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Jia X, Wang Y, Zhang H, Sun D. Current status and quality of prognosis prediction models of non-small cell lung cancer constructed using computed tomography (CT)-based radiomics: a systematic review and radiomics quality score 2.0 assessment. Quant Imaging Med Surg 2024; 14:6978-6989. [PMID: 39281123 PMCID: PMC11400702 DOI: 10.21037/qims-24-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 07/25/2024] [Indexed: 09/18/2024]
Abstract
Background Radiomics extracts specific quantitative data from medical images and explores the characteristics of tumors by analyzing these representations and making predictions. The purpose of this paper is to review computed tomography (CT)-based radiomics articles related to prognostic outcomes in non-small cell lung cancer (NSCLC), assess their scientificity and quality by the latest radiomics quality score (RQS) 2.0 scoring criteria, and provide references for subsequent related studies. Methods CT-based radiomics studies on NSCLC prognosis published from 1 November 2012 to 30 November 2022 in English were screened through the databases of the Cochrane Library, Embase, and PubMed. By excluding criteria such as non-original studies, small sample sizes studies, positron emission tomography (PET)/CT only, and methodological studies only, 17 studies in English were included. The RQS proposed in 2017 is a quality evaluation index specific to radiomics following the PRISMA guidelines, and the latest update of RQS 2.0 has improved the scientificity and completeness of the score. Each checkpoint either belongs to handcrafted radiomics (HCR), deep learning, or both. Results The 17 included studies covered most treatments for NSCLC, including radiotherapy, chemotherapy, surgery, radiofrequency ablation, immunotherapy, and targeted therapy, and predicted outcomes such as overall survival (OS), progression-free survival (PFS), distant metastases, and disease-free survival (DFS). The median score rate for the included studies was 28%, with a range of 12% to 44%. The quality of studies in HCR is not high, and only 4 studies have been validated with independent cohorts. Conclusions The value of radiomics studies needs to be increased, such that clinical application will be possible, and the field of radiomics still has much room for growth. To make prediction models more reliable and stable in forecasting the prognosis of NSCLC and advancing the individualized treatment of NSCLC patients, more clinicians must participate in their development and clinical testing.
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Affiliation(s)
- Xiaoteng Jia
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Yuhang Wang
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Han Zhang
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Daqiang Sun
- Department of Thoracic Surgery, Tianjin Chest Hospital of Tianjin University, Tianjin, China
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Deng C, Chen Z, Bai J, Fu F, Wang S, Li Y, Zhang Y, Chen H. Clinical characteristics and progression of pre-/minimally invasive lung adenocarcinoma harboring ALK or RET rearrangements: a retrospective cohort study. Transl Lung Cancer Res 2023; 12:2440-2447. [PMID: 38205201 PMCID: PMC10775003 DOI: 10.21037/tlcr-23-517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024]
Abstract
Background Patients harboring anaplastic lymphoma kinase (ALK) or rearranged during transfection (RET) rearrangements are usually diagnosed at a relatively late stage with nodal and distant metastasis, and rapid progression course of ALK/RET fusion-positive lung cancer were well-known. However, clinical characteristics and course of pre-/minimally invasive lung adenocarcinoma harboring ALK or RET fusions are poorly described. Identifying patients with gene fusions at early stage may offer surgical options that could cure those patients. Methods We retrospectively included patients with surgically resected pre-/minimally invasive lung adenocarcinomas harboring epidermal growth factor receptor (EGFR) mutations or ALK/RET rearrangements, and further compared the patient clinical characteristics, nodule natural course, and survival outcomes. Radiological characteristics including ground-glass component, cystic airspace, pleural attachment, etc. were specially assessed for this study. EGFR (exons 18-22) was detected by Sanger sequencing and quantitative real-time polymerase chain reaction (qRT-PCR) was used to analyze the ALK/RET rearrangements. Lung cancer-specific survival (LCSS), relapse-free survival (RFS), and overall survival (OS) were all evaluated. Results Of 238 patients with pre-/minimally invasive lung adenocarcinomas, 226 patients had EGFR mutations, 7 patients had ALK fusions, and 5 patients had RET fusions. Average age at surgery was 45.3 years for ALK/RET-positive group and 52.6 years for EGFR-positive group (P=0.049). Radiologically, among the 12 patients with ALK/RET fusions, the majority of lesions (10/12) manifested as mixed ground-glass opacities (mGGOs), which was significantly more prevalent when compared with patients with EGFR mutations (83.4% vs. 24.3%, P<0.001). Moreover, a substantial proportion of cystic airspace was found in ALK/RET-positive group but not in EGFR-positive group (66.7% vs. 14.2%, P<0.001). Among four patients with ALK/RET fusions undergoing surveillance over 1 year before surgery, two of them developed rapid radiologic progression. The 5-year LCSS and RFS were 100%, 100% for ALK/RET-positive group, and 100%, 100% for EGFR-positive group, respectively. Conclusions ALK/RET-positive pre-/minimally invasive lung adenocarcinomas were mostly characterized as mGGOs with cystic airspace developing rapid nodule progression, and no recurrence occurred during long-term follow-up after resection. This provides insights into proper curative surgery timing in the management of patients with gene fusions. However, these findings must be treated with caution and validated in future multi-center studies with larger sample size.
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Affiliation(s)
- Chaoqiang Deng
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zongwei Chen
- Department of Thoracic Surgery, Fudan University Zhongshan Hospital, Shanghai, China
| | - Jinsong Bai
- Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Fangqiu Fu
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shengping Wang
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yuan Li
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yang Zhang
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Haiquan Chen
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China
- Institute of Thoracic Oncology, Fudan University, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Cheng Y, Wang H, Yuan W, Wang H, Zhu Y, Chen H, Jiang W. Combined radiomics of primary tumour and bone metastasis improve the prediction of EGFR mutation status and response to EGFR-TKI therapy for NSCLC. Phys Med 2023; 116:103177. [PMID: 38000098 DOI: 10.1016/j.ejmp.2023.103177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 10/08/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023] Open
Abstract
PURPOSE To develop radiomics models of primary tumour and spinal metastases to predict epidermal growth factor receptor (EGFR) mutations and therapeutic response to EGFR-tyrosine kinase inhibitor (TKI) in patients with metastatic non-small-cell lung cancer (NSCLC). METHODS We enrolled 203 patients with spinal metastases between December 2017 and September 2021, classified as patients with the EGFR mutation or EGFR wild-type. All patients underwent thoracic CT and spinal MRI scans before any treatment. Radiomics analysis was performed to extract features from primary tumour and metastases images and identify predictive features with the least absolute shrinkage and selection operator. Radiomics signatures (RS) were constructed based on primary tumour (RS-Pri), metastases (RS-Met), and in combination (RS-Com) to predict EGFR mutation status and response to EGFR-TKI. Receiver operating characteristic (ROC) curve analysis with 10-fold cross-validation was applied to assess the performance of the models. RESULTS To predict the EGFR mutation status, the RS based on the combination of primary tumour and metastases improved the prediction AUCs compared to those based on the primary tumour or metastasis alone in the training (RS-Com-EGFR: 0.927) and validation (RS-Com-EGFR: 0.812) cohorts. To predict response to EGFR-TKI, the developed RS based on combined primary tumour and metastasis generated the highest AUCs in the training (RS-Com-TKI: 0.880) and validation (RS-Com-TKI: 0.798) cohort. CONCLUSIONS Primary NSCLC and spinal metastases can provide complementary information to predict the EGFR mutation status and response to EGFR-TKI. The developed models that integrate primary lesions and metastases may be potential imaging markers to guide individual treatment decisions.
