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Zhang J, Zhao Y, Lu Y, Li P, Dang S, Li X, Yin B, Zhao L. Meningioma consistency assessment based on the fusion of deep learning features and radiomics features. Eur J Radiol 2024; 170:111250. [PMID: 38071910 DOI: 10.1016/j.ejrad.2023.111250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 09/27/2023] [Accepted: 11/30/2023] [Indexed: 01/16/2024]
Abstract
PURPOSE This study aims to combine deep learning features with radiomics features for the computer-assisted preoperative assessment of meningioma consistency. METHODS 202 patients with surgery and pathological diagnosis of meningiomas at our institution between December 2016 and December 2018 were retrospectively included in the study. The T2-fluid attenuated inversion recovery (T2-Flair) images were evaluated to classify meningioma as soft or hard by professional neurosurgeons based on Zada's consistency grading system. All the patients were split randomly into a training cohort (n = 162) and a testing cohort (n = 40). A convolutional neural network (CNN) model was proposed to extract deep learning features. These deep learning features were combined with radiomics features. After multiple feature selections, selected features were used to construct classification models using four classifiers. AUC was used to evaluate the performance of each classifier. A signature was further constructed by using the least absolute shrinkage and selection operator (LASSO). A nomogram based on the signature was created for predicting meningioma consistency. RESULTS The logistic regression classifier constructed using 17 radiomics features and 9 deep learning features provided the best performance with a precision of 0.855, a recall of 0.854, an F1-score of 0.852 and an AUC of 0.943 (95 % CI, 0.873-1.000) in the testing cohort. The C-index of the nomogram was 0.822 (95 % CI, 0.758-0.885) in the training cohort and 0.943 (95 % CI, 0.873-1.000) in the testing cohort with good calibration. Decision curve analysis further confirmed the clinical usefulness of the nomogram for predicting meningioma consistency. CONCLUSIONS The proposed method for assessing meningioma consistency based on the fusion of deep learning features and radiomics features is potentially clinically valuable. It can be used to assist physicians in the preoperative determination of tumor consistency.
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Affiliation(s)
- Jiatian Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230022, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Yajing Zhao
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China
| | - Yiping Lu
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China
| | - Peng Li
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Shijie Dang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China.
| | - Lingxiao Zhao
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
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Hu Y, Jiang T, Wang H, Song J, Yang Z, Wang Y, Su J, Jin M, Chang S, Deng K, Jiang W. Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC. Phys Med 2024; 117:103200. [PMID: 38160516 DOI: 10.1016/j.ejmp.2023.103200] [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: 01/07/2023] [Revised: 08/18/2023] [Accepted: 12/21/2023] [Indexed: 01/03/2024] Open
Abstract
PURPOSE To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC). METHODS Sixty-six patients from center 1 served as training and internal validation cohorts. Thirty patients from center 2 and thirty patients from center 3 served as external validation 1 and external validation 2 cohorts, respectively. The lesions identified on CT scans were subdivided into two phenotypically consistent subregions by automatic clustering on the patient-level and population-level (denoted as marginal S1 and inner S2). Handcrafted and deep learning-based features were extracted separately from the entire tumor region and subregions, then selected using the intraclass correlation coefficient and least absolute shrinkage and selection operator regression (LASSO). Radiomics signatures (RSs) were built integrating the selected features and correlation coefficients using a logistic regression method. Area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the RSs. RESULTS RSs derived from S1 outperformed those from S2 and the whole tumor region for both handcrafted and deep learning features. The Fusion-RS incorporating the two feature types achieved the best prediction performance in training (AUC = 0.947, 95 % Confidence Interval [CI] 0.905-0.989, SPE = 0.895, SEN = 0.878), internal validation (AUC = 0.875, 95 % CI: 0.782-0.969, SPE = 0.724, SEN = 0.952), external validation 1 (AUC = 0.836, 95 % CI: 0.694-0.977, SPE = 1.000, SEN = 0.533) and external validation 2 (AUC = 0.783, 95 % CI: 0.613-0.953, SPE = 0.765, SEN = 0.692) cohorts. CONCLUSIONS Subregional radiomics analysis can be useful for predicting therapeutic response to anti-PD1 therapy. The developed Fusion-RS may be considered as a potential non-invasive tool for individual treatment managements.
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Affiliation(s)
- Yue Hu
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Tao Jiang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Huan Wang
- Radiation Oncology Department Of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Jiangdian Song
- School of Medical Informatics, China Medical University, Liaoning 110122, PR China
| | - Zhiguang Yang
- Department of Radiology, Shengjing Hospital, Shenyang 110004, PR China
| | - Yan Wang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Juan Su
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Meiqi Jin
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Shijie Chang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China.
