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Mu X, Cui C, Liao J, Wu Z, Hu L. Regional changes in brain metabolism during the progression of mild cognitive impairment: a longitudinal study based on radiomics. EJNMMI REPORTS 2024; 8:19. [PMID: 38945980 PMCID: PMC11214937 DOI: 10.1186/s41824-024-00206-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 04/22/2024] [Indexed: 07/02/2024]
Abstract
BACKGROUND This study aimed to establish radiomics models based on positron emission tomography (PET) images to longitudinally predict transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD). METHODS In our study, 278 MCI patients from the ADNI database were analyzed, where 60 transitioned to AD (pMCI) and 218 remained stable (sMCI) over 48 months. Patients were divided into a training set (n = 222) and a validation set (n = 56). We first employed voxel-based analysis of 18F-FDG PET images to identify brain regions that present significant SUV difference between pMCI and sMCI groups. Radiomic features were extracted from these regions, key features were selected, and predictive models were developed for individual and combined brain regions. The models' effectiveness was evaluated using metrics like AUC to determine the most accurate predictive model for MCI progression. RESULTS Voxel-based analysis revealed four brain regions implicated in the progression from MCI to AD. These include ROI1 within the Temporal lobe, ROI2 and ROI3 in the Thalamus, and ROI4 in the Limbic system. Among the predictive models developed for these individual regions, the model utilizing ROI4 demonstrated superior predictive accuracy. In the training set, the AUC for the ROI4 model was 0.803 (95% CI 0.736, 0.865), and in the validation set, it achieved an AUC of 0.733 (95% CI 0.559, 0.893). Conversely, the model based on ROI3 showed the lowest performance, with an AUC of 0.75 (95% CI 0.685, 0.809). Notably, the comprehensive model encompassing all identified regions (ROI total) outperformed the single-region models, achieving an AUC of 0.884 (95% CI 0.845, 0.921) in the training set and 0.816 (95% CI 0.705, 0.909) in the validation set, indicating significantly enhanced predictive capability for MCI progression to AD. CONCLUSION Our findings underscore the Limbic system as the brain region most closely associated with the progression from MCI to AD. Importantly, our study demonstrates that a PET brain radiomics model encompassing multiple brain regions (ROI total) significantly outperforms models based on single brain regions. This comprehensive approach more accurately identifies MCI patients at high risk of progressing to AD, offering valuable insights for non-invasive diagnostics and facilitating early and timely interventions in clinical settings.
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Affiliation(s)
- Xuxu Mu
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Caozhe Cui
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Jue Liao
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Zhifang Wu
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Lingzhi Hu
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China.
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Chen J, Chen A, Yang S, Liu J, Xie C, Jiang H. Accuracy of machine learning in preoperative identification of genetic mutation status in lung cancer: A systematic review and meta-analysis. Radiother Oncol 2024; 196:110325. [PMID: 38734145 DOI: 10.1016/j.radonc.2024.110325] [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: 11/17/2023] [Revised: 04/12/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND AND PURPOSE We performed this systematic review and meta-analysis to investigate the performance of ML in detecting genetic mutation status in NSCLC patients. MATERIALS AND METHODS We conducted a systematic search of PubMed, Cochrane, Embase, and Web of Science up until July 2023. We discussed the genetic mutation status of EGFR, ALK, KRAS, and BRAF, as well as the mutation status at different sites of EGFR. RESULTS We included a total of 128 original studies, of which 114 constructed ML models based on radiomic features mainly extracted from CT, MRI, and PET-CT data. From a genetic mutation perspective, 121 studies focused on EGFR mutation status analysis. In the validation set, for the detection of EGFR mutation status, the aggregated c-index was 0.760 (95%CI: 0.706-0.814) for clinical feature-based models, 0.772 (95%CI: 0.753-0.791) for CT-based radiomics models, 0.816 (95%CI: 0.776-0.856) for MRI-based radiomics models, and 0.750 (95%CI: 0.712-0.789) for PET-CT-based radiomics models. When combined with clinical features, the aggregated c-index was 0.807 (95%CI: 0.781-0.832) for CT-based radiomics models, 0.806 (95%CI: 0.773-0.839) for MRI-based radiomics models, and 0.822 (95%CI: 0.789-0.854) for PET-CT-based radiomics models. In the validation set, the aggregated c-indexes for radiomics-based models to detect mutation status of ALK and KRAS, as well as the mutation status at different sites of EGFR were all greater than 0.7. CONCLUSION The use of radiomics-based methods for early discrimination of EGFR mutation status in NSCLC demonstrates relatively high accuracy. However, the influence of clinical variables cannot be overlooked in this process. In addition, future studies should also pay attention to the accuracy of radiomics in identifying mutation status of other genes in EGFR.
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Affiliation(s)
- Jinzhan Chen
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China
| | - Ayun Chen
- Department of Endocrinology, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian 361000, People's Republic of China
| | - Shuwen Yang
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China
| | - Jiaxin Liu
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China
| | - Congyi Xie
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China.
| | - Hongni Jiang
- Department of Pulmonary Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian 361000, People's Republic of China.
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Armato SG, Katz SI, Frauenfelder T, Jayasekera G, Catino A, Blyth KG, Theodoro T, Rousset P, Nackaerts K, Opitz I. Imaging in pleural Mesothelioma: A review of the 16th International Conference of the International Mesothelioma Interest Group. Lung Cancer 2024; 193:107832. [PMID: 38875938 DOI: 10.1016/j.lungcan.2024.107832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/21/2024] [Accepted: 05/27/2024] [Indexed: 06/16/2024]
Abstract
Imaging continues to gain a greater role in the assessment and clinical management of patients with mesothelioma. This communication summarizes the oral presentations from the imaging session at the 2023 International Conference of the International Mesothelioma Interest Group (iMig), which was held in Lille, France from June 26 to 28, 2023. Topics at this session included an overview of best practices for clinical imaging of mesothelioma as reported by an iMig consensus panel, emerging imaging techniques for surgical planning, radiologic assessment of malignant pleural effusion, a radiomics-based transfer learning model to predict patient response to treatment, automated assessment of early contrast enhancement, and tumor thickness for response assessment in peritoneal mesothelioma.
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Affiliation(s)
- Samuel G Armato
- Department of Radiology, The University of Chicago, Chicago, IL, USA.
| | - Sharyn I Katz
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Geeshath Jayasekera
- Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK and School of Cancer Sciences, University of Glasgow, UK
| | - Annamaria Catino
- Medical Thoracic Oncology Unit, IRCCS Istituto Tumori "Giovanni Paolo II," BARI, Italy
| | - Kevin G Blyth
- Cancer Research UK Scotland Centre, Glasgow, UK and Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK and School of Cancer Sciences, University of Glasgow, UK
| | - Taylla Theodoro
- Institute of Computing, University of Campinas, Campinas, Brazil and Cancer Research UK Scotland Centre, Glasgow, UK
| | - Pascal Rousset
- Department of Radiology, Lyon Sud University Hospital, Hospices Civils de Lyon, Lyon 1 University, Pierre-Bénite, France
| | - Kristiaan Nackaerts
- Department of Pulmonology/Respiratory Oncology, KU Leuven, University Hospitals Leuven, Leuven, Belgium
| | - Isabelle Opitz
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
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Li X, Zhang L, Ding M. Ultrasound-based radiomics for the differential diagnosis of breast masses: A systematic review and meta-analysis. JOURNAL OF CLINICAL ULTRASOUND : JCU 2024; 52:778-788. [PMID: 38606802 DOI: 10.1002/jcu.23690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/19/2024] [Accepted: 04/01/2024] [Indexed: 04/13/2024]
Abstract
OBJECTIVES Ultrasound-based radiomics has demonstrated excellent diagnostic performance in differentiating benign and malignant breast masses. Given a few clinical studies on their diagnostic role, we conducted a meta-analysis of the potential effects of ultrasound-based radiomics for the differential diagnosis of breast masses, aiming to provide evidence-based medical basis for clinical research. MATERIALS AND METHODS We searched Embase, Web of Science, Cochrane Library, and PubMed databases from inception through to February 2023. The methodological quality assessment of the included studies was performed according to Quality Assessment of Diagnostic Accuracy Studies checklist. A diagnostic test accuracy systematic review and meta-analysis was performed in accordance with PRISMA guidelines. Sensitivity, specificity, and area under curve delineating benign and malignant lesions were recorded. We also used sensitivity analysis and subgroup analysis to explore potential sources of heterogeneity. Deeks' funnel plots was used to examine the publication bias. RESULTS A total of 11 studies were included in this meta-analysis. For the diagnosis of malignant breast masses worldwide, the overall mean rates of sensitivity and specificity of ultrasound-based radiomics were 0.90 (95% confidence interval [CI], 0.83-0.95) and 0.89 (95% CI, 0.82-0.94), respectively. The summary diagnostic odds ratio was 76 (95% CI, 26-219), and the area under the curve for the summary receiver operating characteristic curve was 0.95 (95% CI, 0.93-0.97). CONCLUSION Ultrasound-based radiomics has the potential to improve diagnostic accuracy to discriminate between benign and malignant breast masses, and could reduce unnecessary biopsies.
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Affiliation(s)
- Xuerong Li
- Hebei North University, Zhangjiakou, Hebei, China
| | | | - Manni Ding
- The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China
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Baniasadi A, Das JP, Prendergast CM, Beizavi Z, Ma HY, Jaber MY, Capaccione KM. Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis. J Transl Med 2024; 22:567. [PMID: 38872212 PMCID: PMC11177383 DOI: 10.1186/s12967-024-05379-1] [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: 02/11/2024] [Accepted: 06/06/2024] [Indexed: 06/15/2024] Open
Abstract
Both cancer and fibrosis are diseases involving dysregulation of cell signaling pathways resulting in an altered cellular microenvironment which ultimately leads to progression of the condition. The two disease entities share common molecular pathophysiology and recent research has illuminated the how each promotes the other. Multiple imaging techniques have been developed to aid in the early and accurate diagnosis of each disease, and given the commonalities between the pathophysiology of the conditions, advances in imaging one disease have opened new avenues to study the other. Here, we detail the most up-to-date advances in imaging techniques for each disease and how they have crossed over to improve detection and monitoring of the other. We explore techniques in positron emission tomography (PET), magnetic resonance imaging (MRI), second generation harmonic Imaging (SGHI), ultrasound (US), radiomics, and artificial intelligence (AI). A new diagnostic imaging tool in PET/computed tomography (CT) is the use of radiolabeled fibroblast activation protein inhibitor (FAPI). SGHI uses high-frequency sound waves to penetrate deeper into the tissue, providing a more detailed view of the tumor microenvironment. Artificial intelligence with the aid of advanced deep learning (DL) algorithms has been highly effective in training computer systems to diagnose and classify neoplastic lesions in multiple organs. Ultimately, advancing imaging techniques in cancer and fibrosis can lead to significantly more timely and accurate diagnoses of both diseases resulting in better patient outcomes.
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Affiliation(s)
- Alireza Baniasadi
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA.
| | - Jeeban P Das
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Conor M Prendergast
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | - Zahra Beizavi
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | - Hong Y Ma
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
| | | | - Kathleen M Capaccione
- Department of Radiology, Columbia University Irving Medical Center, 622 W 168Th Street, New York, NY, 10032, USA
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Liu Y, Zhao F, Niu E, Chen L. Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis. Neuroradiology 2024:10.1007/s00234-024-03399-8. [PMID: 38862772 DOI: 10.1007/s00234-024-03399-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 06/06/2024] [Indexed: 06/13/2024]
Abstract
PURPOSE Early identification of hematoma enlargement and persistent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinically effective tools, radiomics has been gradually introduced into the early identification of hematoma enlargement. Though, radiomics has limited predictive accuracy due to variations in procedures. Therefore, we conducted a systematic review and meta-analysis to explore the value of radiomics in the early detection of HE in patients with cerebral hemorrhage. METHODS Eligible studies were systematically searched in PubMed, Embase, Cochrane and Web of Science from inception to April 8, 2024. English articles are considered eligible. The radiomics quality scoring (RQS) tool was used to evaluate included studies. RESULTS A total of 34 studies were identified with sample sizes ranging from 108 to 3016. Eleven types of models were involved, and the types of modeling contained mainly clinical, radiomic, and radiomic plus clinical features. The radiomics models seem to have better performance (0.77 and 0.73 C-index in the training cohort and validation cohort, respectively) than the clinical models (0.69 C-index in the training cohort and 0.70 C-index in the validation cohort) in discriminating HE. However, the C-index was the highest for the combined model in both the training (0.82) and validation (0.79) cohorts. CONCLUSIONS Machine learning based on radiomic plus clinical features has the best predictive performance for HE, followed by machine learning based on radiomic features, and can be used as a potential tool to assist clinicians in early judgment.
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Affiliation(s)
- Yihua Liu
- Department of General medical subjects, Ezhou Central Hospital, Ezhou Hubei, 436000, China
| | - Fengfeng Zhao
- School of Clinical Medicine, Weifang Medical University, Weifang, 261000, China
| | - Enjing Niu
- Department of Adult Internal Medicine, Qingdao Women's and Children's Hospital, No. 217 Liaoyang West Street, Shibei District, Qingdao, 266000, Shandong, China
| | - Liang Chen
- Department of Adult Internal Medicine, Qingdao Women's and Children's Hospital, No. 217 Liaoyang West Street, Shibei District, Qingdao, 266000, Shandong, China.