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Affiliation(s)
- Yuan Cheng
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Wendi Yuan
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Haotian Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Yuheng Zhu
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, 110004 Shenyang, PR China.
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China.
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Pan F, Feng L, Liu B, Hu Y, Wang Q. Application of radiomics in diagnosis and treatment of lung cancer. Front Pharmacol 2023; 14:1295511. [PMID: 38027000 PMCID: PMC10646419 DOI: 10.3389/fphar.2023.1295511] [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: 09/16/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
Radiomics has become a research field that involves the process of converting standard nursing images into quantitative image data, which can be combined with other data sources and subsequently analyzed using traditional biostatistics or artificial intelligence (Al) methods. Due to the capture of biological and pathophysiological information by radiomics features, these quantitative radiomics features have been proven to provide fast and accurate non-invasive biomarkers for lung cancer risk prediction, diagnosis, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics has been emphasized and discussed in lung cancer research, including advantages, challenges, and drawbacks.
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Affiliation(s)
- Feng Pan
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
- Department of CT, Jilin Province FAW General Hospital, Changchun, China
| | - Li Feng
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Baocai Liu
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yue Hu
- Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Qian Wang
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China
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Wolf EV, Müller L, Schoepf UJ, Fink N, Griffith JP, Zsarnoczay E, Baruah D, Suranyi P, Kabakus IM, Halfmann MC, Emrich T, Varga-Szemes A, O'Doherty J. Photon-counting detector CT-based virtual monoenergetic reconstructions: repeatability and reproducibility of radiomics features of an organic phantom and human myocardium. Eur Radiol Exp 2023; 7:59. [PMID: 37875769 PMCID: PMC10597903 DOI: 10.1186/s41747-023-00371-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/17/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Photon-counting detector computed tomography (PCD-CT) may influence imaging characteristics for various clinical conditions due to higher signal and contrast-to-noise ratio in virtual monoenergetic images (VMI). Radiomics analysis relies on quantification of image characteristics. We evaluated the impact of different VMI reconstructions on radiomic features in in vitro and in vivo PCD-CT datasets. METHODS An organic phantom consisting of twelve samples (four oranges, four onions, and four apples) was scanned five times. Twenty-three patients who had undergone coronary computed tomography angiography on a first generation PCD-CT system with the same image acquisitions were analyzed. VMIs were reconstructed at 6 keV levels (40, 55, 70, 90, 120, and 190 keV). The phantoms and the patients' left ventricular myocardium (LVM) were segmented for all reconstructions. Ninety-three original radiomic features were extracted. Repeatability and reproducibility were evaluated through intraclass correlations coefficient (ICC) and post hoc paired samples ANOVA t test. RESULTS There was excellent repeatability for radiomic features in phantom scans (all ICC = 1.00). Among all VMIs, 36/93 radiomic features (38.7%) in apples, 28/93 (30.1%) in oranges, and 33/93 (35.5%) in onions were not significantly different. For LVM, the percentage of stable features was high between VMIs ≥ 90 keV (90 versus 120 keV, 77.4%; 90 versus 190 keV, 83.9%; 120 versus 190 keV, 89.3%), while comparison to lower VMI levels led to fewer reproducible features (40 versus 55 keV, 8.6%). CONCLUSIONS VMI levels influence the stability of radiomic features in an organic phantom and patients' LVM; stability decreases considerably below 90 keV. RELEVANCE STATEMENT Spectral reconstructions significantly influence radiomic features in vitro and in vivo, necessitating standardization and careful attention to these reconstruction parameters before clinical implementation. KEY POINTS • Radiomic features have an excellent repeatability within the same PCD-CT acquisition and reconstruction. • Differences in VMI lead to decreased reproducibility for radiomic features. • VMI ≥ 90 keV increased the reproducibility of the radiomic features.
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Affiliation(s)
- Elias V Wolf
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Nicola Fink
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Joseph P Griffith
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Emese Zsarnoczay
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Dhiraj Baruah
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Pal Suranyi
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Ismael M Kabakus
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Moritz C Halfmann
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany
- German Centre for Cardiovascular Research, Partner site Rhine-Main, Mainz, Germany
| | - Tilman Emrich
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany.
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
- German Centre for Cardiovascular Research, Partner site Rhine-Main, Mainz, Germany.
| | - Akos Varga-Szemes
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - Jim O'Doherty
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
- Siemens Medical Solutions USA Inc, Malvern, PA, USA
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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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Sun J, Li F, Yang J, Lin C, Zhou X, Liu N, Zhang B, Song G, Wang W, Huang C, Song Z, Shi L. Pretherapy investigations using highly robust visualized biomarkers from CT imaging by multiple machine-learning techniques toward its prognosis prediction for ALK-inhibitor therapy in NSCLC: a feasibility study. J Cancer Res Clin Oncol 2023; 149:7341-7353. [PMID: 36928998 DOI: 10.1007/s00432-023-04615-3] [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: 10/29/2022] [Accepted: 01/27/2023] [Indexed: 03/18/2023]
Abstract
PURPOSE Molecularly targeted therapy has revolutionized the therapeutic landscape and is emerging as the first-line treatment option for ALK-rearranged non-small-cell lung cancer (NSCLC). In this study, the highly informative and robust biomarkers based on pre-treatment CT images and clinicopathologic features will be developed and validated to predict the prognosis for ALK-inhibitor therapy in NSCLC patients. METHODS A total of 161 ALK-positive NSCLC patients treated with ALK inhibitors were retrospectively collected as training, validation and test sets from multi-center institutions. Cox proportional hazard regression (CPH) penalized by LASSO and random survival forest (RSF) coupled with recursive feature elimination (RFE) were used for radiomics and clinical features identification and model construction. An overlapping post-processing method was extra added to training process to investigate the stronger biomarker on the whole set. RESULTS 123 of the collected cases progressed after a median follow-up of 15.5 months (IQR, 8.3-25.3). The T and M staging, pericardial effusion, age and ALK inhibitor-alectinib were determined as significant predictors in the survival analysis. Furthermore, we visualized the finally retained 4 radiomics feature. The RSF models built from overlapping-processed clinical and radiomics features respectively reached the maximum C-index of 0.68 and 0.75,but the combination of them,radioclinical signature, improved the score to 0.78. The model on the validation and external test datasets yielded the C-index of 0.73 and 0.79, with the iAUC of 0.76 and 0.83, the IBS of 0.119 and 0.112. CONCLUSION With respect to a simple selection strategy of overlapping optimal radiomics and clinical features from different survival models may promote better progression-free survival(PFS) prediction than conventional survival analysis, which provides a potential method for guiding personalized pre-treatment options of NSCLC.
