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Palmeri F, Zerunian M, Polici M, Nardacci S, De Dominicis C, Allegra B, Monterubbiano A, Mancini M, Ferrari R, Paolantonio P, De Santis D, Laghi A, Caruso D. Virtual biopsy through CT imaging: can radiomics differentiate between subtypes of non-small cell lung cancer? LA RADIOLOGIA MEDICA 2025:10.1007/s11547-025-02022-x. [PMID: 40402434 DOI: 10.1007/s11547-025-02022-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2024] [Accepted: 04/28/2025] [Indexed: 05/23/2025]
Abstract
OBJECTIVE This study evaluated the performance of CT radiomics in distinguishing between lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) at baseline imaging, exploring its potential as a noninvasive virtual biopsy. MATERIALS AND METHODS A retrospective analysis was conducted, enrolling 330 patients between September 2015 and January 2023. Inclusion criteria were histologically proven ADC or SCC and baseline contrast-enhanced chest CT. Exclusion criteria included significant motion artifacts and nodules < 6 mm. Radiological features, including lung lobe affected, peripheral/central location, presence of emphysema, and T/N radiological stage, were assessed for each patient. Volumetric segmentation of lung cancers was performed on baseline CT scans at the portal-venous phase using 3DSlicer software (v5.2.2). A total of 107 radiomic features were extracted and selected using the least absolute shrinkage and selection operator (LASSO) and tenfold cross-validation. Multivariable logistic regression analysis was employed to develop three predictive models: radiological features-only, radiomics-only, and a combined model, with statistical significance set at p < 0.05. Additionally, an independent external validation cohort of 16 patients, meeting the same inclusion and exclusion criteria, was identified. RESULTS The final cohort comprised 200 ADC and 100 SCC patients (mean age 68 ± 10 years, 184 men). Two radiological and 21 radiomic features were selected (p < 0.001). The Radiological model achieved AUC 0.73 (95% CI 0.68-0.78, p < 0.001), 72.3% accuracy. The radiomics model achieved AUC 0.80 (95% CI 0.75-0.85, p < 0.001), 75.6% accuracy. The combined model achieved AUC 0.84 (95% CI 0.80-0.88, p < 0.001), 75.3% accuracy. External validation (n = 15) yielded AUC 0.78 (p = 0.05). CONCLUSION The combined radiologic-radiomics model showed the best performance in differentiating ADC from SCC.
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Affiliation(s)
- Federica Palmeri
- Department of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Rome, Italy
| | - Marta Zerunian
- Department of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Rome, Italy
| | - Michela Polici
- Department of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Rome, Italy
- PhD School in Translational Medicine and Oncology, Department of Medical and Surgical Sciences and Translational Medicine, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Stefano Nardacci
- Department of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Rome, Italy
| | - Chiara De Dominicis
- Department of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Rome, Italy
| | - Bianca Allegra
- Department of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Rome, Italy
| | | | - Massimiliano Mancini
- Morphologic and Molecular Patology Unit, Sant'Andrea University Hospital, Rome, Italy
| | - Riccardo Ferrari
- Emergency Radiology Department, San Camillo-Forlanini Hospital, Rome, Italy
| | | | - Domenico De Santis
- Department of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Rome, Italy
| | - Andrea Laghi
- Department of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Rome, Italy.
| | - Damiano Caruso
- Department of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Rome, Italy
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Zhang R, Zheng H, Lin J, Wang J. Review of the application of dual-energy CT combined with radiomics in the diagnosis and analysis of lung cancer. J Appl Clin Med Phys 2025; 26:e70020. [PMID: 39962757 PMCID: PMC11969089 DOI: 10.1002/acm2.70020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 12/08/2024] [Accepted: 01/13/2025] [Indexed: 04/05/2025] Open
Abstract
Lung cancer is one of the most common malignant tumors in the world. Early detection and precise treatment are of great significance to clinical decision-making and patient prognosis. As an emerging imaging technology, dual-energy computed tomography (DECT) has increasingly prominent advantages in multi-parameter and quantitative analysis in assessing the benign and malignant, classification, and prognosis of lung cancer. Radiomics uses an automated high-throughput method to extract a large number of quantitative features from medical images, quantify tumor heterogeneity, monitor tumor development and prognosis, and provide new ideas for the diagnosis and identification of lung cancer. This article will review the application progress of DECT post-processing technology combined with radiomics in lung cancer diagnosis, identification, biomarker and gene prediction, and prognosis assessment.
