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Varriano G, Nardone V, Correra S, Mercaldo F, Santone A. An automatic radiomic-based approach for disease localization: A pilot study on COVID-19. Comput Med Imaging Graph 2024; 116:102411. [PMID: 38924800 DOI: 10.1016/j.compmedimag.2024.102411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/15/2024] [Accepted: 06/09/2024] [Indexed: 06/28/2024]
Abstract
Radiomics is an innovative field in Personalized Medicine to help medical specialists in diagnosis and prognosis. Mainly, the application of Radiomics to medical images requires the definition and delimitation of the Region Of Interest (ROI) on the medical image to extract radiomic features. The aim of this preliminary study is to define an approach that automatically detects the specific areas indicative of a particular disease and examines them to minimize diagnostic errors associated with false positives and false negatives. This approach aims to create a nxn grid on the DICOM image sequence and each cell in the matrix is associated with a region from which radiomic features can be extracted. The proposed procedure uses the Model Checking technique and produces as output the medical diagnosis of the patient, i.e., whether the patient under analysis is affected or not by a specific disease. Furthermore, the matrix-based method also localizes where appears the disease marks. To evaluate the performance of the proposed methodology, a case study on COVID-19 disease is used. Both results on disease identification and localization seem very promising. Furthermore, this proposed approach yields better results compared to methods based on the extraction of features using the whole image as a single ROI, as evidenced by improvements in Accuracy and especially Recall. Our approach supports the advancement of knowledge, interoperability and trust in the software tool, fostering collaboration among doctors, staff and Radiomics.
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Affiliation(s)
- Giulia Varriano
- University of Molise, Department of Medicine and Health Sciences "V. Tiberio", Campobasso 86100, Italy.
| | - Vittoria Nardone
- University of Molise, Department of Medicine and Health Sciences "V. Tiberio", Campobasso 86100, Italy.
| | - Simona Correra
- University of Molise, Department of Medicine and Health Sciences "V. Tiberio", Campobasso 86100, Italy.
| | - Francesco Mercaldo
- University of Molise, Department of Medicine and Health Sciences "V. Tiberio", Campobasso 86100, Italy.
| | - Antonella Santone
- University of Molise, Department of Medicine and Health Sciences "V. Tiberio", Campobasso 86100, Italy.
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Akramova R, Watanabe Y. Radiomics as a measure superior to common similarity metrics for tumor segmentation performance evaluation. J Appl Clin Med Phys 2024; 25:e14442. [PMID: 38922790 PMCID: PMC11302798 DOI: 10.1002/acm2.14442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 05/04/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024] Open
Abstract
PURPOSE To propose radiomics features as a superior measure for evaluating the segmentation ability of physicians and auto-segmentation tools and to compare its performance with the most commonly used metrics: Dice similarity coefficient (DSC), surface Dice similarity coefficient (sDSC), and Hausdorff distance (HD). MATERIALS/METHODS The data of 10 lung cancer patients' CT images with nine tumor segmentations per tumor were downloaded from the RIDER (Reference Database to Evaluate Response) database. Radiomics features of 90 segmented tumors were extracted using the PyRadiomics program. The intraclass correlation coefficient (ICC) of radiomics features were used to evaluate the segmentation similarity and compare their performance with DSC, sDSC, and HD. We calculated one ICC per radiomics feature and per tumor for nine segmentations and 36 ICCs per radiomics feature for 36 pairs of nine segmentations. Meanwhile, there were 360 DSC, sDSC, and HD values calculated for 36 pairs for 10 tumors. RESULTS The ICC of radiomics features exhibited greater sensitivity to segmentation changes than DSC and sDSC. The ICCs of the wavelet-LLL first order Maximum, wavelet-LLL glcm MCC, wavelet-LLL glcm Cluster Shade features ranged from 0.130 to 0.997, 0.033 to 0.978, and 0.160 to 0.998, respectively. On the other hand, all DSC and sDSC were larger than 0.778 and 0.700, respectively, while HD varied from 0 to 1.9 mm. The results indicated that the radiomics features could capture subtle variations in tumor segmentation characteristics, which could not be easily detected by DSC and sDSC. CONCLUSIONS This study demonstrates the superiority of radiomics features with ICC as a measure for evaluating a physician's tumor segmentation ability and the performance of auto-segmentation tools. Radiomics features offer a more sensitive and comprehensive evaluation, providing valuable insights into tumor characteristics. Therefore, the new metrics can be used to evaluate new auto-segmentation methods and enhance trainees' segmentation skills in medical training and education.