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Affiliation(s)
- Jingjing Sun
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Feng Li
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, 100080, China
| | - Jiantao Yang
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Chen Lin
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Xianglan Zhou
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Na Liu
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Bingqian Zhang
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Ge Song
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Wenxian Wang
- Department of Medical Oncology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, 100080, China
| | - Zhengbo Song
- Department of Clinical Trial, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
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Le VH, Kha QH, Minh TNT, Nguyen VH, Le VL, Le NQK. Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer. J Digit Imaging 2023; 36:911-922. [PMID: 36717518 PMCID: PMC10287593 DOI: 10.1007/s10278-023-00778-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 02/01/2023] Open
Abstract
The malignant tumors in nature share some common morphological characteristics. Radiomics is not only images but also data; we think that a probability exists in a set of radiomics signatures extracted from CT scan images of one cancer tumor in one specific organ also be utilized for overall survival prediction in different types of cancers in different organs. The retrospective study enrolled four data sets of cancer patients in three different organs (420, 157, 137, and 191 patients for lung 1 training, lung 2 testing, and two external validation set: kidney and head and neck, respectively). In the training set, radiomics features were obtained from CT scan images, and essential features were chosen by LASSO algorithm. Univariable and multivariable analyses were then conducted to find a radiomics signature via Cox proportional hazard regression. The Kaplan-Meier curve was performed based on the risk score. The integrated time-dependent area under the ROC curve (iAUC) was calculated for each predictive model. In the training set, Kaplan-Meier curve classified patients as high or low-risk groups (p-value < 0.001; log-rank test). The risk score of radiomics signature was locked and independently evaluated in the testing set, and two external validation sets showed significant differences (p-value < 0.05; log-rank test). A combined model (radiomics + clinical) showed improved iAUC in lung 1, lung 2, head and neck, and kidney data set are 0.621 (95% CI 0.588, 0.654), 0.736 (95% CI 0.654, 0.819), 0.732 (95% CI 0.655, 0.809), and 0.834 (95% CI 0.722, 0.946), respectively. We believe that CT-based radiomics signatures for predicting overall survival in various cancer sites may exist.
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Affiliation(s)
- Viet Huan Le
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- Department of Thoracic Surgery, Khanh Hoa General Hospital, Nha Trang, 65000, Vietnam
| | - Quang Hien Kha
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
| | - Tran Nguyen Tuan Minh
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
| | - Van Hiep Nguyen
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- Oncology Center, Bai Chay Hospital, Quang Ninh, 20000, Vietnam
| | - Van Long Le
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan
- Department of Anesthesiology and Critical Care, Hue University of Medicine and Pharmacy, Hue University, Hue City, 52000, Vietnam
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 106, Taiwan.
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 106, Taiwan.
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan.
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9
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Li H, Wang S, Liu B, Fang M, Cao R, He B, Liu S, Hu C, Dong D, Wang X, Wang H, Tian J. A multi-view co-training network for semi-supervised medical image-based prognostic prediction. Neural Netw 2023; 164:455-463. [PMID: 37182347 DOI: 10.1016/j.neunet.2023.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/07/2023] [Accepted: 04/18/2023] [Indexed: 05/16/2023]
Abstract
Prognostic prediction has long been a hotspot in disease analysis and management, and the development of image-based prognostic prediction models has significant clinical implications for current personalized treatment strategies. The main challenge in prognostic prediction is to model a regression problem based on censored observations, and semi-supervised learning has the potential to play an important role in improving the utilization efficiency of censored data. However, there are yet few effective semi-supervised paradigms to be applied. In this paper, we propose a semi-supervised co-training deep neural network incorporating a support vector regression layer for survival time estimation (Co-DeepSVS) that improves the efficiency in utilizing censored data for prognostic prediction. First, we introduce a support vector regression layer in deep neural networks to deal with censored data and directly predict survival time, and more importantly to calculate the labeling confidence of each case. Then, we apply a semi-supervised multi-view co-training framework to achieve accurate prognostic prediction, where labeling confidence estimation with prior knowledge of pseudo time is conducted for each view. Experimental results demonstrate that the proposed Co-DeepSVS has a promising prognostic ability and surpasses most widely used methods on a multi-phase CT dataset. Besides, the introduction of SVR layer makes the model more robust in the presence of follow-up bias.
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Affiliation(s)
- Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Siwen Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bo Liu
- Lanzhou University Second Hospital, Lanzhou, 730050, Gansu, China; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong University, Jinan, 250021, Shandong, China
| | - Mengjie Fang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Runnan Cao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bingxi He
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Shengyuan Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chaoen Hu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong University, Jinan, 250021, Shandong, China.
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong, China.
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
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10
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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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11
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Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
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Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
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12
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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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Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review. Cancers (Basel) 2022; 14:cancers14205076. [PMID: 36291865 PMCID: PMC9599928 DOI: 10.3390/cancers14205076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers. Abstract Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.
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14
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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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15
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Li Y, Lv X, Wang B, Xu Z, Wang Y, Gao S, Hou D. Differentiating EGFR from ALK mutation status using radiomics signature based on MR sequences of brain metastasis. Eur J Radiol 2022; 155:110499. [PMID: 36049410 DOI: 10.1016/j.ejrad.2022.110499] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/29/2022] [Accepted: 08/20/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE More and more small brain metastases (BMs) in asymptomatic patients can be detected even prior to their primary lung cancer with the development of MRI. The aim of this study was to develop a predictive radiomics model to identify epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutation status in BM and explore the optimal MR sequence for predication. METHODS This retrospective study included 186 patients with proven BM of lung cancer (training cohort: 70 patients with EGFR mutations and 65 patients with ALK rearrangements; testing cohort: 26 patients with EGFR mutations and 25 patients with ALK rearrangements). Radiomics features were separately extracted from contrast-enhanced T1-weighted imaging (T1-CE), T2 fluid-attenuated inversion recovery (T2-FLAIR) and T2WI sequences. The model for three MR sequences were constructed using a random forest classifier. ROC curves were used to validate the capability of the models in the training and testing cohorts. RESULTS The AUCs of the T2-FLAIR model were significantly higher than those of the T1-CE model in training cohort (0.991 versus 0.954) and testing cohort (0.950 versus 0.867) and much higher than those of the T2WI model in training cohort (0.991 versus 0.880) and testing cohort (0.950 versus 0.731). Besides, the F1 scores of the T1-CE model were slightly higher than the T2-FLAIR model and much higher than the T2WI model in two cohorts. CONCLUSION T2-FLAIR and T1-CE radiomics models that can be used as noninvasive tools for identifying EGFR and ALK mutation status are helpful to guide therapeutic strategies.
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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
| | - Bing 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
| | - Yichuan Wang
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
| | - Shan Gao
- Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, 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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Monitoring of Current Cancer Therapy by Positron Emission Tomography and Possible Role of Radiomics Assessment. Int J Mol Sci 2022; 23:ijms23169394. [PMID: 36012657 PMCID: PMC9409366 DOI: 10.3390/ijms23169394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/31/2022] [Accepted: 08/16/2022] [Indexed: 11/17/2022] Open
Abstract
Evaluation of cancer therapy with imaging is crucial as a surrogate marker of effectiveness and survival. The unique response patterns to therapy with immune-checkpoint inhibitors have facilitated the revision of response evaluation criteria using FDG-PET, because the immune response recalls reactive cells such as activated T-cells and macrophages, which show increased glucose metabolism and apparent progression on morphological imaging. Cellular metabolism and function are critical determinants of the viability of active cells in the tumor microenvironment, which would be novel targets of therapies, such as tumor immunity, metabolism, and genetic mutation. Considering tumor heterogeneity and variation in therapy response specific to the mechanisms of therapy, appropriate response evaluation is required. Radiomics approaches, which combine objective image features with a machine learning algorithm as well as pathologic and genetic data, have remarkably progressed over the past decade, and PET radiomics has increased quality and reliability based on the prosperous publications and standardization initiatives. PET and multimodal imaging will play a definitive role in personalized therapeutic strategies by the precise monitoring in future cancer therapy.