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Zhang AD, Shi QL, Zhang HT, Duan WH, Li Y, Ruan L, Han YF, Liu ZK, Li HF, Xiao JS, Shi GF, Wan X, Wang RZ. Pairwise machine learning-based automatic diagnostic platform utilizing CT images and clinical information for predicting radiotherapy locoregional recurrence in elderly esophageal cancer patients. Abdom Radiol (NY) 2024:10.1007/s00261-024-04377-7. [PMID: 38831075 DOI: 10.1007/s00261-024-04377-7] [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: 03/17/2024] [Revised: 05/04/2024] [Accepted: 05/06/2024] [Indexed: 06/05/2024]
Abstract
OBJECTIVE To investigate the feasibility and accuracy of predicting locoregional recurrence (LR) in elderly patients with esophageal squamous cell cancer (ESCC) who underwent radical radiotherapy using a pairwise machine learning algorithm. METHODS The 130 datasets enrolled were randomly divided into a training set and a testing set in a 7:3 ratio. Clinical factors were included and radiomics features were extracted from pretreatment CT scans using pyradiomics-based software, and a pairwise naive Bayes (NB) model was developed. The performance of the model was evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). To facilitate practical application, we attempted to construct an automated esophageal cancer diagnosis system based on trained models. RESULTS To the follow-up date, 64 patients (49.23%) had experienced LR. Ten radiomics features and two clinical factors were selected for modeling. The model demonstrated good prediction performance, with area under the ROC curve of 0.903 (0.829-0.958) for the training cohort and 0.944 (0.849-1.000) for the testing cohort. The corresponding accuracies were 0.852 and 0.914, respectively. Calibration curves showed good agreement, and DCA curve confirmed the clinical validity of the model. The model accurately predicted LR in elderly patients, with a positive predictive value of 85.71% for the testing cohort. CONCLUSIONS The pairwise NB model, based on pre-treatment enhanced chest CT-based radiomics and clinical factors, can accurately predict LR in elderly patients with ESCC. The esophageal cancer automated diagnostic system embedded with the pairwise NB model holds significant potential for application in clinical practice.
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Affiliation(s)
- An-du Zhang
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, 12 Jiankang Road, Shijiazhuang, Hebei, 050011, People's Republic of China
| | - Qing-Lei Shi
- School of Medicine, Chinese University of Hong Kong (Shenzhen), No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China
- Medical Big Data Laboratory, Shenzhen Research Institute of Big Data, Daoyuan Building, No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China
| | - Hong-Tao Zhang
- Department of Oncology, Hebei General Hospital, NO. 348 Heping West Road, Xinhua District, Shijiazhuang, Hebei, 050051, People's Republic of China
| | - Wen-Han Duan
- School of Computer Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Yang Li
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, 12 Jiankang Road, Shijiazhuang, Hebei, 050011, People's Republic of China
| | - Li Ruan
- School of Computer Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Yi-Fan Han
- School of Computer Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Zhi-Kun Liu
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, 12 Jiankang Road, Shijiazhuang, Hebei, 050011, People's Republic of China
| | - Hao-Feng Li
- Medical Big Data Laboratory, Shenzhen Research Institute of Big Data, Daoyuan Building, No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China
| | - Jia-Shun Xiao
- Medical Big Data Laboratory, Shenzhen Research Institute of Big Data, Daoyuan Building, No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China
| | - Gao-Feng Shi
- Department of Radiotherapy, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, 12 Jiankang Road, Shijiazhuang, Hebei, 050011, People's Republic of China.
| | - Xiang Wan
- Medical Big Data Laboratory, Shenzhen Research Institute of Big Data, Daoyuan Building, No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China.
| | - Ren-Zhi Wang
- School of Medicine, Chinese University of Hong Kong (Shenzhen), No. 2001, Longxiang Avenue, Longgang District, Shenzhen, 518172, People's Republic of China.
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Liang Y, Wei Y, Xu F, Wei X. MRI-based radiomic models for the preoperative prediction of extramural venous invasion in rectal cancer: A systematic review and meta-analysis. Clin Imaging 2024; 110:110146. [PMID: 38697000 DOI: 10.1016/j.clinimag.2024.110146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/19/2024] [Accepted: 04/03/2024] [Indexed: 05/04/2024]
Abstract
AIM To estimate the diagnostic value of magnetic resonance imaging (MRI)-based radiomic models in detecting the extramural venous invasion (EMVI) of rectal cancer. MATERIALS AND METHODS Appropriate studies in multiple electronic databases were systematically retrieved. The Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score (RQS) were used to evaluate the eligible studies' methodology quality. Summary accuracy metrics were calculated, and the publication bias was detected using Deek's funnel plot. The sensitivity and meta-regression analysis were performed to investigate the causes of heterogeneity. RESULTS For the seven eligible studies, which included 1175 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.80 (95 % CI, 0.70-0.88), 0.89 (95 % CI, 0.84-0.92), 7.0 (95 % CI, 4.7, 10.4), 0.22 (95 % CI, 0.14, 0.34), and 32 (95 % CI, 16, 65), respectively. The area under the receiver operating characteristic curve (AUC) was 0.91 (95 % CI, 0.88, 0.93). Moderate heterogeneity was found due to I2 values of 38.63 % and 32.29 % in sensitivity and specificity, respectively. Meta-regression analysis suggested that the patient enrollment, number of patients, segmentation method, and RQS score were the source of the heterogeneity. The head-to-head analysis suggested that radiomics model had a higher sensitivity for detection of EMVI than subjective evaluation by radiologist (0.47 vs. 0.73, p ≤ 0.001). CONCLUSION Our study suggests that MRI-based radiomic models have good diagnostic value in detecting EMVI for rectal cancer patients. Nevertheless, more prospective and high-quality studies with larger sample sizes are needed in the future to validate these results.
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Affiliation(s)
- Yingying Liang
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China; Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, 1 Panfu Road, Guangzhou, Guangdong Province 510180, China
| | - Yaxuan Wei
- Guangzhou Medical University, 195 Dongfengxi road, Guangzhou, Guangdong Province 510180, China
| | - Fan Xu
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, 396 Tongfu road, Guangzhou, Guangdong Province 510220, China
| | - Xinhua Wei
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China; Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, 1 Panfu Road, Guangzhou, Guangdong Province 510180, China.
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Wang B, Liu J, Xie J, Zhang X, Wang Z, Cao Z, Wen D, Wan Hasan WZ, Harun Ramli HR, Dong X. Systematic review and meta-analysis of the prognostic value of 18F-Fluorodeoxyglucose ( 18F-FDG) positron emission tomography (PET) and/or computed tomography (CT)-based radiomics in head and neck cancer. Clin Radiol 2024:S0009-9260(24)00282-4. [PMID: 38944542 DOI: 10.1016/j.crad.2024.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 05/16/2024] [Accepted: 05/24/2024] [Indexed: 07/01/2024]
Abstract
AIM Radiomics involves the extraction of quantitative data from medical images to facilitate the diagnosis, prognosis, and staging of tumors. This study provides a comprehensive overview of the efficacy of radiomics in prognostic applications for head and neck cancer (HNC) in recent years. It undertakes a systematic review of prognostic models specific to HNC and conducts a meta-analysis to evaluate their predictive performance. MATERIALS AND METHODS This study adhered rigorously to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for literature searches. The literature databases, including PubMed, Embase, Cochrane, and Scopus were systematically searched individually. The methodological quality of the incorporated studies underwent assessment utilizing the radiomics quality score (RQS) tool. A random-effects meta-analysis employing the Harrell concordance index (C-index) was conducted to evaluate the performance of all radiomics models. RESULTS Among the 388 studies retrieved, 24 studies encompassing a total of 6,978 cases were incorporated into the systematic review. Furthermore, eight studies, focusing on overall survival as an endpoint, were included in the meta-analysis. The meta-analysis revealed that the estimated random effect of the C-index for all studies utilizing radiomics alone was 0.77 (0.71-0.82), with a substantial degree of heterogeneity indicated by an I2 of 80.17%. CONCLUSIONS Based on this review, prognostic modeling utilizing radiomics has demonstrated enhanced efficacy for head and neck cancers; however, there remains room for improvement in this approach. In the future, advancements are warranted in the integration of clinical parameters and multimodal features, balancing multicenter data, as well as in feature screening and model construction within this field.
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Affiliation(s)
- B Wang
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia; Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
| | - J Liu
- Department of Nursing, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Malaysia; Department of Nursing, Chengde Central Hospital, Chengde city, Hebei Province, China.
| | - J Xie
- Department of Automatic, Tsinghua University, Beijing, China.
| | - X Zhang
- Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
| | - Z Wang
- Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
| | - Z Cao
- Department of Radiology, The Affiliated Hospital of Chengde Medical University, Chengde City, Hebei Province, China.
| | - D Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China.
| | - W Z Wan Hasan
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
| | - H R Harun Ramli
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
| | - X Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China; Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde City, Hebei, China; Hebei International Research Center of Medical Engineering, Chengde Medical University, Hebei, China.
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Zhang GY, Du XZ, Xu R, Chen T, Wu Y, Wu XJ, Liu S. Development and Validation of a Machine Learning-Based Model Using CT Radiomics for Predicting Immune Checkpoint Inhibitor-related Pneumonitis in Patients With NSCLC Receiving Anti-PD1 Immunotherapy: A Multicenter Retrospective CaseControl Study. Acad Radiol 2024; 31:2128-2143. [PMID: 37977890 DOI: 10.1016/j.acra.2023.10.039] [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: 08/24/2023] [Revised: 10/06/2023] [Accepted: 10/23/2023] [Indexed: 11/19/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to develop and evaluate a radiomics-based model combined with clinical and qualitative radiological (semantic feature [SF]) features to predict immune checkpoint inhibitor-related pneumonitis (CIP) in patients with non-small cell lung cancer (NSCLC) treated with programmed cell death protein 1 inhibitors. MATERIALS AND METHODS This was a multicenter retrospective casecontrol study conducted from January 1, 2018, to December 31, 2022, at three centers. Patients with NSCLC treated with anti-PD1 were enrolled and randomly divided into two groups (7:3): training (n = 95) and validation (n = 39). Logistic regression (LR) and support vector machine (SVM) algorithms were used to transform features into the models. RESULTS The study comprised 134 participants from three independent centers (male, 114/134, 85%; mean [±standard deviation] age, 63.92 [±7.9] years). The radiomics score (RS) models built based on the LR and SVM algorithms could accurately predict CIP (area under the receiver operating characteristics curve [AUC], 0.860 [0.780, 0.939] and 0.861 [0.781, 0.941], respectively). The AUCs for the RS-clinic-SF combined model were 0.903 (0.839, 0.967) and 0.826 (0.688, 0.964) in the training and validation cohorts, respectively. Decision curve analysis showed that the combined models achieved high clinical net benefit across the majority of the range of reasonable threshold probabilities. CONCLUSION This study demonstrated that the combined model constructed by the identified features of RS, clinical features, and SF has the potential to precisely predict CIP. The RS-clinic-SF combined model has the potential to be used more widely as a practical tool for the noninvasive prediction of CIP to support individualized treatment planning.
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Affiliation(s)
- Guo-Yue Zhang
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (G.-y.Z., X.-z.D., R.X., Y.W., X.-j.W.).
| | - Xian-Zhi Du
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (G.-y.Z., X.-z.D., R.X., Y.W., X.-j.W.).
| | - Rui Xu
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (G.-y.Z., X.-z.D., R.X., Y.W., X.-j.W.).
| | - Ting Chen
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (T.C.).
| | - Yue Wu
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (G.-y.Z., X.-z.D., R.X., Y.W., X.-j.W.).
| | - Xiao-Juan Wu
- Department of Respiratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, P.R. China (G.-y.Z., X.-z.D., R.X., Y.W., X.-j.W.); Department of Respiratory and Critical Care Medicine, Suining Central Hospital, Suining, 629000, Sichuan, P.R. China (X.-j.W.).
| | - Shui Liu
- Department of Respiratory and Critical Care Medicine, People's Hospital of Fengjie, Fengjie, Chongqing, 404600, P.R. China (S.L.).
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11
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Yang C, Fan Y, Zhao D, Wang Z, Wang X, Wang H, Hu Y, He L, Zhang J, Wang Y, Liu Y, Sha X, Su J. Habitat-Based Radiomics for Predicting EGFR Mutations in Exon 19 and 21 From Brain Metastasis. Acad Radiol 2024:S1076-6332(24)00158-2. [PMID: 38599906 DOI: 10.1016/j.acra.2024.03.016] [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: 02/04/2024] [Revised: 03/09/2024] [Accepted: 03/17/2024] [Indexed: 04/12/2024]
Abstract
RATIONALE AND OBJECTIVES To explore and externally validate habitat-based radiomics for preoperative prediction of epidermal growth factor receptor (EGFR) mutations in exon 19 and 21 from MRI imaging of non-small cell lung cancer (NSCLC)-originated brain metastasis (BM). METHODS A total of 170, 62 and 61 patients from center 1, center 2 and center 3, respectively were included. All patients underwent contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI scans. Radiomics features were extracted from the tumor active (TA) and peritumoral edema (PE) regions in each MRI slice. The most important features were selected by the least absolute shrinkage and selection operator regression to develop radiomics signatures based on TA (RS-TA), PE (RS-PE) and their combination (RS-Com). Receiver operating characteristic (ROC) curve analysis was performed to access performance of radiomics models for both internal and external validation cohorts. RESULTS 10, four and six most predictive features were identified to be strongly associated with the EGFR mutation status, exon 19 and exon 21, respectively. The RSs derived from the PE region outperformed those from the TA region for predicting the EGFR mutation, exon 19 and exon 21. The RS-Coms generated the highest performance in the primary training (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.955 vs. 0.946 vs. 0.928), internal validation (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.879 vs. 0.819 vs. 0.882), external validation 1 (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.830 vs. 0.825 vs. 0.822), and external validation 2 (AUCs, RS-EGFR-Com vs. RS-exon 19-Com vs. RS-exon 21-Com, 0.812 vs. 0.818 vs. 0.800) cohort. CONCLUSION The developed habitat-based radiomics model can be used to accurately predict the EGFR mutation subtypes, which may potentially guide personalized treatments for NSCLC patients with BM.
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Affiliation(s)
- Chunna Yang
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Ying Fan
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Dan Zhao
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Zekun Wang
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Yanjun Hu
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Lingzi He
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang 110122, PR China
| | - Jin Zhang
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Yan Wang
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Yan Liu
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Xianzheng Sha
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China
| | - Juan Su
- School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China.