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Affiliation(s)
- Rongyu Zhang
- Department of RadiologyThe First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine)HangzhouChina
- The First School of Clinical MedicineZhejiang Chinese Medical UniversityHangzhouChina
| | - Hao Zheng
- Department of RadiologyThe First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine)HangzhouChina
- The First School of Clinical MedicineZhejiang Chinese Medical UniversityHangzhouChina
| | - Jie Lin
- Department of RadiologyThe First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine)HangzhouChina
- The First School of Clinical MedicineZhejiang Chinese Medical UniversityHangzhouChina
| | - Junna Wang
- Department of RadiologyThe First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine)HangzhouChina
- The First School of Clinical MedicineZhejiang Chinese Medical UniversityHangzhouChina
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Liu X, Chen XL, Yuan Y, Pu H, Li H. Dual-energy CT quantitative parameters for prediction of prognosis in patients with resectable rectal cancer. Eur Radiol 2025:10.1007/s00330-025-11398-3. [PMID: 39921716 DOI: 10.1007/s00330-025-11398-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2024] [Revised: 12/03/2024] [Accepted: 01/05/2025] [Indexed: 02/10/2025]
Abstract
OBJECTIVE To determine whether quantitative parameters derived from dual-energy CT (DECT) could predict prognosis in patients with resectable rectal cancer (RC). MATERIALS AND METHODS One hundred and thirty-four patients (recurrence/distant metastasis group, n = 36; non-metastasis/non-recurrence group, n = 98) with RC who underwent radical resection and DECT were retrospectively included. DECT quantitative parameters, including iodine concentration (IC), normalized iodine concentration (NIC), electron density (Rho), effective atomic number (Zeff), dual-energy index (DEI), the slope of the spectral Hounsfield unit curve (λHU) on arterial and venous phase images. Univariate and multivariate Cox proportional hazards models were employed to identify independent risk factors of prognosis. The area under the receiver operating characteristic curve (AUC) was used to assess the performance. Disease-free survival (DFS) curves were constructed using the Kaplan-Meier method. RESULTS Patients in the metastasis/recurrence group had higher Rho in arterial phase (A-Rho), NIC in venous phase (V-NIC), Rho in venous phase (V-Rho), Zeff in venous phase (V-Zeff), λHU in venous phase (V-λHU), pT stage, pN stage, serum carcinoembryonic antigen (CEA), carbohydrate antigen-199 levels and more frequent in extramural venous invasion than those in non-metastasis/non-recurrence group (all p < 0.05). V-NIC, V-λHU, and CEA were independent risk factors of recurrence/distant metastasis (all p < 0.05). The AUC of combined indicator integrating three independent risk factors achieved the best diagnostic performance (AUC = 0.900). In stratified survival analysis, patients with high V-NIC, V-λHU, and CEA had lower 3-year DFS than those with low V-NIC, V-λHU, and CEA. CONCLUSION Combining V-NIC, V-λHU, and CEA could be used to noninvasively predict prognosis in resectable RC. KEY POINTS Question TNM staging fails to accurately prognosticate; can quantitative parameters derived from dual-energy CT predict prognosis in patients with resectable rectal cancer? Findings Normalized iodine concentration (V-NIC) and the slope of the spectral Hounsfield unit curve in venous phase (V-λHU), and carcinoembryonic antigen (CEA) are independent risk factors for recurrence/metastasis. Clinical relevance The combined indicator integrating V-NIC, V-λHU, and CEA could predict 3-year disease-free survival in patients with resectable rectal cancer and could aid in postoperative survival risk stratification to guide personalized treatment.