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17
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An Investigation on Radiomics Feature Handling for HNSCC Staging Classification. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The incidence of Head and Neck Squamous Cell Carcinoma (HNSCC) has been growing in the last few decades. Its diagnosis is usually performed through clinical evaluation and analyzing radiological images, then confirmed by histopathological examination, an invasive and time-consuming operation. The recent advances in the artificial intelligence field are leading to interesting results in the early diagnosis, personalized treatment and monitoring of HNSCC only by analyzing radiological images, without performing a tissue biopsy. The large amount of radiological images and the increasing interest in radiomics approaches can help to develop machine learning (ML) methods to support diagnosis. In this work, we propose an ML method based on the use of radiomics features, extracted from CT and PET images, to classify the disease in terms of pN-Stage, pT-Stage and Overall Stage. After the extraction of radiomics features, a selection step is performed to remove dataset redundancy. Finally, ML methods are employed to complete the classification task. Our pipeline is applied on the “Head-Neck-PET-CT” TCIA open-source dataset, considering a cohort of 201 patients from four different institutions. An AUC of 97%, 83% and 93% in terms of pN-Stage, pT-Stage and Overall Stage classification, respectively, is achieved. The obtained results are promising, showing the potential efficiency of the use of radiomics approaches in staging classification.
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18
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Prognostic Evaluation of CT Imaging Big Data-Assisted Arterial Chemoembolization Combined with 125I Seed Implantation for Non-Small-Cell Lung Cancer. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3472982. [PMID: 35872936 PMCID: PMC9300324 DOI: 10.1155/2022/3472982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 05/31/2022] [Indexed: 12/02/2022]
Abstract
Objective To investigate the prognostic impact of computed tomography (CT) imaging big data-assisted arterial chemoembolization combined with iodine 125 (125I) seed implantation on patients with non-small-cell lung cancer (NSCLC). Methods A total of 116 patients with intermediate and advanced NSCLC hospitalized in our hospital from August 2019 to August 2020 were selected and divided into a control group and an experiment group (58 cases in each group) by random number table method for the study. The patients in the experiment group were treated with CT imaging big data-assisted arterial chemoembolization combined with 125I seed implantation, while the patients in the control group were treated with arterial chemoembolization alone, with the use of gemcitabine combined with cisplatin (GP) in chemotherapy. The prognostic impact was determined by analyzing recent efficacy; the incidence of adverse effects; tumor size and CT perfusion parameters including blood volume (BV), blood flow (BF), and permeability surface (PS); frailty state and quality of life; and the levels of serum tumor markers including carcinoembryonic antigen (CEA), glycoconjugate antigen 125 (CA125), cytokeratin 19 fragment antigen 21-1 (CYFRA21-1), microRNA- (miRNA-) 137, and miR-379-5p. In addition, frailty status was evaluated using the Fried frailty phenotype (FP) scale, and quality of life was determined according to Karnofsky Performance Status (KPS) score. Kaplan-Meyer (KM) method was used to analyze the survival rate of NSCLC patients after a 12-month follow-up. Results The remission rate in the experiment group (77.59%) was higher than that in the control group (56.90%) (P < 0.05). Tumor size, BV, BF, PS, serum CEA and CA125 levels, and FP value in both groups were dramatically reduced after treatment compared with before treatment, especially in the experiment group after 1 and 3 months of treatment (P < 0.05). Meanwhile, the serum miR-137 and miR-379-5p levels and KPS scores in both groups were higher after treatment than before treatment, especially in the experiment group after 1 and 3 months of treatment (P < 0.05). However, there was no significant difference in the incidence of nausea and vomiting, alopecia, diarrhea, myelosuppression, and hemoptysis of NSCLC patients in both groups after treatment (P > 0.05). Further, the 12-month survival rate of NSCLC patients was higher in the experiment group (84.21%) than in the control group (64.29%) (P < 0.05). Conclusion CT imaging big data-assisted arterial chemoembolization combined with 125I seed implantation for NSCLC can improve recent efficacy and the prognosis of NSCLC patients by inhibiting tumor progression with a certain degree of safety.
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Wang Z, Yang C, Han W, Sui X, Zheng F, Xue F, Xu X, Wu P, Chen Y, Gu W, Song W, Jiang J. Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study. Insights Imaging 2022; 13:82. [PMID: 35482262 PMCID: PMC9050978 DOI: 10.1186/s13244-022-01204-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/04/2022] [Indexed: 11/12/2022] Open
Abstract
Background Radiomics-based image metrics are not used in the clinic despite the rapidly growing literature. We selected eight promising radiomic features and validated their value in decoding lung cancer heterogeneity. Methods CT images of 236 lung cancer patients were obtained from three different institutes, whereupon radiomic features were extracted according to a standardized procedure. The predictive value for patient long-term prognosis and association with routinely used semantic, genetic (e.g., epidermal growth factor receptor (EGFR)), and histopathological cancer profiles were validated. Feature measurement reproducibility was assessed. Results All eight selected features were robust across repeat scans (intraclass coefficient range: 0.81–0.99), and were associated with at least one of the cancer profiles: prognostic, semantic, genetic, and histopathological. For instance, “kurtosis” had a high predictive value of early death (AUC at first year: 0.70–0.75 in two independent cohorts), negative association with histopathological grade (Spearman’s r: − 0.30), and altered expression levels regarding EGFR mutation and semantic characteristics (solid intensity, spiculated shape, juxtapleural location, and pleura tag; all p < 0.05). Combined as a radiomic score, the features had a higher area under curve for predicting 5-year survival (train: 0.855, test: 0.780, external validation: 0.760) than routine characteristics (0.733, 0.622, 0.613, respectively), and a better capability in patient death risk stratification (hazard ratio: 5.828, 95% confidence interval: 2.915–11.561) than histopathological staging and grading. Conclusions We highlighted the clinical value of radiomic features. Following confirmation, these features may change the way in which we approach CT imaging and improve the individualized care of lung cancer patients.
