing radiomic methods.Significance.Feature map visualization of our method shows that it performs complex feature selection while extracting hierarchical feature maps, which qualitatively demonstrates its superiority.
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Affiliation(s)
- Meng Wang
- Software College, Northeastern University, Shenyang 110819, People's Republic of China
| | - Huiyan Jiang
- Software College, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110819, People's Republic of China
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14
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Roisman LC, Kian W, Anoze A, Fuchs V, Spector M, Steiner R, Kassel L, Rechnitzer G, Fried I, Peled N, Bogot NR. Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer. NPJ Precis Oncol 2023; 7:125. [PMID: 37990050 PMCID: PMC10663598 DOI: 10.1038/s41698-023-00473-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/24/2023] [Indexed: 11/23/2023] Open
Abstract
Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.
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Affiliation(s)
- Laila C Roisman
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel.
- Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| | - Waleed Kian
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
- The Institute of Oncology, Assuta Ashdod, Ashdod, Israel
| | - Alaa Anoze
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Vered Fuchs
- Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Maria Spector
- The Department of Radiology, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Roee Steiner
- The Institute for Nuclear Medicine, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Levi Kassel
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Gilad Rechnitzer
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Iris Fried
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Nir Peled
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel.
| | - Naama R Bogot
- The Department of Radiology, Shaare Zedek Medical Center, Jerusalem, Israel
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15
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Li Y, Lv X, Wang Y, Xu Z, Lv Y, Hou D. CT-based nomogram for early identification of T790M resistance in metastatic non-small cell lung cancer before first-line epidermal growth factor receptor-tyrosine kinase inhibitors therapy. Eur Radiol Exp 2023; 7:64. [PMID: 37914925 PMCID: PMC10620367 DOI: 10.1186/s41747-023-00380-7] [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: 07/18/2023] [Accepted: 08/31/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND To evaluate the value of computed tomography (CT) radiomics in predicting the risk of developing epidermal growth factor receptor (EGFR) T790M resistance mutation for metastatic non-small lung cancer (NSCLC) patients before first-line EGFR-tyrosine kinase inhibitors (EGFR-TKIs) therapy. METHODS A total of 162 metastatic NSCLC patients were recruited and split into training and testing cohort. Radiomics features were extracted from tumor lesions on nonenhanced CT (NECT) and contrast-enhanced CT (CECT). Radiomics score (rad-score) of two CT scans was calculated respectively. A nomogram combining two CT scans was developed to evaluate T790M resistance within up to 14 months. Patients were followed up to calculate the time of T790M occurrence. Models were evaluated by area under the curve at receiver operating characteristic analysis (ROC-AUC), calibration curve, and decision curve analysis (DCA). The association of the nomogram with the time of T790M occurrence was evaluated by Kaplan-Meier survival analysis. RESULTS The nomogram constructed with the rad-score of NECT and CECT for predicting T790M resistance within 14 months achieved the highest ROC-AUCs of 0.828 and 0.853 in training and testing cohorts, respectively. The DCA showed that the nomogram was clinically useful. The Kaplan-Meier analysis showed that the occurrence time of T790M difference between the high- and low-risk groups distinguished by the rad-score was significant (p < 0.001). CONCLUSIONS The CT-based radiomics signature may provide prognostic information and improve pretreatment risk stratification in EGFR NSCLC patients before EGFR-TKIs therapy. The multimodal radiomics nomogram further improved the capability. RELEVANCE STATEMENT Radiomics based on NECT and CECT images can effectively identify and stratify the risk of T790M resistance before the first-line TKIs treatment in metastatic non-small cell lung cancer patients. KEY POINTS • Early identification of the risk of T790M resistance before TKIs treatment is clinically relevant. • Multimodel radiomics nomogram holds potential to be a diagnostic tool. • It provided an imaging surrogate for identifying the pretreatment risk of T790M.
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Affiliation(s)
- Ye Li
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China
| | - Xinna Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China
| | - Yichuan Wang
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China
| | - Zexuan Xu
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China
| | - Yan Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China.
| | - Dailun Hou
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China.
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16
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Tang X, Li Y, Shen LT, Yan WF, Qian WL, Yang ZG. CT Radiomics Predict EGFR-T790M Resistance Mutation in Advanced Non-Small Cell Lung Cancer Patients After Progression on First-line EGFR-TKI. Acad Radiol 2023; 30:2574-2587. [PMID: 36941156 DOI: 10.1016/j.acra.2023.01.040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/25/2023] [Accepted: 01/31/2023] [Indexed: 03/23/2023]
Abstract
RATIONALE AND OBJECTIVES We aim to explore the value of chest CT radiomics in predicting the epidermal growth factor receptor (EGFR)-T790M resistance mutation of advanced non-small cell lung cancer (NSCLC) patients after the failure of first-line EGFR-tyrosine kinase inhibitor (EGFR-TKI). MATERIALS AND METHODS A total of 211 and 135 advanced NSCLC patients with tumor tissue-based (Cohort-1) or circulating tumor DNA (ctDNA)-based (Cohort-2) EGFR-T790M testing were included, respectively. Cohort-1 was used for modeling and Cohort-2 was for models' validation. Radiomic features were extracted from tumor lesions on chest nonenhanced CT (NECT) and/or contrast-enhanced CT (CECT). We used eight feature selectors and eight classifier algorithms to establish radiomic models. Models were evaluated by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS CT morphological manifestations of peripheral location and pleural indentation sign were associated with EGFR-T790M. For NECT, CECT, and NECT+CECT radiomic features, the feature selector and classifier algorithms of LASSO and Stepwise logistic regression, Boruta and SVM, and LASSO and SVM were chosen to develop the optimal model, respectively (AUC: 0.844, 0.811, and 0.897). All models performed well in calibration curves and DCA. Independent validation of models in Cohort-2 revealed that both NECT and CECT models individually had limited power for predicting EGFR-T790M mutation detected by ctDNA (AUC: 0.649, 0.675), while the NECT+CECT radiomic model had a satisfactory AUC (0.760). CONCLUSION This study proved the feasibility of using CT radiomic features to predict the EGFR-T790M resistance mutation, which could be helpful in guiding personalized therapeutic strategies.
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Affiliation(s)
- Xin Tang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuan Li
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li-Ting Shen
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei-Feng Yan
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wen-Lei Qian
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhi-Gang Yang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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Zhang S, Liu X, Zhou L, Wang K, Shao J, Shi J, Wang X, Mu J, Gao T, Jiang Z, Chen K, Wang C, Wang G. Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center study. EClinicalMedicine 2023; 65:102270. [PMID: 38106558 PMCID: PMC10725055 DOI: 10.1016/j.eclinm.2023.102270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/27/2023] [Accepted: 09/27/2023] [Indexed: 12/19/2023] Open
Abstract
Background Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients. Methods 20,333 CT images of 6371 patients from June 12, 2009 to March 24, 2022 in West China Hospital of Sichuan University, Mianzhu People's Hospital, Peking University People's Hospital, Chengdu Shangjin Nanfu Hospital and Guangan Peoples' Hospital were included in this retrospective study. We developed the IPES based on self-supervised pre-training and multi-task learning, which aimed to predict an overall survival (OS) risk for each patient. We further evaluated the prognostic accuracy of the IPES and its ability to stratify NSCLC patients with the same TNM stage and with the same EGFR genotype. Findings The IPES was able to predict OS risk for stage I-III resected NSCLC patients in the training set (C-index 0.806; 95% CI: 0.744-0.846), internal validation set (0.783; 95% CI: 0.744-0.825) and external validation set (0.817; 95% CI: 0.786-0.849). In addition, IPES performed well in early-stage (stage I) and EGFR genotype prediction. Furthermore, by adopting IPES-based survival score (IPES-score), resected NSCLC patients in the same stage or with the same EGFR genotype could be divided into low- and high-risk subgroups with good and poor prognosis, respectively (p < 0.05 for all). Interpretation The IPES provided a non-invasive way to obtain prognosis-related information from patients. The identification of IPES for resected NSCLC patients with low and high prognostic risk in the same TNM stage or with the same EGFR genotype suggests that IPES have potential to offer more personalized treatment and surveillance suggestion for NSCLC patients. Funding This study was funded by the National Natural Science Foundation of China (grant 62272055, 92259303, 92059203), New Cornerstone Science Foundation through the XPLORER PRIZE, Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities (K.C.), Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002), BUPT Excellent Ph.D. Students Foundation (CX2022104).
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Affiliation(s)
- Siqi Zhang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Xiaohong Liu
- UCL Cancer Institute, University College London, London, WC1E 6DD, UK
| | - Lixin Zhou
- Thoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
| | - Kai Wang
- College of Future Technology, Peking University and Peking-Tsinghua Center for Life Sciences, Beijing, 100871, China
| | - Jun Shao
- State Key Laboratory of Respiratory Health and Multimorbidity, Department of Pulmonary and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianyu Shi
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Xuan Wang
- Thoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
| | - Jiaxing Mu
- Thoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
| | - Tianrun Gao
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Zeyu Jiang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Kezhong Chen
- Thoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, China
| | - Chengdi Wang
- State Key Laboratory of Respiratory Health and Multimorbidity, Department of Pulmonary and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Guangyu Wang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
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18
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Huang S, Xu F, Zhu W, Xie D, Lou K, Huang D, Hu H. Multi-dimensional radiomics analysis to predict visceral pleural invasion in lung adenocarcinoma of ≤3 cm maximum diameter. Clin Radiol 2023; 78:e847-e855. [PMID: 37607844 DOI: 10.1016/j.crad.2023.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 06/20/2023] [Accepted: 07/21/2023] [Indexed: 08/24/2023]
Abstract
AIM To explore the value of radiomics analysis in preoperatively predicting visceral pleural invasion (VPI) of lung adenocarcinoma (LAC) with ≤3 cm maximum diameter and to compare the performance of two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics models. MATERIALS AND METHODS A total of 391 LAC patients were enrolled retrospectively, of whom 142 were VPI (+) and 249 were VPI (-). Radiomics features were extracted from 2D and 3D regions of interest (ROIs) of tumours in CT images. 2D and 3D radiomics models were developed combining the optimal radiomics features by using the logistic regression machine-learning method and radiomics scores (rad-scores) were calculated. Nomograms were constructed by integrating independent risk factors and rad-scores. The performance of each model was evaluated by using the receiver operator characteristic (ROC) curve, decision curve analysis (DCA), clinical impact curve (CIC), and calculating the area under the curve (AUC). RESULTS There was no difference in the VPI prediction between 2D and 3D radiomics models (training group: 2D AUC=0.835, 3D AUC=0.836, p=0.896; validation group: 2D AUC=0.803, 3D AUC=0.794, p=0.567). The 2D and 3D nomograms performed similarly regarding discrimination (training group: 2D AUC=0.867, 3D AUC=0.862, p=0.409, validation group: 2D AUC=0.835, 3D AUC=0.827, p=0.558), and outperformed their corresponding radiomics models and the clinical model. DCA and CIC revealed that the 2D nomogram had slightly better clinical utility. CONCLUSION The 2D radiomics model has a similar discrimination capability compared with the 3D radiomics model. The 2D nomogram performs slightly better for individual VPI prediction in LAC.
