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Al-Mubarak H, Bane O, Gillingham N, Kyriakakos C, Abboud G, Cuevas J, Gonzalez J, Meilika K, Horowitz A, Huang HHV, Daza J, Fauveau V, Badani K, Viswanath SE, Taouli B, Lewis S. Characterization of renal masses with MRI-based radiomics: assessment of inter-package and inter-observer reproducibility in a prospective pilot study. Abdom Radiol (NY) 2024; 49:3464-3475. [PMID: 38467854 DOI: 10.1007/s00261-024-04212-z] [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: 09/13/2023] [Revised: 01/17/2024] [Accepted: 01/17/2024] [Indexed: 03/13/2024]
Abstract
OBJECTIVES To evaluate radiomics features' reproducibility using inter-package/inter-observer measurement analysis in renal masses (RMs) based on MRI and to employ machine learning (ML) models for RM characterization. METHODS 32 Patients (23M/9F; age 61.8 ± 10.6 years) with RMs (25 renal cell carcinomas (RCC)/7 benign masses; mean size, 3.43 ± 1.73 cm) undergoing resection were prospectively recruited. All patients underwent 1.5 T MRI with T2-weighted (T2-WI), diffusion-weighted (DWI)/apparent diffusion coefficient (ADC), and pre-/post-contrast-enhanced T1-weighted imaging (T1-WI). RMs were manually segmented using volume of interest (VOI) on T2-WI, DWI/ADC, and T1-WI pre-/post-contrast imaging (1-min, 3-min post-injection) by two independent observers using two radiomics software packages for inter-package and inter-observer assessments of shape/histogram/texture features common to both packages (104 features; n = 26 patients). Intra-class correlation coefficients (ICCs) were calculated to assess inter-observer and inter-package reproducibility of radiomics measurements [good (ICC ≥ 0.8)/moderate (ICC = 0.5-0.8)/poor (ICC < 0.5)]. ML models were employed using reproducible features (between observers and packages, ICC > 0.8) to distinguish RCC from benign RM. RESULTS Inter-package comparisons demonstrated that radiomics features from T1-WI-post-contrast had the highest proportion of good/moderate ICCs (54.8-58.6% for T1-WI-1 min), while most features extracted from T2-WI, T1-WI-pre-contrast, and ADC exhibited poor ICCs. Inter-observer comparisons found that radiomics measurements from T1-WI pre/post-contrast and T2-WI had the greatest proportion of features with good/moderate ICCs (95.3-99.1% T1-WI-post-contrast 1-min), while ADC measurements yielded mostly poor ICCs. ML models generated an AUC of 0.71 [95% confidence interval = 0.67-0.75] for diagnosis of RCC vs. benign RM. CONCLUSION Radiomics features extracted from T1-WI-post-contrast demonstrated greater inter-package and inter-observer reproducibility compared to ADC, with fair accuracy for distinguishing RCC from benign RM. CLINICAL RELEVANCE Knowledge of reproducibility of MRI radiomics features obtained on renal masses will aid in future study design and may enhance the diagnostic utility of radiomics models for renal mass characterization.
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Affiliation(s)
- Haitham Al-Mubarak
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Octavia Bane
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Nicolas Gillingham
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai West, New York, NY, 10019, USA
| | - Christopher Kyriakakos
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Ghadi Abboud
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Jordan Cuevas
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Janette Gonzalez
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Kirolos Meilika
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amir Horowitz
- Precision Immunology Institute/Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hsin-Hui Vivien Huang
- Department of Population Sciences and Health Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jorge Daza
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute/Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Valentin Fauveau
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ketan Badani
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Satish E Viswanath
- Department of Biomedical Engineering, School of Medicine, Case School of Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, Case School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA
| | - Sara Lewis
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Hospital, New York, NY, USA.
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, Box 1234, New York, NY, 10029, USA.
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Teng X, Wang Y, Nicol AJ, Ching JCF, Wong EKY, Lam KTC, Zhang J, Lee SWY, Cai J. Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI. Diagnostics (Basel) 2024; 14:1835. [PMID: 39202322 PMCID: PMC11353986 DOI: 10.3390/diagnostics14161835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 08/03/2024] [Accepted: 08/16/2024] [Indexed: 09/03/2024] Open
Abstract
Radiomics, which integrates the comprehensive characterization of imaging phenotypes with machine learning algorithms, is increasingly recognized for its potential in the diagnosis and prognosis of oncological conditions. However, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This review aims to address the paucity of discussion regarding the factors that influence the reproducibility and repeatability of radiomic features and their subsequent impact on the application of radiomic models. We provide a synthesis of the literature on the repeatability and reproducibility of CT/MR-based radiomic features, examining sources of variation, the number of reproducible features, and the availability of individual feature repeatability indices. We differentiate sources of variation into random effects, which are challenging to control but can be quantified through simulation methods such as perturbation, and biases, which arise from scanner variability and inter-reader differences and can significantly affect the generalizability of radiomic model performance in diverse settings. Four suggestions for repeatability and reproducibility studies are suggested: (1) detailed reporting of variation sources, (2) transparent disclosure of calculation parameters, (3) careful selection of suitable reliability indices, and (4) comprehensive reporting of reliability metrics. This review underscores the importance of random effects in feature selection and harmonizing biases between development and clinical application settings to facilitate the successful translation of radiomic models from research to clinical practice.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Yongqiang Wang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Alexander James Nicol
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Jerry Chi Fung Ching
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Edwin Ka Yiu Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Kenneth Tsz Chun Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Shara Wee-Yee Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
- Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518057, China
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Zhang R, Zhu H, Chen M, Sang W, Lu K, Li Z, Wang C, Zhang L, Yin FF, Yang Z. A dual-radiomics model for overall survival prediction in early-stage NSCLC patient using pre-treatment CT images. Front Oncol 2024; 14:1419621. [PMID: 39206157 PMCID: PMC11349529 DOI: 10.3389/fonc.2024.1419621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 07/26/2024] [Indexed: 09/04/2024] Open
Abstract
Introduction Radiation therapy (RT) is one of the primary treatment options for early-stage non-small cell lung cancer (ES-NSCLC). Therefore, accurately predicting the overall survival (OS) rate following radiotherapy is crucial for implementing personalized treatment strategies. This work aims to develop a dual-radiomics (DR) model to (1) predict 3-year OS in ES-NSCLC patients receiving RT using pre-treatment CT images, and (2) provide explanations between feature importanceand model prediction performance. Methods The publicly available TCIA Lung1 dataset with 132 ES-NSCLC patients received RT were studied: 89/43 patients in the under/over 3-year OS group. For each patient, two types of radiomic features were examined: 56 handcrafted radiomic features (HRFs) extracted within gross tumor volume, and 512 image deep features (IDFs) extracted using a pre-trained U-Net encoder. They were combined as inputs to an explainable boosting machine (EBM) model for OS prediction. The EBM's mean absolute scores for HRFs and IDFs were used as feature importance explanations. To evaluate identified feature importance, the DR model was compared with EBM using either (1) key or (2) non-key feature type only. Comparison studies with other models, including supporting vector machine (SVM) and random forest (RF), were also included. The performance was evaluated by the area under the receiver operating characteristic curve (AUCROC), accuracy, sensitivity, and specificity with a 100-fold Monte Carlo cross-validation. Results The DR model showed highestperformance in predicting 3-year OS (AUCROC=0.81 ± 0.04), and EBM scores suggested that IDFs showed significantly greater importance (normalized mean score=0.0019) than HRFs (score=0.0008). The comparison studies showed that EBM with key feature type (IDFs-only demonstrated comparable AUCROC results (0.81 ± 0.04), while EBM with non-key feature type (HRFs-only) showed limited AUCROC (0.64 ± 0.10). The results suggested that feature importance score identified by EBM is highly correlated with OS prediction performance. Both SVM and RF models were unable to explain key feature type while showing limited overall AUCROC=0.66 ± 0.07 and 0.77 ± 0.06, respectively. Accuracy, sensitivity, and specificity showed a similar trend. Discussion In conclusion, a DR model was successfully developed to predict ES-NSCLC OS based on pre-treatment CT images. The results suggested that the feature importance from DR model is highly correlated to the model prediction power.
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Affiliation(s)
- Rihui Zhang
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Haiming Zhu
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Minbin Chen
- Department of Radiotherapy & Oncology, The First People’s Hospital of Kunshan, Kunshan, Jiangsu, China
| | - Weiwei Sang
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Ke Lu
- Deparment of Radiation Oncology, Duke University, Durham, NC, United States
| | - Zhen Li
- Radiation Oncology Department, Shanghai Sixth People’s Hospital, Shanghai, China
| | - Chunhao Wang
- Deparment of Radiation Oncology, Duke University, Durham, NC, United States
| | - Lei Zhang
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Zhenyu Yang
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
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Sadeghi M, Abdalvand N, Mahdavi SR, Abdollahi H, Qasempour Y, Mohammadian F, Birgani MJT, Hosseini K, Hazbavi M. Magnetic Resonance Image Radiomic Reproducibility: The Impact of Preprocessing on Extracted Features from Gross and High-Risk Clinical Tumor Volumes in Cervical Cancer Patients before Brachytherapy. JOURNAL OF MEDICAL SIGNALS & SENSORS 2024; 14:23. [PMID: 39234589 PMCID: PMC11373798 DOI: 10.4103/jmss.jmss_57_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/09/2022] [Accepted: 03/14/2023] [Indexed: 09/06/2024]
Abstract
Background Radiomic feature reproducibility assessment is critical in radiomics-based image biomarker discovery. This study aims to evaluate the impact of preprocessing parameters on the reproducibility of magnetic resonance image (MRI) radiomic features extracted from gross tumor volume (GTV) and high-risk clinical tumor volume (HR-CTV) in cervical cancer (CC) patients. Methods This study included 99 patients with pathologically confirmed cervical cancer who underwent an MRI prior to receiving brachytherapy. The GTV and HR-CTV were delineated on T2-weighted MRI and inputted into 3D Slicer for radiomic analysis. Before feature extraction, all images were preprocessed to a combination of several parameters of Laplacian of Gaussian (1 and 2), resampling (0.5 and 1), and bin width (5, 10, 25, and 50). The reproducibility of radiomic features was analyzed using the intra-class correlation coefficient (ICC). Results Almost all shapes and first-order features had ICC values > 0.95. Most second-order texture features were not reproducible (ICC < 0.95) in GTV and HR-CTV. Furthermore, 20% of all neighboring gray-tone difference matrix texture features had ICC > 0.90 in both GTV and HR-CTV. Conclusion The results presented here showed that MRI radiomic features are vulnerable to changes in preprocessing, and this issue must be understood and applied before any clinical decision-making. Features with ICC > 0.90 were considered the most reproducible features. Shape and first-order radiomic features were the most reproducible features in both GTV and HR-CTV. Our results also showed that GTV and HR-CTV radiomic features had similar changes against preprocessing sets.
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Affiliation(s)
- Mahdi Sadeghi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Finetech in Medicine Research Center, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Neda Abdalvand
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Student Research Committee, Department of Radiology Technology, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Younes Qasempour
- Student Research Committee, Department of Radiology Technology, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Fatemeh Mohammadian
- Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
| | - Mohammad Javad Tahmasebi Birgani
- Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
- Department of Medical Physics, School of Medicine, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
| | - Khadijeh Hosseini
- Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
| | - Maryam Hazbavi
- Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
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Shimozono T, Shiiba T, Takano K. Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson's disease. Eur Radiol 2024:10.1007/s00330-024-10886-2. [PMID: 38958697 DOI: 10.1007/s00330-024-10886-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 04/08/2024] [Accepted: 04/26/2024] [Indexed: 07/04/2024]
Abstract
OBJECTIVES To clarify the association between a radiomics score (Rad-score) derived from T1-weighted signal intensity to T2-weighted signal intensity (T1-w/T2-w) ratio images and the progression of motor symptoms in Parkinson's disease (PD). MATERIALS AND METHODS This retrospective study included patients with PD enrolled in the Parkinson's Progression Markers Initiative. The Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III score ≥ 33 and/or Hoehn and Yahr stage ≥ 3 indicated motor function decline. The Rad-score was constructed using radiomics features extracted from T1-w/T2-w ratio images. The Kaplan-Meier analysis and Cox regression analyses were used to assess the time differences in motor function decline between the high and low Rad-score groups. RESULTS A total of 171 patients with PD were divided into training (n = 101, mean age at baseline, 61.6 ± 9.3 years) and testing (n = 70, mean age at baseline, 61.6 ± 10 years). The patients in the high Rad-score group had a shorter time to motor function decline than those in the low Rad-score group in the training dataset (log-rank test, p < 0.001) and testing dataset (log-rank test, p < 0.001). The multivariate Cox regression using the Rad-score and clinical factors revealed a significant association between the Rad-score and motor function decline in the training dataset (HR = 2.368, 95%CI:1.423-3.943, p < 0.001) and testing dataset (HR = 2.931, 95%CI:1.472-5.837, p = 0.002). CONCLUSION Rad-scores based on radiomics features derived from T1-w/T2-w ratio images were associated with the progression of motor symptoms in PD. CLINICAL RELEVANCE STATEMENT The radiomics score derived from the T1-weighted/T2-weighted ratio images offers a predictive tool for assessing the progression of motor symptom in patients with PD. KEY POINTS Radiomics score derived from T1-weighted/T2-weighted ratio images is correlated with the motor symptoms of Parkinson's disease. A high radiomics score correlated with faster motor function decline in patients with Parkinson's disease. The proposed radiomics score offers predictive insight into the progression of motor symptoms of Parkinson's disease.