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Affiliation(s)
- Zixing Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Cuihong Yang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Wei Han
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Xin Sui
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Fuling Zheng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Fang Xue
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Xiaoli Xu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.,Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Peng Wu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Yali Chen
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Wentao Gu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Wei Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
| | - Jingmei Jiang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 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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Tang X, Li Y, Yan WF, Qian WL, Pang T, Gong YL, Yang ZG. Machine Learning-Based CT Radiomics Analysis for Prognostic Prediction in Metastatic Non-Small Cell Lung Cancer Patients With EGFR-T790M Mutation Receiving Third-Generation EGFR-TKI Osimertinib Treatment. Front Oncol 2021; 11:719919. [PMID: 34660285 PMCID: PMC8511497 DOI: 10.3389/fonc.2021.719919] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/13/2021] [Indexed: 02/05/2023] Open
Abstract
Background and Purpose As a third-generation EGFR tyrosine kinase inhibitor (TKI), osimertinib is approved for treating advanced non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation after progression on first- or second-generation EGFR-TKIs such as gefitinib, erlotinib and afatinib. We aim at exploring the feasibility and effectiveness of using radiomic features from chest CT scan to predict the prognosis of metastatic non-small cell lung cancer (NSCLC) patients with EGFR-T790M mutation receiving second-line osimertinib therapy. Methods Contrast-enhanced and unenhanced chest CT images before osimertinib treatment were collected from 201 and 273 metastatic NSCLC patients with EGFR-T790M mutation, respectively. Radiomic features were extracted from the volume of interest. LASSO regression was used to preliminarily evaluate the prognostic values of different radiomic features. We then performed machine learning-based analyses including random forest (RF), support vector machine (SVM), stepwise regression (SR) and LASSO regression with 5-fold cross-validation (CV) to establish the optimal radiomic model for predicting the progression-free survival (PFS) of osimertinib treatment. Finally, a combined clinical-radiomic model was developed and validated using the concordance index (C-index), decision-curve analysis (DCA) and calibration curve analysis. Results Disease progression occurred in 174/273 (63.7%) cases. CT morphological features had no ability in predicting patients’ prognosis in osimertinib treatment. Univariate COX regression followed by LASSO regression analyses identified 23 and 6 radiomic features from the contrast-enhanced and unenhanced CT with prognostic value, respectively. The 23 contrast-enhanced radiomic features were further used to construct radiomic models using different machine learning strategies. Radiomic model built by SR exhibited superior predictive accuracy than RF, SVR or LASSO model (mean C-index of the 5-fold CV: 0.660 vs. 0.560 vs. 0.598 vs. 0.590). Adding the SR radiomic model to the clinical model could remarkably strengthen the C-index of the latter from 0.672 to 0.755. DCA and calibration curve analyses also demonstrated good performance of the combined clinical-radiomic model. Conclusions Radiomic features extracted from the contrast-enhanced chest CT could be used to evaluate metastatic NSCLC patients’ prognosis in osimertinib treatment. Prognostic models combing both radiomic features and clinical factors had a great performance in predicting patients’ outcomes.
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Affiliation(s)
- Xin Tang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yuan Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Wei-Feng Yan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Wen-Lei Qian
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Tong Pang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - You-Ling Gong
- Department of Thoracic Oncology and State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhi-Gang Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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Virtual Monoenergetic Images of Dual-Energy CT-Impact on Repeatability, Reproducibility, and Classification in Radiomics. Cancers (Basel) 2021; 13:cancers13184710. [PMID: 34572937 PMCID: PMC8467875 DOI: 10.3390/cancers13184710] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/14/2021] [Accepted: 09/17/2021] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Virtual monoenergetic images from dual-energy CT are incrementally used in routine clinical practice. Thus, radiomic analysis will be more often performed on these images in the future. This study characterized the test–retest repeatability and reproducibility of radiomic features from virtual monoenergetic images and their impact on machine-learning-based lesion classification. The results of this study provide a basis to improve radiomic analyses and identify the role of feature stability in classification tasks when using virtual monoenergetic imaging with different scan or reconstruction parameters in multicenter clinical studies. Abstract The purpose of this study was to (i) evaluate the test–retest repeatability and reproducibility of radiomic features in virtual monoenergetic images (VMI) from dual-energy CT (DECT) depending on VMI energy (40, 50, 75, 120, 190 keV), radiation dose (5 and 15 mGy), and DECT approach (dual-source and split-filter DECT) in a phantom (ex vivo), and (ii) to assess the impact of VMI energy and feature repeatability on machine-learning-based classification in vivo in 72 patients with 72 hypodense liver lesions. Feature repeatability and reproducibility were determined by concordance–correlation–coefficient (CCC) and dynamic range (DR) ≥0.9. Test–retest repeatability was high within the same VMI energies and scan conditions (percentage of repeatable features ranging from 74% for SFDE mode at 40 keV and 15 mGy to 86% for DSDE at 190 keV and 15 mGy), while reproducibility varied substantially across different VMI energies and DECTs (percentage of reproducible features ranging from 32.8% for SFDE at 5 mGy comparing 40 with 190 keV to 99.2% for DSDE at 15 mGy comparing 40 with 50 keV). No major differences were observed between the two radiation doses (<10%) in all pair-wise comparisons. In vivo, machine learning classification using penalized regression and random forests resulted in the best discrimination of hemangiomas and metastases at low-energy VMI (40 keV), and for cysts at high-energy VMI (120 keV). Feature selection based on feature repeatability did not improve classification performance. Our results demonstrate the high repeatability of radiomics features when keeping scan and reconstruction conditions constant. Reproducibility diminished when using different VMI energies or DECT approaches. The choice of optimal VMI energy improved lesion classification in vivo and should hence be adapted to the specific task.
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23
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Di Giannatale A, Di Paolo PL, Curione D, Lenkowicz J, Napolitano A, Secinaro A, Tomà P, Locatelli F, Castellano A, Boldrini L. Radiogenomics prediction for MYCN amplification in neuroblastoma: A hypothesis generating study. Pediatr Blood Cancer 2021; 68:e29110. [PMID: 34003574 DOI: 10.1002/pbc.29110] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/13/2021] [Accepted: 04/23/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND MYCN amplification represents a powerful prognostic factor in neuroblastoma (NB) and may occasionally account for intratumoral heterogeneity. Radiomics is an emerging field of advanced image analysis that aims to extract a large number of quantitative features from standard radiological images, providing valuable clinical information. PROCEDURE In this retrospective study, we aimed to create a radiogenomics model by correlating computed tomography (CT) radiomics analysis with MYCN status. NB lesions were segmented on pretherapy CT scans and radiomics features subsequently extracted using a dedicated library. Dimensionality reduction/features selection approaches were then used for features procession and logistic regression models have been developed for the considered outcome. RESULTS Seventy-eight patients were included in this study, as training dataset, of which 24 presented MYCN amplification. In total, 232 radiomics features were extracted. Eight features were selected through Boruta algorithm and two features were lastly chosen through Pearson correlation analysis: mean of voxel intensity histogram (p = .0082) and zone size non-uniformity (p = .038). Five-times repeated three-fold cross-validation logistic regression models yielded an area under the curve (AUC) value of 0.879 on the training set. The model was then applied to an independent validation cohort of 21 patients, of which five presented MYCN amplification. The validation of the model yielded a 0.813 AUC value, with 0.85 accuracy on previously unseen data. CONCLUSIONS CT-based radiomics is able to predict MYCN amplification status in NB, paving the way to the in-depth analysis of imaging based biomarkers that could enhance outcomes prediction.