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Affiliation(s)
- S Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Radiology, Ningbo Medical Center LiHuili Hospital, Ningbo, Zhejiang, China
| | - F Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - W Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - D Xie
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Radiology, Shaoxing Second Hospital, Shaoxing, Zhejiang, China
| | - K Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - D Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - H Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Chang R, Qi S, Wu Y, Yue Y, Zhang X, Qian W. Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy. Cancer Imaging 2023; 23:101. [PMID: 37867196 PMCID: PMC10590525 DOI: 10.1186/s40644-023-00620-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/16/2023] [Indexed: 10/24/2023] Open
Abstract
OBJECTIVES This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment. MATERIALS AND METHODS In a training cohort of 121 NSCLC patients, radiomic features were extracted, selected from intra- and peri-tumoral regions, and used to build signatures (S1 and S2) using a Cox regression model. Deep learning features were obtained from three convolutional neural networks and utilized to build signatures (S3, S4, and S5) that were stratified into over- and under-expression subgroups for survival risk using X-tile. After univariate and multivariate Cox regression analyses, a nomogram incorporating the tumor, node, and metastasis (TNM) stages, radiomic signature, and deep learning signature was established to predict OS and PFS, respectively. The performance was validated using an independent cohort (61 patients). RESULTS TNM stages, S2 and S3 were identified as the significant prognosis factors for both OS and PFS; S2 (OS: (HR (95%), 2.26 (1.40-3.67); PFS: (HR (95%), 2.23 (1.36-3.65)) demonstrated the best ability in discriminating patients with over- and under-expression. For the OS nomogram, the C-index (95% CI) was 0.74 (0.70-0.79) and 0.72 (0.67-0.78) in the training and validation cohorts, respectively; for the PFS nomogram, the C-index (95% CI) was 0.71 (0.68-0.81) and 0.72 (0.66-0.79). The calibration curves for the 3- and 5-year OS and PFS were in acceptable agreement between the predicted and observed survival. The established nomogram presented a higher overall net benefit than the TNM stage for predicting both OS and PFS. CONCLUSION By integrating the TNM stage, CT radiomic signature, and deep learning signatures, the established nomograms can predict the individual prognosis of NSCLC patients who received chemotherapy. The integrated nomogram has the potential to improve the individualized treatment and precise management of NSCLC patients.
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Affiliation(s)
- Runsheng Chang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Yanan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yong Yue
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaoye Zhang
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Wei Qian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
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20
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Li X, Zhang C, Li T, Lin X, Wu D, Yang G, Cao D. Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies. Sci Rep 2023; 13:15586. [PMID: 37730961 PMCID: PMC10511693 DOI: 10.1038/s41598-023-42916-2] [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: 03/10/2023] [Accepted: 09/16/2023] [Indexed: 09/22/2023] Open
Abstract
Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas before radiographic advance cannot be perceived by the naked eye. This study aimed to discover and validate a CT radiomic model to precisely identify the EAR. Training cohort (n = 67) and internal test cohort (n = 29) were from the First Affiliated Hospital of Fujian Medical University, and external test cohort (n = 29) was from the Second Affiliated Hospital of Xiamen Medical College. Follow-up CT images at three different times of each patient were collected: (1) baseline images before EGFR-TKIs therapy; (2) first follow-up images after EGFR-TKIs therapy (FFT); (3) EAR images, which were the last follow-up images before radiographic advance. The features extracted from FFT and EAR were used to construct the classic radiomic model. The delta features which were calculated by subtracting the baseline from either FFT or EAR were used to construct the delta radiomic model. The classic radiomic model achieved AUC 0.682 and 0.641 in training and internal test cohorts, respectively. The delta radiomic model achieved AUC 0.730 and 0.704 in training and internal test cohorts, respectively. Over the external test cohort, the delta radiomic model achieved AUC 0.661. The decision curve analysis showed that when threshold of the probability of the EAR to the EGFR-TKIs was between 0.3 and 0.82, the proposed model was more benefit than treating all patients. Based on two central studies, the delta radiomic model derived from the follow-up non-enhanced CT images can help clinicians to identify the EAR to EGFR-TKIs in lung adenocarcinomas before radiographic advance and optimize clinical outcomes.
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Affiliation(s)
- Xiumei Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China
| | - Tingting Li
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, 361021, Fujian, China
| | - Xiuqiang Lin
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China.
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China.
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, China.
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, Fujian, China.
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital, Fujian Medical University, Shanghai, 200062, China.
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21
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Rinaldi L, Guerini Rocco E, Spitaleri G, Raimondi S, Attili I, Ranghiero A, Cammarata G, Minotti M, Lo Presti G, De Piano F, Bellerba F, Funicelli G, Volpe S, Mora S, Fodor C, Rampinelli C, Barberis M, De Marinis F, Jereczek-Fossa BA, Orecchia R, Rizzo S, Botta F. Association between Contrast-Enhanced Computed Tomography Radiomic Features, Genomic Alterations and Prognosis in Advanced Lung Adenocarcinoma Patients. Cancers (Basel) 2023; 15:4553. [PMID: 37760521 PMCID: PMC10527057 DOI: 10.3390/cancers15184553] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers, are warranted to foster therapy personalization of patients with advanced non-small cell lung cancer (NSCLC). This study investigated the association of contrast-enhanced Computed Tomography (CT) radiomic features of lung adenocarcinoma lesions, alone or integrated with clinical parameters, with tumor mutational status (EGFR, KRAS, ALK alterations) and Overall Survival (OS). In total, 261 retrospective and 48 prospective patients were enrolled. A Radiomic Score (RS) was created with LASSO-Logistic regression models to predict mutational status. Radiomic, clinical and clinical-radiomic models were trained on retrospective data and tested (Area Under the Curve, AUC) on prospective data. OS prediction models were trained and tested on retrospective data with internal cross-validation (C-index). RS significantly predicted each alteration at training (radiomic and clinical-radiomic AUC 0.95-0.98); validation performance was good for EGFR (AUC 0.86), moderate for KRAS and ALK (AUC 0.61-0.65). RS was also associated with OS at univariate and multivariable analysis, in the latter with stage and type of treatment. The validation C-index was 0.63, 0.79, and 0.80 for clinical, radiomic, and clinical-radiomic models. The study supports the potential role of CT radiomics for non-invasive identification of gene alterations and prognosis prediction in patients with advanced lung adenocarcinoma, to be confirmed with independent studies.
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Affiliation(s)
- Lisa Rinaldi
- Radiation Research Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy;
| | - Elena Guerini Rocco
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (E.G.R.); (A.R.); (M.B.)
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy; (S.V.)
| | - Gianluca Spitaleri
- Division of Thoracic Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (G.S.); (I.A.); (F.D.M.)
| | - Sara Raimondi
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Ilaria Attili
- Division of Thoracic Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (G.S.); (I.A.); (F.D.M.)
| | - Alberto Ranghiero
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (E.G.R.); (A.R.); (M.B.)
| | - Giulio Cammarata
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Marta Minotti
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Giuliana Lo Presti
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Francesca De Piano
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Federica Bellerba
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy (F.B.)
| | - Gianluigi Funicelli
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Stefania Volpe
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy; (S.V.)
- Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Serena Mora
- Data Management Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (S.M.); (C.F.)
| | - Cristiana Fodor
- Data Management Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (S.M.); (C.F.)
| | - Cristiano Rampinelli
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
| | - Massimo Barberis
- Division of Pathology, European Institute of Oncology IRCCS, 20141 Milan, Italy; (E.G.R.); (A.R.); (M.B.)
| | - Filippo De Marinis
- Division of Thoracic Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (G.S.); (I.A.); (F.D.M.)
| | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy; (S.V.)
- Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Roberto Orecchia
- Division of Radiology, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy; (M.M.); (C.R.); (R.O.)