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Affiliation(s)
- Takuya Shimozono
- Department of Neuroimaging and Brain Science, Major in Health Science, Graduate School of Health Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Takuro Shiiba
- Department of Molecular Imaging, Clinical Collaboration Unit, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
| | - Kazuki Takano
- Department of Molecular Imaging, Clinical Collaboration Unit, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
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He W, Cui B, Chu Z, Chen X, Liu J, Pang X, Huang X, Yin H, Lin H, Peng L. Radiomics based on HRCT can predict RP-ILD and mortality in anti-MDA5 + dermatomyositis patients: a multi-center retrospective study. Respir Res 2024; 25:252. [PMID: 38902680 PMCID: PMC11191144 DOI: 10.1186/s12931-024-02843-w] [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: 01/09/2024] [Accepted: 05/08/2024] [Indexed: 06/22/2024] Open
Abstract
OBJECTIVES To assess the effectiveness of HRCT-based radiomics in predicting rapidly progressive interstitial lung disease (RP-ILD) and mortality in anti-MDA5 positive dermatomyositis-related interstitial lung disease (anti-MDA5 + DM-ILD). METHODS From August 2014 to March 2022, 160 patients from Institution 1 were retrospectively and consecutively enrolled and were randomly divided into the training dataset (n = 119) and internal validation dataset (n = 41), while 29 patients from Institution 2 were retrospectively and consecutively enrolled as external validation dataset. We generated four Risk-scores based on radiomics features extracted from four areas of HRCT. A nomogram was established by integrating the selected clinico-radiologic variables and the Risk-score of the most discriminative radiomics model. The RP-ILD prediction performance of the models was evaluated by using the area under the receiver operating characteristic curves, calibration curves, and decision curves. Survival analysis was conducted with Kaplan-Meier curves, Mantel-Haenszel test, and Cox regression. RESULTS Over a median follow-up time of 31.6 months (interquartile range: 12.9-49.1 months), 24 patients lost to follow-up and 46 patients lost their lives (27.9%, 46/165). The Risk-score based on bilateral lungs performed best, attaining AUCs of 0.869 and 0.905 in the internal and external validation datasets. The nomogram outperformed clinico-radiologic model and Risk-score with AUCs of 0.882 and 0.916 in the internal and external validation datasets. Patients were classified into low- and high-risk groups with 50:50 based on nomogram. High-risk group patients demonstrated a significantly higher risk of mortality than low-risk group patients in institution 1 (HR = 4.117) and institution 2 cohorts (HR = 7.515). CONCLUSION For anti-MDA5 + DM-ILD, the nomogram, mainly based on radiomics, can predict RP-ILD and is an independent predictor of mortality.
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Affiliation(s)
- Wenzhang He
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, 610000, China
- Department of Radiology, Chongqing General Hospital, Chongqing University, Chongqing, China
| | - Beibei Cui
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan, 610000, China
| | - Zhigang Chu
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoyi Chen
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, 610000, China
| | - Jing Liu
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, 610000, China
| | - Xueting Pang
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, 610000, China
| | - Xuan Huang
- Biomedical Big Data Center, West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision Medical Technology, Beijing, China
| | - Hui Lin
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan, 610000, China.
| | - Liqing Peng
- Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, 610000, China.
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Gitto S, Cuocolo R, Giannetta V, Badalyan J, Di Luca F, Fusco S, Zantonelli G, Albano D, Messina C, Sconfienza LM. Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1187-1200. [PMID: 38332405 PMCID: PMC11169199 DOI: 10.1007/s10278-024-00999-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 12/17/2023] [Accepted: 12/21/2023] [Indexed: 02/10/2024]
Abstract
Segmentation and image intensity discretization impact on radiomics workflow. The aim of this study is to investigate the influence of interobserver segmentation variability and intensity discretization methods on the reproducibility of MRI-based radiomic features in lipoma and atypical lipomatous tumor (ALT). Thirty patients with lipoma or ALT were retrospectively included. Three readers independently performed manual contour-focused segmentation on T1-weighted and T2-weighted sequences, including the whole tumor volume. Additionally, a marginal erosion was applied to segmentations to evaluate its influence on feature reproducibility. After image pre-processing, with included intensity discretization employing both fixed bin number and width approaches, 1106 radiomic features were extracted from each sequence. Intraclass correlation coefficient (ICC) 95% confidence interval lower bound ≥ 0.75 defined feature stability. In contour-focused vs. margin shrinkage segmentation, the rates of stable features extracted from T1-weighted and T2-weighted images ranged from 92.68 to 95.21% vs. 90.69 to 95.66% after fixed bin number discretization and from 95.75 to 97.65% vs. 95.39 to 96.47% after fixed bin width discretization, respectively, with no difference between the two segmentation approaches (p ≥ 0.175). Higher stable feature rates and higher feature ICC values were found when implementing discretization with fixed bin width compared to fixed bin number, regardless of the segmentation approach (p < 0.001). In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies.
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Affiliation(s)
- Salvatore Gitto
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso 173, 20157, Milan, Italy
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Vincenzo Giannetta
- Diagnostic and Interventional Radiology Department, IRCCS Ospedale San Raffaele-Turro, Università Vita-Salute San Raffaele, Milan, Italy
| | - Julietta Badalyan
- Scuola Di Specializzazione in Statistica Sanitaria E Biometria, Università Degli Studi Di Milano, Milan, Italy
| | - Filippo Di Luca
- Scuola Di Specializzazione in Radiodiagnostica, Università Degli Studi Di Milano, Milan, Italy
| | - Stefano Fusco
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy
| | - Giulia Zantonelli
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso 173, 20157, Milan, Italy
- Dipartimento Di Scienze Biomediche, Chirurgiche Ed Odontoiatriche, Università Degli Studi Di Milano, Milan, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso 173, 20157, Milan, Italy
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso 173, 20157, Milan, Italy.
- Dipartimento Di Scienze Biomediche Per La Salute, Università Degli Studi Di Milano, Milan, Italy.
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8
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Chen X, Wang H, Xia Y, Shi F, He L, Liu E. The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma. Discov Oncol 2024; 15:201. [PMID: 38822860 PMCID: PMC11144178 DOI: 10.1007/s12672-024-01067-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/28/2024] [Indexed: 06/03/2024] Open
Abstract
OBJECTIVE Mitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma. MATERIALS AND METHODS 246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status. RESULTS The classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913-0.921) in the training set and 0.858 (95% CI 0.841-0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927-0.934), 0.915 (95% CI 0.912-0.919), and 0.901 (95% CI 0.900-0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859-0.882), 0.860 (95% CI 0.852-0.872), and 0.820 (95% CI 0.813-0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively. CONCLUSIONS CECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.
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Affiliation(s)
- Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China
| | - Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China
| | - Yuwei Xia
- Shanghai United Imaging Intelligence, Co., Ltd, Shanghai, 200030, China
| | - Feng Shi
- Shanghai United Imaging Intelligence, Co., Ltd, Shanghai, 200030, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, 400014, China.
| | - Enmei Liu
- Department of Respiratory Medicine, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Chongqing, 400014, China.
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9
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Xie H, Song C, Jian L, Guo Y, Li M, Luo J, Li Q, Tan T. A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma. BMC Med Imaging 2024; 24:121. [PMID: 38789936 PMCID: PMC11127329 DOI: 10.1186/s12880-024-01300-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
OBJECTIVES At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT. METHODS A total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model. RESULTS By incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model. CONCLUSIONS Leveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.
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Affiliation(s)
- Hui Xie
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China
| | - Chaoling Song
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Lei Jian
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Yeang Guo
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Mei Li
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Jiang Luo
- School of Medical Imaging, Laboratory Science and Rehabilitation, Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Qing Li
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, Hunan province, 423000, People's Republic of China
| | - Tao Tan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
- Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, Netherlands.
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10
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Koçak B, Yüzkan S, Mutlu S, Karagülle M, Kala A, Kadıoğlu M, Solak S, Sunman Ş, Temiz ZH, Ganiyusufoğlu AK. Influence of image preprocessing on the segmentation-based reproducibility of radiomic features: in vivo experiments on discretization and resampling parameters. Diagn Interv Radiol 2024; 30:152-162. [PMID: 38073244 PMCID: PMC11095065 DOI: 10.4274/dir.2023.232543] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 11/14/2023] [Indexed: 05/15/2024]
Abstract
PURPOSE To systematically investigate the impact of image preprocessing parameters on the segmentation-based reproducibility of magnetic resonance imaging (MRI) radiomic features. METHODS The MRI scans of 50 patients were included from the multi-institutional Brain Tumor Segmentation 2021 public glioma dataset. Whole tumor volumes were manually segmented by two independent readers, with the participation of eight readers. Radiomic features were extracted from two sequences: T2-weighted (T2) and contrast-enhanced T1-weighted (T1ce). Two methods were considered for discretization: bin count (i.e., relative discretization) and bin width (i.e., absolute discretization). Ten discretization (five for each method) and five resampling parameters were varied while other parameters were fixed. The intraclass correlation coefficient (ICC) was used for reliability analysis based on two commonly used cut-off values (0.75 and 0.90). RESULTS Image preprocessing parameters had a significant impact on the segmentation-based reproducibility of radiomic features. The bin width method yielded more reproducible features than the bin count method. In discretization experiments using the bin width on both sequences, according to the ICC cut-off values of 0.75 and 0.90, the rate of reproducible features ranged from 70% to 84% and from 35% to 57%, respectively, with an increasing percentage trend as parameter values decreased (from 84 to 5 for T2; 100 to 6 for T1ce). In the resampling experiments, these ranged from 53% to 74% and from 10% to 20%, respectively, with an increasing percentage trend from lower to higher parameter values (physical voxel size; from 1 x 1 x 1 to 2 x 2 x 2 mm3). CONCLUSION The segmentation-based reproducibility of radiomic features appears to be substantially influenced by discretization and resampling parameters. Our findings indicate that the bin width method should be used for discretization and lower bin width and higher resampling values should be used to allow more reproducible features.
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Affiliation(s)
- Burak Koçak
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Sabahattin Yüzkan
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Samet Mutlu
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Mehmet Karagülle
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Ahmet Kala
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Mehmet Kadıoğlu
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Sıla Solak
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Şeyma Sunman
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Zişan Hayriye Temiz
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Ali Kürşad Ganiyusufoğlu
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
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11
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Huang XS, Dai N, Xu JX, Xiang JY, Zheng XZ, Ke TY, Ma LY, Shi QH, Fan SF. MRI quantitative assessment of the effects of low-carbohydrate therapy on Hashimoto's thyroiditis. Endocr Connect 2024; 13:e230477. [PMID: 38552311 PMCID: PMC11046326 DOI: 10.1530/ec-23-0477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 03/28/2024] [Indexed: 04/24/2024]
Abstract
Objective Hashimoto's thyroiditis is an inflammatory disease, and research suggests that a low-carbohydrate diet may have potential anti-inflammatory effects. This study aims to utilize Dixon-T2-weighted imaging (WI) sequence for a semi-quantitative assessment of the impact of a low-carbohydrate diet on the degree of thyroid inflammation in patients with Hashimoto's thyroiditis. Methods Forty patients with Hashimoto's thyroiditis were recruited for this study and randomly divided into two groups: one with a normal diet and the other with a low-carbohydrate diet. Antibodies against thyroid peroxidase (TPOAb) and thyroglobulin (TgAb) were measured for all participants. Additionally, thyroid water content was semi-quantitatively measured using Dixon-T2WI. The same tests and measurements were repeated for all participants after 6 months. Results After 6 months of a low-carbohydrate diet, patients with Hashimoto's thyroiditis showed a significant reduction in thyroid water content (94.84 ± 1.57% vs 93.07 ± 2.05%, P < 0.05). Concurrently, a decrease was observed in levels of TPOAb and TgAb (TPOAb: 211.30 (92.63-614.62) vs 89.45 (15.9-215.67); TgAb: 17.05 (1.47-81.64) vs 4.1 (0.51-19.42), P < 0.05). In contrast, there were no significant differences in thyroid water content or TPOAb and TgAb levels for patients with Hashimoto's thyroiditis following a normal diet after 6 months (P < 0.05). Conclusion Dixon-T2WI can quantitatively assess the degree of thyroid inflammation in patients with Hashimoto's thyroiditis. Following a low-carbohydrate diet intervention, there is a significant reduction in thyroid water content and a decrease in levels of TPOAb and TgAb. These results suggest that a low-carbohydrate diet may help alleviate inflammation in patients with Hashimoto's thyroiditis.