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Affiliation(s)
- Rukhsora Akramova
- Department of Radiation OncologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Yoichi Watanabe
- Department of Radiation OncologyUniversity of MinnesotaMinneapolisMinnesotaUSA
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Ramlee S, Manavaki R, Aloj L, Escudero Sanchez L. Mitigating the impact of image processing variations on tumour [ 18F]-FDG-PET radiomic feature robustness. Sci Rep 2024; 14:16294. [PMID: 39009706 PMCID: PMC11251269 DOI: 10.1038/s41598-024-67239-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 07/09/2024] [Indexed: 07/17/2024] Open
Abstract
Radiomics analysis of [18F]-fluorodeoxyglucose ([18F]-FDG) PET images could be leveraged for personalised cancer medicine. However, the inherent sensitivity of radiomic features to intensity discretisation and voxel interpolation complicates its clinical translation. In this work, we evaluated the robustness of tumour [18F]-FDG-PET radiomic features to 174 different variations in intensity resolution or voxel size, and determined whether implementing parameter range conditions or dependency corrections could improve their robustness. Using 485 patient images spanning three cancer types: non-small cell lung cancer (NSCLC), melanoma, and lymphoma, we observed features were more sensitive to intensity discretisation than voxel interpolation, especially texture features. In most of our investigations, the majority of non-robust features could be made robust by applying parameter range conditions. Correctable features, which were generally fewer than conditionally robust, showed systematic dependence on bin configuration or voxel size that could be minimised by applying corrections based on simple mathematical equations. Melanoma images exhibited limited robustness and correctability relative to NSCLC and lymphoma. Our study provides an in-depth characterisation of the sensitivity of [18F]-FDG-PET features to image processing variations and reinforces the need for careful selection of imaging biomarkers prior to any clinical application.
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Affiliation(s)
- Syafiq Ramlee
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.
| | - Roido Manavaki
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Luigi Aloj
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Lorena Escudero Sanchez
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
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Pistel M, Brock L, Laun FB, Erber R, Weiland E, Uder M, Wenkel E, Ohlmeyer S, Bickelhaupt S. Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions. Diagnostics (Basel) 2024; 14:1427. [PMID: 39001317 PMCID: PMC11241112 DOI: 10.3390/diagnostics14131427] [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] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.