- Scientific Direction, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Stefania Rizzo
- Clinica di Radiologia EOC, Istituto Imaging della Svizzera Italiana (IIMSI), Via Tesserete 46, 6900 Lugano, Switzerland;
- Faculty of Biomedical Sciences, Università della Svizzera italiana, Via G. Buffi 13, 6900 Lugano, Switzerland
| | - Francesca Botta
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy;
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22
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Shao J, Feng J, Li J, Liang S, Li W, Wang C. Novel tools for early diagnosis and precision treatment based on artificial intelligence. CHINESE MEDICAL JOURNAL PULMONARY AND CRITICAL CARE MEDICINE 2023; 1:148-160. [PMID: 39171128 PMCID: PMC11332840 DOI: 10.1016/j.pccm.2023.05.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Indexed: 08/23/2024]
Abstract
Lung cancer has the highest mortality rate among all cancers in the world. Hence, early diagnosis and personalized treatment plans are crucial to improving its 5-year survival rate. Chest computed tomography (CT) serves as an essential tool for lung cancer screening, and pathology images are the gold standard for lung cancer diagnosis. However, medical image evaluation relies on manual labor and suffers from missed diagnosis or misdiagnosis, and physician heterogeneity. The rapid development of artificial intelligence (AI) has brought a whole novel opportunity for medical task processing, demonstrating the potential for clinical application in lung cancer diagnosis and treatment. AI technologies, including machine learning and deep learning, have been deployed extensively for lung nodule detection, benign and malignant classification, and subtype identification based on CT images. Furthermore, AI plays a role in the non-invasive prediction of genetic mutations and molecular status to provide the optimal treatment regimen, and applies to the assessment of therapeutic efficacy and prognosis of lung cancer patients, enabling precision medicine to become a reality. Meanwhile, histology-based AI models assist pathologists in typing, molecular characterization, and prognosis prediction to enhance the efficiency of diagnosis and treatment. However, the leap to extensive clinical application still faces various challenges, such as data sharing, standardized label acquisition, clinical application regulation, and multimodal integration. Nevertheless, AI holds promising potential in the field of lung cancer to improve cancer care.
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Affiliation(s)
- Jun Shao
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jiaming Feng
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Jingwei Li
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Shufan Liang
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chengdi Wang
- Department of Pulmonary and Critical Care Medicine, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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23
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Wang TW, Hsu MS, Lin YH, Chiu HY, Chao HS, Liao CY, Lu CF, Wu YT, Huang JW, Chen YM. Application of Radiomics in Prognosing Lung Cancer Treated with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 15:3542. [PMID: 37509204 PMCID: PMC10377421 DOI: 10.3390/cancers15143542] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/01/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
In the context of non-small cell lung cancer (NSCLC) patients treated with EGFR tyrosine kinase inhibitors (TKIs), this research evaluated the prognostic value of CT-based radiomics. A comprehensive systematic review and meta-analysis of studies up to April 2023, which included 3111 patients, was conducted. We utilized the Quality in Prognosis Studies (QUIPS) tool and radiomics quality scoring (RQS) system to assess the quality of the included studies. Our analysis revealed a pooled hazard ratio for progression-free survival of 2.80 (95% confidence interval: 1.87-4.19), suggesting that patients with certain radiomics features had a significantly higher risk of disease progression. Additionally, we calculated the pooled Harrell's concordance index and area under the curve (AUC) values of 0.71 and 0.73, respectively, indicating good predictive performance of radiomics. Despite these promising results, further studies with consistent and robust protocols are needed to confirm the prognostic role of radiomics in NSCLC.
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Affiliation(s)
- Ting-Wei Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Ming-Sheng Hsu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yi-Hui Lin
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Hwa-Yen Chiu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Heng-Sheng Chao
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Jing-Wen Huang
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
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24
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Zheng Y, Dong J, Yang X, Shuai P, Li Y, Li H, Dong S, Gong Y, Liu M, Zeng Q. Benign-malignant classification of pulmonary nodules by low-dose spiral computerized tomography and clinical data with machine learning in opportunistic screening. Cancer Med 2023. [PMID: 37248730 DOI: 10.1002/cam4.5886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 03/14/2023] [Accepted: 03/19/2023] [Indexed: 05/31/2023] Open
Abstract
BACKGROUND Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology. METHODS The subjects' demographic data, baseline examination results, and annual follow-up low-dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model. RESULTS A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%-0.46%, which was lower than the baseline level except the follow-up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re-examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p < 0.001), and the proportion of highly suspicious nodules was not different between the baseline screening and other follow-up years (p > 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67-0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively. CONCLUSIONS It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow-up.
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Affiliation(s)
- Yansong Zheng
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jing Dong
- Research of Medical Big Data Center & National Engineering Laboratory for Medical Big Data Application Technology, Chinese PLA General Hospital, Beijing, China
| | - Xue Yang
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Ping Shuai
- Health Management Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongli Li
- Department of Health Management/ Henan Provincial People's Hospital of Zhengzhou University, Henan Key Laboratory of Chronic Disease Management, Zhengzhou, China
| | - Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Shengyong Dong
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yan Gong
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Miao Liu
- Graduate School, Chinese PLA general hospital, Beijing, China
| | - Qiang Zeng
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
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25
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Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study. Eur Radiol 2023; 33:1653-1667. [PMID: 36149481 DOI: 10.1007/s00330-022-09137-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 07/05/2022] [Accepted: 09/01/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To investigate the value of R2* mapping-based radiomics nomograms in staging liver fibrosis in patients with chronic hepatitis B. METHODS Between January 2020 and December 2020, 151 patients with chronic hepatitis B were randomly divided into training (n = 103) and validation (n = 48) cohorts. From January to February 2021, 58 patients were included in a test cohort. Radiomics features were selected using the interclass correlation coefficient and least absolute shrinkage and selection operator method. Three radiomics nomograms, combining the radiomics score (Radscore) derived from R2* mapping and clinical variables, were used for staging significant and advanced fibrosis, and cirrhosis. Performance of the model was evaluated using the AUC. The utility and clinical benefits were evaluated using the continuous net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). RESULTS The Radscore calculated by 12 radiomics features and independent factors (laminin and platelet) of advanced fibrosis were used to construct the radiomics nomograms. In the test cohort, the AUCs of the radiomics nomograms for staging significant fibrosis, advanced fibrosis, and cirrhosis were 0.738 (95% confidence interval [CI]: 0.604-0.872), 0.879 (95% CI: 0.779-0.98), and 0.952 (95% CI: 0.878-1), respectively. NRI, IDI, and DCA confirmed that radiomics nomograms demonstrated varying degrees of clinical benefit and improvement for advanced fibrosis and cirrhosis, but not for significant fibrosis. CONCLUSIONS Radiomics nomograms combined with R2* mapping-based Radscore, laminin, and platelet have value in staging advanced fibrosis and cirrhosis but limited value for staging significant fibrosis. KEY POINTS • Laminin and platelets were independent predictors of advanced fibrosis. • Radiomics analysis based on R2* mapping was beneficial for evaluating advanced fibrosis and cirrhosis. • It was difficult to distinguish significant fibrosis using a radiomics nomogram, which is possibly due to the complex pathological microenvironment of chronic liver diseases.
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Li J, Qiu Z, Zhang C, Chen S, Wang M, Meng Q, Lu H, Wei L, Lv H, Zhong W, Zhang X. ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features. Eur Radiol 2023; 33:893-903. [PMID: 36001124 DOI: 10.1007/s00330-022-09055-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 06/15/2022] [Accepted: 07/24/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To quantify intra-tumor heterogeneity (ITH) in non-small cell lung cancer (NSCLC) from computed tomography (CT) images. METHODS We developed a quantitative ITH measurement-ITHscore-by integrating local radiomic features and global pixel distribution patterns. The associations of ITHscore with tumor phenotypes, genotypes, and patient's prognosis were examined on six patient cohorts (n = 1399) to validate its effectiveness in characterizing ITH. RESULTS For stage I NSCLC, ITHscore was consistent with tumor progression from stage IA1 to IA3 (p < 0.001) and captured key pathological change in terms of malignancy (p < 0.001). ITHscore distinguished the presence of lymphovascular invasion (p = 0.003) and pleural invasion (p = 0.001) in tumors. ITHscore also separated patient groups with different overall survival (p = 0.004) and disease-free survival conditions (p = 0.005). Radiogenomic analysis showed that the level of ITHscore in stage I and stage II NSCLC is correlated with heterogeneity-related pathways. In addition, ITHscore was proved to be a stable measurement and can be applied to ITH quantification in head-and-neck cancer (HNC). CONCLUSIONS ITH in NSCLC can be quantified from CT images by ITHscore, which is an indicator for tumor phenotypes and patient's prognosis. KEY POINTS • ITHscore provides a radiomic quantification of intra-tumor heterogeneity in NSCLC. • ITHscore is an indicator for tumor phenotypes and patient's prognosis. • ITHscore has the potential to be generalized to other cancer types such as HNC.
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Affiliation(s)
- Jiaqi Li
- Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
| | - Zhenbin Qiu
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chao Zhang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Sijie Chen
- Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
| | - Mengmin Wang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Qiuchen Meng
- Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
| | - Haiming Lu
- Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
| | - Lei Wei
- Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
| | - Hairong Lv
- Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
- Fuzhou Institute of Data Technology, Fuzhou, China
| | - Wenzhao Zhong
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Xuegong Zhang
- Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China.
- School of Medicine, Tsinghua University, Beijing, China.
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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Lu CF, Liao CY, Chao HS, Chiu HY, Wang TW, Lee Y, Chen JR, Shiao TH, Chen YM, Wu YT. A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer. Cancer Imaging 2023; 23:9. [PMID: 36670497 PMCID: PMC9854198 DOI: 10.1186/s40644-023-00522-5] [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: 09/21/2022] [Accepted: 01/05/2023] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSCLC) with EGFR mutations. Approximately half of the patients with EGFR-mutated NSCLC are treated with EGFR-TKIs and develop disease progression within 1 year. Therefore, the early prediction of tumor progression in patients who receive EGFR-TKIs can facilitate patient management and development of treatment strategies. We proposed a deep learning approach based on both quantitative computed tomography (CT) characteristics and clinical data to predict progression-free survival (PFS) in patients with advanced NSCLC after EGFR-TKI treatment. METHODS A total of 593 radiomic features were extracted from pretreatment chest CT images. The DeepSurv models for the progression risk stratification of EGFR-TKI treatment were proposed based on CT radiomic and clinical features from 270 stage IIIB-IV EGFR-mutant NSCLC patients. Time-dependent PFS predictions at 3, 12, 18, and 24 months and estimated personalized PFS curves were calculated using the DeepSurv models. RESULTS The model combining clinical and radiomic features demonstrated better prediction performance than the clinical model. The model achieving areas under the curve of 0.76, 0.77, 0.76, and 0.86 can predict PFS at 3, 12, 18, and 24 months, respectively. The personalized PFS curves showed significant differences (p < 0.003) between groups with good (PFS > median) and poor (PFS < median) tumor control. CONCLUSIONS The DeepSurv models provided reliable multi-time-point PFS predictions for EGFR-TKI treatment. The personalized PFS curves can help make accurate and individualized predictions of tumor progression. The proposed deep learning approach holds promise for improving the pre-TKI personalized management of patients with EGFR-mutated NSCLC.