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Affiliation(s)
- Xiao-Shan Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Ning Dai
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Jian-Xia Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jun-Yi Xiang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiao-Zhong Zheng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Tian-Yu Ke
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Lin-Ying Ma
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Qi-Hao Shi
- Zhejiang Chinese Medical University, Hangzhou, China
| | - Shu-Feng Fan
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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Zhou H, Hua Z, Gao J, Lin F, Chen Y, Zhang S, Zheng T, Wang Z, Shao H, Li W, Liu F, Li Q, Chen J, Wang X, Zhao F, Qu N, Xie H, Ma H, Zhang H, Mao N. Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study. J Magn Reson Imaging 2024; 59:1710-1722. [PMID: 37497811 DOI: 10.1002/jmri.28913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/02/2023] [Accepted: 07/03/2023] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience. PURPOSE To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis. STUDY TYPE Retrospective. POPULATION 1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets. FIELD STRENGTH/SEQUENCE 3.0T/dynamic contrast-enhanced (DCE)-MRI sequence. ASSESSMENT DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data. STATISTICAL TESTS Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value. RESULTS The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation. DATA CONCLUSION The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 3.
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Affiliation(s)
- Heng Zhou
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China
| | - Zhen Hua
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China
| | - Jing Gao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Yuqian Chen
- School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, Shandong, China
| | - Shijie Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Tiantian Zheng
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Zhongyi Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Huafei Shao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Wenjuan Li
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Fengjie Liu
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Qin Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jingjing Chen
- Department of Radiology, Qingdao University Affiliated Hospital, Qingdao, Shandong, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital, Jinan, Shandong, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
| | - Nina Qu
- Department of Ultrasound, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China
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Zhao W, Hu Z, Kazerooni AF, Körzdörfer G, Nittka M, Davatzikos C, Viswanath SE, Wang X, Badve C, Ma D. Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI. Invest Radiol 2024; 59:359-371. [PMID: 37812483 PMCID: PMC10997475 DOI: 10.1097/rli.0000000000001026] [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] [Indexed: 10/10/2023]
Abstract
OBJECTIVE Given the limited repeatability and reproducibility of radiomic features derived from weighted magnetic resonance imaging (MRI), there may be significant advantages to using radiomics in conjunction with quantitative MRI. This study introduces a novel physics-informed discretization (PID) method for reproducible radiomic feature extraction and evaluates its performance using quantitative MRI sequences including magnetic resonance fingerprinting (MRF) and apparent diffusion coefficient (ADC) mapping. MATERIALS AND METHODS A multiscanner, scan-rescan dataset comprising whole-brain 3D quantitative (MRF T1, MRF T2, and ADC) and weighted MRI (T1w MPRAGE, T2w SPACE, and T2w FLAIR) from 5 healthy subjects was prospectively acquired. Subjects underwent 2 repeated acquisitions on 3 distinct 3 T scanners each, for a total of 6 scans per subject (30 total scans). First-order statistical (n = 23) and second-order texture (n = 74) radiomic features were extracted from 56 brain tissue regions of interest using the proposed PID method (for quantitative MRI) and conventional fixed bin number (FBN) discretization (for quantitative MRI and weighted MRI). Interscanner radiomic feature reproducibility was measured using the intraclass correlation coefficient (ICC), and the effect of image sequence (eg, MRF T1 vs T1w MPRAGE), as well as image discretization method (ie, PID vs FBN), on radiomic feature reproducibility was assessed using repeated measures analysis of variance. The robustness of PID and FBN discretization to segmentation error was evaluated by simulating segmentation differences in brainstem regions of interest. Radiomic features with ICCs greater than 0.75 following simulated segmentation were determined to be robust to segmentation. RESULTS First-order features demonstrated higher reproducibility in quantitative MRI than weighted MRI sequences, with 30% (n = 7/23) features being more reproducible in MRF T1 and MRF T2 than weighted MRI. Gray level co-occurrence matrix (GLCM) texture features extracted from MRF T1 and MRF T2 were significantly more reproducible using PID compared with FBN discretization; for all quantitative MRI sequences, PID yielded the highest number of texture features with excellent reproducibility (ICC > 0.9). Comparing texture reproducibility of quantitative and weighted MRI, a greater proportion of MRF T1 (n = 225/370, 61%) and MRF T2 (n = 150/370, 41%) texture features had excellent reproducibility (ICC > 0.9) compared with T1w MPRAGE (n = 148/370, 40%), ADC (n = 115/370, 32%), T2w SPACE (n = 98/370, 27%), and FLAIR (n = 102/370, 28%). Physics-informed discretization was also more robust than FBN discretization to segmentation error, as 46% (n = 103/222, 46%) of texture features extracted from quantitative MRI using PID were robust to simulated 6 mm segmentation shift compared with 19% (n = 42/222, 19%) of weighted MRI texture features extracted using FBN discretization. CONCLUSIONS The proposed PID method yields radiomic features extracted from quantitative MRI sequences that are more reproducible and robust than radiomic features extracted from weighted MRI using conventional (FBN) discretization approaches. Quantitative MRI sequences also demonstrated greater scan-rescan robustness and first-order feature reproducibility than weighted MRI.
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Affiliation(s)
- Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Zheyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Satish E. Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Xiaofeng Wang
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Cleveland, Ohio 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA
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Khodabakhshi Z, Gabrys H, Wallimann P, Guckenberger M, Andratschke N, Tanadini-Lang S. Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization. Phys Imaging Radiat Oncol 2024; 30:100585. [PMID: 38799810 PMCID: PMC11127267 DOI: 10.1016/j.phro.2024.100585] [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: 12/12/2023] [Revised: 04/23/2024] [Accepted: 05/02/2024] [Indexed: 05/29/2024] Open
Abstract
Background and purpose Magnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs). Materials and methods Two T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient >0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance. Results Applying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization. Conclusions To develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.
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Affiliation(s)
- Zahra Khodabakhshi
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Hubert Gabrys
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Philipp Wallimann
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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Ansari G, Mirza-Aghazadeh-Attari M, Afyouni S, Mohseni A, Shahbazian H, Kamel IR. Utilization of texture features of volumetric ADC maps in differentiating between serous cystadenoma and intraductal papillary neoplasms. Abdom Radiol (NY) 2024; 49:1175-1184. [PMID: 38378839 DOI: 10.1007/s00261-024-04187-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 01/04/2024] [Accepted: 01/07/2024] [Indexed: 02/22/2024]
Abstract
INTRODUCTION The rising incidence of incidental detection of pancreatic cystic neoplasms has compelled radiologists to determine new diagnostic methods for the differentiation of various kinds of lesions. We aim to demonstrate the utility of texture features extracted from ADC maps in differentiating intraductal papillary mucinous neoplasms (IPMN) from serous cystadenomas (SCA). METHODS This retrospective study was performed on 136 patients (IPMN = 87, SCA = 49) split into testing and training datasets. A total of 851 radiomics features were extracted from volumetric contours drawn by an expert radiologist on ADC maps of the lesions. LASSO regression analysis was used to determine the most predictive set of features and a radiomics score was developed based on their respective coefficients. A hyper-optimized support vector machine was then utilized to classify the lesions based on their radiomics score. RESULTS A total of four Wavelet features (LHL/GLCM/LCM2, HLL/GLCM/LCM2, /LLL/First Order/90percent, /LLL/GLCM/MCC) were selected from all of the features to be included in our classifier. The classifier was optimized by altering hyperparameters and the trained model was applied to the validation dataset. The model achieved a sensitivity of 92.8, specificity of 90%, and an AUC of 0.97 in the training data set, and a sensitivity of 83.3%, specificity of 66.7%, and AUC of 0.90 in the testing dataset. CONCLUSION A support vector machine model trained and validated on volumetric texture features extracted from ADC maps showed the possible beneficence of these features in differentiating IPMNs from SCAs. These results are in line with previous regarding the role of ADC maps in classifying cystic lesions and offers new evidence regarding the role of texture features in differentiation of potentially neoplastic and benign lesions.
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Affiliation(s)
- Golnoosh Ansari
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Mohammad Mirza-Aghazadeh-Attari
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Shadi Afyouni
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Alireza Mohseni
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Haneyeh Shahbazian
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Ihab R Kamel
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA.
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Riley BA, Stevens JB, Li X, Yang Z, Wang C, Mowery YM, Brizel DM, Yin FF, Lafata KJ. Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma. J Med Imaging (Bellingham) 2024; 11:024007. [PMID: 38549835 PMCID: PMC10966359 DOI: 10.1117/1.jmi.11.2.024007] [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: 09/06/2023] [Revised: 02/29/2024] [Accepted: 03/04/2024] [Indexed: 07/09/2024] Open
Abstract
Purpose We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer. Approach A prospective clinical trial (NCT01908504) enrolled patients with oropharyngeal squamous cell carcinoma (N = 69 ; mixed HPV status) undergoing definitive radiotherapy and evaluated intra-treatment 18fluorodeoxyglucose PET as a potential imaging biomarker of early metabolic response. The primary tumor volume was manually segmented by a radiation oncologist on PET/CT images acquired two weeks into treatment (20 Gy). From this, 54 radiomic texture features were extracted. Two image discretization techniques-fixed bin number (FBN) and fixed bin size (FBS)-were considered to evaluate systematic changes in the bin number ({32, 64, 128, 256} gray levels) and bin size ({0.10, 0.15, 0.22, 0.25} bin-widths). For each discretization-specific radiomic feature space, an LASSO-regularized logistic regression model was independently trained to predict residual and/or recurrent disease. The model training was based on Monte Carlo cross-validation with a 20% testing hold-out, 50 permutations, and minor-class up-sampling to account for imbalanced outcomes data. Performance differences among the discretization-specific models were quantified via receiver operating characteristic curve analysis. A final parameter-optimized logistic regression model was developed by incorporating different settings parameterizations into the same model. Results FBN outperformed FBS in predicting residual and/or recurrent disease. The four FBN models achieved AUC values of 0.63, 0.61, 0.65, and 0.62 for 32, 64, 128, and 256 gray levels, respectively. By contrast, the average AUC of the four FBS models was 0.53. The parameter-optimized model, comprising features joint entropy (FBN = 64) and information measure correlation 1 (FBN = 128), achieved an AUC of 0.70. Kaplan-Meier analyses identified these features to be associated with disease-free survival (p = 0.0158 and p = 0.0180 , respectively; log-rank test). Conclusions Our findings suggest that the prognostic value of individual radiomic features may depend on feature-specific discretization parameter settings.
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Affiliation(s)
- Breylon A. Riley
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Jack B. Stevens
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Xiang Li
- Duke University Pratt School of Engineering, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
| | - Zhenyu Yang
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Chunhao Wang
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Yvonne M. Mowery
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
- University of Pittsburgh, UPMC Hillman Cancer Center, Department of Radiation Oncology, Pittsburgh, North Carolina, United States
| | - David M. Brizel
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
- Duke University School of Medicine, Department of Head and Neck Surgery and Communication Sciences, Durham, North Carolina, United States
| | - Fang-Fang Yin
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
| | - Kyle J. Lafata
- Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
- Duke University Pratt School of Engineering, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Radiology, Durham, North Carolina, United States
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Santinha J, Katsaros V, Stranjalis G, Liouta E, Boskos C, Matos C, Viegas C, Papanikolaou N. Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:31-44. [PMID: 38343254 DOI: 10.1007/s10278-023-00918-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/08/2023] [Accepted: 08/23/2023] [Indexed: 03/02/2024]
Abstract
Radiogenomics has shown potential to predict genomic phenotypes from medical images. The development of models using standard-of-care pre-operative MRI images, as opposed to advanced MRI images, enables a broader reach of such models. In this work, a radiogenomics model for IDH mutation status prediction from standard-of-care MRIs in patients with glioma was developed and validated using multicentric data. A cohort of 142 (wild-type: 32.4%) patients with glioma retrieved from the TCIA/TCGA was used to train a logistic regression model to predict the IDH mutation status. The model was evaluated using retrospective data collected in two distinct hospitals, comprising 36 (wild-type: 63.9%) and 53 (wild-type: 75.5%) patients. Model development utilized ROC analysis. Model discrimination and calibration were used for validation. The model yielded an AUC of 0.741 vs. 0.716 vs. 0.938, a sensitivity of 0.784 vs. 0.739 vs. 0.875, and a specificity of 0.657 vs. 0.692 vs. 1.000 on the training, test cohort 1, and test cohort 2, respectively. The assessment of model fairness suggested an unbiased model for age and sex, and calibration tests showed a p < 0.05. These results indicate that the developed model allows the prediction of the IDH mutation status in gliomas using standard-of-care MRI images and does not appear to hold sex and age biases.
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Affiliation(s)
- João Santinha
- Computational Clinical Imaging Group, Champalimaud Research , Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal.
- Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal.
| | - Vasileios Katsaros
- Department of Radiology, General Anti-Cancer and Oncological Hospital of Athens, St. Savvas, Athens, Greece
| | - George Stranjalis
- Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece
- Hellenic Center for Neurosurgical Research "Prof. Petros Kokkalis", Athens, Greece
- Athens Microneurosurgery Laboratory, Athens, Greece
| | - Evangelia Liouta
- Department of Neurosurgery, National and Kapodistrian University of Athens, Evangelismos Hospital, Athens, Greece
- Hellenic Center for Neurosurgical Research "Prof. Petros Kokkalis", Athens, Greece
| | - Christos Boskos
- Athens Microneurosurgery Laboratory, Athens, Greece
- IATROPOLIS CyberKnife Center, Hellenic Neuro-Oncology Society, Chalandri, Greece
| | - Celso Matos
- Radiology Department, Champalimaud Clinical Centre, Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal
| | - Catarina Viegas
- Department of Neurosurgery, Hospital Garcia de Orta, Almada, Portugal
| | - Nickolas Papanikolaou
- Computational Clinical Imaging Group, Champalimaud Research , Champalimaud Foundation, Av. Brasília, 1400-038, Lisbon, Portugal
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Du P, Liu X, Xiang R, Lv K, Chen H, Liu W, Cao A, Chen L, Wang X, Yu T, Ding J, Li W, Li J, Li Y, Yu Z, Zhu L, Liu J, Geng D. Development and validation of a radiomics-based prediction pipeline for the response to stereotactic radiosurgery therapy in brain metastases. Eur Radiol 2023; 33:8925-8935. [PMID: 37505244 DOI: 10.1007/s00330-023-09930-4] [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: 04/30/2022] [Revised: 03/31/2023] [Accepted: 05/02/2023] [Indexed: 07/29/2023]
Abstract
OBJECTIVES The first treatment strategy for brain metastases (BM) plays a pivotal role in the prognosis of patients. Among all strategies, stereotactic radiosurgery (SRS) is considered a promising therapy method. Therefore, we developed and validated a radiomics-based prediction pipeline to prospectively identify BM patients who are insensitive to SRS therapy, especially those who are at potential risk of progressive disease. METHODS A total of 337 BM patients (277, 30, and 30 in the training set, internal validation set, and external validation set, respectively) were enrolled in the study. 19,377 radiomics features (3 masks × 3 MRI sequences × 2153 features) extracted from 9 ROIs were filtered through LASSO and Max-Relevance and Min-Redundancy (mRMR) algorithms. The selected radiomics features were combined with 4 clinical features to construct a two-stage cascaded model for the prediction of BM patients' response to SRS therapy using SVM and an ensemble learning classifier. The performance of the model was evaluated by its accuracy, specificity, sensitivity, and AUC curve. RESULTS Radiomics features were integrated with the clinical features of patients in our optimal model, which showed excellent discriminative performance in the training set (AUC: 0.95, 95% CI: 0.88-0.98). The model was also verified in the internal validation set and external validation set (AUC 0.93, 95% CI: 0.76-0.95 and AUC 0.90, 95% CI: 0.73-0.93, respectively). CONCLUSIONS The proposed prediction pipeline could non-invasively predict the response to SRS therapy in patients with brain metastases thus assisting doctors to precisely designate individualized first treatment decisions. CLINICAL RELEVANCE STATEMENT The proposed prediction pipeline combines the radiomics features of multi-modal MRI with clinical features to construct machine learning models that noninvasively predict the response of patients with brain metastases to stereotactic radiosurgery therapy, assisting neuro-oncologists to develop personalized first treatment plans. KEY POINTS • The proposed prediction pipeline can non-invasively predict the response to SRS therapy. • The combination of multi-modality and multi-mask contributes significantly to the prediction. • The edema index also shows a certain predictive value.
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Affiliation(s)
- Peng Du
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao Liu
- School of Computer and Information Technology, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing, 100044, China
| | - Rui Xiang
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Kun Lv
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China
| | - Hongyi Chen
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Weifan Liu
- Department of Mathematics, Syracuse University, Syracuse, NY, USA
| | - Aihong Cao
- Department of Radiology, the Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Lang Chen
- Department of Radiology, the Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Xuefeng Wang
- Department of Radiotherapy, the Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Tonggang Yu
- Department of Radiology, Shanghai Gamma Hospital, Huashan Hospital, Fudan University, Shanghai, China
| | - Jian Ding
- Department of Radiology, Shanghai Gamma Hospital, Huashan Hospital, Fudan University, Shanghai, China
| | - Wuchao Li
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China
| | - Jie Li
- Department of Gynecology, Jinan Central Hospital, Jinan, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Huashan Hospital, Fudan University, Shanghai, China
- Department of Mathematics, Syracuse University, Syracuse, NY, USA
| | - Zekuan Yu
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Department of Mathematics, Syracuse University, Syracuse, NY, USA
| | - Li Zhu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, 241 West Huaihai Road, Shanghai, 200030, China.
| | - Jie Liu
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Huashan Hospital, Fudan University, Shanghai, China.
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China.
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Huashan Hospital, Fudan University, Shanghai, China.
- Academy for Engineering and Technology, Fudan University, Shanghai, China.
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Dewi DEO, Sunoqrot MRS, Nketiah GA, Sandsmark E, Giskeødegård GF, Langørgen S, Bertilsson H, Elschot M, Bathen TF. The impact of pre-processing and disease characteristics on reproducibility of T2-weighted MRI radiomics features. MAGMA (NEW YORK, N.Y.) 2023; 36:945-956. [PMID: 37556085 PMCID: PMC10667400 DOI: 10.1007/s10334-023-01112-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/11/2023] [Accepted: 07/25/2023] [Indexed: 08/10/2023]
Abstract
PURPOSE To evaluate the reproducibility of radiomics features derived via different pre-processing settings from paired T2-weighted imaging (T2WI) prostate lesions acquired within a short interval, to select the setting that yields the highest number of reproducible features, and to evaluate the impact of disease characteristics (i.e., clinical variables) on features reproducibility. MATERIALS AND METHODS A dataset of 50 patients imaged using T2WI at 2 consecutive examinations was used. The dataset was pre-processed using 48 different settings. A total of 107 radiomics features were extracted from manual delineations of 74 lesions. The inter-scan reproducibility of each feature was measured using the intra-class correlation coefficient (ICC), with ICC values > 0.75 considered good. Statistical differences were assessed using Mann-Whitney U and Kruskal-Wallis tests. RESULTS The pre-processing parameters strongly influenced the reproducibility of radiomics features of T2WI prostate lesions. The setting that yielded the highest number of features (25 features) with high reproducibility was the relative discretization with a fixed bin number of 64, no signal intensity normalization, and outlier filtering by excluding outliers. Disease characteristics did not significantly impact the reproducibility of radiomics features. CONCLUSION The reproducibility of T2WI radiomics features was significantly influenced by pre-processing parameters, but not by disease characteristics. The selected pre-processing setting yielded 25 reproducible features.
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Affiliation(s)
- Dyah Ekashanti Octorina Dewi
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, 7030, Trondheim, Norway
| | - Mohammed R S Sunoqrot
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, 7030, Trondheim, Norway.
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway.
| | - Gabriel Addio Nketiah
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, 7030, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway
| | - Elise Sandsmark
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway
| | - Guro F Giskeødegård
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, 7030, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, NTNU-Norwegian University of Science and Technology, 7030, Trondheim, Norway
| | - Sverre Langørgen
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway
| | - Helena Bertilsson
- Department of Cancer Research and Molecular Medicine, NTNU-Norwegian University of Science and Technology, 7030, Trondheim, Norway
- Department of Urology, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, 7030, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway
| | - Tone Frost Bathen
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, 7030, Trondheim, Norway.
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030, Trondheim, Norway.
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Kremer LE, Chapman AB, Armato SG. Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype. J Med Imaging (Bellingham) 2023; 10:064503. [PMID: 38156331 PMCID: PMC10752557 DOI: 10.1117/1.jmi.10.6.064503] [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: 06/22/2023] [Revised: 11/08/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023] Open
Abstract
Purpose Our study aims to investigate the impact of preprocessing on magnetic resonance imaging (MRI) radiomic features extracted from the noncystic kidney parenchyma of patients with autosomal dominant polycystic kidney disease (ADPKD) in the task of classifying PKD1 versus PKD2 genotypes, which differ with regard to cyst burden and disease outcome. Approach The effect of preprocessing on radiomic features was investigated using a single T2-weighted fat saturated (T2W-FS) MRI scan from PKD1 and PKD2 subjects (29 kidneys in total) from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study. Radiomic feature reproducibility using the intraclass correlation coefficient (ICC) was computed across MRI normalizations (z -score, reference-tissue, and original image), gray-level discretization, and upsampling and downsampling pixel schemes. A second dataset for genotype classification from 136 subjects T2W-FS MRI images previously enrolled in the HALT Progression of Polycystic Kidney Disease study was matched for age, gender, and Mayo imaging classification class. Genotype classification was performed using a logistic regression classifier and radiomic features extracted from (1) the noncystic kidney parenchyma and (2) the entire kidney. The area under the receiver operating characteristic curve (AUC) was used to evaluate the classification performance across preprocessing methods. Results Radiomic features extracted from the noncystic kidney parenchyma were sensitive to preprocessing parameters, with varying reproducibility depending on the parameter. The percentage of features with good-to-excellent ICC scores ranged from 14% to 58%. AUC values ranged between 0.47 to 0.68 and 0.56 to 0.73 for the noncystic kidney parenchyma and entire kidney, respectively. Conclusions Reproducibility of radiomic features extracted from the noncystic kidney parenchyma was dependent on the preprocessing parameters used, and the effect on genotype classification was sensitive to preprocessing parameters. The results suggest that texture features may be indicative of genotype expression in ADPKD.
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Affiliation(s)
- Linnea E. Kremer
- The University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States
| | - Arlene B. Chapman
- The University of Chicago, Department of Medicine, Chicago, Illinois, United States
| | - Samuel G. Armato
- The University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States
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Yang Z, Wang C, Wang Y, Lafata KJ, Zhang H, Ackerson BG, Kelsey C, Tong B, Yin FF. Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients. Front Oncol 2023; 13:1185771. [PMID: 37781201 PMCID: PMC10534017 DOI: 10.3389/fonc.2023.1185771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/21/2023] [Indexed: 10/03/2023] Open
Abstract
Objective To develop a Multi-Feature-Combined (MFC) model for proof-of-concept in predicting local failure (LR) in NSCLC patients after surgery or SBRT using pre-treatment CT images. This MFC model combines handcrafted radiomic features, deep radiomic features, and patient demographic information in an integrated machine learning workflow. Methods The MFC model comprised three key steps. (1) Extraction of 92 handcrafted radiomic features from the GTV segmented on pre-treatment CT images. (2) Extraction of 512 deep radiomic features from pre-trained U-Net encoder. (3) The extracted handcrafted radiomic features, deep radiomic features, along with 4 patient demographic information (i.e., gender, age, tumor volume, and Charlson comorbidity index), were concatenated as a multi-dimensional input to the classifiers for LR prediction. Two NSCLC patient cohorts from our institution were investigated: (1) the surgery cohort includes 83 patients with segmentectomy or wedge resection (7 LR), and (2) the SBRT cohort includes 84 patients with lung SBRT (9 LR). The MFC model was developed and evaluated independently for both cohorts, and was subsequently compared against the prediction models based on only handcrafted radiomic features (R models), patient demographic information (PI models), and deep learning modeling (DL models). ROC with AUC was adopted to evaluate model performance with leave-one-out cross-validation (LOOCV) and 100-fold Monte Carlo random validation (MCRV). The t-test was performed to identify the statistically significant differences. Results In LOOCV, the AUC range (surgery/SBRT) of the MFC model was 0.858-0.895/0.868-0.913, which was higher than the three other models: 0.356-0.480/0.322-0.650 for PI models, 0.559-0.618/0.639-0.682 for R models, and 0.809/0.843 for DL models. In 100-fold MCRV, the MFC model again showed the highest AUC results (surgery/SBRT): 0.742-0.825/0.888-0.920, which were significantly higher than PI models: 0.464-0.564/0.538-0.628, R models: 0.557-0.652/0.551-0.732, and DL models: 0.702/0.791. Conclusion We successfully developed an MFC model that combines feature information from multiple sources for proof-of-concept prediction of LR in patients with surgical and SBRT early-stage NSCLC. Initial results suggested that incorporating pre-treatment patient information from multiple sources improves the ability to predict the risk of local failure.