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12
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Rozynek M, Tabor Z, Kłęk S, Wojciechowski W. Body composition radiomic features as a predictor of survival in patients with non-small cellular lung carcinoma: A multicenter retrospective study. Nutrition 2024; 120:112336. [PMID: 38237479 DOI: 10.1016/j.nut.2023.112336] [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: 08/14/2023] [Revised: 12/14/2023] [Accepted: 12/20/2023] [Indexed: 02/24/2024]
Abstract
OBJECTIVES This study combined two novel approaches in oncology patient outcome predictions-body composition and radiomic features analysis. The aim of this study was to validate whether automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer. METHODS The study included 178 patients with non-small cell lung cancer receiving concurrent platinum-based chemoradiotherapy. Abdominal imaging was conducted as a part of whole-body positron emission tomography/computed tomography performed before therapy. Methods used included automated assessment of the volume of interest using densely connected convolutional network classification model - DenseNet121, automated muscle and adipose tissue segmentation using U-net architecture implemented in nnUnet framework, and radiomic features extraction. Acquired body composition radiomic features and clinical data were used for overall and 1-y survival prediction using machine learning classification algorithms. RESULTS The volume of interest detection model achieved the following metric scores: 0.98 accuracy, 0.89 precision, 0.96 recall, and 0.92 F1 score. Automated segmentation achieved a median dice coefficient >0.99 in all segmented regions. We extracted 330 body composition radiomic features for every patient. For overall survival prediction using clinical and radiomic data, the best-performing feature selection and prediction method achieved areas under the curve-receiver operating characteristic (AUC-ROC) of 0.73 (P < 0.05); for 1-y survival prediction AUC-ROC was 0.74 (P < 0.05). CONCLUSION Automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.
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Affiliation(s)
- Miłosz Rozynek
- Department of Radiology, Jagiellonian University Medical College, Krakow, Poland
| | - Zbisław Tabor
- AGH University of Science and Technology, Krakow, Poland
| | - Stanisław Kłęk
- Surgical Oncology Clinic, Maria Skłodowska-Curie National Cancer Institute, Krakow, Poland
| | - Wadim Wojciechowski
- Department of Radiology, Jagiellonian University Medical College, Krakow, Poland.
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Coroller T, Sahiner B, Amatya A, Gossmann A, Karagiannis K, Moloney C, Samala RK, Santana-Quintero L, Solovieff N, Wang C, Amiri-Kordestani L, Cao Q, Cha KH, Charlab R, Cross FH, Hu T, Huang R, Kraft J, Krusche P, Li Y, Li Z, Mazo I, Paul R, Schnakenberg S, Serra P, Smith S, Song C, Su F, Tiwari M, Vechery C, Xiong X, Zarate JP, Zhu H, Chakravartty A, Liu Q, Ohlssen D, Petrick N, Schneider JA, Walderhaug M, Zuber E. Methodology for Good Machine Learning with Multi-Omics Data. Clin Pharmacol Ther 2024; 115:745-757. [PMID: 37965805 DOI: 10.1002/cpt.3105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023]
Abstract
In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4-year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio-genomics-based prognostic and predictive factors for HR+/HER- metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi-omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as "maintaining effective communication" and "following good data science practices," followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.
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Affiliation(s)
| | - Berkman Sahiner
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Anup Amatya
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Alexej Gossmann
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Konstantinos Karagiannis
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Ravi K Samala
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Luis Santana-Quintero
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Nadia Solovieff
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Craig Wang
- Novartis Pharma AG, Rotkreuz, Switzerland
| | - Laleh Amiri-Kordestani
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Qian Cao
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Kenny H Cha
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rosane Charlab
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Frank H Cross
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Tingting Hu
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ruihao Huang
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jeffrey Kraft
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Yutong Li
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Zheng Li
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Ilya Mazo
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rahul Paul
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Paolo Serra
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Sean Smith
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Chi Song
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Fei Su
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Mohit Tiwari
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Colin Vechery
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Xin Xiong
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Hao Zhu
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Qi Liu
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - David Ohlssen
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Nicholas Petrick
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Julie A Schneider
- Oncology Center of Excellence, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Mark Walderhaug
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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Chen Z, Wang Z, Liu S, Zhang S, Zhou Y, Zhang R, Yang W. Nomograms based on multiparametric MRI radiomics integrated with clinical-radiological features for predicting the response to induction chemotherapy in nasopharyngeal carcinoma. Eur J Radiol 2024; 175:111438. [PMID: 38613869 DOI: 10.1016/j.ejrad.2024.111438] [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: 10/06/2023] [Revised: 01/27/2024] [Accepted: 03/20/2024] [Indexed: 04/15/2024]
Abstract
OBJECTIVE To establish nomograms integrating multiparametric MRI radiomics with clinical-radiological features to identify the responders and non-responders to induction chemotherapy (ICT) in nasopharyngeal carcinoma (NPC). METHODS We retrospectively analyzed the clinical and MRI data of 168 NPC patients between December 2015 and April 2022. We used 3D-Slicer to segment the regions of interest (ROIs) and the "Pyradiomic" package to extract radiomics features. We applied the least absolute shrinkage and selection operator regression to select radiomics features. We developed clinical-only, radiomics-only, and the combined clinical-radiomics nomograms using logistic regression analysis. The receiver operating characteristic curves, DeLong test, calibration, and decision curves were used to assess the discriminative performance of the models. The model was internally validated using 10-fold cross-validation. RESULTS A total of 14 optimal features were finally selected to develop a radiomic signature, with an AUC of 0.891 (95 % CI, 0.825-0.946) in the training cohort and 0.837 (95 % CI, 0.723-0.932) in the testing cohort. The nomogram based on the Rad-Score and clinical-radiological factors for evaluating tumor response to ICT yielded an AUC of 0.926 (95 % CI, 0.875-0.965) and 0.901 (95 % CI, 0.815-0.979) in the two cohorts, respectively. Decision curves demonstrated that the combined clinical-radiomics nomograms were clinically useful. CONCLUSION Nomograms integrating multiparametric MRI-based radiomics and clinical-radiological features could non-invasively discriminate ICT responders from non-responders in NPC patients.
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Affiliation(s)
- Zhiqiang Chen
- Department of Radiology, the First Affiliated Hospital of Hainan Medical University, Haikou, Hainan 570102, China; Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China.
| | - Zhuo Wang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Shili Liu
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Shaoru Zhang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Yunshu Zhou
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Ruodi Zhang
- Department of Radiology, the General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China
| | - Wenjun Yang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Basic Medicine and Life Sciences, Hainan Medical University, Haikou, Hainan 571199, China.
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15
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Kong Y, Su M, Zhu Y, Li X, Zhang J, Gu W, Yang F, Zhou J, Ni J, Yang X, Zhu Z, Huang J. Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study. Strahlenther Onkol 2024:10.1007/s00066-024-02221-x. [PMID: 38498173 DOI: 10.1007/s00066-024-02221-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/25/2024] [Indexed: 03/20/2024]
Abstract
OBJECTIVE This study aims to examine the ability of deep learning (DL)-derived imaging features for the prediction of radiation pneumonitis (RP) in locally advanced non-small-cell lung cancer (LA-NSCLC) patients. MATERIALS AND METHODS The study cohort consisted of 90 patients from the Fudan University Shanghai Cancer Center and 59 patients from the Affiliated Hospital of Jiangnan University. Occurrences of RP were used as the endpoint event. A total of 512 3D DL-derived features were extracted from two regions of interest (lung-PTV and PTV-GTV) delineated on the pre-radiotherapy planning CT. Feature selection was done using LASSO regression, and the classification models were built using the multilayered perceptron method. Performances of the developed models were evaluated by receiver operating characteristic curve analysis. In addition, the developed models were supplemented with clinical variables and dose-volume metrics of relevance to search for increased predictive value. RESULTS The predictive model using DL features derived from lung-PTV outperformed the one based on features extracted from PTV-GTV, with AUCs of 0.921 and 0.892, respectively, in the internal test dataset. Furthermore, incorporating the dose-volume metric V30Gy into the predictive model using features from lung-PTV resulted in an improvement of AUCs from 0.835 to 0.881 for the training data and from 0.690 to 0.746 for the validation data, respectively (DeLong p < 0.05). CONCLUSION Imaging features extracted from pre-radiotherapy planning CT using 3D DL networks could predict radiation pneumonitis and may be of clinical value for risk stratification and toxicity management in LA-NSCLC patients. CLINICAL RELEVANCE STATEMENT Integrating DL-derived features with dose-volume metrics provides a promising noninvasive method to predict radiation pneumonitis in LA-NSCLC lung cancer radiotherapy, thus improving individualized treatment and patient outcomes.
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Affiliation(s)
- Yan Kong
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
| | - Mingming Su
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
- Department of Medical Oncology, Affiliated Huishan Hospital of Xinglin College, Nantong University, Wuxi Huishan District People's Hospital, 214187, Wuxi, Jiangsu, China
| | - Yan Zhu
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
| | - Xuan Li
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
- Department of Medical Oncology, Affiliated Huishan Hospital of Xinglin College, Nantong University, Wuxi Huishan District People's Hospital, 214187, Wuxi, Jiangsu, China
| | - Jinmeng Zhang
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, 305-8577, Ibaraki, Japan
| | - Fei Yang
- Department of Radiation Oncology, University of Miami, 33136, Miami, FL, USA
| | - Jialiang Zhou
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China
| | - Jianjiao Ni
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Xi Yang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China
| | - Zhengfei Zhu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui, 200032, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, 200032, Shanghai, China.
| | - Jianfeng Huang
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, 214122, Wuxi, Jiangsu, China.
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Tárnoki ÁD, Tárnoki DL, Dąbrowska M, Knetki-Wróblewska M, Frille A, Stubbs H, Blyth KG, Juul AD. New developments in the imaging of lung cancer. Breathe (Sheff) 2024; 20:230176. [PMID: 38595936 PMCID: PMC11003524 DOI: 10.1183/20734735.0176-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 01/25/2024] [Indexed: 04/11/2024] Open
Abstract
Radiological and nuclear medicine methods play a fundamental role in the diagnosis and staging of patients with lung cancer. Imaging is essential in the detection, characterisation, staging and follow-up of lung cancer. Due to the increasing evidence, low-dose chest computed tomography (CT) screening for the early detection of lung cancer is being introduced to the clinical routine in several countries. Radiomics and radiogenomics are emerging fields reliant on artificial intelligence to improve diagnosis and personalised risk stratification. Ultrasound- and CT-guided interventions are minimally invasive methods for the diagnosis and treatment of pulmonary malignancies. In this review, we put more emphasis on the new developments in the imaging of lung cancer.
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Affiliation(s)
- Ádám Domonkos Tárnoki
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
- National Tumour Biology Laboratory, Oncologic Imaging and Invasive Diagnostic Centre, National Institute of Oncology, Budapest, Hungary
| | - Dávid László Tárnoki
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
- National Tumour Biology Laboratory, Oncologic Imaging and Invasive Diagnostic Centre, National Institute of Oncology, Budapest, Hungary
| | - Marta Dąbrowska
- Department of Internal Medicine, Pulmonary Diseases and Allergy, Medical University of Warsaw, Warsaw, Poland
| | | | - Armin Frille
- Department of Respiratory Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Harrison Stubbs
- Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Kevin G. Blyth
- Glasgow Pleural Disease Unit, Queen Elizabeth University Hospital, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
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17
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Suh YJ, Han K, Kwon Y, Kim H, Lee S, Hwang SH, Kim MH, Shin HJ, Lee CY, Shim HS. Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas. Yonsei Med J 2024; 65:163-173. [PMID: 38373836 PMCID: PMC10896671 DOI: 10.3349/ymj.2023.0368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/19/2023] [Accepted: 10/25/2023] [Indexed: 02/21/2024] Open
Abstract
PURPOSE To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. MATERIALS AND METHODS This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance. CONCLUSION A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.
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Affiliation(s)
- Young Joo Suh
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Yonghan Kwon
- Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea
| | - Hwiyoung Kim
- Department of Biomedical System Informatics, Yonsei University College of Medicine, Seoul, Korea
| | - Suji Lee
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sung Ho Hwang
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Myung Hyun Kim
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Hyun Joo Shin
- Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea
| | - Chang Young Lee
- Thoracic and Cardiovascular Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hyo Sup Shim
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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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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Wang T, Li YY, Ma NN, Wang PA, Zhang B. A MRI radiomics-based model for prediction of pelvic lymph node metastasis in cervical cancer. World J Surg Oncol 2024; 22:55. [PMID: 38365759 PMCID: PMC10873981 DOI: 10.1186/s12957-024-03333-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 02/06/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Cervical cancer (CC) is a common malignancy of the female reproductive tract, and preoperative prediction of lymph node metastasis (LNM) is essential. This study aims to design and validate a magnetic resonance imaging (MRI) radiomics-based predictive model capable of detecting LNM in patients diagnosed with CC. METHODS This retrospective analysis incorporated 86 and 38 CC patients into the training and testing groups, respectively. Radiomics features were extracted from MRI T2WI, T2WI-SPAIR, and axial apparent diffusion coefficient (ADC) sequences. Selected features identified in the training group were then used to construct a radiomics scoring model, with relevant LNM-related risk factors having been identified through univariate and multivariate logistic regression analyses. The resultant predictive model was then validated in the testing cohort. RESULTS In total, 16 features were selected for the construction of a radiomics scoring model. LNM-related risk factors included worse differentiation (P < 0.001), more advanced International Federation of Gynecology and Obstetrics (FIGO) stages (P = 0.03), and a higher radiomics score from the combined MRI sequences (P = 0.01). The equation for the predictive model was as follows: -0.0493-2.1410 × differentiation level + 7.7203 × radiomics score of combined sequences + 1.6752 × FIGO stage. The respective area under the curve (AUC) values for the T2WI radiomics score, T2WI-SPAIR radiomics score, ADC radiomics score, combined sequence radiomics score, and predictive model were 0.656, 0.664, 0.658, 0.835, and 0.923 in the training cohort, while these corresponding AUC values were 0.643, 0.525, 0.513, 0.826, and 0.82 in the testing cohort. CONCLUSIONS This MRI radiomics-based model exhibited favorable accuracy when used to predict LNM in patients with CC. Relative to the use of any individual MRI sequence-based radiomics score, this predictive model yielded superior diagnostic accuracy.
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Affiliation(s)
- Tao Wang
- Suzhou Medical College of Soochow University, Suzhou, China
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Yan-Yu Li
- Department of Gynaecology and Obstetrics, Xuzhou Central Hospital, Xuzhou, China
| | - Nan-Nan Ma
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Pei-An Wang
- Hospital Administration Office, Xuzhou Central Hospital, Xuzhou, China.
| | - Bei Zhang
- Suzhou Medical College of Soochow University, Suzhou, China.
- Department of Gynaecology and Obstetrics, Xuzhou Central Hospital, Xuzhou, China.