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Affiliation(s)
- Chia-Feng Lu
- grid.260539.b0000 0001 2059 7017Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chien-Yi Liao
- grid.260539.b0000 0001 2059 7017Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Heng-Sheng Chao
- grid.278247.c0000 0004 0604 5314Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Hwa-Yen Chiu
- grid.278247.c0000 0004 0604 5314Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ting-Wei Wang
- grid.260539.b0000 0001 2059 7017Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen Lee
- grid.260539.b0000 0001 2059 7017Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jyun-Ru Chen
- grid.260539.b0000 0001 2059 7017Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tsu-Hui Shiao
- grid.278247.c0000 0004 0604 5314Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yuh-Min Chen
- grid.278247.c0000 0004 0604 5314Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Te Wu
- grid.260539.b0000 0001 2059 7017Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan ,grid.260539.b0000 0001 2059 7017Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
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Cao R, Chen H, Wang H, Wang Y, Cui EN, Jiang W. Comprehensive analysis of prediction of the EGFR mutation and subtypes based on the spinal metastasis from primary lung adenocarcinoma. Front Oncol 2023; 13:1154327. [PMID: 37143947 PMCID: PMC10151709 DOI: 10.3389/fonc.2023.1154327] [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: 01/30/2023] [Accepted: 03/30/2023] [Indexed: 05/06/2023] Open
Abstract
Purpose To investigate the use of multiparameter MRI-based radiomics in the in-depth prediction of epidermal growth factor receptor (EGFR) mutation and subtypes based on the spinal metastasis in patients with primary lung adenocarcinoma. Methods A primary cohort was conducted with 257 patients who pathologically confirmed spinal bone metastasis from the first center between Feb. 2016 and Oct. 2020. An external cohort was developed with 42 patients from the second center between Apr. 2017 and Jun. 2021. All patients underwent sagittal T1-weighted imaging (T1W) and sagittal fat-suppressed T2-weight imaging (T2FS) MRI imaging. Radiomics features were extracted and selected to build radiomics signatures (RSs). Machine learning classify with 5-fold cross-validation were used to establish radiomics models for predicting the EGFR mutation and subtypes. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors. Nomogram models were developed integrating the RSs and important clinical factors. Results The RSs derived from T1W showed better performance for predicting the EGFR mutation and subtypes compared with those from T2FS in terms of AUC, accuracy and specificity. The nomogram models integrating RSs from combination of the two MRI sequences and important clinical factors achieved the best prediction capabilities in the training (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.829 vs. 0.885 vs.0.919), internal validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.760 vs. 0.777 vs.0.811), external validation (AUCs, EGFR vs. Exon 19 vs. Exon 21, 0.780 vs. 0.846 vs.0.818). DCA curves indicated potential clinical values of the radiomics models. Conclusions This study indicated potentials of multi-parametric MRI-based radiomics to assess the EGFR mutation and subtypes. The proposed clinical-radiomics nomogram models can be considered as non-invasive tools to assist clinicians in making individual treatment plans.
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Affiliation(s)
- Ran Cao
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Liaoning, Shenyang, China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, Shenyang, China
| | - Yan Wang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Liaoning, Shenyang, China
| | - E-Nuo Cui
- School of Computer Science and Engineering, Shenyang University, Shenyang, China
- *Correspondence: E-Nuo Cui, ; Wenyan Jiang,
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, Shenyang, China
- *Correspondence: E-Nuo Cui, ; Wenyan Jiang,
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Liu H, Zhao D, Huang Y, Li C, Dong Z, Tian H, Sun Y, Lu Y, Chen C, Wu H, Zhang Y. Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma. Front Oncol 2023; 13:1129918. [PMID: 37025592 PMCID: PMC10072214 DOI: 10.3389/fonc.2023.1129918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/13/2023] [Indexed: 04/08/2023] Open
Abstract
Purpose To propose and evaluate a comprehensive modeling approach combing radiomics, dosiomics and clinical components, for more accurate prediction of locoregional recurrence risk after radiotherapy for patients with locoregionally advanced HPSCC. Materials and methods Clinical data of 77 HPSCC patients were retrospectively investigated, whose median follow-up duration was 23.27 (4.83-81.40) months. From the planning CT and dose distribution, 1321 radiomics and dosiomics features were extracted respectively from planning gross tumor volume (PGTV) region each patient. After stability test, feature dimension was further reduced by Principal Component Analysis (PCA), yielding Radiomic and Dosiomic Principal Components (RPCs and DPCs) respectively. Multiple Cox regression models were constructed using various combinations of RPC, DPC and clinical variables as the predictors. Akaike information criterion (AIC) and C-index were used to evaluate the performance of Cox regression models. Results PCA was performed on 338 radiomic and 873 dosiomic features that were tested as stable (ICC1 > 0.7 and ICC2 > 0.95), yielding 5 RPCs and DPCs respectively. Three comprehensive features (RPC0, P<0.01, DPC0, P<0.01 and DPC3, P<0.05) were found to be significant in the individual Radiomic or Dosiomic Cox regression models. The model combining the above features and clinical variable (total stage IVB) provided best risk stratification of locoregional recurrence (C-index, 0.815; 95%CI, 0.770-0.859) and prevailing balance between predictive accuracy and complexity (AIC, 143.65) than any other investigated models using either single factors or two combined components. Conclusion This study provided quantitative tools and additional evidence for the personalized treatment selection and protocol optimization for HPSCC, a relatively rare cancer. By combining complementary information from radiomics, dosiomics, and clinical variables, the proposed comprehensive model provided more accurate prediction of locoregional recurrence risk after radiotherapy.
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Affiliation(s)
- Hongjia Liu
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Dan Zhao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yuliang Huang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Chenguang Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Zhengkun Dong
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Hongbo Tian
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yijie Sun
- School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Yanye Lu
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Chen Chen
- School of Electronics Engineering and Computer Science, Peking University, Beijing, China
| | - Hao Wu
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yibao Zhang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
- *Correspondence: Yibao Zhang,
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Deep learning for preoperative prediction of the EGFR mutation and subtypes based on the MRI image of spinal metastasis from primary NSCLC. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Zhang X, Lu B, Yang X, Lan D, Lin S, Zhou Z, Li K, Deng D, Peng P, Zeng Z, Long L. Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature. Eur Radiol 2023; 33:825-835. [PMID: 36166088 PMCID: PMC9889474 DOI: 10.1007/s00330-022-09123-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 08/05/2022] [Accepted: 08/19/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To evaluate the value of time-serial CT radiomics features in predicting progression-free survival (PFS) for lung adenocarcinoma (LUAD) patients after epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) therapy. MATERIALS AND METHODS LUAD patients treated with EGFR-TKIs were retrospectively included from three independent institutes and divided into training and validation cohorts. Intratumoral and peritumoral features were extracted from time-serial non-contrast chest CT (including pre-therapy and first follow-up images); moreover, the percentage variation per unit time (day) was introduced to adjust for the different follow-up periods of each patient. Test-retest was performed to exclude irreproducible features, while the Boruta algorithm was used to select critical radiomics features. Radiomics signatures were constructed with random forest survival models in the training cohort and compared against baseline clinical characteristics through Cox regression and nonparametric testing of concordance indices (C-indices). RESULTS The training cohort included 131 patients (74 women, 56.5%) from one institute and the validation cohort encompassed 41 patients (24 women, 58.5%) from two other institutes. The optimal signature contained 10 features and 7 were unit time feature variations. The comprehensive radiomics model outperformed the pre-therapy clinical characteristics in predicting PFS (training: 0.78, 95% CI: [0.72, 0.84] versus 0.55, 95% CI: [0.49, 0.62], p < 0.001; validation: 0.72, 95% CI: [0.60, 0.84] versus 0.54, 95% CI: [0.42, 0.66], p < 0.001). CONCLUSION Radiomics signature derived from time-serial CT images demonstrated optimal prognostic performance of disease progression. This dynamic imaging biomarker holds the promise of monitoring treatment response and achieving personalized management. KEY POINTS • The intrinsic tumor heterogeneity can be highly dynamic under the therapeutic effect of EGFR-TKI treatment, and the inevitable development of drug resistance may disrupt the duration of clinical benefit. Decision-making remained challenging in practice to detect the emergence of acquired resistance during the early response phase. • Time-serial CT-based radiomics signature integrating intra- and peritumoral features offered the potential to predict progression-free survival for LUAD patients treated with EGFR-TKIs. • The dynamic imaging signature allowed for prognostic risk stratification.