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Affiliation(s)
- Zhenyu Yang
- Department of Radiation Oncology, Duke University, Durham, NC, United States
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
- Medical Physics Graduate Program, Duke University, Durham, NC, United States
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University, Durham, NC, United States
| | - Yuqi Wang
- Medical Physics Graduate Program, Duke University, Durham, NC, United States
| | - Kyle J. Lafata
- Department of Radiation Oncology, Duke University, Durham, NC, United States
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States
- Department of Radiology, Duke University, Durham, NC, United States
| | - Haozhao Zhang
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Bradley G. Ackerson
- Department of Radiation Oncology, Duke University, Durham, NC, United States
| | - Christopher Kelsey
- Department of Radiation Oncology, Duke University, Durham, NC, United States
| | - Betty Tong
- Department of Surgery, Duke University, Durham, NC, United States
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University, Durham, NC, United States
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
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Waldenberg C, Brisby H, Hebelka H, Lagerstrand KM. Associations between Vertebral Localized Contrast Changes and Adjacent Annular Fissures in Patients with Low Back Pain: A Radiomics Approach. J Clin Med 2023; 12:4891. [PMID: 37568293 PMCID: PMC10420134 DOI: 10.3390/jcm12154891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 08/13/2023] Open
Abstract
Low back pain (LBP) is multifactorial and associated with various spinal tissue changes, including intervertebral disc fissures, vertebral pathology, and damaged endplates. However, current radiological markers lack specificity and individualized diagnostic capability, and the interactions between the various markers are not fully clear. Radiomics, a data-driven analysis of radiological images, offers a promising approach to improve evaluation and deepen the understanding of spinal changes related to LBP. This study investigated possible associations between vertebral changes and annular fissures using radiomics. A dataset of 61 LBP patients who underwent conventional magnetic resonance imaging followed by discography was analyzed. Radiomics features were extracted from segmented vertebrae and carefully reduced to identify the most relevant features associated with annular fissures. The results revealed three important texture features that display concentrated high-intensity gray levels, extensive regions with elevated gray levels, and localized areas with reduced gray levels within the vertebrae. These features highlight patterns within vertebrae that conventional classification systems cannot reflect on distinguishing between vertebrae adjacent to an intervertebral disc with or without an annular fissure. As such, the present study reveals associations that contribute to the understanding of pathophysiology and may provide improved diagnostics of LBP.
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Affiliation(s)
- Christian Waldenberg
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden;
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Helena Brisby
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Orthopaedics, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Hanna Hebelka
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Radiology, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Kerstin Magdalena Lagerstrand
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden;
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
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23
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Rezaeijo SM, Chegeni N, Baghaei Naeini F, Makris D, Bakas S. Within-Modality Synthesis and Novel Radiomic Evaluation of Brain MRI Scans. Cancers (Basel) 2023; 15:3565. [PMID: 37509228 PMCID: PMC10377568 DOI: 10.3390/cancers15143565] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 06/27/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
One of the most common challenges in brain MRI scans is to perform different MRI sequences depending on the type and properties of tissues. In this paper, we propose a generative method to translate T2-Weighted (T2W) Magnetic Resonance Imaging (MRI) volume from T2-weight-Fluid-attenuated-Inversion-Recovery (FLAIR) and vice versa using Generative Adversarial Networks (GAN). To evaluate the proposed method, we propose a novel evaluation schema for generative and synthetic approaches based on radiomic features. For the evaluation purpose, we consider 510 pair-slices from 102 patients to train two different GAN-based architectures Cycle GAN and Dual Cycle-Consistent Adversarial network (DC2Anet). The results indicate that generative methods can produce similar results to the original sequence without significant change in the radiometric feature. Therefore, such a method can assist clinics to make decisions based on the generated image when different sequences are not available or there is not enough time to re-perform the MRI scans.
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Affiliation(s)
- Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; (S.M.R.)
| | - Nahid Chegeni
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; (S.M.R.)
| | - Fariborz Baghaei Naeini
- Faculty of Engineering, Computing and the Environment, Kingston University, Penrhyn Road Campus, Kingston upon Thames, London KT1 2EE, UK; (F.B.N.); (D.M.)
| | - Dimitrios Makris
- Faculty of Engineering, Computing and the Environment, Kingston University, Penrhyn Road Campus, Kingston upon Thames, London KT1 2EE, UK; (F.B.N.); (D.M.)
| | - Spyridon Bakas
- Faculty of Engineering, Computing and the Environment, Kingston University, Penrhyn Road Campus, Kingston upon Thames, London KT1 2EE, UK; (F.B.N.); (D.M.)
- Richards Medical Research Laboratories, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Floor 7, 3700 Hamilton Walk, Philadelphia, PA 19104, USA
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24
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Kiser K, Zhang J, Kim SG. Textural Features of Mouse Glioma Models Measured by Dynamic Contrast-Enhanced MR Images with 3D Isotropic Resolution. Tomography 2023; 9:721-735. [PMID: 37104129 PMCID: PMC10141208 DOI: 10.3390/tomography9020058] [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: 02/04/2023] [Revised: 03/20/2023] [Accepted: 03/23/2023] [Indexed: 04/28/2023] Open
Abstract
This paper investigates the effect of anisotropic resolution on the image textural features of pharmacokinetic (PK) parameters of a murine glioma model using dynamic contrast-enhanced (DCE) MR images acquired with an isotropic resolution at 7T with pre-contrast T1 mapping. The PK parameter maps of whole tumors at isotropic resolution were generated using the two-compartment exchange model combined with the three-site-two-exchange model. The textural features of these isotropic images were compared with those of simulated, thick-slice, anisotropic images to assess the influence of anisotropic voxel resolution on the textural features of tumors. The isotropic images and parameter maps captured distributions of high pixel intensity that were absent in the corresponding anisotropic images with thick slices. A significant difference was observed in 33% of the histogram and textural features extracted from anisotropic images and parameter maps, compared to those extracted from corresponding isotropic images. Anisotropic images in different orthogonal orientations demonstrated 42.1% of the histogram and textural features to be significantly different from those of isotropic images. This study demonstrates that the anisotropy of voxel resolution needs to be carefully considered when comparing the textual features of tumor PK parameters and contrast-enhanced images.
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Affiliation(s)
- Karl Kiser
- Department of Radiology, Weill Cornell Medical College, New York, NY 10065, USA
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25
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Zhong JG, Shi L, Liu J, Cao F, Ma YQ, Zhang Y. Predicting prostate cancer in men with PSA levels of 4-10 ng/mL: MRI-based radiomics can help junior radiologists improve the diagnostic performance. Sci Rep 2023; 13:4846. [PMID: 36964192 PMCID: PMC10038986 DOI: 10.1038/s41598-023-31869-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 03/20/2023] [Indexed: 03/26/2023] Open
Abstract
To develop MRI-based radiomics model for predicting prostate cancer (PCa) in men with prostate-specific antigen (PSA) levels of 4-10 ng/mL, to compare the performance of radiomics model and PI-RADS v2.1, and to further verify the predictive ability of radiomics model for lesions with different PI-RADS v2.1 score. 171 patients with PSA levels of 4-10 ng/mL were divided into training (n = 119) and testing (n = 52) groups. PI-RADS v2.1 score was assessed by two radiologists. All volumes of interest were segmented on T2-weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences, from which quantitative radiomics features were extracted. Multivariate logistic regression analysis was performed to establish radiomics model for predicting PCa. The diagnostic performance was assessed using receiver operating characteristic curve analysis. The radiomics model exhibited the best performance in predicting PCa, which was better than the performance of PI-RADS v2.1 scoring by the junior radiologist in the training group [area under the curve (AUC): 0.932 vs 0.803], testing group (AUC: 0.922 vs 0.797), and the entire cohort (AUC: 0.927 vs 0.801) (P < 0.05). The radiomics model performed well for lesions with PI-RADS v2.1 score of 3 (AUC = 0.854, sensitivity = 84.62%, specificity = 84.34%) and PI-RADS v2.1 score of 4-5 (AUC = 0.967, sensitivity = 98.11%, specificity = 86.36%) assigned by junior radiologist. The radiomics model quantitatively outperformed PI-RADS v2.1 for noninvasive prediction of PCa in men with PSA levels of 4-10 ng/mL. The model can help improve the diagnostic performance of junior radiologists and facilitate better decision-making by urologists for management of lesions with different PI-RADS v2.1 score.
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Affiliation(s)
- Jian-Guo Zhong
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Lin Shi
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jing Liu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Fang Cao
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yan-Qing Ma
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yang Zhang
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
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26
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Elmahdy M, Sebro R. Radiomics analysis in medical imaging research. J Med Radiat Sci 2023; 70:3-7. [PMID: 36762402 PMCID: PMC9977659 DOI: 10.1002/jmrs.662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/21/2023] [Indexed: 02/11/2023] Open
Abstract
This article discusses the current research in the field of radiomics in medical imaging with emphasis on its role in fighting coronavirus disease 2019 (COVID-19). This article covers the building of radiomic models in a simple straightforward manner, while discussing radiomic models potential to help us face this pandemic.
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Affiliation(s)
- Mahmoud Elmahdy
- Department of RadiologyMayo ClinicJacksonvilleFloridaUSA,Center for Augmented IntelligenceMayo ClinicJacksonvilleFloridaUSA
| | - Ronnie Sebro
- Department of RadiologyMayo ClinicJacksonvilleFloridaUSA,Center for Augmented IntelligenceMayo ClinicJacksonvilleFloridaUSA,Department of Orthopedic SurgeryMayo ClinicJacksonvilleFloridaUSA,Department of BiostatisticsCentre for Quantitative Health SciencesJacksonvilleFloridaUSA
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27
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Wichtmann BD, Harder FN, Weiss K, Schönberg SO, Attenberger UI, Alkadhi H, Pinto Dos Santos D, Baeßler B. Influence of Image Processing on Radiomic Features From Magnetic Resonance Imaging. Invest Radiol 2023; 58:199-208. [PMID: 36070524 DOI: 10.1097/rli.0000000000000921] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Before implementing radiomics in routine clinical practice, comprehensive knowledge about the repeatability and reproducibility of radiomic features is required. The aim of this study was to systematically investigate the influence of image processing parameters on radiomic features from magnetic resonance imaging (MRI) in terms of feature values as well as test-retest repeatability. MATERIALS AND METHODS Utilizing a phantom consisting of 4 onions, 4 limes, 4 kiwifruits, and 4 apples, we acquired a test-retest dataset featuring 3 of the most commonly used MRI sequences on a 3 T scanner, namely, a T1-weighted, a T2-weighted, and a fluid-attenuated inversion recovery sequence, each at high and low resolution. After semiautomatic image segmentation, image processing with systematic variation of image processing parameters was performed, including spatial resampling, intensity discretization, and intensity rescaling. For each respective image processing setting, a total of 45 radiomic features were extracted, corresponding to the following 7 matrices/feature classes: conventional indices, histogram matrix, shape matrix, gray-level zone length matrix, gray-level run length matrix, neighboring gray-level dependence matrix, and gray-level cooccurrence matrix. Systematic differences of individual features between different resampling steps were assessed using 1-way analysis of variance with Tukey-type post hoc comparisons to adjust for multiple testing. Test-retest repeatability of radiomic features was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient. RESULTS Image processing influenced radiological feature values. Regardless of the acquired sequence and feature class, significant differences ( P < 0.05) in feature values were found when the size of the resampled voxels was too large, that is, bigger than 3 mm. Almost all higher-order features depended strongly on intensity discretization. The effects of intensity rescaling were negligible except for some features derived from T1-weighted sequences. For all sequences, the percentage of repeatable features (concordance correlation coefficient and dynamic range ≥ 0.9) varied considerably depending on the image processing settings. The optimal image processing setting to achieve the highest percentage of stable features varied per sequence. Irrespective of image processing, the fluid-attenuated inversion recovery sequence in high-resolution overall yielded the highest number of stable features in comparison with the other sequences (89% vs 64%-78% for the respective optimal image processing settings). Across all sequences, the most repeatable features were generally obtained for a spatial resampling close to the originally acquired voxel size and an intensity discretization to at least 32 bins. CONCLUSION Variation of image processing parameters has a significant impact on the values of radiomic features as well as their repeatability. Furthermore, the optimal image processing parameters differ for each MRI sequence. Therefore, it is recommended that these processing parameters be determined in corresponding test-retest scans before clinical application. Extensive repeatability, reproducibility, and validation studies as well as standardization are required before quantitative image analysis and radiomics can be reliably translated into routine clinical care.
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Affiliation(s)
- Barbara D Wichtmann
- From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Felix N Harder
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | | | - Stefan O Schönberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Germany
| | - Ulrike I Attenberger
- From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
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28
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Ubaldi L, Saponaro S, Giuliano A, Talamonti C, Retico A. Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning. Phys Med 2023; 107:102538. [PMID: 36796177 DOI: 10.1016/j.ejmp.2023.102538] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 01/15/2023] [Accepted: 01/31/2023] [Indexed: 02/16/2023] Open
Abstract
PURPOSE Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas. METHODS The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings. RESULTS The results show that using MRI-reliable features improves the performance in glioma grade classification (AUC=0.93±0.05) with respect to the use of raw (AUC=0.88±0.08) and robust features (AUC=0.83±0.08), defined as those not depending on image normalization and intensity discretization. CONCLUSIONS These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out.