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20
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Grenier PA, Brun AL, Mellot F. [The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. Rev Mal Respir 2024; 41:110-126. [PMID: 38129269 DOI: 10.1016/j.rmr.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.
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Affiliation(s)
- P A Grenier
- Délégation à la recherche clinique et l'innovation, hôpital Foch, Suresnes, France.
| | - A L Brun
- Service de radiologie, hôpital Foch, Suresnes, France
| | - F Mellot
- Service de radiologie, hôpital Foch, Suresnes, France
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21
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Li Y, Lyu B, Wang R, Peng Y, Ran H, Zhou B, Liu Y, Bai G, Huai Q, Chen X, Zeng C, Wu Q, Zhang C, Gao S. Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis. Thorac Cancer 2024; 15:466-476. [PMID: 38191149 PMCID: PMC10883857 DOI: 10.1111/1759-7714.15216] [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: 12/01/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results. METHODS A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical-radiological semantic features; (2) The radiomics models (radiomics+ and radiomics-) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite-) incorporated both above. RESULTS In the testing set, the radiomics+/- models and the composite+/- models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/- models and the composite+/- models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/- and composite+/- models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics-, composite+ model vs. composite-) (p > 0.05). CONCLUSIONS The present study established a machine learning-based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability.
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Affiliation(s)
- Yuan Li
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Baihan Lyu
- CAS Key Laboratory of Behavioral Science, Institute of PsychologyChinese Academy of SciencesBeijingChina
| | - Rong Wang
- Department of Echocardiography, Fuwai Hospital/ National Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yue Peng
- Department of Thoracic Surgery, Beijing Chao‐Yang HospitalCapital Medical UniversityBeijingChina
| | - Haoyu Ran
- Department of Cardiothoracic Surgerythe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Bolun Zhou
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yang Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Guangyu Bai
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Qilin Huai
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Xiaowei Chen
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Chun Zeng
- Department of Radiologythe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Qingchen Wu
- Department of Cardiothoracic Surgerythe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Cheng Zhang
- Department of Cardiothoracic Surgerythe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Shugeng Gao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Zhou T, Yang M, Xiong W, Zhu F, Li Q, Zhao L, Zhao Z. The value of intratumoral and peritumoral radiomics features in differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes. Transl Cancer Res 2024; 13:202-216. [PMID: 38410219 PMCID: PMC10894356 DOI: 10.21037/tcr-23-1324] [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: 07/26/2023] [Accepted: 11/29/2023] [Indexed: 02/28/2024]
Abstract
Background The identification of different subtypes of early-stage lung invasive adenocarcinoma before surgery contributes to the precision treatment. Radiomics could be one of the effective and noninvasive identification methods. The value of peritumoral radiomics in predicting the subtypes of early-stage lung invasive adenocarcinoma perhaps clinically useful. Methods This retrospective study included 937 lung adenocarcinomas which were randomly divided into the training set (n=655) and testing set (n=282) with a ratio of 7:3. This study used the univariate and multivariate analysis to choose independent clinical predictors. Radiomics features were extracted from 18 regions of interest (1 intratumoral region and 17 peritumoral regions). Independent and conjoint prediction models were constructed based on radiomics and clinical features. The performance of the models was evaluated using receiver operating characteristic (ROC) curves, accuracy (ACC), sensitivity (SEN), and specificity (SPE). Significant differences between areas under the ROC (AUCs) were estimated using in the Delong test. Results Patient age, smoking history, carcinoembryonic antigen (CEA), lesion location, length, width and clinic behavior were the independent predictors of differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes. The highest AUC value among the 19 independent models was obtained for the PTV0~+3 radiomics model with 0.849 for the training set and 0.854 for the testing set. As the peritumoral distance increased, the predictive power of the models decreased. The radiomics-clinical conjoint model was statistically significantly different from the other models in the Delong test (P<0.05). Conclusions The intratumoral and peritumoral regions contained a wealth of clinical information. The diagnostic efficacy of intra-peritumoral radiomics combined clinical model was further improved, which was particularly important for preoperative staging and treatment decision-making.
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Affiliation(s)
- Tong Zhou
- School of Medicine, Shaoxing University, Shaoxing, China
| | - Minxia Yang
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Wanrong Xiong
- School of Medicine, Shaoxing University, Shaoxing, China
| | - Fandong Zhu
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Qianling Li
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Li Zhao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
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Zhang H, Deng Y, Xiaojie M, Zou Q, Liu H, Tang N, Luo Y, Xiang X. CT radiomics for predicting the prognosis of patients with stage II rectal cancer during the three-year period after surgery, chemotherapy and radiotherapy. Heliyon 2024; 10:e23923. [PMID: 38223741 PMCID: PMC10787243 DOI: 10.1016/j.heliyon.2023.e23923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 11/29/2023] [Accepted: 12/15/2023] [Indexed: 01/16/2024] Open
Abstract
Objective Pre-treatment enhanced CT image data were used to train and build models to predict the efficacy of non-small cell lung cancer after conventional radiotherapy and chemotherapy using two classification algorithms, Logistic Regression (LR) and Gaussian Naive Baye (GNB). Methods In this study, we used pre-treatment enhanced CT image data for region of interest (ROI) sketching and feature extraction. We utilized the least absolute shrinkage and selection operator (LASSO) mutual confidence method for feature screening. We pre-screened logistic regression (LR) and Gaussian naive Bayes (GNB) classification algorithms and trained and modeled the screened features. We plotted 5-fold and 10-fold cross-validated receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC). We performed DeLong's test for validation and plotted calibration curves and decision curves to assess model performance. Results A total of 102 patients were included in this study, and after a comparative analysis of the two models, LR had only slightly lower specificity than GNB, and higher sensitivity, accuracy, AUC value, precision, and F1 value than GNB (training set accuracy: 0.787, AUC value: 0.851; test set accuracy: 0.772, AUC value: 0.849), and the LR model has better performance in both the decision curve and the calibration curve. Conclusion CT can be used for efficacy prediction after radiotherapy and chemotherapy in NSCLC patients. LR is more suitable for predicting whether NSCLC prognosis is in remission without considering the computing speed.
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Affiliation(s)
- Hanjing Zhang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Yu Deng
- The Affiliated Cancer Hospital of Guizhou Medical University, GuiYang, Guizhou Province, 550000, China
| | - M.A. Xiaojie
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Qian Zou
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Huanhui Liu
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Ni Tang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Yuanyuan Luo
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Xuejing Xiang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
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Lu J, Ji X, Liu X, Jiang Y, Li G, Fang P, Li W, Zuo A, Guo Z, Yang S, Ji Y, Lu D. Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC. Sci Rep 2024; 14:446. [PMID: 38172228 PMCID: PMC10764785 DOI: 10.1038/s41598-023-50984-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 12/28/2023] [Indexed: 01/05/2024] Open
Abstract
The epidermal growth factor receptor (EGFR) Thr790 Met (T790M) mutation is responsible for approximately half of the acquired resistance to EGFR-tyrosine kinase inhibitor (TKI) in non-small-cell lung cancer (NSCLC) patients. Identifying patients at diagnosis who are likely to develop this mutation after first- or second-generation EGFR-TKI treatment is crucial for better treatment outcomes. This study aims to develop and validate a radiomics-based machine learning (ML) approach to predict the T790M mutation in NSCLC patients at diagnosis. We collected retrospective data from 210 positive EGFR mutation NSCLC patients, extracting 1316 radiomics features from CT images. Using the LASSO algorithm, we selected 10 radiomics features and 2 clinical features most relevant to the mutations. We built models with 7 ML approaches and assessed their performance through the receiver operating characteristic (ROC) curve. The radiomics model and combined model, which integrated radiomics features and relevant clinical factors, achieved an area under the curve (AUC) of 0.80 (95% confidence interval [CI] 0.79-0.81) and 0.86 (0.87-0.88), respectively, in predicting the T790M mutation. Our study presents a convenient and noninvasive radiomics-based ML model for predicting this mutation at the time of diagnosis, aiding in targeted treatment planning for NSCLC patients with EGFR mutations.
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Affiliation(s)
- Jiameng Lu
- Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical Medicine, 16766 Jingshilu, Lixia, Jinan, 250014, Shandong, People's Republic of China
- School of Microelectronics, Shandong University, Jinan, 250100, Shandong, People's Republic of China
| | - Xiaoqing Ji
- Department of Nursing, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, 250014, Shandong, People's Republic of China
| | - Xinyi Liu
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Yunxiu Jiang
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Gang Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medicine Imaging, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, 250000, Shandong, China
| | - Ping Fang
- Department of Blood Transfusion, The First Affiliated Hospital of Shandong First Medical University and Shandong Province Qianfoshan Hospital, Jinan, 250014, Shandong, China
| | - Wei Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medicine Imaging, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, 250000, Shandong, China
| | - Anli Zuo
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Zihan Guo
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Shuran Yang
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Yanbo Ji
- Department of Nursing, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, 250014, Shandong, People's Republic of China
| | - Degan Lu
- Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical Medicine, 16766 Jingshilu, Lixia, Jinan, 250014, Shandong, People's Republic of China.
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Zhu L, Huang J, Jin C, Zhou A, Chen Y, Zhang B, Venuta F, Pua BB, Shen Y. Retrospective cohort study on the correlation analysis among peri-procedural factors, complications, and local tumor progression of lung tumors treated with CT-guided microwave ablation. J Thorac Dis 2023; 15:6915-6927. [PMID: 38249890 PMCID: PMC10797391 DOI: 10.21037/jtd-23-1799] [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: 11/23/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Background Despite adherence to guidelines, recurrence of lesions remains possible in lung tumor microwave ablation (MWA) even when termination is enabled by 5-10 mm ground glass changes. Limited evidence exists regarding the correlation between timely management of perioperative complications (including pneumothorax, pleural effusion, hemorrhage, cavity formation, and infection) and local tumor progression. This retrospective study aimed to investigate the relationship among peri-procedural factors, complications, and local tumor progression in 164 cases of lung tumors treated with computed tomography-guided MWA (CT-MWA), and improve the local prognosis and reduce the complication rate of CT-guided lung tumor ablation. Methods We reviewed 164 consecutive patients who underwent CT-MWA at Fudan University Shanghai Cancer Center's Minimally Invasive Therapy Center for lung cancer from September 2019 to May 2020. Correlative analysis was performed between peri-procedural factors, complications and outcomes (local tumor progression rates). Patients who have had prior surgery or previous MWA were excluded. Ablation was the first treatment of choice, and all patients who have had other treatments were excluded. Patients were followed every 3 months with CT. Outcomes of ablation including complications and local tumor progression were evaluated. Peri-procedural factors included demographical factors, tumor features, ablation parameters, management of intra-procedural pneumothorax, and CT features. Complications included pneumothorax, post-procedural refractory infection, and pleural effusion. Results The study included 98 males and 68 females, with an average age of 56.1 years. Local tumor progression rate was negatively correlated with intra-procedural management of pneumothorax (R=-0.550, P=0.0003) and Hounsfield unit (HU) difference between HU before and after procedure (R=-0.855, P=0.006), and positively correlated with the average HU value of immediate post-procedural CT at the measurement points (R=0.857, P=0.00002). The correlation analysis results also showed a positive correlation between infection after procedure and pneumothorax (R=0.340, P=0.0001). Conclusions A greater difference between HU before and after the procedure or a decrease in CT values immediately after ablation may predict a higher rate of local complete ablation. Prompt management of intraoperative pneumothorax may lower local tumor progression rates and decrease incidence of post-procedural infection.
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Affiliation(s)
- Liming Zhu
- Department of Oncology, Wuxi Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi, China
| | - Jiaxi Huang
- Department of Pediatric Cardiothoracic Surgery, Children’s Hospital of Fudan University, Shanghai, China
| | - Chunhui Jin
- Department of Oncology, Wuxi Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi, China
| | - Acheng Zhou
- Department of Oncology, Wuxi Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi, China
| | - Ying Chen
- Department of Oncology, Wuxi Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi, China
| | - Baonan Zhang
- Department of Oncology, Wuxi Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi, China
| | - Federico Venuta
- Università di Roma “Sapienza”, Cattedra di Chirurgia Toracica, Policlinico Umberto I, Rome, Italy
| | - Bradley B. Pua
- Division of Interventional Radiology, Weill Cornell Medical College, New York-Presbyterian Hospital, New York, NY, USA
| | - Yehua Shen
- Minimally Invasive Therapy Center, Department of Integrative Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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Luo W, Xiu Z, Wang X, McGarry R, Allen J. A Novel Method for Evaluating Early Tumor Response Based on Daily CBCT Images for Lung SBRT. Cancers (Basel) 2023; 16:20. [PMID: 38201447 PMCID: PMC10778260 DOI: 10.3390/cancers16010020] [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: 11/03/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND We aimed to develop a new tumor response assessment method for lung SBRT. METHODS In total, 132 lung cancer patients with 134 tumors who received SBRT treatment with daily CBCT were included in this study. The information about tumor size (area), contrast (contrast-to-noise ratio (CNR)), and density/attenuation (μ) was derived from the CBCT images for the first and the last fractions. The ratios of tumor area, CNR, and μ (RA, RCNR, Rμ) between the last and first fractions were calculated for comparison. The product of the three rations was defined as a new parameter (R) for assessment. Tumor response was independently assessed by a radiologist based on a comprehensive analysis of the CBCT images. RESULTS R ranged from 0.27 to 1.67 with a mean value of 0.95. Based on the radiologic assessment results, a receiver operation characteristic (ROC) curve with the area under the curve (AUC) of 95% was obtained and the optimal cutoff value (RC) was determined as 1.1. The results based on RC achieved a 94% accuracy, 94% specificity, and 90% sensitivity. CONCLUSION The results show that R was correlated with early tumor response to lung SBRT and that using R for evaluating tumor response to SBRT would be viable and efficient.