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Affiliation(s)
- Xiaobo Zhang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Bingfeng Lu
- Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | - Xinguan Yang
- Department of Radiology, Guilin People’s Hospital, Guilin, Guangxi China
| | - Dong Lan
- Department of Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | | | - Zhipeng Zhou
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi China
| | - Kai Li
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Dong Deng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Peng Peng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Zisan Zeng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
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She Y, He B, Wang F, Zhong Y, Wang T, Liu Z, Yang M, Yu B, Deng J, Sun X, Wu C, Hou L, Zhu Y, Yang Y, Hu H, Dong D, Chen C, Tian J. Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study. EBioMedicine 2022; 86:104364. [PMID: 36395737 PMCID: PMC9672965 DOI: 10.1016/j.ebiom.2022.104364] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 11/15/2022] Open
Abstract
Background This study, based on multicentre cohorts, aims to utilize computed tomography (CT) images to construct a deep learning model for predicting major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) and further explore the biological basis under its prediction. Methods 274 patients undergoing curative surgery after neoadjuvant chemoimmunotherapy for NSCLC at 4 centres from January 2019 to December 2021 were included and divided into a training cohort, an internal validation cohort, and an external validation cohort. ShuffleNetV2x05-based features of the primary tumour on the CT scans within the 2 weeks preceding neoadjuvant administration were employed to develop a deep learning score for distinguishing MPR and non-MPR. To reveal the underlying biological basis of the deep learning score, a genetic analysis was conducted based on 25 patients with RNA-sequencing data. Findings MPR was achieved in 54.0% (n = 148) patients. The area under the curve (AUC) of the deep learning score to predict MPR was 0.73 (95% confidence interval [CI]: 0.58–0.86) and 0.72 (95% CI: 0.58–0.85) in the internal validation and external validation cohorts, respectively. After integrating the clinical characteristic into the deep learning score, the combined model achieved satisfactory performance in the internal validation (AUC: 0.77, 95% CI: 0.64–0.89) and external validation cohorts (AUC: 0.75, 95% CI: 0.62–0.87). In the biological basis exploration for the deep learning score, a high deep learning score was associated with the downregulation of pathways mediating tumour proliferation and the promotion of antitumour immune cell infiltration in the microenvironment. Interpretation The proposed deep learning model could effectively predict MPR in NSCLC patients treated with neoadjuvant chemoimmunotherapy. Funding This study was supported by National Key Research and Development Program of China, China (2017YFA0205200); National Natural Science Foundation of China, China (91959126, 82022036, 91959130, 81971776, 81771924, 6202790004, 81930053, 9195910169, 62176013, 8210071009); Beijing Natural Science Foundation, China (L182061); Strategic Priority Research Program of Chinese Academy of Sciences, China (XDB38040200); Chinese Academy of Sciences, China (GJJSTD20170004, QYZDJ-SSW-JSC005); Shanghai Hospital Development Center, China (SHDC2020CR3047B); and Science and Technology Commission of Shanghai Municipality, China (21YF1438200).
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Fan Y, Zhao Z, Wang X, Ai H, Yang C, Luo Y, Jiang X. Radiomics for prediction of response to EGFR-TKI based on metastasis/brain parenchyma (M/BP)-interface. LA RADIOLOGIA MEDICA 2022; 127:1342-1354. [PMID: 36284030 DOI: 10.1007/s11547-022-01569-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/14/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE To evaluate the potential of subregional radiomics as a novel tumor marker in predicting epidermal growth factor receptor (EGFR) mutation status and response to EGFR-tyrosine kinase inhibitor (TKI) therapy in NSCLC patients with brain metastasis (BM). MATERIALS AND METHODS We included 230 patients from center 1, and 80 patients were included from center 2 to form a primary and external validation cohort, respectively. Patients underwent contrast-enhanced T1-weighted and T2-weighted MRI scans before treatment. The individual- and population-level clustering was used to partition the peritumoral edema area (POA) into phenotypically consistent subregions. Radiomics features were calculated and selected from the tumor active area (TAA), POA and subregions, and used to develop models. Prediction values of each region were investigated and compared with receiver operating characteristic curves and Delong test. RESULTS For predicting EGFR mutations, a multi-region combined model (EGFR-Fusion) was developed based on joint of the partitioned metastasis/brain parenchyma (M/BP)-interface and TAA, and generated the highest prediction performance in the training (AUC = 0.945, SEN = 0.878, SPE = 0.937), internal validation (AUC = 0.880, SEN = 0.733, SPE = 0.969), and external validation (AUC = 0.895, SEN = 0.875, SPE = 0.800) cohorts. For predicting response to EGFR-TKI, the developed multi-region combined model (TKI-Fusion) yielded predictive AUCs of 0.869 (SEN = 0.717, SPE = 0.884), 0.786 (SEN = 0.708, SPE = 0.818), and 0.802 (SEN = 0.750, SPE = 0.800) in the training, internal validation and external validation cohort, respectively. CONCLUSION Our study revealed that complementary information regarding the EGFR status and response to EGFR-TKI can be provided by subregional radiomics. The proposed radiomics models may be new markers to guide treatment plans for NSCLC patients with BM.
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Affiliation(s)
- Ying Fan
- School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Zilong Zhao
- Department of Neurosurgery, The First Affiliated Hospital of China Medical University, Shenyang, 110001, People's Republic of China
| | - Xingling Wang
- Department of Gynecology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People's Republic of China
| | - Hua Ai
- School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Chunna Yang
- School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People's Republic of China.
| | - Xiran Jiang
- School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China.
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Xie D, Xu F, Zhu W, Pu C, Huang S, Lou K, Wu Y, Huang D, He C, Hu H. Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy. Front Oncol 2022; 12:990608. [PMID: 36276082 PMCID: PMC9583844 DOI: 10.3389/fonc.2022.990608] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 09/22/2022] [Indexed: 11/22/2022] Open
Abstract
Objective To assess the validity of pre- and posttreatment computed tomography (CT)-based radiomics signatures and delta radiomics signatures for predicting progression-free survival (PFS) in stage III-IV non-small-cell lung cancer (NSCLC) patients after immune checkpoint inhibitor (ICI) therapy. Methods Quantitative image features of the largest primary lung tumours were extracted on CT-enhanced imaging at baseline (time point 0, TP0) and after the 2nd-3rd immunotherapy cycles (time point 1, TP1). The critical features were selected to construct TP0, TP1 and delta radiomics signatures for the risk stratification of patient survival after ICI treatment. In addition, a prediction model integrating the clinicopathologic risk characteristics and phenotypic signature was developed for the prediction of PFS. Results The C-index of TP0, TP1 and delta radiomics models in the training and validation cohort were 0.64, 0.75, 0.80, and 0.61, 0.68, 0.78, respectively. The delta radiomics score exhibited good accuracy for distinguishing patients with slow and rapid progression to ICI treatment. The predictive accuracy of the combined prediction model was higher than that of the clinical prediction model in both training and validation sets (P<0.05), with a C-index of 0.83 and 0.70, respectively. Additionally, the delta radiomics model (C-index of 0.86) had a higher predictive accuracy compared to PD-L1 expression (C-index of 0.50) (P<0.0001). Conclusions The combined prediction model including clinicopathologic characteristics (tumour anatomical classification and brain metastasis) and the delta radiomics signature could achieve the individualized prediction of PFS in ICIs-treated NSCLC patients.
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Affiliation(s)
- Dong Xie
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Shaoxing Second Hospital, Shaoxing, China
| | - Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenchao Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cailing Pu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shaoyu Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Ningbo Medical Center LiHuili Hospital, Ningbo, China
| | - Kaihua Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yan Wu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dingpin Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cong He
- Department of Radiology, Shaoxing Second Hospital, Shaoxing, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Hongjie Hu,
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Jiang M, Yang P, Li J, Peng W, Pu X, Chen B, Li J, Wang J, Wu L. Computed tomography-based radiomics quantification predicts epidermal growth factor receptor mutation status and efficacy of first-line targeted therapy in lung adenocarcinoma. Front Oncol 2022; 12:985284. [PMID: 36052262 PMCID: PMC9424619 DOI: 10.3389/fonc.2022.985284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 07/25/2022] [Indexed: 11/23/2022] Open
Abstract
Background Biomarkers that predict the efficacy of first-line tyrosine kinase inhibitors (TKIs) are pivotal in epidermal growth factor receptor (EGFR) mutant advanced lung adenocarcinoma. Imaging-based biomarkers have attracted much attention in anticancer therapy. This study aims to use the machine learning method to distinguish EGFR mutation status and further explores the predictive role of EGFR mutation-related radiomics features in response to first-line TKIs. Methods We retrospectively analyzed pretreatment CT images and clinical information from a cohort of lung adenocarcinomas. We entered the top-ranked features into a support vector machine (SVM) classifier to establish a radiomics signature that predicted EGFR mutation status. Furthermore, we identified the best response-related features based on EGFR mutant-related features in first-line TKI therapy patients. Then we test and validate the predictive effect of the best response-related features for progression-free survival (PFS). Results Six hundred ninety-two patients were enrolled in building radiomics signatures. The 13 top-ranked features were input into an SVM classifier to establish the radiomics signature of the training cohort (n = 514), and the predictive score of the radiomics signature was assessed on an independent validation group with 178 patients and obtained an area under the curve (AUC) of 74.13%, an F1 score of 68.29%, a specificity of 79.55%, an accuracy of 70.79%, and a sensitivity of 62.22%. More importantly, the skewness-Low (≤0.882) or 10th percentile-Low group (≤21.132) had a superior partial response (PR) rate than the skewness-High or 10th percentile-High group (p < 0.01). Higher skewness (hazard ratio (HR) = 1.722, p = 0.001) was also found to be significantly associated with worse PFS. Conclusions The radiomics signature can be used to predict EGFR mutation status. Skewness may contribute to the stratification of disease progression in lung cancer patients treated with first-line TKIs.