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Affiliation(s)
- Mona Pistel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
- Siemens Healthineers AG, 91052 Erlangen, Germany
| | - Luise Brock
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Ramona Erber
- Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthineers AG, 91052 Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Evelyn Wenkel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
- Radiologie München, 80331 München, Germany
| | - Sabine Ohlmeyer
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Sebastian Bickelhaupt
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
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Takeyama N, Sasaki Y, Ueda Y, Tashiro Y, Tanaka E, Nagai K, Morioka M, Ogawa T, Tate G, Hashimoto T, Ohgiya Y. Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study. Jpn J Radiol 2024; 42:731-743. [PMID: 38472624 PMCID: PMC11217043 DOI: 10.1007/s11604-024-01545-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/02/2024] [Indexed: 03/14/2024]
Abstract
PURPOSE To retrospectively evaluate the diagnostic potential of magnetic resonance imaging (MRI)-based features and radiomics analysis (RA)-based features for discriminating ovarian clear cell carcinoma (CCC) from endometrioid carcinoma (EC). MATERIALS AND METHODS Thirty-five patients with 40 ECs and 42 patients with 43 CCCs who underwent pretherapeutic MRI examinations between 2011 and 2022 were enrolled. MRI-based features of the two groups were compared. RA-based features were extracted from the whole tumor volume on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (cT1WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation method was performed to select features. Logistic regression analysis was conducted to construct the discriminating models. Receiver operating characteristic curve (ROC) analyses were performed to predict CCC. RESULTS Four features with the highest absolute value of the LASSO algorithm were selected for the MRI-based, RA-based, and combined models: the ADC value, absence of thickening of the uterine endometrium, absence of peritoneal dissemination, and growth pattern of the solid component for the MRI-based model; Gray-Level Run Length Matrix (GLRLM) Long Run Low Gray-Level Emphasis (LRLGLE) on T2WI, spherical disproportion and Gray-Level Size Zone Matrix (GLSZM), Large Zone High Gray-Level Emphasis (LZHGE) on cT1WI, and GLSZM Normalized Gray-Level Nonuniformity (NGLN) on ADC map for the RA-based model; and the ADC value, spherical disproportion and GLSZM_LZHGE on cT1WI, and GLSZM_NGLN on ADC map for the combined model. Area under the ROC curves of those models were 0.895, 0.910, and 0.956. The diagnostic performance of the combined model was significantly superior (p = 0.02) to that of the MRI-based model. No significant differences were observed between the combined and RA-based models. CONCLUSION Conventional MRI-based analysis can effectively distinguish CCC from EC. The combination of RA-based features with MRI-based features may assist in differentiating between the two diseases.
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Affiliation(s)
- Nobuyuki Takeyama
- Department of Radiology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo, 142-8666, Japan.
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan.
| | - Yasushi Sasaki
- Department of Obstetrics and Gynecology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, Kanagawa, 227-8501, Japan
| | - Yasuo Ueda
- Department of Pathology and Laboratory Medicine, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Yuki Tashiro
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Eliko Tanaka
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
- Department of Radiology, Kawasaki Saiwai Hospital, 31-27 Ohmiya-Tyo, Saiwai-Ku, Kawasaki City, Kanagawa, 212-0014, Japan
| | - Kyoko Nagai
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Miki Morioka
- Department of Obstetrics and Gynecology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, Kanagawa, 227-8501, Japan
| | - Takafumi Ogawa
- Department of Pathology and Laboratory Medicine, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Genshu Tate
- Department of Pathology and Laboratory Medicine, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Toshi Hashimoto
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Yoshimitsu Ohgiya
- Department of Radiology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo, 142-8666, Japan
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Brown KH, Kerr BN, Pettigrew M, Connor K, Miller IS, Shiels L, Connolly C, McGarry C, Byrne AT, Butterworth KT. A comparative analysis of preclinical computed tomography radiomics using cone-beam and micro-computed tomography scanners. Phys Imaging Radiat Oncol 2024; 31:100615. [PMID: 39157293 PMCID: PMC11328005 DOI: 10.1016/j.phro.2024.100615] [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: 04/11/2024] [Revised: 07/17/2024] [Accepted: 07/20/2024] [Indexed: 08/20/2024] Open
Abstract
Background and purpose Radiomics analysis extracts quantitative data (features) from medical images. These features could potentially reflect biological characteristics and act as imaging biomarkers within precision medicine. However, there is a lack of cross-comparison and validation of radiomics outputs which is paramount for clinical implementation. In this study, we compared radiomics outputs across two computed tomography (CT)-based preclinical scanners. Materials and methods Cone beam CT (CBCT) and µCT scans were acquired using different preclinical CT imaging platforms. The reproducibility of radiomics features on each scanner was assessed using a phantom across imaging energies (40 & 60 kVp) and segmentation volumes (44-238 mm3). Retrospective mouse scans were used to compare feature reliability across varying tissue densities (lung, heart, bone), scanners and after voxel size harmonisation. Reliable features had an intraclass correlation coefficient (ICC) > 0.8. Results First order and GLCM features were the most reliable on both scanners across different volumes. There was an inverse relationship between tissue density and feature reliability, with the highest number of features in lung (CBCT=580, µCT=734) and lowest in bone (CBCT=110, µCT=560). Comparable features for lung and heart tissues increased when voxel sizes were harmonised. We have identified tissue-specific preclinical radiomics signatures in mice for the lung (133), heart (35), and bone (15). Conclusions Preclinical CBCT and µCT scans can be used for radiomics analysis to support the development of meaningful radiomics signatures. This study demonstrates the importance of standardisation and emphasises the need for multi-centre studies.