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Affiliation(s)
- Xia Liu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiao-Li Chen
- Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, Chengdu, China
| | - Yi Yuan
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Pu
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hang Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
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Jiang J, Sheng K, Li M, Zhao H, Guan B, Dai L, Li Y. A dual-energy computed tomography-based radiomics nomogram for predicting time since stroke onset: a multicenter study. Eur Radiol 2024; 34:7373-7385. [PMID: 38834786 DOI: 10.1007/s00330-024-10802-8] [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: 12/26/2023] [Revised: 03/25/2024] [Accepted: 04/06/2024] [Indexed: 06/06/2024]
Abstract
OBJECTIVES We aimed to develop and validate a radiomics nomogram based on dual-energy computed tomography (DECT) images and clinical features to classify the time since stroke (TSS), which could facilitate stroke decision-making. MATERIALS AND METHODS This retrospective three-center study consecutively included 488 stroke patients who underwent DECT between August 2016 and August 2022. The eligible patients were divided into training, test, and validation cohorts according to the center. The patients were classified into two groups based on an estimated TSS threshold of ≤ 4.5 h. Virtual images optimized the visibility of early ischemic lesions with more CT attenuation. A total of 535 radiomics features were extracted from polyenergetic, iodine concentration, virtual monoenergetic, and non-contrast images reconstructed using DECT. Demographic factors were assessed to build a clinical model. A radiomics nomogram was a tool that the Rad score and clinical factors to classify the TSS using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) was used to compare the clinical utility and benefits of different models. RESULTS Twelve features were used to build the radiomics model. The nomogram incorporating both clinical and radiomics features showed favorable predictive value for TSS. In the validation cohort, the nomogram showed a higher AUC than the radiomics-only and clinical-only models (AUC: 0.936 vs 0.905 vs 0.824). DCA demonstrated the clinical utility of the radiomics nomogram model. CONCLUSIONS The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients. CLINICAL RELEVANCE STATEMENT The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS. KEY POINTS Accurately determining the TSS onset is crucial in deciding a treatment approach. The radiomics-clinical nomogram showed the best performance for predicting the TSS. Using the developed model to identify patients at different times since stroke can facilitate individualized management.
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Affiliation(s)
- Jingxuan Jiang
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Kai Sheng
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Minda Li
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Huilin Zhao
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Baohui Guan
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lisong Dai
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuehua Li
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Shi L, Zhao J, Wei Z, Wu H, Sheng M. Radiomics in distinguishing between lung adenocarcinoma and lung squamous cell carcinoma: a systematic review and meta-analysis. Front Oncol 2024; 14:1381217. [PMID: 39381037 PMCID: PMC11458374 DOI: 10.3389/fonc.2024.1381217] [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: 02/09/2024] [Accepted: 09/05/2024] [Indexed: 10/10/2024] Open
Abstract
Objectives The aim of this study was to systematically review the studies on radiomics models in distinguishing between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and evaluate the classification performance of radiomics models using images from various imaging techniques. Materials and methods PubMed, Embase and Web of Science Core Collection were utilized to search for radiomics studies that differentiate between LUAD and LUSC. The assessment of the quality of studies included utilized the improved Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS). Meta-analysis was conducted to assess the classification performance of radiomics models using various imaging techniques. Results The qualitative analysis included 40 studies, while the quantitative synthesis included 21 studies. Median RQS for 40 studies was 12 (range -5~19). Sixteen studies were deemed to have a low risk of bias and low concerns regarding applicability. The radiomics model based on CT images had a pooled sensitivity of 0.78 (95%CI: 0.71~0.83), specificity of 0.85 (95%CI:0.73~0.92), and the area under summary receiver operating characteristic curve (SROC-AUC) of 0.86 (95%CI:0.82~0.89). As for PET images, the pooled sensitivity was 0.80 (95%CI: 0.61~0.91), specificity was 0.77 (95%CI: 0.60~0.88), and the SROC-AUC was 0.85 (95%CI: 0.82~0.88). PET/CT images had a pooled sensitivity of 0.87 (95%CI: 0.72~0.94), specificity of 0.88 (95%CI: 0.80~0.93), and an SROC-AUC of 0.93 (95%CI: 0.91~0.95). MRI images had a pooled sensitivity of 0.73 (95%CI: 0.61~0.82), specificity of 0.80 (95%CI: 0.65~0.90), and an SROC-AUC of 0.79 (95%CI: 0.75~0.82). Conclusion Radiomics models demonstrate potential in distinguishing between LUAD and LUSC. Nevertheless, it is crucial to conduct a well-designed and powered prospective radiomics studies to establish their credibility in clinical application. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=412851, identifier CRD42023412851.