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Affiliation(s)
- Meilin Jiang
- The Second Department of Thoracic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Pei Yang
- The General Surgery Department of Xiangya Hospital Affiliated to Central South University, Changsha, China
- The National Clinical Research Center for Geriatric Disorders of Xiangya Hospital Affiliated to Central South University, Changsha, China
| | - Jing Li
- Medical Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Wenying Peng
- The Second Department of Oncology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Center, Kunming, China
| | - Xingxiang Pu
- The Second Department of Thoracic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Bolin Chen
- The Second Department of Thoracic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Jia Li
- The Second Department of Thoracic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Jingyi Wang
- The Second Department of Thoracic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Lin Wu
- The Second Department of Thoracic Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
- *Correspondence: Lin Wu,
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Fan Y, Dong Y, Sun X, Wang H, Zhao P, Wang H, Jiang X. Development and validation of MRI-based radiomics signatures as new markers for preoperative assessment of EGFR mutation and subtypes from bone metastases. BMC Cancer 2022; 22:889. [PMID: 35964032 PMCID: PMC9375915 DOI: 10.1186/s12885-022-09985-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/08/2022] [Indexed: 11/10/2022] Open
Abstract
Background This study aimed to develop and externally validate contrast-enhanced (CE) T1-weighted MRI-based radiomics for the identification of epidermal growth factor receptor (EGFR) mutation, exon-19 deletion and exon-21 L858R mutation from MR imaging of spinal bone metastasis from primary lung adenocarcinoma. Methods A total of 159 patients from our hospital between January 2017 and September 2021 formed a primary set, and 24 patients from another center between January 2017 and October 2021 formed an independent validation set. Radiomics features were extracted from the CET1 MRI using the Pyradiomics method. The least absolute shrinkage and selection operator (LASSO) regression was applied for selecting the most predictive features. Radiomics signatures (RSs) were developed based on the primary training set to predict EGFR mutations and differentiate between exon-19 deletion and exon-21 L858R. The RSs were validated on the internal and external validation sets using the Receiver Operating Characteristic (ROC) curve analysis. Results Eight, three, and five most predictive features were selected to build RS-EGFR, RS-19, and RS-21 for predicting EGFR mutation, exon-19 deletion and exon-21 L858R, respectively. The RSs generated favorable prediction efficacies for the primary (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.851 vs. 0.816 vs. 0.814) and external validation (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.807 vs. 0.742 vs. 0.792) sets. Conclusions Radiomics features from the CE MRI could be used to detect the EGFR mutation, increasing the certainty of identifying exon-19 deletion and exon-21 L858R mutations based on spinal metastasis MR imaging. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09985-4.
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Affiliation(s)
- Ying Fan
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, 110122, People's Republic of China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, 110042, People's Republic of China
| | - Xinyan Sun
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, 110042, People's Republic of China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, 110042, People's Republic of China
| | - Peng Zhao
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, 110042, People's Republic of China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China
| | - Xiran Jiang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, 110122, People's Republic of China.
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Radiomics for Detection of the EGFR Mutation in Liver Metastatic NSCLC. Acad Radiol 2022; 30:1039-1046. [PMID: 35907759 DOI: 10.1016/j.acra.2022.06.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 12/09/2022]
Abstract
RATIONALE AND OBJECTIVES The research aims to investigate whether MRI radiomics on hepatic metastasis from primary nonsmall cell lung cancer (NSCLC) can be used to differentiate patients with epidermal growth factor receptor (EGFR) mutations from those with EGFR wild-type, and develop a prediction model based on combination of primary tumor and the metastasis. MATERIALS AND METHODS A total of 130 patients were enrolled between Aug. 2017 and Dec. 2021, all pathologically confirmed harboring hepatic metastasis from primary NSCLC. The pyradiomics was used to extract radiomics features from intra- and peritumoral areas of both primary tumor and metastasis. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify most predictive features and to develop radiomics signatures (RSs) for prediction of the EGFR mutation status. The receiver operating characteristic (ROC) curve analysis was performed to assess the prediction capability of the developed RSs. RESULTS A RS-Primary and a RS-Metastasis were derived from the primary tumor and metastasis, respectively. The RS-Combine by combination of the primary tumor and metastasis achieved the highest prediction performance in the training (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.826 vs. 0.821 vs. 0.908) and testing (AUCs, RS-Primary vs. RS-Metastasis vs. RS-Combine, 0.760 vs. 0.791 vs. 0.884) set. The smoking status showed significant difference between EGFR mutant and wild-type groups (p < 0.05) in the training set. CONCLUSION The study indicates that hepatic metastasis-based radiomics can be used to detect the EGFR mutation. The developed multiorgan combined radiomics signature may be helpful to guide individual treatment strategies for patients with metastatic NSCLC.
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Luo B, Yang M, Han Z, Que Z, Luo T, Tian J. Establishment of a Nomogram-Based Prognostic Model (LASSO-COX Regression) for Predicting Progression-Free Survival of Primary Non-Small Cell Lung Cancer Patients Treated with Adjuvant Chinese Herbal Medicines Therapy: A Retrospective Study of Case Series. Front Oncol 2022; 12:882278. [PMID: 35875082 PMCID: PMC9304868 DOI: 10.3389/fonc.2022.882278] [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] [Received: 02/23/2022] [Accepted: 06/03/2022] [Indexed: 11/13/2022] Open
Abstract
Nowadays, Jin-Fu-Kang oral liquid (JFK), one of Chinese herbal medicines (CHMs) preparations, has been widely used as an adjuvant therapy for primary non-small cell lung cancer (PNSCLC) patients with the syndrome of deficiency of both Qi and Yin (Qi–Yin deficiency pattern) based on Traditional Chinese Medicine (TCM) theory. However, we found insufficient evidence of how long-term CHM treatment influence PNSCLC patients’ progression-free survival (PFS). Thus, using electronic medical records, we established a nomograph-based prognostic model for predicting PNSCLC patients’ PFS involved with JFK supplementary formulas (JFK-SFs) over 6 months, in order to preliminarily investigate potential predictors highly related to adjuvant CHMs therapies in theoretical epidemiology. In our retrospective study, a series of 197 PNSCLC cases from Long Hua Hospital were enrolled by non-probability sampling and divided into 2 datasets at the ratio of 5:4 by Kennard–Stone algorithm, as a result of 109 in training dataset and 88 in validation dataset. Besides, TNM stage, operation history, sIL-2R, and CA724 were considered as 4 highly correlated predictors for modeling based on LASSO-Cox regression. Additionally, we respectively used training dataset and validation dataset for establishment including internal validation and external validation, and the prediction performance of model was measured by concordance index (C-index), integrated discrimination improvement, and net reclassification indices (NRI). Moreover, we found that the model containing clinical characteristics and bio-features presented the best performance by pairwise comparison. Next, the result of sensitivity analysis proved its stability. Then, for preliminarily examination of its discriminative power, all eligible cases were divided into high-risk or low-risk progression by the cut-off value of 57, in the light of predicted nomogram scores. Ultimately, a completed TRIPOD checklist was used for self-assessment of normativity and integrity in modeling. In conclusion, our model might offer crude probability of uncertainly individualized PFS with long-term CHMs therapy in the real-world setting, which could discern the individuals implicated with worse prognosis from the better ones. Nevertheless, our findings were prone to unmeasured bias caused by confounding factors, owing to retrospective cases series.
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Affiliation(s)
- Bin Luo
- Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ming Yang
- Department of Good Practice Criterion, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zixin Han
- School of Pharmacy, Jiangxi University of Traditional Chinese Medicine, Nanchang, China
| | - Zujun Que
- Cancer Institute of Traditional Chinese Medicine, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tianle Luo
- Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jianhui Tian
- Department of Oncology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Oncology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Cancer Institute of Traditional Chinese Medicine, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- *Correspondence: Jianhui Tian,
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Liu J, Tang J, Xia B, Gu Z, Yin H, Zhang H, Yang H, Song B. Novel Radiomics-Clinical Model for the Noninvasive Prediction of New Fractures After Vertebral Augmentation. Acad Radiol 2022; 30:1092-1100. [PMID: 35915030 DOI: 10.1016/j.acra.2022.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/25/2022] [Accepted: 06/26/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE To investigate the noninvasive prediction model for new fractures after percutaneous vertebral augmentation (PVA) based on radiomics signature and clinical parameters. METHODS Data from patients who were diagnosed with osteoporotic vertebral compression fracture (OVCF) and treated with PVA in our hospital between May 2014 and April 2019 were retrospectively analyzed. Radiomics features were extracted from T1-weighted magnetic resonance imaging (MRI) of the T11-L5 segments taken before PVA. Different radiomics models was developed by using linear discriminant analysis (LDA), multilayer perceptron (MLP), and stochastic gradient descent (SGD) classifiers. A nomogram was constructed by integrating clinical parameters and Radscore that calculated by the best radiomics model. The model performance was quantified in terms of discrimination, calibration and clinical usefulness. RESULT Four clinical parameters and 16 selected radiomics features were used for model development. The clinical model showed poor discrimination capability with area under the curves (AUCs) yielding of 0.522 in the training dataset and 0.517 in the validation dataset. The LDA, MLP and SGD classifier-based radiomics model had achieved AUCs of 0.793, 0.810, and 0.797 in the training dataset, and 0.719, 0.704, and 0.725 in the validation dataset, respectively. The nomogram showed the best performance with AUCs achieving 0.810 and 0.754 in the training and validation datasets, respectively. The decision curve analysis demonstrated the net benefit of the nomogram was higher than that of other models. CONCLUSION Our findings indicate that combining clinical features with radiomics features from pre-augmentation T1-weighted MRI can be used to develop a nomogram that can predict new fractures in patients after PVA.
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Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters. JOURNAL OF ONCOLOGY 2022; 2022:3335048. [PMID: 35813867 PMCID: PMC9262528 DOI: 10.1155/2022/3335048] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 06/08/2022] [Indexed: 01/17/2023]
Abstract
Objective To investigate the value of apparent diffusion coefficient (ADC) value of endometrial cancer (EC) primary lesion and magnetic resonance imaging (MRI) three-dimensional (3D) radiomics features combined with clinical parameters for preoperative prediction of pelvic lymph node metastasis (PLNM). Methods A total of 136 patients with EC confirmed by postoperative pathology were retrospectively reviewed and analyzed. Patients were randomly divided into training set (n = 95) and test set (n = 41) at a ratio of 7 : 3. Radiomics features based on T2WI, DWI, and contrast-enhanced T1WI (CE-T1WI) sequence were extracted and screened, and then radiomics score (Rads-score) was calculated. Clinical parameters and ADC value of EC primary lesion were measured and collected, and their correlation with PLNM was analyzed. Receiver operating characteristic (ROC) curve was plotted to assess the diagnostic efficacy of the model. A nomogram for PLNM was created based on the multivariate logistic regression model. Results The ADC value of the EC primary lesion showed inverse correlation with PLNM, while CA125 and Rads-score were positively associated with PLNM. A predictive model was proposed based on ADC value, Rads-score, CA125, and MR-reported pelvic lymph node status (PLNS) for PLNM in EC. The area under the curve (AUC) of the model is 0.940; the sensitivity and specificity (87.1% and 90.6%) of the model were significantly higher than that of the MRI morphological signs. Conclusion A combination of ADC value, MRI 3D radiomics features of the EC primary lesion, and clinical parameters generated a prediction model for PLNM in EC and had a good diagnostic performance; it was a useful supplement to MR-reported PLNS based on MRI morphological signs.