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Affiliation(s)
- Wei Luo
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Zijian Xiu
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Xiaoqin Wang
- Department of Radiology, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA;
| | - Ronald McGarry
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Joshua Allen
- AdventHealth, 2501 N Orange Ave, Orlando, FL 32804, USA;
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Fan Y, Wang X, Yang C, Chen H, Wang H, Wang X, Hou S, Wang L, Luo Y, Sha X, Yang H, Yu T, Jiang X. Brain-Tumor Interface-Based MRI Radiomics Models to Determine EGFR Mutation, Response to EGFR-TKI and T790M Resistance Mutation in Non-Small Cell Lung Carcinoma Brain Metastasis. J Magn Reson Imaging 2023; 58:1838-1847. [PMID: 37144750 DOI: 10.1002/jmri.28751] [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: 02/21/2023] [Revised: 04/10/2023] [Accepted: 04/10/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Preoperative assessment of epidermal growth factor receptor (EGFR) status, response to EGFR-tyrosine kinase inhibitors (TKI) and development of T790M mutation in non-small cell lung carcinoma (NSCLC) patients with brain metastases (BM) is important for clinical decision-making, while previous studies were only based on the whole BM. PURPOSE To investigate values of brain-to-tumor interface (BTI) for determining the EGFR mutation, response to EGFR-TKI and T790M mutation. STUDY TYPE Retrospective. POPULATION Two hundred thirty patients from Hospital 1 (primary cohort) and 80 patients from Hospital 2 (external validation cohort) with BM and histological diagnosis of primary NSCLC, and with known EGFR status (biopsy) and T790M mutation status (gene sequencing). FIELD STRENGTH/SEQUENCE Contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) fast spin echo sequences at 3.0T MRI. ASSESSMENT Treatment response to EGFR-TKI therapy was determined by the Response Evaluation Criteria in Solid Tumors. Radiomics features were extracted from the 4 mm thickness BTI and selected by least shrinkage and selection operator regression. The selected BTI features and volume of peritumoral edema (VPE) were combined to construct models using logistic regression. STATISTICAL TESTS The performance of each radiomics model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). RESULTS A total of 7, 3, and 3 features were strongly associated with the EGFR mutation status, response to EGFR-TKI and T790M mutation status, respectively. The developed models combining BTI features and VPE can improve the performance than those based on BTI features alone, generating AUCs of 0.814, 0.730, and 0.774 for determining the EGFR mutation, response to EGFR-TKI and T790M mutation, respectively, in the external validation cohort. DATA CONCLUSION BTI features and VPE were associated with the EGFR mutation status, response to EGFR-TKI and T790M mutation status in NSCLC patients with BM. EVIDENCE LEVEL 3 Technical Efficacy: Stage 2.
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Affiliation(s)
- Ying Fan
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Xinti Wang
- The First Clinical Department of China Medical University, Shenyang, Liaoning, China
| | - Chunna Yang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Shaoping Hou
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Lihua Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Xianzheng Sha
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Huazhe Yang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Xiran Jiang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
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Dercle L, Yang M, Gönen M, Flynn J, Moskowitz CS, Connors DE, Yang H, Lu L, Reidy-Lagunes D, Fojo T, Karovic S, Zhao B, Schwartz LH, Henick BS. Ethnic diversity in treatment response for colorectal cancer: proof of concept for radiomics-driven enrichment trials. Eur Radiol 2023; 33:9254-9261. [PMID: 37368111 DOI: 10.1007/s00330-023-09862-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 05/11/2023] [Accepted: 05/15/2023] [Indexed: 06/28/2023]
Abstract
BACKGROUND Several barriers hamper recruitment of diverse patient populations in multicenter clinical trials which determine efficacy of new systemic cancer therapies. PURPOSE We assessed if quantitative analysis of computed tomography (CT) scans of metastatic colorectal cancer (mCRC) patients using imaging features that predict overall survival (OS) can unravel the association between ethnicity and efficacy. METHODS We retrospectively analyzed CT images from 1584 mCRC patients in two phase III trials evaluating FOLFOX ± panitumumab (n = 331, 350) and FOLFIRI ± aflibercept (n = 437, 466) collected from August 2006 to March 2013. Primary and secondary endpoints compared RECIST1.1 response at month-2 and delta tumor volume at month-2, respectively. An ancillary study compared imaging phenotype using a peer-reviewed radiomics-signature combining 3 imaging features to predict OS landmarked from month-2. Analysis was stratified by ethnicity. RESULTS In total, 1584 patients were included (mean age, 60.25 ± 10.57 years; 969 men). Ethnicity was as follows: African (n = 50, 3.2%), Asian (n = 66, 4.2%), Caucasian (n = 1413, 89.2%), Latino (n = 27, 1.7%), Other (n = 28, 1.8%). Overall baseline tumor volume demonstrated Africans and Caucasians had more advanced disease (p < 0.001). Ethnicity was associated with treatment response. Response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. Overall delta tumor volume at month-2 demonstrated that Latino patients more likely experienced response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023). CONCLUSION This study highlights how clinical trials that inadequately represent minority groups may impact associated translational work. In appropriately powered studies, radiomics features may allow us to unravel associations between ethnicity and treatment efficacy, better elucidate mechanisms of resistance, and promote diversity in trials through predictive enrichment. CLINICAL RELEVANCE STATEMENT Radiomics could promote clinical trial diversity through predictive enrichment, hence benefit to historically underrepresented racial/ethnic groups that may respond variably to treatment due to socioeconomic factors and built environment, collectively referred to as social determinants of health. KEY POINTS •Findings indicate ethnicity was associated with treatment response across all 3 endpoints. First, response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. •Second, the overall delta tumor volume at month-2 demonstrated that Latino patients were more likely to experience response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023).
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Affiliation(s)
- Laurent Dercle
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA.
| | - Melissa Yang
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA
| | - Mithat Gönen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Jessica Flynn
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Chaya S Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Dana E Connors
- Foundation for the National Institutes of Health (FNIH), 11400 Rockville Pike, Suite 600, North Bethesda, MD, 20852, USA
| | - Hao Yang
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA
| | - Lin Lu
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA
| | - Diane Reidy-Lagunes
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Tito Fojo
- Columbia University Herbert Irving Comprehensive Cancer Center, 161 Fort Washington Ave, New York, NY, 10032, USA
| | - Sanja Karovic
- Inova Center for Personalized Health and Schar Cancer Institute, 8100 Innovation Park Dr, Fairfax, VA, 22031, USA
- University of Virginia Cancer Center, 1240 Lee St, Charlottesville, VA, 22903, USA
| | - Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA
| | - Brian S Henick
- Columbia University Herbert Irving Comprehensive Cancer Center, 161 Fort Washington Ave, New York, NY, 10032, USA
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Wang MM, Li JQ, Dou SH, Li HJ, Qiu ZB, Zhang C, Yang XW, Zhang JT, Qiu XH, Xie HS, Tang WF, Cheng ML, Yan HH, Yang XN, Wu YL, Zhang XG, Yang L, Zhong WZ. Lack of incremental value of three-dimensional measurement in assessing invasiveness for lung cancer. Eur J Cardiothorac Surg 2023; 64:ezad373. [PMID: 37975876 PMCID: PMC10753921 DOI: 10.1093/ejcts/ezad373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 06/22/2023] [Accepted: 11/16/2023] [Indexed: 11/19/2023] Open
Abstract
OBJECTIVES The aim of this study was to evaluate the performance of consolidation-to-tumour ratio (CTR) and the radiomic models in two- and three-dimensional modalities for assessing radiological invasiveness in early-stage lung adenocarcinoma. METHODS A retrospective analysis was conducted on patients with early-stage lung adenocarcinoma from Guangdong Provincial People's Hospital and Shenzhen People's Hospital. Manual delineation of pulmonary nodules along the boundary was performed on cross-sectional images to extract radiomic features. Clinicopathological characteristics and radiomic signatures were identified in both cohorts. CTR and radiomic score for every patient were calculated. The performance of CTR and radiomic models were tested and validated in the respective cohorts. RESULTS A total of 818 patients from Guangdong Provincial People's Hospital were included in the primary cohort, while 474 patients from Shenzhen People's Hospital constituted an independent validation cohort. Both CTR and radiomic score were identified as independent factors for predicting pathological invasiveness. CTR in two- and three-dimensional modalities exhibited comparable results with areas under the receiver operating characteristic curves and were demonstrated in the validation cohort (area under the curve: 0.807 vs 0.826, P = 0.059) Furthermore, both CTR in two- and three-dimensional modalities was able to stratify patients with significant relapse-free survival (P < 0.000 vs P < 0.000) and overall survival (P = 0.003 vs P = 0.001). The radiomic models in two- and three-dimensional modalities demonstrated favourable discrimination and calibration in independent cohorts (P = 0.189). CONCLUSIONS Three-dimensional measurement provides no additional clinical benefit compared to two-dimensional.
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Affiliation(s)
- Meng-Min Wang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Jia-Qi Li
- Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
| | - Shi-Hua Dou
- Department of Thoracic Surgery, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen, China
| | - Hong-Ji Li
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Zhen-Bin Qiu
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Chao Zhang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Xiong-Wen Yang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Jia-Tao Zhang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xin-Hua Qiu
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Hong-Sheng Xie
- Department of Thoracic Surgery, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen, China
| | - Wen-Fang Tang
- Department of Cardiothoracic Surgery, Zhongshan City People's Hospital, Zhongshan, China
| | - Mei-Ling Cheng
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Hong-Hong Yan
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xue-Ning Yang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yi-Long Wu
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xue-Gong Zhang
- Bioinformatics Division, BNRIST and MOE Key Lab of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
| | - Lin Yang
- Department of Thoracic Surgery, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen, China
| | - Wen-Zhao Zhong
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, 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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Ma Y, Li Q. An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning. Cancer Radiother 2023; 27:705-711. [PMID: 37932182 DOI: 10.1016/j.canrad.2023.05.005] [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: 05/13/2023] [Revised: 05/22/2023] [Accepted: 05/24/2023] [Indexed: 11/08/2023]
Abstract
PURPOSE The purpose of this study was to develop a model for predicting chemoradiation response in non-small cell lung cancer (NSCLC) patients by integrating radiomics and deep-learning features and combined intra- and peritumoral regions with pre-treated CT images. MATERIALS AND METHODS This study enrolled 462 patients with NSCLC who received chemoradiation. On the basis of pretreated CT images, we developed three models to compare the prediction of chemoradiation: intratumoral, peritumoral and combined regions. To further illustrate each model, we established different feature integration methods: a) radiomics model with 1500 features; b) deep learning model with a multiple instance learning algorithm; c) integrated model by integrating radiomic and deep learning features. For radiomics and integrated models, support vector machine and the least absolute shrinkage and selection operator were used to extract and select features. Transfer learning and max pooling algorithms were used to identify high informative features in deep learning models. We applied ten-fold cross validation in model training and testing. RESULTS The best area under the curve (AUC) of intratumoral, peritumoral and combined models were 0.89 (95% CI, 0.74-0.93), 0.86 (95% CI, 0.75-0.92) and 0.92 (95% CI, 0.81-0.95), respectively. It indicated the importance of the peritumoral region for treatment response prediction and should be used in combination with the intratumoral region. Integrated models gave better results than models with radiomics and deep learning features alone in all regions of interest and radiomics models outperformed deep learning models in any comparative models. CONCLUSIONS The model that integrate radiomic and deep learning features and combined intra- and peritumoral regions provide valuable information in predicting treatment response of chemoradiation. It can help oncologists customize personalized clinical treatment plans for NSCLC patients.
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Affiliation(s)
- Y Ma
- The First Affiliated Hospital of China Medical University, Department of Pathology, 110001 Shenyang, China.
| | - Q Li
- The First Affiliated Hospital of China Medical University, Department of Pathology, 110001 Shenyang, China.
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Xu Z, An C, Shi F, Ren H, Li Y, Chen S, Dou J, Wang Y, Yan S, Lu J, Chen H. Automatic prediction of hepatic arterial infusion chemotherapy response in advanced hepatocellular carcinoma with deep learning radiomic nomogram. Eur Radiol 2023; 33:9038-9051. [PMID: 37498380 DOI: 10.1007/s00330-023-09953-x] [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: 10/16/2022] [Revised: 05/15/2023] [Accepted: 05/22/2023] [Indexed: 07/28/2023]
Abstract
OBJECTIVES Hepatic arterial infusion chemotherapy (HAIC) using the FOLFOX regimen (oxaliplatin plus fluorouracil and leucovorin) is a promising option for advanced hepatocellular carcinoma (Ad-HCC). As identifying patients with Ad-HCC who would obtain objective response (OR) to HAIC preoperatively remains a challenge, we aimed to develop an automatic and non-invasive model for predicting HAIC response. METHODS A total of 458 patients with Ad-HCC who underwent HAIC were retrospectively included from three hospitals (310 for training, 77 for internal validation, and 71 for external validation). The deep learning and radiomic features were extracted from the automatically segmented liver region on contrast-enhanced computed tomography images. Then, a deep learning radiomic nomogram (DLRN) was constructed by integrating deep learning scores, radiomic scores, and significant clinical variables with multivariate logistic regression. Model performance was assessed by AUC and Kaplan-Meier estimator. RESULTS After automatic segmentation, only a few modifications were needed (less than 30 min for 458 patients). The DLRN achieved an AUC of 0.988 in the training cohort, 0.915 in the internal validation cohort, and 0.896 in the external validation cohort, respectively, outperforming other models in HAIC response prediction. Moreover, survival risk stratification was also successfully performed by the DLRN. The overall survival (OS) of the predictive OR group was significantly longer than that of the predictive non-OR group (median OS: 26.0 vs. 12.3 months, p < 0.001). CONCLUSIONS The DLRN provided a satisfactory performance for predicting HAIC response, which is essential to identify Ad-HCC patients for HAIC and may potentially benefit personalized pre-treatment decision-making. CLINICAL RELEVANCE STATEMENT This study presents an accurate and automatic method for predicting response to hepatic arterial infusion chemotherapy in patients with advanced hepatocellular carcinoma, and therefore help in defining the best candidates for this treatment. KEY POINTS • Deep learning radiomic nomogram (DLRN) based on automatic segmentation of CECT can accurately predict hepatic arterial infusion chemotherapy (HAIC) response of advanced HCC patients. • The proposed prediction model can perform survival risk stratification and is an easy-to-use tool for personalized pre-treatment decision-making for advanced HCC patients.