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Affiliation(s)
- Leonardo Ubaldi
- National Institute for Nuclear Physics (INFN), Firenze Division, Firenze, Italy, Firenze, Italy; Department Biomedical Experimental and Clinical Science "Mario Serio", Univeristy of Firenze, Firenze, Italy
| | - Sara Saponaro
- University of Pisa, Pisa, Italy; National Institute for Nuclear Physics (INFN), Pisa Division, Pisa, Italy.
| | | | - Cinzia Talamonti
- National Institute for Nuclear Physics (INFN), Firenze Division, Firenze, Italy, Firenze, Italy; Department Biomedical Experimental and Clinical Science "Mario Serio", Univeristy of Firenze, Firenze, Italy
| | - Alessandra Retico
- National Institute for Nuclear Physics (INFN), Pisa Division, Pisa, Italy
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29
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Salome P, Sforazzini F, Grugnara G, Kudak A, Dostal M, Herold-Mende C, Heiland S, Debus J, Abdollahi A, Knoll M. MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma. Cancers (Basel) 2023; 15:cancers15030965. [PMID: 36765922 PMCID: PMC9913466 DOI: 10.3390/cancers15030965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models' performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). METHODS MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods' performance. RESULTS Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13); however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman's rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. CONCLUSION The IN method impacted the predictive power of survival models; thus, performance is sequence-dependent.
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Affiliation(s)
- Patrick Salome
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- Heidelberg Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Correspondence: (P.S.); (M.K.)
| | - Francesco Sforazzini
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- Heidelberg Medical Faculty, Heidelberg University, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
| | - Gianluca Grugnara
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Andreas Kudak
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- CCU Radiation Therapy, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
| | - Matthias Dostal
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- CCU Radiation Therapy, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
| | - Christel Herold-Mende
- Brain Tumour Group, European Organization for Research and Treatment of Cancer, 1200 Brussels, Belgium
- Division of Neurosurgical Research, Department of Neurosurgery, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, 69120 Heidelberg, Germany
| | - Jürgen Debus
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
| | - Amir Abdollahi
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
| | - Maximilian Knoll
- Clinical Cooperation Unit (CCU) Radiation Oncology, German Cancer Research Centre, INF 280, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK) Core Centre Heidelberg, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), INF 450, 69120 Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, INF 400, 69120 Heidelberg, Germany
- Correspondence: (P.S.); (M.K.)
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30
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Huang EP, O'Connor JPB, McShane LM, Giger ML, Lambin P, Kinahan PE, Siegel EL, Shankar LK. Criteria for the translation of radiomics into clinically useful tests. Nat Rev Clin Oncol 2023; 20:69-82. [PMID: 36443594 PMCID: PMC9707172 DOI: 10.1038/s41571-022-00707-0] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2022] [Indexed: 11/29/2022]
Abstract
Computer-extracted tumour characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an extension of CAD involving high-throughput computer-extracted quantitative characterization of healthy or pathological structures and processes as captured by medical imaging, interest in such computer-extracted measurements has increased substantially. However, despite the thousands of radiomic studies, the number of settings in which radiomics has been successfully translated into a clinically useful tool or has obtained FDA clearance is comparatively small. This relative dearth might be attributable to factors such as the varying imaging and radiomic feature extraction protocols used from study to study, the numerous potential pitfalls in the analysis of radiomic data, and the lack of studies showing that acting upon a radiomic-based tool leads to a favourable benefit-risk balance for the patient. Several guidelines on specific aspects of radiomic data acquisition and analysis are already available, although a similar roadmap for the overall process of translating radiomics into tools that can be used in clinical care is needed. Herein, we provide 16 criteria for the effective execution of this process in the hopes that they will guide the development of more clinically useful radiomic tests in the future.
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Affiliation(s)
- Erich P Huang
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
| | - James P B O'Connor
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London, UK
| | - Lisa M McShane
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | | | - Philippe Lambin
- Department of Precision Medicine, Maastricht University, Maastricht, Netherlands
| | - Paul E Kinahan
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Eliot L Siegel
- Department of Diagnostic Radiology, University of Maryland, Baltimore, MD, USA
| | - Lalitha K Shankar
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
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Tippareddy C, Onyewadume L, Sloan AE, Wang GM, Patil NT, Hu S, Barnholtz-Sloan JS, Boyacıoğlu R, Gulani V, Sunshine J, Griswold M, Ma D, Badve C. Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study. Eur Radiol 2023; 33:836-844. [PMID: 35999374 DOI: 10.1007/s00330-022-09067-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/16/2022] [Accepted: 07/27/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms. METHODS 3D MRF acquisition was performed on 78 patients with newly diagnosed brain tumors including 33 glioblastomas (grade IV), 6 grade III gliomas, 12 grade II gliomas, and 27 patients with brain metastases. Regions of enhancing tumor, non-enhancing tumor, and peritumoral edema were segmented and radiomics analysis with gray-level co-occurrence matrices and gray-level run-length matrices was performed. Statistical analysis was performed to identify features capable of differentiating tumors based on type, grade, and isocitrate dehydrogenase (IDH1) status. Receiver operating curve analysis was performed and the area under the curve (AUC) was calculated for tumor classification and grading. For gliomas, Kaplan-Meier analysis for overall survival was performed using MRF T1 features from enhancing tumor region. RESULTS Multiple MRF T1 and T2 features from enhancing tumor region were capable of differentiating glioblastomas from brain metastases. Although no differences were identified between grade 2 and grade 3 gliomas, differentiation between grade 2 and grade 4 gliomas as well as between grade 3 and grade 4 gliomas was achieved. MRF radiomics features were also able to differentiate IDH1 mutant from the wild-type gliomas. Radiomics T1 features for enhancing tumor region in gliomas correlated to overall survival (p < 0.05). CONCLUSION Radiomics analysis of 3D MRF maps allows differentiating glioblastomas from metastases and is capable of differentiating glioblastomas from metastases and characterizing gliomas based on grade, IDH1 status, and survival. KEY POINTS • 3D MRF data analysis using radiomics offers novel tissue characterization of brain tumors. • 3D MRF with radiomics offers glioma characterization based on grade, IDH1 status, and overall patient survival.
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Affiliation(s)
- Charit Tippareddy
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Louisa Onyewadume
- Department of Neurosurgery, West Virginia University Health Sciences Center, Morgantown, WV, USA
| | - Andrew E Sloan
- Departments of Neurosurgery and Pathology, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Gi-Ming Wang
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Research and Education Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Nirav T Patil
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Jill S Barnholtz-Sloan
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
- Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Rasim Boyacıoğlu
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Vikas Gulani
- Department of Radiology, Michigan Institute of Imaging Technology and Translation, Michigan Medicine, Ann Arbor, MI, USA
| | - Jeffrey Sunshine
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Mark Griswold
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Dan Ma
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA.
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Koçak B, Cuocolo R, dos Santos DP, Stanzione A, Ugga L. Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning. Balkan Med J 2023; 40:3-12. [PMID: 36578657 PMCID: PMC9874249 DOI: 10.4274/balkanmedj.galenos.2022.2022-11-51] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 12/06/2022] [Indexed: 12/30/2022] Open
Abstract
In the field of computer science, known as artificial intelligence, algorithms imitate reasoning tasks that are typically performed by humans. The techniques that allow machines to learn and get better at tasks such as recognition and prediction, which form the basis of clinical practice, are referred to as machine learning, which is a subfield of artificial intelligence. The number of artificial intelligence-and machine learnings-related publications in clinical journals has grown exponentially, driven by recent developments in computation and the accessibility of simple tools. However, clinicians are often not included in data science teams, which may limit the clinical relevance, explanability, workflow compatibility, and quality improvement of artificial intelligence solutions. Thus, this results in the language barrier between clinicians and artificial intelligence developers. Healthcare practitioners sometimes lack a basic understanding of artificial intelligence research because the approach is difficult for non-specialists to understand. Furthermore, many editors and reviewers of medical publications might not be familiar with the fundamental ideas behind these technologies, which may prevent journals from publishing high-quality artificial intelligence studies or, worse still, could allow for the publication of low-quality works. In this review, we aim to improve readers’ artificial intelligence literacy and critical thinking. As a result, we concentrated on what we consider the 10 most important qualities of artificial intelligence research: valid scientific purpose, high-quality data set, robust reference standard, robust input, no information leakage, optimal bias-variance tradeoff, proper model evaluation, proven clinical utility, transparent reporting, and open science. Before designing a study, one should have defined a sound scientific purpose. Then, it should be backed by a high-quality data set, robust input, and a solid reference standard. The artificial intelligence development pipeline should prevent information leakage. For the models, optimal bias-variance tradeoff should be achieved, and generalizability assessment must be adequately performed. The clinical value of the final models must also be established. After the study, thought should be given to transparency in publishing the process and results as well as open science for sharing data, code, and models. We hope this work may improve the artificial intelligence literacy and mindset of the readers.
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Affiliation(s)
- Burak Koçak
- Clinic of Radiology, University of Health Sciences Turkey, Başakşehir Çam and Sakura City Hospital, İstanbul, Turkey
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry University of Salerno, Baronissi, Italy
| | - Daniel Pinto dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Napoli, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Napoli, Italy
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Caba B, Cafaro A, Lombard A, Arnold DL, Elliott C, Liu D, Jiang X, Gafson A, Fisher E, Belachew SM, Paragios N. Single-timepoint low-dimensional characterization and classification of acute versus chronic multiple sclerosis lesions using machine learning. Neuroimage 2023; 265:119787. [PMID: 36473647 DOI: 10.1016/j.neuroimage.2022.119787] [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: 09/20/2022] [Revised: 11/16/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease characterized by the appearance of focal lesions across the central nervous system. The discrimination of acute from chronic MS lesions may yield novel biomarkers of inflammatory disease activity which may support patient management in the clinical setting and provide endpoints in clinical trials. On a single timepoint and in the absence of a prior reference scan, existing methods for acute lesion detection rely on the segmentation of hyperintense foci on post-gadolinium T1-weighted magnetic resonance imaging (MRI), which may underestimate recent acute lesion activity. In this paper, we aim to improve the sensitivity of acute MS lesion detection in the single-timepoint setting, by developing a novel machine learning approach for the automatic detection of acute MS lesions, using single-timepoint conventional non-contrast T1- and T2-weighted brain MRI. The MRI input data are supplemented via the use of a convolutional neural network generating "lesion-free" reconstructions from original "lesion-present" scans using image inpainting. A multi-objective statistical ranking module evaluates the relevance of textural radiomic features from the core and periphery of lesion sites, compared within "lesion-free" versus "lesion-present" image pairs. Then, an ensemble classifier is optimized through a recursive loop seeking consensus both in the feature space (via a greedy feature-pruning approach) and in the classifier space (via model selection repeated after each pruning operation). This leads to the identification of a compact textural signature characterizing lesion phenotype. On the patch-level task of acute versus chronic MS lesion classification, our method achieves a balanced accuracy in the range of 74.3-74.6% on fully external validation cohorts.
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Affiliation(s)
- Bastien Caba
- Biogen Digital Health, Biogen, Cambridge, MA, USA.
| | | | | | - Douglas L Arnold
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada; NeuroRx Research, Montreal, QC, Canada
| | | | - Dawei Liu
- Biogen Digital Health, Biogen, Cambridge, MA, USA
| | | | - Arie Gafson
- Biogen Digital Health, Biogen, Cambridge, MA, USA
| | | | | | - Nikos Paragios
- CentraleSupélec, University of Paris-Saclay, Gif-sur-Yvette, France; TheraPanacea, Paris, France
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Jiang ZY, Qi LS, Li JT, Cui N, Li W, Liu W, Wang KZ. Radiomics: Status quo and future challenges. Artif Intell Med Imaging 2022; 3:87-96. [DOI: 10.35711/aimi.v3.i4.87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/08/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Noninvasive imaging (computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography) as an important part of the clinical workflow in the clinic, but it still provides limited information for diagnosis, treatment effect evaluation and prognosis prediction. In addition, judgment and diagnoses made by experts are usually based on multiple years of experience and subjective impression which lead to variable results in the same case. With accumulation of medical imaging data, radiomics emerges as a relatively new approach for analysis. Via artificial intelligence techniques, high-throughput quantitative data which is invisible to the naked eyes extracted from original images can be used in the process of patients’ management. Several studies have evaluated radiomics combined with clinical factors, pathological, or genetic information would assist in the diagnosis, particularly in the prediction of biological characteristics, risk of recurrence, and survival with encouraging results. In various clinical settings, there are limitations and challenges needing to be overcome before transformation. Therefore, we summarize the concepts and method of radiomics including image acquisition, region of interest segmentation, feature extraction and model development. We also set forth the current applications of radiomics in clinical routine. At last, the limitations and related deficiencies of radiomics are pointed out to direct the future opportunities and development.