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Cao R, Pang Z, Wang X, Du Z, Chen H, Liu J, Yue Z, Wang H, Luo Y, Jiang X. Radiomics evaluates the EGFR mutation status from the brain metastasis: a multi-center study. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/19/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. To develop and externally validate habitat-based MRI radiomics for preoperative prediction of the EGFR mutation status based on brain metastasis (BM) from primary lung adenocarcinoma (LA). Approach. We retrospectively reviewed 150 and 38 patients from hospital 1 and hospital 2 between January 2017 and December 2021 to form a primary and an external validation cohort, respectively. Radiomics features were calculated from the whole tumor (W), tumor active area (TAA) and peritumoral oedema area (POA) in the contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI image. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures (RSs) based on W (RS-W), TAA (RS-TAA), POA (RS-POA) and in combination (RS-Com). The area under receiver operating characteristic curve (AUC) and accuracy analysis were performed to assess the performance of radiomics models. Main results. RS-TAA and RS-POA outperformed RS-W in terms of AUC, ACC and sensitivity. The multi-region combined RS-Com showed the best prediction performance in the primary validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.901 versus 0.699 versus 0.812 versus 0.883) and external validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.900 versus 0.637 versus 0.814 versus 0.842) cohort. Significance. The developed habitat-based radiomics models can accurately detect the EGFR mutation in patients with BM from primary LA, and may provide a preoperative basis for personal treatment planning.
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Random Walk Algorithm-Based Computer Tomography (CT) Image Segmentation Analysis Effect of Spiriva Combined with Symbicort on Immunologic Function of Non-Small-Cell Lung Cancer. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1986647. [PMID: 35693265 PMCID: PMC9187478 DOI: 10.1155/2022/1986647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 11/18/2022]
Abstract
The objective of this research was to explore the effect of the treatment regimen of Spiriva combined with Symbicort on the immune function of non-small-cell lung cancer (NSCLC) based on computed tomography (CT) imaging features. An automatic CT image segmentation algorithm (RW-CT) was constructed based on random walk (RW) and image segmentation technology. The image segmentation algorithm based on the Toboggan method (C-CT) was introduced to compare with the traditional RW algorithm. 60 subjects were divided into four groups: a Chinese combined with Western medicine group (treated with Spiriva combined with Symbicort, group C+W), a Chinese medicine group (treated with Spiriva, group C), a Western medicine group (treated with Symbicort, group W), and a model group for control (group M). The results show that the Dice coefficient of the RW-CT algorithm was obviously larger than that of the C-CT algorithm and the RW algorithm, while the Hausdorff distance (HD) of the RW-CT algorithm was much smaller than that of the other two algorithms (
). The proportion of positive cells of hypoxia-inducible factor-1α (HIF-1α) in group C+W was the least (15%-23%), followed by the group W (21%-29%) and the group C (28%-37%), and that in the group M was the highest (39%-49%). There was a remarkable difference in the immunohistochemical scores (HIS) of vascular endothelial growth factor (VEGF) in the tumor tissues between group C+W and the group M (
,
), but there was no great difference from the group C and the group W (
). There was a notable difference in the IHS of vascular endothelial factor recepto-2 (VEGFR-2) between the group C+W medication group and the group M (
,
), and there was no statistical difference between the group C and W (
). In short, the RW-CT constructed based on RW was better than the traditional algorithms for CT image segmentation. The Spiriva combined with Symbicort could effectively inhibit the expression of VEGF, VEGFR-2, and HIF-1α in NSCLC and promote the immunologic function of the body.
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Zhang Y, Zhang K, Jia H, Xia B, Zang C, Liu Y, Qian L, Dong J. IVIM-DWI and MRI-based radiomics in cervical cancer: Prediction of concurrent chemoradiotherapy sensitivity in combination with clinical prognostic factors. Magn Reson Imaging 2022; 91:37-44. [PMID: 35568271 DOI: 10.1016/j.mri.2022.05.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 05/02/2022] [Accepted: 05/03/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE To identify the feasibility and value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and magnetic resonance imaging (MRI)-based radiomics combined with clinical prognostic factors (CPF) in predicting concurrent chemoradiotherapy (CCRT) sensitivity of locally advanced cervical cancer (LACC). METHODS A retrospective analysis of 163 patients (assigned to training or test groups) who underwent conventional MRI and IVIM-DWI before CCRT were divided into sensitive and resistant groups according to their efficacy at 6 months after CCRT. Per-treatment IVIM-DWI parameters (ADC, D, D⁎ and f value), 3D texture features (from axial T2WI) and CPF were measured, analyzed and screened. The prediction model and its nomogram were developed by combining screened parameters and then validated internally and externally. RESULTS Clinical stage, f value, D value, InverseVariance, SizeZoneNonUniformity, and Minimum were selected to construct prediction model. All parameters except D value showed independent diagnostic value in multivariate Logistic regression analysis and composed prediction model, with AUCs of 0.987 and 0.984 for training and test groups, respectively. The calibration curve (Brier score of 0.042, C-index of 0.987), decision curve and clinical impact curve further demonstrated the reliability and clinical value of prediction model. CONCLUSION IVIM-DWI, MRI-based radiomics and CPF showed high clinical value in predicting CCRT sensitivity for LACC with better predictive performance when combined.
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Affiliation(s)
- Yu Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, 17 Lujiang Road, Hefei, Anhui 230001, China
| | - Kaiyue Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, 17 Lujiang Road, Hefei, Anhui 230001, China
| | - Haodong Jia
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, 17 Lujiang Road, Hefei, Anhui 230001, China; Department of Radiology, West Branch of the First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, 107 Huanhu East Road, Hefei, Anhui 230031, China
| | - Bairong Xia
- Department of Radiology, West Branch of the First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, 107 Huanhu East Road, Hefei, Anhui 230031, China; Department of Radiation Oncology, West Branch of the First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, 107 Huanhu East Road, Hefei, Anhui 230031, China
| | - Chunbao Zang
- Department of Radiation Oncology, West Branch of the First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, 107 Huanhu East Road, Hefei, Anhui 230031, China
| | - Yunqin Liu
- Department of Radiation Oncology, West Branch of the First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, 107 Huanhu East Road, Hefei, Anhui 230031, China
| | - Liting Qian
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, 17 Lujiang Road, Hefei, Anhui 230001, China; Department of Radiation Oncology, West Branch of the First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, 107 Huanhu East Road, Hefei, Anhui 230031, China.
| | - Jiangning Dong
- Department of Radiology, West Branch of the First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, 107 Huanhu East Road, Hefei, Anhui 230031, China.
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Zhu JM, Sun L, Wang L, Zhou TC, Yuan Y, Zhen X, Liao ZW. Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutation. BMC Res Notes 2022; 15:140. [PMID: 35422007 PMCID: PMC9008953 DOI: 10.1186/s13104-022-06019-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 03/27/2022] [Indexed: 11/11/2022] Open
Abstract
Objective This study was to explore the most appropriate radiomics modeling method to predict the progression-free survival of EGFR-TKI treatment in advanced non-small cell lung cancer with EGFR mutations. Different machine learning methods may vary considerably and the selection of a proper model is essential for accurate treatment outcome prediction. Our study were established 176 discrimination models constructed with 22 feature selection methods and 8 classifiers. The predictive performance of each model were evaluated using the AUC, ACC, sensitivity and specificity, where the optimal model was identified. Results There were totally 107 radiomics features and 7 clinical features obtained from each patient. After feature selection, the top-ten most relevant features were fed to train 176 models. Significant performance variations were observed in the established models, with the best performance achieved by the logistic regression model using gini-index feature selection (AUC = 0.797, ACC = 0.722, sensitivity = 0.758, specificity = 0.693). The median R-score was 0.518 (IQR, 0.023–0.987), and the patients were divided into high-risk and low-risk groups based on this cut-off value. The KM survival curves of the two groups demonstrated evident stratification results (p = 0.000). Supplementary Information The online version contains supplementary material available at 10.1186/s13104-022-06019-x.
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Affiliation(s)
- Jian-Man Zhu
- Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong, China
| | - Lei Sun
- Department of Radiation Oncology, Affiliated Zhujiang Hospital of Southern Medical University, Guangzhou, 510280, Guangdong, China
| | - Linjing Wang
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong, China
| | - Tong-Chong Zhou
- Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong, China
| | - Yawei Yuan
- Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong, China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Zhi-Wei Liao
- Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong, China.