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Affiliation(s)
- Ziming Xu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, No. 30 Shuangqing Road, Haidian District, Beijing, 100084, China
| | - Chao An
- Department of Minimal Invasive Intervention, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Feng Shi
- Department of Minimal Invasive Intervention, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - He Ren
- Department of Ultrasound, The Sixth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yuze Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, No. 30 Shuangqing Road, Haidian District, Beijing, 100084, China
| | - Song Chen
- Department of Minimal Invasive Intervention, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiaqi Dou
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, No. 30 Shuangqing Road, Haidian District, Beijing, 100084, China
| | - Yajie Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, No. 30 Shuangqing Road, Haidian District, Beijing, 100084, China
| | - Shaozhen Yan
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No. 45, Changchun Street, Xicheng District, Beijing, 100053, China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, No. 45, Changchun Street, Xicheng District, Beijing, 100053, China.
| | - Huijun Chen
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, No. 30 Shuangqing Road, Haidian District, Beijing, 100084, China.
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Wu Z, Lin Q, Song H, Chen J, Wang G, Fu G, Cui C, Su X, Li L, Bian T. Evaluation of Lymphatic Vessel Invasion Determined by D2-40 Using Preoperative MRI-Based Radiomics for Invasive Breast Cancer. Acad Radiol 2023; 30:2458-2468. [PMID: 36586760 DOI: 10.1016/j.acra.2022.11.024] [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: 10/12/2022] [Revised: 11/14/2022] [Accepted: 11/18/2022] [Indexed: 12/30/2022]
Abstract
RATIONALE AND OBJECTIVES Preoperative prediction of LVI status can facilitate personalized therapeutic planning. This study aims to investigate the efficacy of preoperative MRI-based radiomics for predicting lymphatic vessel invasion (LVI) determined by D2-40 in patients with invasive breast cancer. MATERIALS AND METHODS A total of 203 patients with pathologically confirmed invasive breast cancer, who underwent preoperative breast MRI, were retrospectively enrolled and randomly assigned to the following cohorts: training cohort (n=141) and test cohort (n=62). Then, univariate and multivariate logistic regression were performed to select independent risk factors and build a clinical model. Afterwards, least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select predictive features extracted from the early and delay enhancement dynamic contrast-enhanced (DCE)-MRI images, and a radiomics signature was established. Subsequently, a nomogram model was constructed by incorporating the radiomics score and risk factors. Receiver operating characteristic curves were performed to determine the performance of various models. The efficacy of the various models was evaluated using calibration and decision curves. RESULTS Fourteen radiomics features were selected to construct the radiomics model. The size of the lymph node was identified as an independent risk factor of the clinical model. The nomogram model demonstrated the best calibration and discrimination performance in both the training and test cohorts, with an area under the curve of 0.873 (95% confidence interval [CI]: 0.807-0.923) and 0.902 (95% CI: 0.800-0.963), respectively. The decision curve illustrated that the nomogram model added more net benefits, when compared to the radiomics signature and clinical model. CONCLUSION The nomogram model based on preoperative DCE-MRI images exhibits satisfactory efficacy for the noninvasive prediction of LVI determined by D2-40 in invasive breast cancer.
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Affiliation(s)
- Zengjie Wu
- Department of Radiology, the Affiliated Hospital of Qingdao University, Shandong, China
| | - Qing Lin
- Breast Disease Center, the Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao 266000, Shandong, China
| | - Hongming Song
- Breast Disease Center, the Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao 266000, Shandong, China
| | - Jingjing Chen
- Breast Disease Center, the Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao 266000, Shandong, China
| | - Guanqun Wang
- Department of Pathology, the Affiliated Hospital of Qingdao University, Shandong, China
| | - Guangming Fu
- Department of Pathology, the Affiliated Hospital of Qingdao University, Shandong, China
| | - Chunxiao Cui
- Breast Disease Center, the Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao 266000, Shandong, China
| | - Xiaohui Su
- Breast Disease Center, the Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao 266000, Shandong, China
| | - Lili Li
- Breast Disease Center, the Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao 266000, Shandong, China
| | - Tiantian Bian
- Breast Disease Center, the Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao 266000, Shandong, China..
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Liu M, Chang N, Zhang S, Du Y, Zhang X, Ren W, Sun J, Bai J, Wang L, Zhang G. Identification of vulnerable carotid plaque with CT-based radiomics nomogram. Clin Radiol 2023; 78:e856-e863. [PMID: 37633746 DOI: 10.1016/j.crad.2023.07.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 07/08/2023] [Accepted: 07/26/2023] [Indexed: 08/28/2023]
Abstract
AIM To develop and validate a radiomics nomogram for identifying high-risk carotid plaques on computed tomography (CT) angiography (CTA). MATERIALS AND METHODS A total of 280 patients with symptomatic (n=131) and asymptomatic (n=139) carotid plaques were divided into a training set (n=135), validation set (n=58), and external test set (n=87). Radiomic features were extracted from CTA images. A radiomics model was constructed based on selected features and a radiomics score (rad-score) was calculated. A clinical factor model was constructed by demographics and CT findings. A radiomics nomogram combining independent clinical factors and the rad-score was constructed. The diagnostic performance of three models was evaluated and validated by region of characteristic curves. RESULTS Calcification and maximum plaque thickness were the independent clinical factors. Twenty-four features were used to build the radiomics signature. In the validation set, the nomogram (area under the curve [AUC], 0.977; 95% CI, 0.899-0.999) performed better (p=0.017 and p=0.031) than the clinical factor model (AUC, 0.862; 95% CI, 0.746-0.938) and radiomics signature (AUC, 0.944; 95% CI, 0.850-0.987). In external test set, the nomogram (AUC, 0.952; 95% CI, 0.884-0.987) and radiomics signature (AUC, 0.932; 95% CI, 0.857-0.975) showed better discrimination capability (p=0.002 and p=0.037) than clinical factor model (AUC, 0.818; 95% CI, 0.721-0.892). CONCLUSION The CT-based nomogram showed satisfactory performance in identification of high-risk plaques in carotid arteries, and it may serve as a potential non-invasive tool to identify carotid plaque vulnerability and risk stratification.
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Affiliation(s)
- M Liu
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - N Chang
- Department of Medical Technology, Jinan Nursing Vocational College, No. 3636 Gangxi Road, Jinan 250021, Shandong, China
| | - S Zhang
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan China; Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Y Du
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - X Zhang
- Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - W Ren
- Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - J Sun
- Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - J Bai
- Department of Computed Tomography, Liaocheng Traditional Chinese Medicine Hospital, Liaocheng, China
| | - L Wang
- Physical Examination Centre, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
| | - G Zhang
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
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Fan Y, Wang X, Dong Y, Cui E, Wang H, Sun X, Su J, Luo Y, Yu T, Jiang X. Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC. Eur Radiol 2023; 33:7902-7912. [PMID: 37142868 DOI: 10.1007/s00330-023-09709-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 02/12/2023] [Accepted: 03/16/2023] [Indexed: 05/06/2023]
Abstract
OBJECTIVES To develop radiomics signatures from multiparametric magnetic resonance imaging (MRI) scans to detect epidermal growth factor receptor (EGFR) mutations and predict the response to EGFR-tyrosine kinase inhibitors (EGFR-TKIs) in non-small cell lung cancer (NSCLC) patients with brain metastasis (BM). METHODS We included 230 NSCLC patients with BM treated at our hospital between January 2017 and December 2021 and 80 patients treated at another hospital between July 2014 and October 2021 to form the primary and external validation cohorts, respectively. All patients underwent contrast-enhanced T1-weighted (T1C) and T2-weighted (T2W) MRI, and radiomics features were extracted from both the tumor active area (TAA) and peritumoral edema area (POA) for each patient. The least absolute shrinkage and selection operator (LASSO) was used to identify the most predictive features. Radiomics signatures (RSs) were constructed using logistic regression analysis. RESULTS For predicting the EGFR mutation status, the created RS-EGFR-TAA and RS-EGFR- POA showed similar performance. By combination of TAA and POA, the multi-region combined RS (RS-EGFR-Com) achieved the highest prediction performance, with AUCs of 0.896, 0.856, and 0.889 in the primary training, internal validation, and external validation cohort, respectively. For predicting response to EGFR-TKI, the multi-region combined RS (RS-TKI-Com) generated the highest AUCs in the primary training (AUC = 0.817), internal validation (AUC = 0.788), and external validation (AUC = 0.808) cohort, respectively. CONCLUSIONS Our findings suggested values of multiregional radiomics of BM for predicting EGFR mutations and response to EGFR-TKI. CLINICAL RELEVANCE STATEMENT The application of radiomic analysis of multiparametric brain MRI has proven to be a promising tool to stratify which patients can benefit from EGFR-TKI therapy and to facilitate the precise therapeutics of NSCLC patients with brain metastases. KEY POINTS • Multiregional radiomics can improve efficacy in predicting therapeutic response to EGFR-TKI therapy in NSCLC patients with brain metastasis. • The tumor active area (TAA) and peritumoral edema area (POA) may hold complementary information related to the therapeutic response to EGFR-TKI. • The developed multi-region combined radiomics signature achieved the best predictive performance and may be considered as a potential tool for predicting response to EGFR-TKI.
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Affiliation(s)
- Ying Fan
- School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Xinti Wang
- The First Clinical Department, China Medical University, Shenyang, 110122, People's Republic of China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, 110042, People's Republic of China
| | - Enuo Cui
- School of Computer Science and Engineering, Shenyang University, Shenyang, 110044, People's Republic of China
| | - Huan Wang
- Radiation Oncology Department of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, 110042, People's Republic of China
| | - Xinyan Sun
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, 110042, People's Republic of China
| | - Juan Su
- School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, 110042, People's Republic of China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, 110042, People's Republic of China.
| | - Xiran Jiang
- School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China.
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Yang J, Cao Y, Zhou F, Li C, Lv J, Li P. Combined deep-learning MRI-based radiomic models for preoperative risk classification of endometrial endometrioid adenocarcinoma. Front Oncol 2023; 13:1231497. [PMID: 37909025 PMCID: PMC10613647 DOI: 10.3389/fonc.2023.1231497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
Background Differences exist between high- and low-risk endometrial cancer (EC) in terms of whether lymph node dissection is performed. Factors such as tumor grade, myometrial invasion (MDI), and lymphovascular space invasion (LVSI) in the European Society for Medical Oncology (ESMO), European SocieTy for Radiotherapy & Oncology (ESTRO) and European Society of Gynaecological Oncology (ESGO) guidelines risk classification can often only be accurately assessed postoperatively. The aim of our study was to estimate the risk classification of patients with endometrial endometrioid adenocarcinoma before surgery and offer individualized treatment plans based on their risk classification. Methods Clinical information and last preoperative pelvic magnetic resonance imaging (MRI) of patients with postoperative pathologically determined endometrial endometrioid adenocarcinoma were collected retrospectively. The region of interest (ROI) was subsequently plotted in T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) MRI scans, and the traditional radiomics features and deep-learning image features were extracted. A final radiomics nomogram model integrating traditional radiomics features, deep learning image features, and clinical information was constructed to distinguish between low- and high-risk patients (based on the 2020 ESMO-ESGO-ESTRO guidelines). The efficacy of the model was evaluated in the training and validation sets of the model. Results We finally included 168 patients from January 1, 2020 to July 29, 2021, of which 95 patients in 2021 were classified as the training set and 73 patients in 2020 were classified as the validation set. In the training set, the area under the curve (AUC) of the radiomics nomogram was 0.923 (95%CI: 0.865-0.980) and in the validation set, the AUC of the radiomics nomogram was 0.842 (95%CI: 0.762-0.923). The nomogram had better predictions than both the traditional radiomics model and the deep-learning radiomics model. Conclusion MRI-based radiomics models can be useful for preoperative risk classification of patients with endometrial endometrioid adenocarcinoma.
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Affiliation(s)
| | | | | | | | | | - Pu Li
- Clinical School of Obstetrics and Gynecology Center, Tianjin Medical University, Tianjin, China
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Xue Y, Zhang H, Zheng Z, Liu X, Yin J, Zhang J. Predictive performance of radiomics for peritoneal metastasis in patients with gastric cancer: a meta-analysis and radiomics quality assessment. J Cancer Res Clin Oncol 2023; 149:12103-12113. [PMID: 37422882 DOI: 10.1007/s00432-023-05096-0] [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: 05/23/2023] [Accepted: 06/30/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE The purpose of this meta-analysis is to systematically review the diagnostic performance of radiomic techniques in predicting peritoneal metastasis in patients with gastric cancer, and to evaluate the quality of current research. METHODS We searched PubMed, Web of Science, EBSCO, Embase, and Cochrane databases for relevant studies up to April 3, 2023. Data extraction and quality evaluation were performed by two independent reviewers. Then we performed statistical analysis, including plotting the forest plot and summary receiver operating characteristic (SROC) curve, and source of heterogeneity analysis, through the MIDAS module in Stata 15. We performed meta-regression and subgroup analyses to analyze the sources of heterogeneity. Using the QUADAS-2 scale and the RQS scale to assess the quality of retrieved studies. RESULTS Ten studies with 6199 patients were finally included in our meta-analysis. Pooled sensitivity and specificity were 0.77 (95% confidence interval [CI]: 0.66, 0.86), and 0.88 (95% CI 0.80, 0.93), respectively. The overall AUC was 0.89 (95% CI 0.86, 0.92). The heterogeneity of this meta-analysis was high, with I2 = 88% (95% CI 75,100). The result of meta-regression showed that QUADAS-2 results, RQS results and machine learning method led to heterogeneity in sensitivity and specificity (P < 0.05). Furthermore, the image segmentation area and the presence or absence of combined clinical factors were associated with sensitivity heterogeneity and specificity heterogeneity, respectively. CONCLUSION Undoubtedly, radiomics has potential value in diagnosing peritoneal metastasis of gastric cancer, but the quality of current research is inconsistent, and more standardized and high-quality research is still needed in the future to achieve the transformation of radiomics results into clinical applications.
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Affiliation(s)
- Yasheng Xue
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Haiqiao Zhang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Zhi Zheng
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Xiaoye Liu
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Jie Yin
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Jun Zhang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China.