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Anhui 230036, PR China.
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China.
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Alshammari A. DenseNet_ HybWWoA: A DenseNet-Based Brain Metastasis Classification with a Hybrid Metaheuristic Feature Selection Strategy. Biomedicines 2023; 11:biomedicines11051354. [PMID: 37239025 DOI: 10.3390/biomedicines11051354] [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: 03/12/2023] [Revised: 04/01/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Brain metastases (BM) are the most severe consequence of malignancy in the brain, resulting in substantial illness and death. The most common primary tumors that progress to BM are lung, breast, and melanoma. Historically, BM patients had poor clinical outcomes, with limited treatment options including surgery, stereotactic radiation therapy (SRS), whole brain radiation therapy (WBRT), systemic therapy, and symptom control alone. Magnetic Resonance Imaging (MRI) is a valuable tool for detecting cerebral tumors, though it is not infallible, as cerebral matter is interchangeable. This study offers a novel method for categorizing differing brain tumors in this context. This research additionally presents a combination of optimization algorithms called the Hybrid Whale and Water Waves Optimization Algorithm (HybWWoA), which is used to identify features by reducing the size of recovered features. This algorithm combines whale optimization and water waves optimization. The categorization procedure is consequently carried out using a DenseNet algorithm. The suggested cancer categorization method is evaluated on a number of factors, including precision, specificity, and sensitivity. The final assessment findings showed that the suggested approach exceeded the authors' expectations, with an F1-score of 97% and accuracy, precision, memory, and recollection of 92.1%, 98.5%, and 92.1%, respectively.
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Affiliation(s)
- Abdulaziz Alshammari
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
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Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features. LA RADIOLOGIA MEDICA 2023; 128:160-170. [PMID: 36670236 DOI: 10.1007/s11547-023-01594-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 01/10/2023] [Indexed: 01/21/2023]
Abstract
PURPOSE To build an automatic computer-aided diagnosis (CAD) pipeline based on multiparametric magnetic resonance imaging (mpMRI) and explore the role of different imaging features in the classification of breast cancer. MATERIALS AND METHODS A total of 222 histopathology-confirmed breast lesions, together with their BI-RADS scores, were included in the analysis. The cohort was randomly split into training (159) and test (63) cohorts, and another 50 lesions were collected as an external cohort. An nnUNet-based lesion segmentation model was trained to automatically segment lesion ROI, from which radiomics features were extracted for diffusion-weighted imaging (DWI), T2-weighted imaging (T2WI), and contrast-enhanced (DCE) pharmacokinetic parametric maps. Models based on combinations of sequences were built using support vector machine (SVM) and logistic regression (LR). Also, the performance of these sequence combinations and BI-RADS scores were compared. The Dice coefficient and AUC were calculated to evaluate the segmentation and classification results. Decision curve analysis (DCA) was used to assess clinical utility. RESULTS The segmentation model achieved a Dice coefficient of 0.831 in the test cohort. The radiomics model used only three features from diffusion coefficient (ADC) images, T2WI, and DCE-derived kinetic mapping, and achieved an AUC of 0.946 [0.883-0.990], AUC of 0.842 [0.6856-0.998] in the external cohort, which was higher than the BI-RADS score with an AUC of 0.872 [0.752-0.975]. The joint model using both radiomics score and BI-RADS score achieved the highest test AUC of 0.975 [0.935-1.000], with a sensitivity of 0.920 and a specificity of 0.923. CONCLUSION Three radiomics features can be used to construct an automatic radiomics-based pipeline to improve the diagnosis of breast lesions and reduce unnecessary biopsies, especially when using jointly with BI-RADS scores.