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Affiliation(s)
- Zhi-Yun Jiang
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Li-Shuang Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| | - Jia-Tong Li
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Nan Cui
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Wei Li
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
- Department of Interventional Vascular Surgery, The 4th Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China
| | - Wei Liu
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
| | - Ke-Zheng Wang
- Department of Positron Emission Tomography-Computed Tomography/Magnetic Resonance Imaging, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China
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A Framework of Analysis to Facilitate the Harmonization of Multicenter Radiomic Features in Prostate Cancer. J Clin Med 2022; 12:jcm12010140. [PMID: 36614941 PMCID: PMC9821561 DOI: 10.3390/jcm12010140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/16/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Pooling radiomic features coming from different centers in a statistical framework is challenging due to the variability in scanner models, acquisition protocols, and reconstruction settings. To remove technical variability, commonly called batch effects, different statistical harmonization strategies have been widely used in genomics but less considered in radiomics. The aim of this work was to develop a framework of analysis to facilitate the harmonization of multicenter radiomic features extracted from prostate T2-weighted magnetic resonance imaging (MRI) and to improve the power of radiomics for prostate cancer (PCa) management in order to develop robust non-invasive biomarkers translating into clinical practice. To remove technical variability and correct for batch effects, we investigated four different statistical methods (ComBat, SVA, Arsynseq, and mixed effect). The proposed approaches were evaluated using a dataset of 210 prostate cancer (PCa) patients from two centers. The impacts of the different statistical approaches were evaluated by principal component analysis and classification methods (LogitBoost, random forest, K-nearest neighbors, and decision tree). The ComBat method outperformed all other methods by achieving 70% accuracy and 78% AUC with the random forest method to automatically classify patients affected by PCa. The proposed statistical framework enabled us to define and develop a standardized pipeline of analysis to harmonize multicenter T2W radiomic features, yielding great promise to support PCa clinical practice.
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Soleymani Y, Jahanshahi AR, Pourfarshid A, Khezerloo D. Reproducibility assessment of radiomics features in various ultrasound scan settings and different scanner vendors. J Med Imaging Radiat Sci 2022; 53:664-671. [PMID: 36266173 DOI: 10.1016/j.jmir.2022.09.018] [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: 03/19/2022] [Revised: 09/09/2022] [Accepted: 09/22/2022] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Radiomics in Ultrasound (US) imaging has been investigated for the prediction and prognosis of cancers. However, inter-scanner and intra-scanner variations may affect the reproducibility of radiomics results. This study aims to evaluate the reproducibility of US textural radiomics features across various scan settings and scanner vendors. MATERIALS AND METHODS US images in quality control (QC) phantom were obtained by three scanners (Philips, Samsung, and Siemens) with different scan settings and parameters. Circular regions of interest (ROIs) inside isoechoic, hypoechoic, and hyperechoic objects were manually delineated. Forty textural radiomics features were extracted from each ROI, and then the robust features that could distinguish different echogenic objects were obtained by the Mann-Whitney U test. Reproducibility of the robust radiomics features was assessed by the intraclass correlation coefficient (ICC) and coefficient of variation (CV); ICC>0.90 and %CV<20 were considered reproducible. RESULTS According to the Mann-Whitney U test results, ten robust features could differentiate the hypoechoic, and 15 robust features could differentiate the hyperechoic objects from the isoechoic objects (P<0.001). The total ICC of the robust features for each echogenic object was >0.95 in different scanners and scan settings. Four and seven features were individually reproducible (%CV < 20, ICC>0.90) in hypoechoic and hyperechoic objects, respectively. Also, four features seem reproducible by changing the ROI location across the horizontal and vertical lines for both convex and linear array transducers. CONCLUSIONS Most of the US textural radiomics features in this study were not reproducible. However, several features showed high reproducibility at different scan settings and scanners. These features may also be reproducible when ROI size and location change slightly.
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Affiliation(s)
- Yunus Soleymani
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Jahanshahi
- Tabriz University of Medical Sciences, Faculty of Medicine, Imam Reza Hospital, Department of Radiology
| | - Amin Pourfarshid
- Department of Medical Physics, Faculty of Medicine, Urmia University of Medical Sciences, Urmia, Iran
| | - Davood Khezerloo
- Department of Radiology, Faculty of Alliance Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran & Medical Radiation Sciences Research Group, Tabriz University of Medical Sciences, Tabriz, Iran.
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Stamoulou E, Spanakis C, Manikis GC, Karanasiou G, Grigoriadis G, Foukakis T, Tsiknakis M, Fotiadis DI, Marias K. Harmonization Strategies in Multicenter MRI-Based Radiomics. J Imaging 2022; 8:303. [PMID: 36354876 PMCID: PMC9695920 DOI: 10.3390/jimaging8110303] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/28/2022] [Accepted: 10/31/2022] [Indexed: 08/13/2023] Open
Abstract
Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating digital diagnostics, it is susceptible to bias and variation due to numerous inter-patient factors (e.g., patient age and gender) as well as inter-scanner ones (different protocol acquisition depending on the scanner center). A variety of image and feature based harmonization methods has been developed to compensate for these effects; however, to the best of our knowledge, none of these techniques has been established as the most effective in the analysis pipeline so far. To this end, this review provides an overview of the challenges in optimizing radiomics analysis, and a concise summary of the most relevant harmonization techniques, aiming to provide a thorough guide to the radiomics harmonization process.
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Affiliation(s)
- Elisavet Stamoulou
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
| | - Constantinos Spanakis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
| | - Georgios C. Manikis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Oncology-Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Georgia Karanasiou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
| | - Grigoris Grigoriadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
| | - Theodoros Foukakis
- Department of Oncology-Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Manolis Tsiknakis
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 714 10 Heraklion, Greece
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology—FORTH, University Campus of Ioannina, 451 15 Ioannina, Greece
| | - Kostas Marias
- Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology—Hellas (FORTH), 700 13 Heraklion, Greece
- Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 714 10 Heraklion, Greece
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Basran PS, McDonough S, Palmer S, Reesink HL. Radiomics Modeling of Catastrophic Proximal Sesamoid Bone Fractures in Thoroughbred Racehorses Using μCT. Animals (Basel) 2022; 12:3033. [PMID: 36359157 PMCID: PMC9658779 DOI: 10.3390/ani12213033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 09/08/2024] Open
Abstract
Proximal sesamoid bone (PSB) fractures are the most common musculoskeletal injury in race-horses. X-ray CT imaging can detect expressed radiological features in horses that experienced catastrophic fractures. Our objective was to assess whether expressed radiomic features in the PSBs of 50 horses can be used to develop machine learning models for predicting PSB fractures. The μCTs of intact contralateral PSBs from 50 horses, 30 of which suffered catastrophic fractures, and 20 controls were studied. From the 129 intact μCT images of PSBs, 102 radiomic features were computed using a variety of voxel resampling dimensions. Decision Trees and Wrapper methods were used to identify the 20 top expressed features, and six machine learning algorithms were developed to model the risk of fracture. The accuracy of all machine learning models ranged from 0.643 to 0.903 with an average of 0.754. On average, Support Vector Machine, Random Forest (RUS Boost), and Log-regression models had higher performance than K-means Nearest Neighbor, Neural Network, and Random Forest (Bagged Trees) models. Model accuracy peaked at 0.5 mm and decreased substantially when the resampling resolution was greater than or equal to 1 mm. We find that, for this in vitro dataset, it is possible to differentiate between unfractured PSBs from case and control horses using μCT images. It may be possible to extend these findings to the assessment of fracture risk in standing horses.
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Affiliation(s)
- Parminder S. Basran
- Clinical Sciences, Cornell University, Ithaca, NY 14853, USA
- Biomedical Sciences, Cornell University, Ithaca, NY 14853, USA
| | - Sean McDonough
- Biomedical Sciences, Cornell University, Ithaca, NY 14853, USA
| | - Scott Palmer
- Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853, USA
| | - Heidi L. Reesink
- Biomedical Sciences, Cornell University, Ithaca, NY 14853, USA
- Clinical Sciences, Equine and Farm Animal Hospital & Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853, USA
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Brancato V, Cavaliere C, Garbino N, Isgrò F, Salvatore M, Aiello M. The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study. Front Oncol 2022; 12:1005805. [PMID: 36276163 PMCID: PMC9582951 DOI: 10.3389/fonc.2022.1005805] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/21/2022] [Indexed: 12/01/2022] Open
Abstract
Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman's correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as "virtual biopsy", introducing new insights for omics integration toward a personalized medicine approach.
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Affiliation(s)
| | | | | | - Francesco Isgrò
- Department of Electrical Engineering and Information Technologies, University of Napoli Federico II, Napoli, Italy
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Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
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Update on quantitative radiomics of pancreatic tumors. Abdom Radiol (NY) 2022; 47:3118-3160. [PMID: 34292365 DOI: 10.1007/s00261-021-03216-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors.
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Effect of Gray Value Discretization and Image Filtration on Texture Features of the Pancreas Derived from Magnetic Resonance Imaging at 3T. J Imaging 2022; 8:jimaging8080220. [PMID: 36005463 PMCID: PMC9409719 DOI: 10.3390/jimaging8080220] [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: 06/28/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 11/17/2022] Open
Abstract
Radiomics of pancreas magnetic resonance (MR) images is positioned well to play an important role in the management of diseases characterized by diffuse involvement of the pancreas. The effect of image pre-processing configurations on these images has been sparsely investigated. Fifteen individuals with definite chronic pancreatitis (an exemplar diffuse disease of the pancreas) and 15 healthy individuals were included in this age- and sex-matched case-control study. MR images of the pancreas were acquired using a single 3T scanner. A total of 93 first-order and second-order texture features of the pancreas were compared between the study groups, by subjecting MR images of the pancreas to 7 image pre-processing configurations related to gray level discretization and image filtration. The studied parameters of intensity discretization did not vary in terms of their effect on the number of significant first-order texture features. The number of statistically significant first-order texture features varied after filtering (7 with the use of logarithm filter and 3 with the use of Laplacian of Gaussian filter with 5 mm σ). Intensity discretization generally affected the number of significant second-order texture features more markedly than filtering. The use of fixed bin number of 16 yielded 42 significant second-order texture features, fixed bin number of 128–38 features, fixed bin width of 6–24 features, and fixed bin width of 42–26 features. The specific parameters of filtration and intensity discretization had differing effects on radiomics signature of the pancreas. Relative discretization with fixed bin number of 16 and use of logarithm filter hold promise as pre-processing configurations of choice in future radiomics studies in diffuse diseases of the pancreas.
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Xu A, Chu X, Zhang S, Zheng J, Shi D, Lv S, Li F, Weng X. Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma. BMC Cancer 2022; 22:872. [PMID: 35945526 PMCID: PMC9364617 DOI: 10.1186/s12885-022-09967-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 07/26/2022] [Indexed: 11/17/2022] Open
Abstract
Background The determination of HER2 expression status contributes significantly to HER2-targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive assessment of HER2 is lacking. We aimed to develop a clinicoradiomic nomogram based on radiomics scores extracted from multiparametric MRI (mpMRI, including ADC-map, T2W1, DCE-T1WI) and clinical risk factors to assess HER2 status. Methods We retrospectively collected 214 patients with pathologically confirmed invasive ductal carcinoma between January 2018 to March 2021 from Fudan University Shanghai Cancer Center, and randomly divided this cohort into training set (n = 128, 42 HER2-positive and 86 HER2-negative cases) and validation set (n = 86, 28 HER2-positive and 58 HER2-negative cases) at a ratio of 6:4. The original and transformed pretherapy mpMRI images were treated by semi-automated segmentation and manual modification on the DeepWise scientific research platform v1.6 (http://keyan.deepwise.com/), then radiomics feature extraction was implemented with PyRadiomics library. Recursive feature elimination (RFE) based on logistic regression (LR) and LASSO regression were adpoted to identify optimal features before modeling. LR, Linear Discriminant Analysis (LDA), support vector machine (SVM), random forest (RF), naive Bayesian (NB) and XGBoost (XGB) algorithms were used to construct the radiomics signatures. Independent clinical predictors were identified through univariate logistic analysis (age, tumor location, ki-67 index, histological grade, and lymph node metastasis). Then, the radiomics signature with the best diagnostic performance (Rad score) was further combined with significant clinical risk factors to develop a clinicoradiomic model (nomogram) using multivariate logistic regression. The discriminative power of the constructed models were evaluated by AUC, DeLong test, calibration curve, and decision curve analysis (DCA). Results 70 (32.71%) of the enrolled 214 cases were HER2-positive, while 144 (67.29%) were HER2-negative. Eleven best radiomics features were retained to develop 6 radiomcis classifiers in which RF classifier showed the highest AUC of 0.887 (95%CI: 0.827–0.947) in the training set and acheived the AUC of 0.840 (95%CI: 0.758–0.922) in the validation set. A nomogram that incorporated the Rad score with two selected clinical factors (Ki-67 index and histological grade) was constructed and yielded better discrimination compared with Rad score (p = 0.374, Delong test), with an AUC of 0.945 (95%CI: 0.904–0.987) in the training set and 0.868 (95%CI: 0.789–0.948; p = 0.123) in the validation set. Moreover, calibration with the p-value of 0.732 using Hosmer–Lemeshow test demonstrated good agreement, and the DCA verified the benefits of the nomogram. Conclusion Post largescale validation, the clinicoradiomic nomogram may have the potential to be used as a non-invasive tool for determination of HER2 expression status in clinical HER2-targeted therapy prediction. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09967-6.
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Affiliation(s)
- Aqiao Xu
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, 312030, China.
| | - Xiufeng Chu
- Department of Surgical, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, 312030, China
| | - Shengjian Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Jing Zheng
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, 312030, China
| | - Dabao Shi
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, 312030, China
| | - Shasha Lv
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, 312030, China
| | - Feng Li
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, 100080, P.R. China
| | - Xiaobo Weng
- Department of Radiology, The Central Hospital Affiliated to Shaoxing University (Shaoxing Central Hospital), Shaoxing, 312030, China.