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A Deep Learning Radiomics Analysis for Survival Prediction in Esophageal Cancer. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4034404. [PMID: 35368956 PMCID: PMC8970800 DOI: 10.1155/2022/4034404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/03/2022] [Indexed: 11/18/2022]
Abstract
The purpose of this study was to explore the deep learning radiomics (DLR) nomogram to predict the overall 3-year survival after chemoradiotherapy in patients with esophageal cancer. The 154 patients' data were used in this study, which was randomly split into training (116) and validation (38) data. Deep learning and handcrafted features were obtained via the preprocessing diagnostic computed tomography images. The selected features were used to construct radiomics signatures through the least absolute shrinkage and selection operator (LASSO) regression, maximizing relevance while minimizing redundancy. The DLR signature, handcrafted features' radiomics (HCR) signature, and clinical factors were incorporated to develop a DLR nomogram. The DLR nomogram was evaluated in terms of discrimination and calibration with comparison to the HCR signature-based radiomics model. The experimental results showed the outperforming discrimination ability of the proposed DLR over the HCR model in terms of Harrel's concordance index, 0.76 and 0.784, for training and validation sets, respectively. Also, the proposed DLR nomogram calibrates and classifies better than the HCR model in terms of AUC, 0.984 (vs. 0.797) and 0.942 (vs. 0.665) for training and validation sets, respectively. Furthermore, the nomogram-predicted Kaplan–Meier survival (KMS) curves differed significantly from the nonsurvival groups in the log-rank test (p value <0.05). The proposed DLR model based on conventional CT images showed the outperforming performance over the HCR signature model in noninvasively individualized prediction of the 3-year survival rate in esophageal cancer patients. The proposed model can potentially provide prognostic information that guides and helps the clinical decisions between observation and treatment.
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Zhu Y, Wang L, Yu H, Liu M, Zhang M, Li W, Wang S, Tian J. Decouple-Couple Network for Drug-Resistant EGFR Mutation Subtype Prediction with Lung Cancer CT Images. 2022 IEEE 19TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) 2022. [DOI: 10.1109/isbi52829.2022.9761599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Yongbei Zhu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University,China
| | - Liusu Wang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University,China
| | - He Yu
- West China Hospital of Sichuan University,Department of Critical Care and Respiratory Medicine,Chengdu,China
| | - Meili Liu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University,China
| | - Mingyu Zhang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University,China
| | - Weimin Li
- West China Hospital of Sichuan University,Department of Critical Care and Respiratory Medicine,Chengdu,China
| | - Shuo Wang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University,China
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University,China
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Cao R, Dong Y, Wang X, Ren M, Wang X, Zhao N, Yu T, Zhang L, Luo Y, Cui EN, Jiang X. MRI-Based Radiomics Nomogram as a Potential Biomarker to Predict the EGFR Mutations in Exon 19 and 21 Based on Thoracic Spinal Metastases in Lung Adenocarcinoma. Acad Radiol 2022; 29:e9-e17. [PMID: 34332860 DOI: 10.1016/j.acra.2021.06.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/01/2021] [Accepted: 06/08/2021] [Indexed: 01/17/2023]
Abstract
RATIONALE AND OBJECTIVES Preoperative identifications of epidermal growth factor receptor (EGFR) mutation subtypes based on the MRI image of spinal metastases are needed to provide individualized therapy, but has not been previously investigated. This study aims to develop and evaluate an MRI-based radiomics nomogram for differentiating the exon 19 and 21 in EGFR mutation from spinal bone metastases in patients with primary lung adenocarcinoma. MATERIALS AND METHODS A total of 76 patients underwent T1-weighted and T2-weighted fat-suppressed MRI scans were enrolled in this study, 38 were positive for EGFR mutation in exon 19 and 38 were in exon 21.MRI imaging features were extracted and selected from each MRI pulse sequence, and used to form the radiomics signature. A radiomics nomogram was developed integrating the radiomics signature and important clinical factors with receiver operating characteristic, calibration and decision curve analysis to assess the nomogram. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors. RESULTS A total of 6 features were selected as the most discriminative predictors from the two MRI pulse sequences. The nomogram integrating the combined radiomics signature, age and CEA level generated good prediction performance in the training (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.90 vs. 0.87 vs. 0.59) and validation (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.88 vs. 0.86 vs. 0.72) cohort. DCA analysis confirmed the potential clinical utility of the nomogram. CONCLUSION This study demonstrated that MRI features from spinal bone metastases can be used to prognosticate EGFR mutation subtypes in exon 19 and 21. The developed pre-treatment nomogram can potentially guide treatments for lung adenocarcinoma patients.
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Forouzannezhad P, Maes D, Hippe DS, Thammasorn P, Iranzad R, Han J, Duan C, Liu X, Wang S, Chaovalitwongse WA, Zeng J, Bowen SR. Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer. Cancers (Basel) 2022; 14:cancers14051228. [PMID: 35267535 PMCID: PMC8909466 DOI: 10.3390/cancers14051228] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/23/2022] [Accepted: 02/25/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Medical imaging provides quantitative and spatial information to evaluate treatment response in the management of patients with non-small cell lung cancer (NSCLC). High throughput extraction of radiomic features on these images can potentially phenotype tumors non-invasively and support risk stratification based on survival outcome prediction. The prognostic value of radiomics from different imaging modalities and time points prior to and during chemoradiation therapy of NSCLC, relative to conventional imaging biomarker or delta radiomics models, remains uncharacterized. We investigated the utility of multitask learning of multi-time point radiomic features, as opposed to single-task learning, for improving survival outcome prediction relative to conventional clinical imaging feature model benchmarks. (2) Methods: Survival outcomes were prospectively collected for 45 patients with unresectable NSCLC enrolled on the FLARE-RT phase II trial of risk-adaptive chemoradiation and optional consolidation PD-L1 checkpoint blockade (NCT02773238). FDG-PET, CT, and perfusion SPECT imaging pretreatment and week 3 mid-treatment was performed and 110 IBSI-compliant pyradiomics shape-/intensity-/texture-based features from the metabolic tumor volume were extracted. Outcome modeling consisted of a fused Laplacian sparse group LASSO with component-wise gradient boosting survival regression in a multitask learning framework. Testing performance under stratified 10-fold cross-validation was evaluated for multitask learning radiomics of different imaging modalities and time points. Multitask learning models were benchmarked against conventional clinical imaging and delta radiomics models and evaluated with the concordance index (c-index) and index of prediction accuracy (IPA). (3) Results: FDG-PET radiomics had higher prognostic value for overall survival in test folds (c-index 0.71 [0.67, 0.75]) than CT radiomics (c-index 0.64 [0.60, 0.71]) or perfusion SPECT radiomics (c-index 0.60 [0.57, 0.63]). Multitask learning of pre-/mid-treatment FDG-PET radiomics (c-index 0.71 [0.67, 0.75]) outperformed benchmark clinical imaging (c-index 0.65 [0.59, 0.71]) and FDG-PET delta radiomics (c-index 0.52 [0.48, 0.58]) models. Similarly, the IPA for multitask learning FDG-PET radiomics (30%) was higher than clinical imaging (26%) and delta radiomics (15%) models. Radiomics models performed consistently under different voxel resampling conditions. (4) Conclusion: Multitask learning radiomics for outcome modeling provides a clinical decision support platform that leverages longitudinal imaging information. This framework can reveal the relative importance of different imaging modalities and time points when designing risk-adaptive cancer treatment strategies.
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Affiliation(s)
- Parisa Forouzannezhad
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
| | - Dominic Maes
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
| | - Daniel S. Hippe
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA;
| | - Phawis Thammasorn
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Reza Iranzad
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Jie Han
- Department of Industrial, Manufacturing, and System Engineering, University of Texas, Arlington, TX 76019, USA; (J.H.); (S.W.)
| | - Chunyan Duan
- Department of Mechanical Engineering, Tongji University, Shanghai 200092, China;
| | - Xiao Liu
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Shouyi Wang
- Department of Industrial, Manufacturing, and System Engineering, University of Texas, Arlington, TX 76019, USA; (J.H.); (S.W.)
| | - W. Art Chaovalitwongse
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA; (P.T.); (R.I.); (X.L.); (W.A.C.)
| | - Jing Zeng
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
| | - Stephen R. Bowen
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, WA 98195, USA; (P.F.); (D.M.); (J.Z.)
- Department of Radiology, School of Medicine, University of Washington, Seattle, WA 98195, USA
- Correspondence:
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Wu LL, Zhong JD, Zhu JL, Kang L, Huang YY, Lin P, Long H, Zhang LJ, Ma QL, Qiu LH, Ma GW. Postoperative survival effect of the number of examined lymph nodes on esophageal squamous cell carcinoma with pathological stage T1–3N0M0. BMC Cancer 2022; 22:118. [PMID: 35090428 PMCID: PMC8800278 DOI: 10.1186/s12885-022-09207-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 01/12/2022] [Indexed: 12/31/2022] Open
Abstract
Background The postoperative survival effect of the number of examined lymph nodes on patients of R0-resected esophageal squamous cell carcinoma with pathological stage T1–3N0M0 is still unclear. Methods Patients diagnosed with pathological stage T1–3N0M0 esophageal squamous cell carcinoma from two cancer databases—our cancer center (N = 707), and Surveillance Epidemiology and End Results (N = 151). The primary clinical endpoint was overall survival. The X-tile software was used to determine the optimal cutoff value of the number of examined lymph nodes, and propensity score matching was conducted to reduce selection bias according to the results of X-tile software. The cohort of 151 patients from another database was used for validation. Results X-tile software provided an optimal cutoff value of 15 examined lymph nodes based on 707 patients, and 231 pairs of matched patients were included. In the unmatched cohort, Cox proportional hazard regression analysis revealed better overall survival in patients with more than 15 examined lymph nodes (adjusted hazard ratio, 0.566, 95% confidence interval, 0.445–0.720; p < 0.001) compared with patients with 15 or fewer examined lymph nodes. In the validation cohort, patients with more than 15 examined lymph nodes also had better overall survival (adjusted hazard ratio 0.665, p = 0.047). Conclusions The number of examined lymph nodes is a significant prognostic factor in esophageal squamous cell carcinoma patients with pathological stage T1–3N0M0, and more than 15 examined lymph nodes are associated with better overall survival. Although the difference is not significant, the survival curve of patients with examined lymph nodes > 30 is better than those with examined lymph nodes 15–30. We believe that the number of examined lymph nodes can provide prognostic guidance for those patients, and the more examined lymph nodes cause lesser occult lymph nodes metastasis and lead to a better prognosis. Therefore, surgeons and pathologists should try to examine as many lymph nodes as possible to evaluate the pathological stage precisely. However, we need more validation from other studies.
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