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Gu J, Li B, Shu H, Zhu J, Qiu Q, Bai T. Development and verification of radiomics framework for computed tomography image segmentation. Med Phys 2022; 49:6527-6537. [PMID: 35917213 PMCID: PMC9805121 DOI: 10.1002/mp.15904] [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: 03/31/2022] [Revised: 07/19/2022] [Accepted: 07/21/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Radiomics has been considered an imaging marker for capturing quantitative image information (QII). The introduction of radiomics to image segmentation is desirable but challenging. PURPOSE This study aims to develop and validate a radiomics-based framework for image segmentation (RFIS). METHODS RFIS is designed using features extracted from volume (svfeatures) created by sliding window (swvolume). The 53 svfeatures are extracted from 11 phantom series. Outliers in the svfeature datasets are detected by isolation forest (iForest) and specified as the mean value. The percentage coefficient of variation (%COV) is calculated to evaluate the reproducibility of svfeatures. RFIS is constructed and applied to the gross target volume (GTV) segmentation from the peritumoral region (GTV with a 10 mm margin) to assess its feasibility. The 127 lung cancer images are enrolled. The test-retest method, correlation matrix, and Mann-Whitney U test (p < 0.05) are used to select non-redundant svfeatures of statistical significance from the reproducible svfeatures. The synthetic minority over-sampling technique is utilized to balance the minority group in the training sets. The support vector machine is employed for RFIS construction, which is tuned in the training set using 10-fold stratified cross-validation and then evaluated in the test sets. The swvolumes with the consistent classification results are grouped and merged. Mode filtering is performed to remove very small subvolumes and create relatively large regions of completely uniform character. In addition, RFIS performance is evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, and Dice similarity coefficient (DSC). RESULTS 30249 phantom and 145008 patient image swvolumes were analyzed. Forty-nine (92.45% of 53) svfeatures represented excellent reproducibility(%COV<15). Forty-five features (91.84% of 49) included five categories that passed test-retest analysis. Thirteen svfeatures (28.89% of 45) svfeatures were selected for RFIS construction. RFIS showed an average (95% confidence interval) sensitivity of 0.848 (95% CI:0.844-0.883), a specificity of 0.821 (95% CI: 0.818-0.825), an accuracy of 83.48% (95% CI: 83.27%-83.70%), and an AUC of 0.906 (95% CI: 0.904-0.908) with cross-validation. The sensitivity, specificity, accuracy, and AUC were equal to 0.762 (95% CI: 0.754-0.770), 0.840 (95% CI: 0.837-0.844), 82.29% (95% CI: 81.90%-82.60%), and 0.877 (95% CI: 0.873-0.881) in the test set, respectively. GTV was segmented by grouping and merging swvolume with identical classification results. The mean DSC after mode filtering was 0.707 ± 0.093 in the training sets and 0.688 ± 0.072 in the test sets. CONCLUSION Reproducible svfeatures can capture the differences in QII among swvolumes. RFIS can be applied to swvolume classification, which achieves image segmentation by grouping and merging the swvolume with similar QII.
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Affiliation(s)
- Jiabing Gu
- Southeast UniversityLaboratory of Image Science and TechnologyJiangsu Provincial Joint International Research Laboratory of Medical Information ProcessingCentre de Recherche en Information Biomédicale Sino‐français (CRIBs)NanjingP. R. China,Department of Radiation Oncology Physics and TechnologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Baosheng Li
- Southeast UniversityLaboratory of Image Science and TechnologyJiangsu Provincial Joint International Research Laboratory of Medical Information ProcessingCentre de Recherche en Information Biomédicale Sino‐français (CRIBs)NanjingP. R. China,Department of Radiation Oncology Physics and TechnologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Huazhong Shu
- Southeast UniversityLaboratory of Image Science and TechnologyJiangsu Provincial Joint International Research Laboratory of Medical Information ProcessingCentre de Recherche en Information Biomédicale Sino‐français (CRIBs)NanjingP. R. China
| | - Jian Zhu
- Department of Radiation Oncology Physics and TechnologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina,Shandong Key Laboratory of Digital Medicine and Computer Assisted SurgeryThe Affiliated Hospital of Qingdao UniversityQingdaoP. R. China
| | - Qingtao Qiu
- Department of Radiation Oncology Physics and TechnologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Tong Bai
- Department of Radiation Oncology Physics and TechnologyShandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
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Deep learning with multiresolution handcrafted features for brain MRI segmentation. Artif Intell Med 2022; 131:102365. [DOI: 10.1016/j.artmed.2022.102365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 06/28/2022] [Accepted: 07/09/2022] [Indexed: 12/26/2022]
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Shi J, Zhao Z, Jiang T, Ai H, Liu J, Chen X, Luo Y, Fan H, Jiang X. A deep learning approach with subregion partition in MRI image analysis for metastatic brain tumor. Front Neuroinform 2022; 16:973698. [PMID: 35991287 PMCID: PMC9382021 DOI: 10.3389/fninf.2022.973698] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeTo propose a deep learning network with subregion partition for predicting metastatic origins and EGFR/HER2 status in patients with brain metastasis.MethodsWe retrospectively enrolled 140 patients with clinico-pathologically confirmed brain metastasis originated from primary NSCLC (n = 60), breast cancer (BC, n = 60) and other tumor types (n = 20). All patients underwent contrast-enhanced brain MRI scans. The brain metastasis was subdivided into phenotypically consistent subregions using patient-level and population-level clustering. A residual network with a global average pooling layer (RN-GAP) was proposed to calculate deep learning-based features. Features from each subregion were selected with least absolute shrinkage and selection operator (LASSO) to build logistic regression models (LRs) for predicting primary tumor types (LR-NSCLC for the NSCLC origin and LR-BC for the BC origin), EGFR mutation status (LR-EGFR) and HER2 status (LR-HER2).ResultsThe brain metastasis can be partitioned into a marginal subregion (S1) and an inner subregion (S2) in the MRI image. The developed models showed good predictive performance in the training (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.860 vs. 0.909 vs. 0.850 vs. 0.900) and validation (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.819 vs. 0.872 vs. 0.750 vs. 0.830) set.ConclusionOur proposed deep learning network with subregion partitions can accurately predict metastatic origins and EGFR/HER2 status of brain metastasis, and hence may have the potential to be non-invasive and preoperative new markers for guiding personalized treatment plans in patients with brain metastasis.