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Bhonde SB, Wagh SK, Prasad JR. Identification of cancer types from gene expressions using learning techniques. Comput Methods Biomech Biomed Engin 2023; 26:1951-1965. [PMID: 36562388 DOI: 10.1080/10255842.2022.2160243] [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: 04/11/2022] [Revised: 10/15/2022] [Accepted: 11/15/2022] [Indexed: 12/24/2022]
Abstract
Tumor is the major cause of death all around the world in recent days. Early detection and prediction of a cancer type are important for a patient's well-being. Functional genomic data has recently been used in the effective and early detection of cancer. According to previous research, the use of microarray data in cancer prediction has evidenced two main problems as high dimensionality and limited sample size. Several researchers have used numerous statistical and machine learning-based methods to classify cancer types but still, limitations are there which makes cancer classification a difficult job. Deep Learning (DL) and Convolutional Neural Networks (CNN) have been proven with effective analyses of unstructured data including gene expression data. In the proposed method gene expression data for five types of cancer is collected from The Cancer Genome Atlas (TCGA). Prominent features are selected using a hybrid Particle Swarm Optimization (PSO) and Random Forest (RF) algorithm followed by the use of Principal Component Analysis (PCA) for dimensionality reduction. Finally, for classification blend of Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) is used to predict the target type of cancer. Experimental results demonstrate that accuracy of the proposed method is 96.89%. As compared to existing work, our method outperformed with better results.
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Affiliation(s)
- Swati B Bhonde
- Smt. Kashibai Navale College of Engineering, Pune, India
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Yu C, Li T, Yang X, Xin L, Zhao Z, Yang Z, Zhang R. The maximal contrast-enhanced range of CT for differentiating the WHO pathological subtypes and risk subgroups of thymic epithelial tumors. Br J Radiol 2023; 96:20221076. [PMID: 37486626 PMCID: PMC10546431 DOI: 10.1259/bjr.20221076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 07/01/2023] [Accepted: 07/11/2023] [Indexed: 07/25/2023] Open
Abstract
OBJECTIVE To explore the value of maximal contrast-enhanced (CEmax) range using contrast-enhanced CT (CECT) imaging in differentiating the pathological subtypes and risk subgroups of thymic epithelial tumors (TETs). METHODS The pre-treatment-CECT images of 319 TET patients from May 2012 to November 2021 were analyzed retrospectively. The CEmax was defined as the maximum difference between the CT value of the solid tumor on pre-contrast and contrast-enhanced images. The mean CEmax value was calculated at three different tumor levels. RESULTS There was a significant difference in the CEmax among the eight main pathological subtypes [types A, AB, B1, B2, and B3 thymoma, thymic carcinoma (TC), low-grade neuroendocrine tumor (NET) and high-grade NET] (p < 0.001). Among the eight subtypes, the CEmax values of types A, AB, and low-risk NET were higher than those of the other subtypes (all p < 0.001), and there was no difference among types B1-B3 and high-risk NET (all p > 0.05). There was no difference for CEmax values between NET and TC (p = 0.491). For the risk subgroups, the CEmax of TC (including NET) was 35.35 ± 11.41 HU, which was lower than that of low-risk thymoma (A and AB) (57.73±21.24 HU) (P < 0.001) and was higher than that of high-risk thymoma (B1-B3) (27.37±8.27 HU) (P < 0.001). The CEmax cut-off values were 38.5 HU and 30.5 HU respectively (AUC: 0.829 and 0.712; accuracy, 72.4% and 67.7%). CONCLUSION The tumor CEmax on CECT helps differentiate the pathological subtypes and risk subgroups of TETs. ADVANCES IN KNOWLEDGE In this study, an improved simplified risk grouping method was proposed based on the traditional (2004 edition) simplified risk grouping method for TETs. If Type B1 thymoma is classified as high-risk, radiologists using this improved method may improve the accuracy in differentiating risk level of TETs compared with the traditional method.
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Affiliation(s)
- Chunhai Yu
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Ting Li
- Department of Nephrology, Taiyuan People's Hospital, Taiyuan, China
| | - Xiaotang Yang
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Lei Xin
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Zhikai Zhao
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Zhao Yang
- Department of Radiology, Cancer Hospital Affiliated to Shanxi Medical University, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, China
| | - Ruiping Zhang
- First Hospital of Shanxi Medical University, Taiyuan, China
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Wang M, Mei T, Gong Y. The quality and clinical translation of radiomics studies based on MRI for predicting Ki-67 levels in patients with breast cancer. Br J Radiol 2023; 96:20230172. [PMID: 37724784 PMCID: PMC10546437 DOI: 10.1259/bjr.20230172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/13/2023] [Accepted: 08/02/2023] [Indexed: 09/21/2023] Open
Abstract
OBJECTIVE To evaluate the methodological quality of radiomics literature predicting Ki-67 levels based on MRI in patients with breast cancer (BC) and to propose suggestions for clinical translation. METHODS In this review, we searched PubMed, Embase, and Web of Science for studies published on radiomics in patients with BC. We evaluated the methodological quality of the studies using the Radiomics Quality Score (RQS). The Cochrane Collaboration's software (RevMan 5.4), Meta-DiSc (v. 1.4) and IBM SPSS (v. 26.0) were used for all statistical analyses. RESULTS Eighteen studies met our inclusion criteria, and the average RQS was 10.17 (standard deviation [SD]: 3.54). None of these studies incorporated any of the following items: a phantom study on all scanners, cut-off analyses, prospective study, cost-effectiveness analysis, or open science and data. In the meta-analysis, it showed apparent diffusion coefficient (ADC) played a better role to predict Ki-67 level than dynamic contrast-enhanced (DCE) MRI in the radiomics, with the pooled area under the curve (AUC) of 0.969. CONCLUSION Ki-67 index is a common tumor biomarker with high clinical value. Radiomics is an ever-growing quantitative data-mining method helping predict tumor biomarkers from medical images. However, the quality of the reviewed studies evaluated by the RQS was not so satisfactory and there are ample opportunities for improvement. Open science and data, external validation, phantom study, publicly open radiomics database and standardization in the radiomics practice are what researchers should pay more attention to in the future. ADVANCES IN KNOWLEDGE The RQS tool considered the radiomics used to predict the Ki-67 level was of poor quality. ADC performed better than DCE in radiomic prediction. We propose some measures to facilitate the clinical translation of radiomics.
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Affiliation(s)
- Min Wang
- Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Mei
- Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Youling Gong
- Division of Thoracic Tumor Multidisciplinary Treatment, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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Liang X, Guan R, Zhu J, Meng Y, Zhu J, Yang Y, Cui Y, Dai J, Mao W, Lv L, Shen D, Guo R. A clinical decision support system to predict the efficacy for EGFR-TKIs based on artificial neural network. J Cancer Res Clin Oncol 2023; 149:12265-12274. [PMID: 37434091 DOI: 10.1007/s00432-023-05104-3] [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: 05/18/2023] [Accepted: 06/30/2023] [Indexed: 07/13/2023]
Abstract
BACKGROUND The efficacy of epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) was affected by numerous factors. In the study, we developed and validated an artificial neural network (ANN) system based on clinical characteristics and next-generation sequencing (NGS) to support clinical decisions. METHODS A multicenter retrospective non-interventional study was conducted. 240 patients from three hospitals with advanced non-small cell lung cancer (NSCLC) and EGFR mutation were tested by NGS before the first treatment. All patients received formal EGFR-TKIs treatment. Five different models were individually trained to predict the efficacy of EGFR-TKIs based on one medical center with 188 patients. Two independent cohorts from other medical centers were collected for external validation. RESULTS Compared with logistic regression, four machine learning methods showed better predicting abilities for EGFR-TKIs. The inclusion of NGS tests improved the predictive power of models. ANN performed best on the dataset with mutations TP53, RB1, PIK3CA, EGFR mutation sites, and tumor mutation burden (TMB). The prediction accuracy, recall and AUC were 0.82, 0.82, and 0.82, respectively in our final model. In the external validation set, ANN still showed good performance and differentiated patients with poor outcomes. Finally, a clinical decision support software based on ANN was developed and provided a visualization interface for clinicians. CONCLUSION This study provides an approach to assess the efficacy of NSCLC patients with first-line EGFR-TKI treatment. Software is developed to support clinical decisions.
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Affiliation(s)
- Xiao Liang
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Oncology, Jiangyin People's Hospital, Jiangyin, China
| | - Runwei Guan
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Jiamin Zhu
- Department of Oncology, Jiangyin People's Hospital, Jiangyin, China
| | - Yue Meng
- Department of Oncology, Affiliated Hospital of Nantong University, Nantong, China
| | - Jing Zhu
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Oncology, The Affiliated Jiangning Hospital With Nanjing Medical University, Nanjing, China
| | - Yuxiang Yang
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Yanan Cui
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiali Dai
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Weidong Mao
- Department of Oncology, Jiangyin People's Hospital, Jiangyin, China
| | - Liting Lv
- Department of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
| | - Dong Shen
- Department of Oncology, Jiangyin People's Hospital, Jiangyin, China.
| | - Renhua Guo
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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Salmanpour MR, Hosseinzadeh M, Rezaeijo SM, Rahmim A. Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107714. [PMID: 37473589 DOI: 10.1016/j.cmpb.2023.107714] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 05/19/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous features are commonly generated in radiomics applications as applied to medical imaging, and identification of robust radiomics features (RFs) can be an important step to derivation of reliable, reproducible solutions. In this work, we utilize a tensor radiomics (TR) framework, where numerous fusions are explored, to generate different flavours of RFs, and we aimed to identify RFs that are robust to fusion techniques in head and neck cancer. Overall, we aimed to predict progression-free survival (PFS) using Hybrid Machine Learning Systems (HMLS) and reproducible RFs. METHODS The study was performed on 408 patients with head and neck cancer from The Cancer Imaging Archive. After image preprocessing, 15 fusion techniques were employed to combine Positron Emission Tomography (PET) and Computed Tomography (CT) images. Subsequently, 215 RFs were extracted through a standardized radiomics software, with 17 'flavours' generated using PET-only, CT-only, and 15 fused PET&CT images. The variability of RFs across flavours was studied using the Intraclass Correlation Coefficient (ICC). Furthermore, the features were categorized into seven reliability groups, 106 reproducible RFs with ICC>0.75 were selected, highly correlated flavours were removed, Principal Component Analysis was used to convert 17 flavours to 1 attribute, the polynomial function was utilized to increase RFs, and Analysis of variance (ANOVA) was used to select the relevant attributes. Finally, 3 classifiers including Random Forest (RFC), Logistic regression (LR), and Multi-layer perceptron were applied to the preselected relevant attributes to predict binary PFS. In 5-fold cross-validation, 80% of 4 divisions were utilized to train the model, and the remaining 20% was utilized to evaluate the model. Further, the remaining fold was used for external nested testing. RESULTS Reliability analysis indicated that most morphological features belong to the high-reliability category. By contrast, local intensity and statistical features extracted from images belong to the low-reliability category. In the tensor framework, the highest 5-fold cross-validation accuracy of 76.7%±3.3% with an external nested testing of 70.6%±6.7% resulted from the reproducible TR+polynomial function+ANOVA+LR algorithm while the accuracy of 70.0%±4.2% with the external nested testing of 67.7%±4.9% was achieved through the PCA fusion+RFC (non-tensor paradigm). CONCLUSIONS This study demonstrated that using reproducible RFs as utilized within a tensor fusion radiomics framework, linked with ANOVA and LR, added value to prediction of progression-free survival outcome in head and neck cancer patients.
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Affiliation(s)
- Mohammad R Salmanpour
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC, Canada.
| | - Mahdi Hosseinzadeh
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC, Canada; Department of Electrical & Computer Engineering, University of Tarbiat Modares, Tehran, Iran
| | - Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
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Hou J, Zheng G, Han L, Shu Z, Wang H, Yuan Z, Peng J, Gong X. Coronary computed tomography angiography imaging features combined with computed tomography-fractional flow reserve, pericoronary fat attenuation index, and radiomics for the prediction of myocardial ischemia. J Nucl Cardiol 2023; 30:1838-1850. [PMID: 36859595 DOI: 10.1007/s12350-023-03221-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 01/19/2023] [Indexed: 03/03/2023]
Abstract
BACKGROUND This study aimed to predict myocardial ischemia (MIS) by constructing models with imaging features, CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics based on coronary computed tomography angiography (CCTA). METHODS AND RESULTS This study included 96 patients who underwent CCTA and single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI). According to SPECT-MPI results, there were 72 vessels with MIS in corresponding supply area and 105 vessels with no-MIS. The conventional model [lesion length (LL), MDS (maximum stenosis diameter × 100% / reference vessel diameter), MAS (maximum stenosis area × 100% / reference vessel area) and CT value], radiomics model (radiomics features), and multi-faceted model (all features) were constructed using support vector machine. Conventional and radiomics models showed similar predictive efficacy [AUC: 0.76, CI 0.62-0.90 vs. 0.74, CI 0.61-0.88; p > 0.05]. Adding pFAI to the conventional model showed better predictive efficacy than adding CT-FFR (AUC: 0.88, CI 0.79-0.97 vs. 0.80, CI 0.68-0.92; p < 0.05). Compared with conventional and radiomics model, the multi-faceted model showed the highest predictive efficacy (AUC: 0.92, CI 0.82-0.98, p < 0.05). CONCLUSION pFAI is more effective for predicting MIS than CT-FFR. A multi-faceted model combining imaging features, CT-FFR, pFAI, and radiomics is a potential diagnostic tool for MIS.
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Affiliation(s)
- Jie Hou
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Address: No. 158 Shangtang Road, Hanghzou City, 310014, Zhejiang Province, China
| | - Guangying Zheng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Lu Han
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Zhenyu Shu
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Address: No. 158 Shangtang Road, Hanghzou City, 310014, Zhejiang Province, China
| | - Haochu Wang
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Address: No. 158 Shangtang Road, Hanghzou City, 310014, Zhejiang Province, China
| | - Zhongyu Yuan
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Jiaxuan Peng
- Jinzhou Medical University, Jinzhou, Liaoning Province, China
| | - Xiangyang Gong
- Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Address: No. 158 Shangtang Road, Hanghzou City, 310014, Zhejiang Province, China.