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Affiliation(s)
- Angela Di Giannatale
- Department of Pediatric Hematology/Oncology and Cell and Gene Therapy, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | | | - Davide Curione
- Department of Imaging, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Jacopo Lenkowicz
- UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Aurelio Secinaro
- Department of Imaging, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Paolo Tomà
- Department of Imaging, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Franco Locatelli
- Department of Pediatric Hematology/Oncology and Cell and Gene Therapy, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy.,Department of Gynecology/Obstetrics and Pediatrics, Sapienza University of Rome, Rome, Italy
| | - Aurora Castellano
- Department of Pediatric Hematology/Oncology and Cell and Gene Therapy, IRCCS Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Luca Boldrini
- UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
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Le VH, Kha QH, Hung TNK, Le NQK. Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer. Cancers (Basel) 2021; 13:cancers13143616. [PMID: 34298828 PMCID: PMC8304936 DOI: 10.3390/cancers13143616] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/14/2021] [Accepted: 07/16/2021] [Indexed: 12/17/2022] Open
Abstract
Simple Summary Despite recent advancements in lung cancer treatment, individuals with lung cancer have a dismal 5-year survival rate of only 15%. In patients with non-small cell lung cancer (NSCLC), medical images have lately been employed as a valuable marker for predicting overall survival. The primary goal of this study was to develop a risk score based on computed tomography (CT) based radiomics feature signatures that may be used to predict survival in NSCLC patients. After analyzing 577 NSCLC patients from two data sets, we discovered that the risk score model’s prediction ability as a prognostic indicator was superior to other clinical indicators (age, stage, and gender), and the possibility of patient risk stratification with survival was evaluated using a risk score representation of 10 radiomics signatures. According to this study, the risk score generated using CT-based radiomics signatures promises to predict overall survival in NSCLC patients. Abstract This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; p < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.
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Affiliation(s)
- Viet-Huan Le
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (V.-H.L.); (Q.-H.K.); (T.N.K.H.)
- Department of Thoracic Surgery, Khanh Hoa General Hospital, Nha Trang City 65000, Vietnam
| | - Quang-Hien Kha
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (V.-H.L.); (Q.-H.K.); (T.N.K.H.)
| | - Truong Nguyen Khanh Hung
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (V.-H.L.); (Q.-H.K.); (T.N.K.H.)
- Department of Orthopedic and Trauma, Cho Ray Hospital, Ho Chi Minh City 70000, Vietnam
| | - Nguyen Quoc Khanh Le
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; (V.-H.L.); (Q.-H.K.); (T.N.K.H.)
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Correspondence: ; Tel.: +886-2-66382736 (ext. 1992); Fax: +886-02-27321956
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Liu G, Xu Z, Zhang Y, Jiang B, Zhang L, Wang L, de Bock GH, Vliegenthart R, Xie X. Machine-Learning-Derived Nomogram Based on 3D Radiomic Features and Clinical Factors Predicts Progression-Free Survival in Lung Adenocarcinoma. Front Oncol 2021; 11:692329. [PMID: 34249741 PMCID: PMC8260977 DOI: 10.3389/fonc.2021.692329] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 06/07/2021] [Indexed: 01/06/2023] Open
Abstract
Background To establish a machine-learning-derived nomogram based on radiomic features and clinical factors to predict post-surgical 2-year progression-free survival (PFS) in patients with lung adenocarcinoma. Methods Patients with >2 years post-surgical prognosis results of lung adenocarcinoma were included in Hospital-1 for model training (n = 100) and internal validation (n = 50), and in Hospital-2 for external testing (n = 50). A total of 1,672 radiomic features were extracted from 3D segmented CT images. The Rad-score was established using random survival forest by accumulating and weighting the top-20 imaging features contributive to PFS. A nomogram for predicting PFS was established, which comprised the Rad-score and clinical factors highly relevant to PFS. Results In the training, internal validation, and external test groups, 69/100 (69%), 37/50 (74%) and 36/50 (72%) patients were progression-free at two years, respectively. According to the Rad-score, the integral of area under the curve (iAUC) for discriminating high and low risk of progression was 0.92 (95%CI: 0.77-1.0), 0.70 (0.41-0.98) and 0.90 (0.65-1.0), respectively. The C-index of Rad-score was 0.781 and 0.860 in the training and external test groups, higher than 0.707 and 0.606 for TNM stage, respectively. The nomogram integrating Rad-score and clinical factors (lung nodule type, cM stage and histological type) achieved a C-index of 0.845 and 0.837 to predict 2-year PFS, respectively, significantly higher than by only radiomic features (all p < 0.01). Conclusion The nomogram comprising CT-derived radiomic features and risk factors showed a high performance in predicting post-surgical 2-year PFS of patients with lung adenocarcinoma, which may help personalize the treatment decisions.
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Affiliation(s)
- Guixue Liu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhihan Xu
- DI CT Collaboration, Siemens Healthineers Ltd., Shanghai, China
| | - Yaping Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Beibei Jiang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lingyun Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Geertruida H de Bock
- Department of Epidemiology, Hanzeplein 1, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, Hanzeplein 1, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Hope A, Verduin M, Dilling TJ, Choudhury A, Fijten R, Wee L, Aerts HJWL, El Naqa I, Mitchell R, Vooijs M, Dekker A, de Ruysscher D, Traverso A. Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers. Cancers (Basel) 2021; 13:2382. [PMID: 34069307 PMCID: PMC8156328 DOI: 10.3390/cancers13102382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/21/2021] [Accepted: 05/03/2021] [Indexed: 11/16/2022] Open
Abstract
Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients' data (imaging, electronic health records, patients' reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.
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Affiliation(s)
- Andrew Hope
- Department of Radiation Oncology, University of Toronto, Toronto, ON 5MT 1P5, Canada;
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON 5MT 1P5, Canada
| | - Maikel Verduin
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Thomas J Dilling
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Ananya Choudhury
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Rianne Fijten
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Leonard Wee
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Hugo JWL Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA;
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, 6228 ET Maastricht, The Netherlands
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (I.E.N.); (R.M.)
| | - Ross Mitchell
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (I.E.N.); (R.M.)
| | - Marc Vooijs
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Andre Dekker
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Dirk de Ruysscher
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
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Combining radiomic phenotypes of non-small cell lung cancer with liquid biopsy data may improve prediction of response to EGFR inhibitors. Sci Rep 2021; 11:9984. [PMID: 33976268 PMCID: PMC8113313 DOI: 10.1038/s41598-021-88239-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/09/2021] [Indexed: 12/13/2022] Open
Abstract
Among non-small cell lung cancer (NSCLC) patients with therapeutically targetable tumor mutations in epidermal growth factor receptor (EGFR), not all patients respond to targeted therapy. Combining circulating-tumor DNA (ctDNA), clinical variables, and radiomic phenotypes may improve prediction of EGFR-targeted therapy outcomes for NSCLC. This single-center retrospective study included 40 EGFR-mutant advanced NSCLC patients treated with EGFR-targeted therapy. ctDNA data included number of mutations and detection of EGFR T790M. Clinical data included age, smoking status, and ECOG performance status. Baseline chest CT scans were analyzed to extract 429 radiomic features from each primary tumor. Unsupervised hierarchical clustering was used to group tumors into phenotypes. Kaplan–Meier (K–M) curves and Cox proportional hazards regression were modeled for progression-free survival (PFS) and overall survival (OS). Likelihood ratio test (LRT) was used to compare fit between models. Among 40 patients (73% women, median age 62 years), consensus clustering identified two radiomic phenotypes. For PFS, the model combining radiomic phenotypes with ctDNA and clinical variables had c-statistic of 0.77 and a better fit (LRT p = 0.01) than the model with clinical and ctDNA variables alone with a c-statistic of 0.73. For OS, adding radiomic phenotypes resulted in c-statistic of 0.83 versus 0.80 when using clinical and ctDNA variables (LRT p = 0.08). Both models showed separation of K–M curves dichotomized by median prognostic score (p < 0.005). Combining radiomic phenotypes, ctDNA, and clinical variables may enhance precision oncology approaches to managing advanced non-small cell lung cancer with EGFR mutations.