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Affiliation(s)
- Lili Shi
- Medical School, Nantong University, Nantong, China
| | - Jinli Zhao
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Zhichao Wei
- Medical School, Nantong University, Nantong, China
| | - Huiqun Wu
- Medical School, Nantong University, Nantong, China
| | - Meihong Sheng
- Department of Radiology, The Second Affiliated Hospital of Nantong University and Nantong First People’s Hospital, Nantong, China
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Cai F, Cheng L, Liao X, Xie Y, Wang W, Zhang H, Lu J, Chen R, Chen C, Zhou X, Mo X, Hu G, Huang L. An Integrated Clinical and Computerized Tomography-Based Radiomic Feature Model to Separate Benign from Malignant Pleural Effusion. Respiration 2024; 103:406-416. [PMID: 38422997 DOI: 10.1159/000536517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
INTRODUCTION Distinguishing between malignant pleural effusion (MPE) and benign pleural effusion (BPE) poses a challenge in clinical practice. We aimed to construct and validate a combined model integrating radiomic features and clinical factors using computerized tomography (CT) images to differentiate between MPE and BPE. METHODS A retrospective inclusion of 315 patients with pleural effusion (PE) was conducted in this study (training cohort: n = 220; test cohort: n = 95). Radiomic features were extracted from CT images, and the dimensionality reduction and selection processes were carried out to obtain the optimal radiomic features. Logistic regression (LR), support vector machine (SVM), and random forest were employed to construct radiomic models. LR analyses were utilized to identify independent clinical risk factors to develop a clinical model. The combined model was created by integrating the optimal radiomic features with the independent clinical predictive factors. The discriminative ability of each model was assessed by receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). RESULTS Out of the total 1,834 radiomic features extracted, 15 optimal radiomic features explicitly related to MPE were picked to develop the radiomic model. Among the radiomic models, the SVM model demonstrated the highest predictive performance [area under the curve (AUC), training cohort: 0.876, test cohort: 0.774]. Six clinically independent predictive factors, including age, effusion laterality, procalcitonin, carcinoembryonic antigen, carbohydrate antigen 125 (CA125), and neuron-specific enolase (NSE), were selected for constructing the clinical model. The combined model (AUC: 0.932, 0.870) exhibited superior discriminative performance in the training and test cohorts compared to the clinical model (AUC: 0.850, 0.820) and the radiomic model (AUC: 0.876, 0.774). The calibration curves and DCA further confirmed the practicality of the combined model. CONCLUSION This study presented the development and validation of a combined model for distinguishing MPE and BPE. The combined model was a powerful tool for assisting in the clinical diagnosis of PE patients.
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Affiliation(s)
- Fangqi Cai
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China,
| | - Liwei Cheng
- Department of Spine Osteopathia, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xiaoling Liao
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yuping Xie
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Wu Wang
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Haofeng Zhang
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Jinhua Lu
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Ru Chen
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Chunxia Chen
- Department of Clinical Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Xing Zhou
- Department of Clinical Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Xiaoyun Mo
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Guoping Hu
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Luying Huang
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
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