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Affiliation(s)
- Jiaxin Shi
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Zilong Zhao
- Department of Neurosurgery, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Tao Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Hua Ai
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Jiani Liu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Xinpu Chen
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Huijie Fan
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
- *Correspondence: Huijie Fan,
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
- Xiran Jiang,
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Shi J, Cui L, Wang H, Dong Y, Yu T, Yang H, Wang X, Liu G, Jiang W, Luo Y, Yang Z, Jiang X. MRI-based intratumoral and peritumoral radiomics on prediction of lymph-vascular space invasion in cervical cancer: A multi-center study. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103373] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Kendrick J, Francis R, Hassan GM, Rowshanfarzad P, Jeraj R, Kasisi C, Rusanov B, Ebert M. Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies. Front Oncol 2021; 11:771787. [PMID: 34790581 PMCID: PMC8591174 DOI: 10.3389/fonc.2021.771787] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 10/11/2021] [Indexed: 12/21/2022] Open
Abstract
Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.
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Affiliation(s)
- Jake Kendrick
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Roslyn Francis
- Medical School, University of Western Australia, Crawley, WA, Australia
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Robert Jeraj
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Collin Kasisi
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Branimir Rusanov
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Martin Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, WA, Australia
- 5D Clinics, Claremont, WA, Australia
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Fan Y, Dong Y, Yang H, Chen H, Yu Y, Wang X, Wang X, Yu T, Luo Y, Jiang X. Subregional radiomics analysis for the detection of the EGFR mutation on thoracic spinal metastases from lung cancer. Phys Med Biol 2021; 66. [PMID: 34633298 DOI: 10.1088/1361-6560/ac2ea7] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/11/2021] [Indexed: 01/20/2023]
Abstract
The present study intended to use radiomic analysis of spinal metastasis subregions to detect epidermal growth factor receptor (EGFR) mutation. In total, 94 patients with thoracic spinal metastasis originated from primary lung adenocarcinoma (2017-2020) were studied. All patients underwent T1-weighted (T1W) and T2 fat-suppressed (T2FS) MRI scans. The spinal metastases (tumor region) were subdivided into phenotypically consistent subregions based on patient- and population-level clustering: Three subregions, S1, S2 and S3, and the total tumor region. Radiomics features were extracted from each subregion and from the whole tumor region as well. Least shrinkage and selection operator (LASSO) regression were used for feature selection and radiomics signature definition. Detection performance of S3 was better than all other regions using T1W (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.720 versus 0.764 versus 0.786 versus 0.758) and T2FS (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.791 versus 0.708 versus 0.838 versus 0.797) MRI. The multi-regional radiomics signature derived from the joint of inner subregion S3 from T1W and T2FS MRI achieved the best detection capabilities with AUCs of 0.879 (ACC = 0.774, SEN = 0.838, SPE = 0.840) and 0.777 (ACC = 0.688, SEN = 0.947, SPE = 0.615) in the training and test sets, respectively. Our study revealed that MRI-based radiomic analysis of spinal metastasis subregions has the potential to detect the EGFR mutation in patients with primary lung adenocarcinoma.
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Affiliation(s)
- Ying Fan
- Department of Biomedical Engineering, School of Intelligent Medicine, 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, 110042, People's Republic of China
| | - Huazhe Yang
- Department of Biophysics, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
| | - Huanhuan Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, 110004, People's Republic of China
| | - Yalian Yu
- Department of Otorhinolaryngology, the First Affiliated Hospital of China Medical University, Shenyang, 110122, People's Republic of China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Xinling Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, 110042, People's Republic of China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China
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