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Sugai Y, Kadoya N, Tanaka S, Tanabe S, Umeda M, Yamamoto T, Takeda K, Dobashi S, Ohashi H, Takeda K, Jingu K. Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients. Radiat Oncol 2021; 16:80. [PMID: 33931085 PMCID: PMC8086112 DOI: 10.1186/s13014-021-01810-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/21/2021] [Indexed: 02/08/2023] Open
Abstract
Background Radiomics is a new technology to noninvasively predict survival prognosis with quantitative features extracted from medical images. Most radiomics-based prognostic studies of non-small-cell lung cancer (NSCLC) patients have used mixed datasets of different subgroups. Therefore, we investigated the radiomics-based survival prediction of NSCLC patients by focusing on subgroups with identical characteristics. Methods A total of 304 NSCLC (Stages I–IV) patients treated with radiotherapy in our hospital were used. We extracted 107 radiomic features (i.e., 14 shape features, 18 first-order statistical features, and 75 texture features) from the gross tumor volume drawn on the free breathing planning computed tomography image. Three feature selection methods [i.e., test–retest and multiple segmentation (FS1), Pearson's correlation analysis (FS2), and a method that combined FS1 and FS2 (FS3)] were used to clarify how they affect survival prediction performance. Subgroup analysis for each histological subtype and each T stage applied the best selection method for the analysis of All data. We used a least absolute shrinkage and selection operator Cox regression model for all analyses and evaluated prognostic performance using the concordance-index (C-index) and the Kaplan–Meier method. For subgroup analysis, fivefold cross-validation was applied to ensure model reliability. Results In the analysis of All data, the C-index for the test dataset is 0.62 (FS1), 0.63 (FS2), and 0.62 (FS3). The subgroup analysis indicated that the prediction model based on specific histological subtypes and T stages had a higher C-index for the test dataset than that based on All data (All data, 0.64 vs. SCCall, 060; ADCall, 0.69; T1, 0.68; T2, 0.65; T3, 0.66; T4, 0.70). In addition, the prediction models unified for each T stage in histological subtype showed a different trend in the C-index for the test dataset between ADC-related and SCC-related models (ADCT1–ADCT4, 0.72–0.83; SCCT1–SCCT4, 0.58–0.71). Conclusions Our results showed that feature selection methods moderately affected the survival prediction performance. In addition, prediction models based on specific subgroups may improve the prediction performance. These results may prove useful for determining the optimal radiomics-based predication model. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-021-01810-9.
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Affiliation(s)
- Yuto Sugai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Shunpei Tanabe
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Mariko Umeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Suguru Dobashi
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Haruna Ohashi
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Ken Takeda
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
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On the performance of lung nodule detection, segmentation and classification. Comput Med Imaging Graph 2021; 89:101886. [PMID: 33706112 DOI: 10.1016/j.compmedimag.2021.101886] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 01/11/2021] [Accepted: 02/02/2021] [Indexed: 01/10/2023]
Abstract
Computed tomography (CT) screening is an effective way for early detection of lung cancer in order to improve the survival rate of such a deadly disease. For more than two decades, image processing techniques such as nodule detection, segmentation, and classification have been extensively studied to assist physicians in identifying nodules from hundreds of CT slices to measure shapes and HU distributions of nodules automatically and to distinguish their malignancy. Thanks to new parallel computation, multi-layer convolution, nonlinear pooling operation, and the big data learning strategy, recent development of deep-learning algorithms has shown great progress in lung nodule screening and computer-assisted diagnosis (CADx) applications due to their high sensitivity and low false positive rates. This paper presents a survey of state-of-the-art deep-learning-based lung nodule screening and analysis techniques focusing on their performance and clinical applications, aiming to help better understand the current performance, the limitation, and the future trends of lung nodule analysis.
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30
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Chang C, Sun X, Wang G, Yu H, Zhao W, Ge Y, Duan S, Qian X, Wang R, Lei B, Wang L, Liu L, Ruan M, Yan H, Liu C, Chen J, Xie W. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma. Front Oncol 2021; 11:603882. [PMID: 33738250 PMCID: PMC7962599 DOI: 10.3389/fonc.2021.603882] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 01/08/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives Anaplastic lymphoma kinase (ALK) rearrangement status examination has been widely used in clinic for non-small cell lung cancer (NSCLC) patients in order to find patients that can be treated with targeted ALK inhibitors. This study intended to non-invasively predict the ALK rearrangement status in lung adenocarcinomas by developing a machine learning model that combines PET/CT radiomic features and clinical characteristics. Methods Five hundred twenty-six patients of lung adenocarcinoma with PET/CT scan examination were enrolled, including 109 positive and 417 negative patients for ALK rearrangements from February 2016 to March 2019. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images. The maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were further employed to select the most distinguishable radiomic features to construct predictive models. The mRMR is a feature selection method, which selects the features with high correlation to the pathological results (maximum correlation), meanwhile retain the features with minimum correlation between them (minimum redundancy). LASSO is a statistical formula whose main purpose is the feature selection and regularization of data model. LASSO method regularizes model parameters by shrinking the regression coefficients, reducing some of them to zero. The feature selection phase occurs after the shrinkage, where every non-zero value is selected to be used in the model. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the models, and the performance of different models was compared by the DeLong test. Results A total of 22 radiomic features were extracted from PET/CT images for constructing the PET/CT radiomic model, and majority of these features used were based on CT features (20 out of 22), only 2 PET features were included (PET percentile 10 and PET difference entropy). Moreover, three clinical features associated with ALK mutation (age, burr and pleural effusion) were also employed to construct a combined model of PET/CT and clinical model. We found that this combined model PET/CT-clinical model has a significant advantage to predict the ALK mutation status in the training group (AUC = 0.87) and the testing group (AUC = 0.88) compared with the clinical model alone in the training group (AUC = 0.76) and the testing group (AUC = 0.74) respectively. However, there is no significant difference between the combined model and PET/CT radiomic model. Conclusions This study demonstrated that PET/CT radiomics-based machine learning model has potential to be used as a non-invasive diagnostic method to help diagnose ALK mutation status for lung adenocarcinoma patients in the clinic.