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Wang SY, Sun K, Jin S, Wang KY, Jiang N, Shan SQ, Lu Q, Lv GY, Dong JH. Predicting the outcomes of hepatocellular carcinoma downstaging with the use of clinical and radiomics features. BMC Cancer 2023; 23:858. [PMID: 37700255 PMCID: PMC10496191 DOI: 10.1186/s12885-023-11386-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 09/07/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Downstaging of hepatocellular carcinoma (HCC) makes it possible for patients beyond the criteria to have the chance of liver transplantation (LT) and improved outcomes. Thus, a procedure to predict the prognosis of the treatment is an urgent requisite. The present study aimed to construct a comprehensive framework with clinical information and radiomics features to accurately predict the prognosis of downstaging treatment. METHODS Specifically, three-dimensional (3D) tumor segmentation from contrast-enhanced computed tomography (CT) is employed to extract spatial information of the lesions. Then, the radiomics features within the segmented region are calculated. Combining radiomics features and clinical data prompts the development of feature selection to enhance the robustness and generalizability of the model. Finally, we adopt the support vector machine (SVM) algorithm to establish a classification model for predicting HCC downstaging outcomes. RESULTS Herein, a comparative study was conducted on three different models: a radiomics features-based model (R model), a clinical features-based model (C model), and a joint radiomics clinical features-based model (R-C model). The average accuracy of the three models was 0.712, 0.792, and 0.844, and the average area under the receiver-operating characteristic (AUROC) of the three models was 0.775, 0.804, and 0.877, respectively. CONCLUSIONS The novel and practical R-C model accurately predicted the downstaging outcomes, which could be utilized to guide the HCC downstaging toward LT treatment.
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Affiliation(s)
- Si-Yuan Wang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
- Research Unit of Precision hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing, China
| | - Kai Sun
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
- Research Unit of Precision hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing, China
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Shuo Jin
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
- Research Unit of Precision hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing, China
| | - Kai-Yu Wang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
- Research Unit of Precision hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing, China
| | - Nan Jiang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
- Research Unit of Precision hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing, China
| | - Si-Qiao Shan
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
- Research Unit of Precision hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing, China
| | - Qian Lu
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
- Research Unit of Precision hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing, China
| | - Guo-Yue Lv
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, China
| | - Jia-Hong Dong
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
- Research Unit of Precision hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences, Beijing, China.
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Wang Y, Ding Y, Liu X, Li X, Jia X, Li J, Zhang H, Song Z, Xu M, Ren J, Sun D. Preoperative CT-based radiomics combined with tumour spread through air spaces can accurately predict early recurrence of stage I lung adenocarcinoma: a multicentre retrospective cohort study. Cancer Imaging 2023; 23:83. [PMID: 37679806 PMCID: PMC10485937 DOI: 10.1186/s40644-023-00605-3] [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: 05/03/2023] [Accepted: 08/27/2023] [Indexed: 09/09/2023] Open
Abstract
OBJECTIVE To develop and validate a prediction model for early recurrence of stage I lung adenocarcinoma (LUAD) that combines radiomics features based on preoperative CT with tumour spread through air spaces (STAS). MATERIALS AND METHODS The most recent preoperative thin-section chest CT scans and postoperative pathological haematoxylin and eosin-stained sections were retrospectively collected from patients with a postoperative pathological diagnosis of stage I LUAD. Regions of interest were manually segmented, and radiomics features were extracted from the tumour and peritumoral regions extended by 3 voxel units, 6 voxel units, and 12 voxel units, and 2D and 3D deep learning image features were extracted by convolutional neural networks. Then, the RAdiomics Integrated with STAS model (RAISm) was constructed. The performance of RAISm was then evaluated in a development cohort and validation cohort. RESULTS A total of 226 patients from two medical centres from January 2015 to December 2018 were retrospectively included as the development cohort for the model and were randomly split into a training set (72.6%, n = 164) and a test set (27.4%, n = 62). From June 2019 to December 2019, 51 patients were included in the validation cohort. RAISm had excellent discrimination in predicting the early recurrence of stage I LUAD in the training cohort (AUC = 0.847, 95% CI 0.762-0.932) and validation cohort (AUC = 0.817, 95% CI 0.625-1.000). RAISm outperformed single modality signatures and other combinations of signatures in terms of discrimination and clinical net benefits. CONCLUSION We pioneered combining preoperative CT-based radiomics with STAS to predict stage I LUAD recurrence postoperatively and confirmed the superior effect of the model in validation cohorts, showing its potential to assist in postoperative treatment strategies.
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Affiliation(s)
- Yuhang Wang
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Yun Ding
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Xin Liu
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Xin Li
- Department of Thoracic Surgery, Tianjin Chest Hospital of Tianjin University, No. 261, Taierzhuang South Road, Jinnan District, Tianjin, 300222, China
| | - Xiaoteng Jia
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Jiuzhen Li
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Han Zhang
- Graduate School, Tianjin Medical University, Tianjin, China
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Zhenchun Song
- Department of Imaging, Tianjin Chest Hospital of Tianjin University, Tianjin, China
| | - Meilin Xu
- Department of Pathology, Tianjin Chest Hospital of Tianjin University, Tianjin, China
| | - Jie Ren
- Graduate School, Tianjin Medical University, Tianjin, China
- Department of Thoracic Surgery, Tianjin Jinnan Hospital, Tianjin, China
| | - Daqiang Sun
- Graduate School, Tianjin Medical University, Tianjin, China.
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China.
- Department of Thoracic Surgery, Tianjin Chest Hospital of Tianjin University, No. 261, Taierzhuang South Road, Jinnan District, Tianjin, 300222, China.
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Li Z, Wang F, Zhang H, Zheng H, Zhou X, Wang Z, Xie S, Peng L, Wang X, Wang Y. The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein. Quant Imaging Med Surg 2023; 13:5622-5640. [PMID: 37711814 PMCID: PMC10498270 DOI: 10.21037/qims-22-1050] [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: 09/30/2022] [Accepted: 06/20/2023] [Indexed: 09/16/2023]
Abstract
Background The aim of this study was to develop a radiomics machine learning model based on computed tomography (CT) that can predict whether thymic epithelial tumors (TETs) can be separated from veins during surgery and to compare the accuracy of the radiomics model to that of radiologists. Methods Patients who underwent thymectomy at our hospital from 2009 to 2017 were included in the screening process. After the selection of patients according to the inclusion and exclusion criteria, the cohort was randomly divided into training and testing groups, and CT images of these patients were collected. Subsequently, two-dimensional (2D) and three-dimensional (3D) regions of interest were labelled using ITK-SNAP 3.8.0 software, and Radiomics features were extracted using Python software (Python Software Foundation) and selected through the least absolute shrinkage and selection operator (LASSO) regression model. To construct the classifier, a support vector machine (SVM) was employed, and a nomogram was created using logistic regression to predict vascular inseparable TETs based on the radiomics score (radscore) and image features. To assess the accuracy of these models, area under receiver operating characteristic (ROC) curves of these models were calculated, and differences among the models were identified using the Delong test. Results In this retrospective study, 204 patients with TETs were included, among whom 21 were diagnosed with surgical vascularly inseparable TETs. The area under ROC curve (AUC) of the 2D model, 3D model, 2D + 3D model, and radiologist diagnoses were 0.94, 0.92, 0.95, and 0.87 in the training cohort and 0.95, 0.92, 0.98, and 0.78 in testing cohort, respectively. The Delong test revealed a significant improvement in the performance of the radiomics models compared to radiologists' diagnoses. The logistic regression selected 3 image features, namely maximum diameter of the tumor, degree of abutment of vessel circumference >50%, and absence of the mediastinal fat layer or space between the tumor and surrounding structures. These features, along with the radscore, were included to develop a nomogram. The AUCs of this nomogram were 0.99 in both the training set and testing set, and the Delong test did not find a significant difference between ROC plots of the nomogram and radiomics models. Conclusions The proposed radiomics model could accurately predict surgical vascularly inseparable TETs preoperatively and was shown to have a higher predictive value than the radiologists.
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Affiliation(s)
- Zhiyang Li
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Fuqiang Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Hanlu Zhang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Zheng
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xue Zhou
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhensong Wang
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shenglong Xie
- Department of Thoracic Surgery, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Peng
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xuyang Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yun Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
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Bevilacqua R, Barbarossa F, Fantechi L, Fornarelli D, Paci E, Bolognini S, Giammarchi C, Lattanzio F, Paciaroni L, Riccardi GR, Pelliccioni G, Biscetti L, Maranesi E. Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer's Disease: A Systematic Review of Studies in the Field. J Clin Med 2023; 12:5432. [PMID: 37629474 PMCID: PMC10455452 DOI: 10.3390/jcm12165432] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/17/2023] [Accepted: 08/19/2023] [Indexed: 08/27/2023] Open
Abstract
The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer's disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer's disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer's disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer's disease patients-both at the dementia and pre-dementia phases of the disease-from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer's disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer's.
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Affiliation(s)
- Roberta Bevilacqua
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Federico Barbarossa
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Lorenzo Fantechi
- Unit of Nuclear Medicine, IRCCS INRCA, 60127 Ancona, Italy; (L.F.); (D.F.)
| | - Daniela Fornarelli
- Unit of Nuclear Medicine, IRCCS INRCA, 60127 Ancona, Italy; (L.F.); (D.F.)
| | - Enrico Paci
- Unit of Radiology, IRCCS INRCA, 60127 Ancona, Italy;
| | - Silvia Bolognini
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Cinzia Giammarchi
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Fabrizia Lattanzio
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
| | - Lucia Paciaroni
- Unit of Neurology, IRCCS INRCA, 60127 Ancona, Italy; (L.P.); (G.P.)
| | | | | | | | - Elvira Maranesi
- Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy; (R.B.); (F.B.); (S.B.); (C.G.); (F.L.); (E.M.)
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Chen M, Copley SJ, Viola P, Lu H, Aboagye EO. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol 2023; 93:97-113. [PMID: 37211292 DOI: 10.1016/j.semcancer.2023.05.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 05/23/2023]
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.
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Affiliation(s)
- Mitchell Chen
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Susan J Copley
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK; Imperial College Healthcare NHS Trust, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK
| | - Patrizia Viola
- North West London Pathology, Charing Cross Hospital, Fulham Palace Rd, London W6 8RF, UK
| | - Haonan Lu
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, The Commonwealth Building, Du Cane Road, Hammersmith Campus, Imperial College, London W12 0NN, UK.
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Tang FH, Fong YW, Yung SH, Wong CK, Tu CL, Chan MT. Radiomics-Clinical AI Model with Probability Weighted Strategy for Prognosis Prediction in Non-Small Cell Lung Cancer. Biomedicines 2023; 11:2093. [PMID: 37626590 PMCID: PMC10452490 DOI: 10.3390/biomedicines11082093] [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: 06/01/2023] [Revised: 06/29/2023] [Accepted: 07/19/2023] [Indexed: 08/27/2023] Open
Abstract
In this study, we propose a radiomics clinical probability-weighted model for the prediction of prognosis for non-small cell lung cancer (NSCLC). The model combines radiomics features extracted from radiotherapy (RT) planning images with clinical factors such as age, gender, histology, and tumor stage. CT images with radiotherapy structures of 422 NSCLC patients were retrieved from The Cancer Imaging Archive (TCIA). Radiomic features were extracted from gross tumor volumes (GTVs). Five machine learning algorithms, namely decision trees (DT), random forests (RF), extreme boost (EB), support vector machine (SVM) and generalized linear model (GLM) were optimized by a voted ensemble machine learning (VEML) model. A probabilistic weighted approach is used to incorporate the uncertainty associated with both radiomic and clinical features and to generate a probabilistic risk score for each patient. The performance of the model is evaluated using a receiver operating characteristic (ROC). The Radiomic model, clinical factor model, and combined radiomic clinical probability-weighted model demonstrated good performance in predicting NSCLC survival with AUC of 0.941, 0.856 and 0.949, respectively. The combined radiomics clinical probability-weighted enhanced model achieved significantly better performance than the radiomic model in 1-year survival prediction (chi-square test, p < 0.05). The proposed model has the potential to improve NSCLC prognosis and facilitate personalized treatment decisions.
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Affiliation(s)
- Fuk-Hay Tang
- School of Medical and Health Sciences, Tung Wah College, Hong Kong, China
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Pellegrino S, Fonti R, Hakkak Moghadam Torbati A, Bologna R, Morra R, Damiano V, Matano E, De Placido S, Del Vecchio S. Heterogeneity of Glycolytic Phenotype Determined by 18F-FDG PET/CT Using Coefficient of Variation in Patients with Advanced Non-Small Cell Lung Cancer. Diagnostics (Basel) 2023; 13:2448. [PMID: 37510192 PMCID: PMC10378511 DOI: 10.3390/diagnostics13142448] [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: 06/21/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
We investigated the role of Coefficient of Variation (CoV), a first-order texture parameter derived from 18F-FDG PET/CT, in the prognosis of Non-Small Cell Lung Cancer (NSCLC) patients. Eighty-four patients with advanced NSCLC who underwent 18F-FDG PET/CT before therapy were retrospectively studied. SUVmax, SUVmean, CoV, total Metabolic Tumor Volume (MTVTOT) and whole-body Total Lesion Glycolysis (TLGWB) were determined by an automated contouring program (SUV threshold at 2.5). We analyzed 194 lesions: primary tumors (n = 84), regional (n = 48) and non-regional (n = 17) lymph nodes and metastases in liver (n = 9), bone (n = 23) and other sites (n = 13); average CoVs were 0.36 ± 0.13, 0.36 ± 0.14, 0.42 ± 0.18, 0.30 ± 0.14, 0.37 ± 0.17, 0.34 ± 0.13, respectively. No significant differences were found between the CoV values among the different lesion categories. Survival analysis included age, gender, histology, stage, MTVTOT, TLGWB and imaging parameters derived from primary tumors. At univariate analysis, CoV (p = 0.0184), MTVTOT (p = 0.0050), TLGWB (p = 0.0108) and stage (p = 0.0041) predicted Overall Survival (OS). At multivariate analysis, age, CoV, MTVTOT and stage were retained in the model (p = 0.0001). Patients with CoV > 0.38 had significantly better OS than those with CoV ≤ 0.38 (p = 0.0143). Patients with MTVTOT ≤ 89.5 mL had higher OS than those with MTVTOT > 89.5 mL (p = 0.0063). Combining CoV and MTVTOT, patients with CoV ≤ 0.38 and MTVTOT > 89.5 mL had the worst prognosis. CoV, by reflecting the heterogeneity of glycolytic phenotype, can predict clinical outcomes in NSCLC patients.
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Affiliation(s)
- Sara Pellegrino
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Rosa Fonti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | | | - Roberto Bologna
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Rocco Morra
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Vincenzo Damiano
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Elide Matano
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Sabino De Placido
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Silvana Del Vecchio
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
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