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Cui Y, Yin FF. Impact of image quality on radiomics applications. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7fd7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.
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Moskowitz CS, Welch ML, Jacobs MA, Kurland BF, Simpson AL. Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies. Radiology 2022; 304:265-273. [PMID: 35579522 PMCID: PMC9340236 DOI: 10.1148/radiol.211597] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 01/30/2022] [Accepted: 02/02/2022] [Indexed: 12/13/2022]
Abstract
Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described.
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Affiliation(s)
| | | | - Michael A. Jacobs
- From the Department of Epidemiology and Biostatistics, Memorial Sloan
Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017
(C.S.M.); Cancer Digital Intelligence Program, University Health Network,
Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and
Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns
Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa
(B.F.K.); and School of Computing, Department of Biomedical and Molecular
Sciences, Queen’s University, Kingston, ON, Canada (A.L.S.)
| | - Brenda F. Kurland
- From the Department of Epidemiology and Biostatistics, Memorial Sloan
Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017
(C.S.M.); Cancer Digital Intelligence Program, University Health Network,
Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and
Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns
Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa
(B.F.K.); and School of Computing, Department of Biomedical and Molecular
Sciences, Queen’s University, Kingston, ON, Canada (A.L.S.)
| | - Amber L. Simpson
- From the Department of Epidemiology and Biostatistics, Memorial Sloan
Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017
(C.S.M.); Cancer Digital Intelligence Program, University Health Network,
Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and
Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns
Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa
(B.F.K.); and School of Computing, Department of Biomedical and Molecular
Sciences, Queen’s University, Kingston, ON, Canada (A.L.S.)
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AutoComBat: a generic method for harmonizing MRI-based radiomic features. Sci Rep 2022; 12:12762. [PMID: 35882891 PMCID: PMC9325761 DOI: 10.1038/s41598-022-16609-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 07/12/2022] [Indexed: 11/09/2022] Open
Abstract
The use of multicentric data is becoming essential for developing generalizable radiomic signatures. In particular, Magnetic Resonance Imaging (MRI) data used in brain oncology are often heterogeneous in terms of scanners and acquisitions, which significantly impact quantitative radiomic features. Various methods have been proposed to decrease dependency, including methods acting directly on MR images, i.e., based on the application of several preprocessing steps before feature extraction or the ComBat method, which harmonizes radiomic features themselves. The ComBat method used for radiomics may be misleading and presents some limitations, such as the need to know the labels associated with the "batch effect". In addition, a statistically representative sample is required and the applicability of a signature whose batch label is not present in the train set is not possible. This work aimed to compare a priori and a posteriori radiomic harmonization methods and propose a code adaptation to be machine learning compatible. Furthermore, we have developed AutoComBat, which aims to automatically determine the batch labels, using either MRI metadata or quality metrics as inputs of the proposed constrained clustering. A heterogeneous dataset consisting of high and low-grade gliomas coming from eight different centers was considered. The different methods were compared based on their ability to decrease relative standard deviation of radiomic features extracted from white matter and on their performance on a classification task using different machine learning models. ComBat and AutoComBat using image-derived quality metrics as inputs for batch assignment and preprocessing methods presented promising results on white matter harmonization, but with no clear consensus for all MR images. Preprocessing showed the best results on the T1w-gd images for the grading task. For T2w-flair, AutoComBat, using either metadata plus quality metrics or metadata alone as inputs, performs better than the conventional ComBat, highlighting its potential for data harmonization. Our results are MRI weighting, feature class and task dependent and require further investigations on other datasets.
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Keek SA, Beuque M, Primakov S, Woodruff HC, Chatterjee A, van Timmeren JE, Vallières M, Hendriks LEL, Kraft J, Andratschke N, Braunstein SE, Morin O, Lambin P. Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics. Front Oncol 2022; 12:920393. [PMID: 35912214 PMCID: PMC9326101 DOI: 10.3389/fonc.2022.920393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionThere is a cumulative risk of 20–40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain. It is therefore of clinical interest to identify patients at a high risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, and patient characteristics or their combination can predict ARE risk in patients with BM before SRT.MethodsGadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (N = 1,404) and a validation cohort (N = 237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features, and patient characteristics or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per patient developing ARE were investigated.ResultsThe best-performing XGBoost model on a lesion level was trained on a combination of radiomics features and DL features (AUC of 0.71 and recall of 0.80). On a patient level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72 and recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per lesion and an AUC of 0.70 and recall of 0.60 per patient.ConclusionMachine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion levels. These models could be used in clinical decision making, informing patients on their risk of ARE and allowing physicians to opt for different therapies.
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Affiliation(s)
- Simon A. Keek
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Manon Beuque
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Sergey Primakov
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- Department of Radiology and Nuclear Medicine, GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Avishek Chatterjee
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
| | - Janita E. van Timmeren
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Martin Vallières
- Medical Physics Unit, Department of Oncology, Faculty of Medicine, McGill University, Montréal, QC, Canada
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Lizza E. L. Hendriks
- Department of Pulmonary Diseases, GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Johannes Kraft
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
- Department of Radiation Oncology, University Hospital Würzburg, Würzburg, Germany
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Steve E. Braunstein
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States
| | - Olivier Morin
- Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW- School for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands
- Department of Radiology and Nuclear Medicine, GROW – School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
- *Correspondence: Philippe Lambin,
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Mbanu P, Saunders MP, Mistry H, Mercer J, Malcomson L, Yousif S, Price G, Kochhar R, Renehan AG, van Herk M, Osorio EV. Clinical and radiomics prediction of complete response in rectal cancer pre-chemoradiotherapy. Phys Imaging Radiat Oncol 2022; 23:48-53. [PMID: 35800297 PMCID: PMC9253904 DOI: 10.1016/j.phro.2022.06.010] [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: 09/29/2021] [Revised: 06/18/2022] [Accepted: 06/20/2022] [Indexed: 11/11/2022] Open
Abstract
Background and purpose Patients with rectal cancer could avoid major surgery if they achieve clinical complete response (cCR) post neoadjuvant treatment. Therefore, prediction of treatment outcomes before treatment has become necessary to select the best neo-adjuvant treatment option. This study investigates clinical and radiomics variables' ability to predict cCR in patients pre chemoradiotherapy. Materials and methods Using the OnCoRe database, we recruited a matched cohort of 304 patients (152 with cCR; 152 without cCR) deriving training (N = 200) and validation (N = 104) sets. We collected pre-treatment MR (magnetic resonance) images, demographics and blood parameters (haemoglobin, neutrophil, lymphocyte, alkaline phosphate and albumin). We segmented the gross tumour volume on T2 Weighted MR Images and extracted 1430 stable radiomics features per patient. We used principal component analysis (PCA) and receiver operating characteristic area under the curve (ROC AUC) to reduce dimensionality and evaluate the models produced. Results Using Logistic regression analysis, PCA-derived combined model (radiomics plus clinical variables) gave a ROC AUC of 0.76 (95% CI: 0.69-0.83) in the training set and 0.68 (95% CI 0.57-0.79) in the validation set. The clinical only model achieved an AUC of 0.73 (95% CI 0.66-0.80) and 0.62 (95% CI 0.51-0.74) in the training and validation set, respectively. The radiomics model had an AUC of 0.68 (95% CI 0.61-0.75) and 0.66 (95% CI 0.56-0.77) in the training and validation sets. Conclusion The predictive characteristics of both clinical and radiomics variables for clinical complete response remain modest but radiomics predictability is improved with addition of clinical variables.
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Affiliation(s)
- Peter Mbanu
- Department of Clinical Oncology, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Mark P. Saunders
- Department of Clinical Oncology, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Hitesh Mistry
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
- Division of Pharmacy, University of Manchester, Manchester, United Kingdom
| | - Joe Mercer
- Department of Radiological Oncology, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Lee Malcomson
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
- Colorectal and Peritoneal Oncology Centre, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Saif Yousif
- Department of Clinical Oncology, Lancashire Teaching Hospital, Preston, United Kingdom
| | - Gareth Price
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
| | - Rohit Kochhar
- Department of Radiological Oncology, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Andrew G. Renehan
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
- Colorectal and Peritoneal Oncology Centre, Christie Hospital NHS Foundation Trust, Manchester, United Kingdom
| | - Marcel van Herk
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
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Marfisi D, Tessa C, Marzi C, Del Meglio J, Linsalata S, Borgheresi R, Lilli A, Lazzarini R, Salvatori L, Vignali C, Barucci A, Mascalchi M, Casolo G, Diciotti S, Traino AC, Giannelli M. Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy. Sci Rep 2022; 12:10186. [PMID: 35715531 PMCID: PMC9205876 DOI: 10.1038/s41598-022-13937-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 03/21/2022] [Indexed: 12/24/2022] Open
Abstract
Radiomics is emerging as a promising and useful tool in cardiac magnetic resonance (CMR) imaging applications. Accordingly, the purpose of this study was to investigate, for the first time, the effect of image resampling/discretization and filtering on radiomic features estimation from quantitative CMR T1 and T2 mapping. Specifically, T1 and T2 maps of 26 patients with hypertrophic cardiomyopathy (HCM) were used to estimate 98 radiomic features for 7 different resampling voxel sizes (at fixed bin width), 9 different bin widths (at fixed resampling voxel size), and 7 different spatial filters (at fixed resampling voxel size/bin width). While we found a remarkable dependence of myocardial radiomic features from T1 and T2 mapping on image filters, many radiomic features showed a limited sensitivity to resampling voxel size/bin width, in terms of intraclass correlation coefficient (> 0.75) and coefficient of variation (< 30%). The estimate of most textural radiomic features showed a linear significant (p < 0.05) correlation with resampling voxel size/bin width. Overall, radiomic features from T2 maps have proven to be less sensitive to image preprocessing than those from T1 maps, especially when varying bin width. Our results might corroborate the potential of radiomics from T1/T2 mapping in HCM and hopefully in other myocardial diseases.
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Affiliation(s)
- Daniela Marfisi
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Carlo Tessa
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Apuane Hospital, 54100, Massa, Italy
| | - Chiara Marzi
- Institute of Applied Physics "Nello Carrara", Italian National Research Council, 50019, Sesto Fiorentino, Italy
| | - Jacopo Del Meglio
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Stefania Linsalata
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Rita Borgheresi
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Alessio Lilli
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Riccardo Lazzarini
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Luca Salvatori
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Claudio Vignali
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Andrea Barucci
- Institute of Applied Physics "Nello Carrara", Italian National Research Council, 50019, Sesto Fiorentino, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50121, Florence, Italy
| | - Giancarlo Casolo
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 47522, Cesena, Italy
| | - Antonio Claudio Traino
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy.
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50
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Liberini V, Laudicella R, Balma M, Nicolotti DG, Buschiazzo A, Grimaldi S, Lorenzon L, Bianchi A, Peano S, Bartolotta TV, Farsad M, Baldari S, Burger IA, Huellner MW, Papaleo A, Deandreis D. Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics. Eur Radiol Exp 2022; 6:27. [PMID: 35701671 PMCID: PMC9198151 DOI: 10.1186/s41747-022-00282-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 04/20/2022] [Indexed: 11/21/2022] Open
Abstract
In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.
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Affiliation(s)
- Virginia Liberini
- Medical Physiopathology - A.O.U. Città della Salute e della Scienza di Torino, Division of Nuclear Medicine, Department of Medical Science, University of Torino, 10126, Torino, Italy. .,Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy.
| | - Riccardo Laudicella
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8006, Zurich, Switzerland.,Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and of Morpho-Functional Imaging, University of Messina, 98125, Messina, Italy.,Nuclear Medicine Unit, Fondazione Istituto G. Giglio, Ct.da Pietrapollastra Pisciotto, Cefalù, Palermo, Italy
| | - Michele Balma
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | | | - Ambra Buschiazzo
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | - Serena Grimaldi
- Medical Physiopathology - A.O.U. Città della Salute e della Scienza di Torino, Division of Nuclear Medicine, Department of Medical Science, University of Torino, 10126, Torino, Italy
| | - Leda Lorenzon
- Medical Physics Department, Central Bolzano Hospital, 39100, Bolzano, Italy
| | - Andrea Bianchi
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | - Simona Peano
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | | | - Mohsen Farsad
- Nuclear Medicine, Central Hospital Bolzano, 39100, Bolzano, Italy
| | - Sergio Baldari
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and of Morpho-Functional Imaging, University of Messina, 98125, Messina, Italy
| | - Irene A Burger
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8006, Zurich, Switzerland.,Department of Nuclear Medicine, Kantonsspital Baden, 5004, Baden, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8006, Zurich, Switzerland
| | - Alberto Papaleo
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy
| | - Désirée Deandreis
- Medical Physiopathology - A.O.U. Città della Salute e della Scienza di Torino, Division of Nuclear Medicine, Department of Medical Science, University of Torino, 10126, Torino, Italy
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