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Affiliation(s)
- Cheng Chang
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xiaoyan Sun
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Gang Wang
- Statistical Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Hong Yu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wenlu Zhao
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yaqiong Ge
- Pharmaceutical Diagnostic Department, GE Healthcare China, Shanghai, China
| | - Shaofeng Duan
- Pharmaceutical Diagnostic Department, GE Healthcare China, Shanghai, China
| | - Xiaohua Qian
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Rui Wang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Bei Lei
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Lihua Wang
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Liu Liu
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Maomei Ruan
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Hui Yan
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Ciyi Liu
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Chen
- Department of Ultrasound, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wenhui Xie
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
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Song J, Wang L, Ng NN, Zhao M, Shi J, Wu N, Li W, Liu Z, Yeom KW, Tian J. Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant-Positive Non-Small Cell Lung Cancer. JAMA Netw Open 2020; 3:e2030442. [PMID: 33331920 PMCID: PMC7747022 DOI: 10.1001/jamanetworkopen.2020.30442] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
IMPORTANCE An end-to-end efficacy evaluation approach for identifying progression risk after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy in patients with stage IV EGFR variant-positive non-small cell lung cancer (NSCLC) is lacking. OBJECTIVE To propose a clinically applicable large-scale bidirectional generative adversarial network for predicting the efficacy of EGFR-TKI therapy in patients with NSCLC. DESIGN, SETTING, AND PARTICIPANTS This diagnostic/prognostic study enrolled 465 patients from January 1, 2010, to August 1, 2017, with follow-up from February 1, 2010, to June 1, 2020. A deep learning (DL) semantic signature to predict progression-free survival (PFS) was constructed in the training cohort, validated in 2 external validation and 2 control cohorts, and compared with the radiomics signature. EXPOSURES An end-to-end bidirectional generative adversarial network framework was designed to predict the progression risk in patients with NSCLC. MAIN OUTCOMES AND MEASURES The primary end point was PFS, considering the time from the initiation of therapy to the date of recurrence, confirmed disease progression, or death. RESULTS A total of 342 patients with stage IV EGFR variant-positive NSCLC receiving EGFR-TKI therapy met the inclusion criteria. Of these, 145 patients from 2 of the hospitals (n = 117 and 28) formed a training cohort (mean [SD] age, 61 [11] years; 87 [60.0%] female), and the patients from 2 other hospitals comprised 2 external validation cohorts (validation cohort 1: n = 101; mean [SD] age, 57 [12] years; 60 [59.4%] female; and validation cohort 2: n = 96, mean [SD] age, 58 [9] years; 55 [57.3%] female). Fifty-six patients with advanced-stage EGFR variant-positive NSCLC (mean [SD] age, 52 [11] years; 26 [46.4%] female) and 67 patients with advanced-stage EGFR wild-type NSCLC (mean [SD] age, 54 [10] years; 10 [15.0%] female) who received first-line chemotherapy were included. A total of 90 (26%) receiving EGFR-TKI therapy with a high risk of rapid disease progression were identified (median [range] PFS, 7.3 [1.4-32.0] months in the training cohort, 5.0 [0.6-34.6] months in validation cohort 1, and 6.4 [1.8-20.1] months, in validation cohort 2) using the DL semantic signature.The PFS decreased by 36% (hazard ratio, 2.13; 95% CI, 1.30-3.49; P < .001) compared with that in other patients (median [range] PFS, 11.5 [1.5-64.2] months in the training cohort, 10.9 [1.1-50.5] in validation cohort 1, and 8.9 [0.8-40.6] months in validation cohort 2. No significant differences were observed when comparing the PFS of high-risk patients receiving EGFR-TKI therapy with the chemotherapy cohorts (median PFS, 6.9 vs 4.4 months; P = .08). In terms of predicting the tumor progression risk after EGFR-TKI therapy, clinical decisions based on the DL semantic signature led to better survival outcomes than those based on radiomics signature across all risk probabilities by the decision curve analysis. CONCLUSIONS AND RELEVANCE This diagnostic/prognostic study provides a clinically applicable approach for identifying patients with stage IV EGFR variant-positive NSCLC who are not likely to benefit from EGFR-TKI therapy. The end-to-end DL-derived semantic features eliminated all manual interventions required while using previous radiomics methods and have a better prognostic performance.
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Affiliation(s)
- Jiangdian Song
- Department of Biomedical Engineering, College of Medicine and Biological Information Engineering, Northeastern University. Shenyang, Liaoning, China
- Department of Radiology, School of Medicine Stanford University, Stanford, California
| | - Lu Wang
- Department of Medical Informatics, China Medical University, Shenyang, Liaoning, China
| | - Nathan Norton Ng
- Department of Radiology, School of Medicine Stanford University, Stanford, California
| | - Mingfang Zhao
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jingyun Shi
- Department of Radiology, Shanghai Pulmonary Hospital, Shanghai, China
| | - Ning Wu
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, Sichuan, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Kristen W. Yeom
- Department of Radiology, School of Medicine Stanford University, Stanford, California
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
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Positron Emission Tomography for Response Evaluation in Microenvironment-Targeted Anti-Cancer Therapy. Biomedicines 2020; 8:biomedicines8090371. [PMID: 32972006 PMCID: PMC7556039 DOI: 10.3390/biomedicines8090371] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 12/31/2022] Open
Abstract
Therapeutic response is evaluated using the diameter of tumors and quantitative parameters of 2-[18F] fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET). Tumor response to molecular-targeted drugs and immune checkpoint inhibitors is different from conventional chemotherapy in terms of temporal metabolic alteration and morphological change after the therapy. Cancer stem cells, immunologically competent cells, and metabolism of cancer are considered targets of novel therapy. Accumulation of FDG reflects the glucose metabolism of cancer cells as well as immune cells in the tumor microenvironment, which differs among patients according to the individual immune function; however, FDG-PET could evaluate the viability of the tumor as a whole. On the other hand, specific imaging and cell tracking of cancer cell or immunological cell subsets does not elucidate tumor response in a complexed interaction in the tumor microenvironment. Considering tumor heterogeneity and individual variation in therapeutic response, a radiomics approach with quantitative features of multimodal images and deep learning algorithm with reference to pathologic and genetic data has the potential to improve response assessment for emerging cancer therapy.
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33
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Chen B, Yang L, Zhang R, Luo W, Li W. Radiomics: an overview in lung cancer management-a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1191. [PMID: 33241040 PMCID: PMC7576016 DOI: 10.21037/atm-20-4589] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Radiomics is a novel approach for optimizing the analysis massive data from medical images to provide auxiliary guidance in clinical issues. Quantitative feature extraction is one of the critical steps of radiomics. The association between radiomics features and the clinicopathological information of diseases can be identified by several statistics methods. For instance, although significant progress has been made in the field of lung cancer, too many questions remain, especially for the individualized decisions. Radiomics offers a new tool to encode the characteristics of lung cancer which is the leading cause of cancer-related deaths worldwide. Here, we reviewed the workflow and clinical utility of radiomics in lung cancer management, including pulmonary nodules detection, classification, histopathology and genetics evaluation, clinical staging, therapy response, and prognosis prediction. Most of these studies showed positive results, indicating the potential value of radiomics in clinical practice. The implementation of radiomics is both feasible and invaluable, and has aided clinicians in ascertaining the nature of a disease with greater precision. However, it should be noted that radiomics in its current state cannot completely replace the work of therapists or tissue examination. The potential future trends of this modality were also remarked. More efforts are needed to overcome the limitations identified above in order to facilitate the widespread application of radiomics in the reasonably near future.
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Affiliation(s)
- Bojiang Chen
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Lan Yang
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Rui Zhang
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Wenxin Luo
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
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