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Affiliation(s)
- Kathryn H Brown
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Brianna N Kerr
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Mihaela Pettigrew
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
| | - Kate Connor
- Department of Physiology and Medical Physics and Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Ian S Miller
- Department of Physiology and Medical Physics and Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin 2, Ireland
- National Preclinical Imaging Centre, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Liam Shiels
- Department of Physiology and Medical Physics and Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Colum Connolly
- Department of Physiology and Medical Physics and Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Conor McGarry
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Belfast, United Kingdom
| | - Annette T Byrne
- Department of Physiology and Medical Physics and Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin 2, Ireland
- National Preclinical Imaging Centre, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Karl T Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom
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Zhang Z, Lan H, Zhao S. Analysis of the Value of Quantitative Features in Multimodal MRI Images to Construct a Radio-Omics Model for Breast Cancer Diagnosis. BREAST CANCER (DOVE MEDICAL PRESS) 2024; 16:305-318. [PMID: 38895649 PMCID: PMC11182731 DOI: 10.2147/bctt.s458036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024]
Abstract
Objective To analyze the diagnostic value of quantitative features in multimodal magnetic resonance imaging (MRI) images to construct a radio-omics model for breast cancer. Methods Ninety-five patients with breast-related diseases from January 2020 to January 2021 were grouped into the benign group (n=57) and malignant group (n=38) according to the pathological findings. All cases were randomized as the training group (n=66) and validation group (n=29) in a 7:3 ratio based on the examination time. All subjects were examined by T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), dynamic contrast enhancement (DCE), and apparent diffusion coefficient (ADC) multimodality MRI. The MRI findings were analyzed against pathological findings. A diagnostic breast cancer radiomics model was constructed. The diagnostic efficacy of the model in the validation group was analyzed, and the diagnostic efficacy was analyzed via the ROC curve. Results Fibroadenoma accounted for 49.12% of benign breast diseases, and invasive ductal carcinoma accounted for 73.68% of malignant breast diseases. The sensitivity of T1WI, T2WI, DWI, ADC, and DCE in diagnosing breast cancer was 61.14%, 66.67%, 73.30%, 78.95%, and 85.96%, using the four-fold table method. The area under the curves (AUCs) of T1WI, T2WI, DWI, ADC, and DCE for diagnosing breast cancer were 0.715, 0.769, 0.785, 0.835, and 0.792, respectively. The AUCs of plain scan, diffuse, enhanced, plain scan + diffuse, plain scan + enhanced, enhanced + diffuse, and plain scan + enhanced + diffuse for diagnosing breast cancer were 0.746, 0.798, 0.816, 0.839, 0.890, 0.906, and 0.927, respectively. Conclusion The construction of a radio-omics model by quantitative features in multimodal MRI images was valuable in the diagnosis of breast cancer. The value of radio-omics models such as plain scan + enhanced + diffuse was higher than the other models in diagnosing breast cancer and could be widely applied in clinical practice.
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Affiliation(s)
- Zhitao Zhang
- Department of Galactophore, Fujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, 350001, People’s Republic of China
| | - Huan Lan
- Department of Galactophore, Fujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, 350001, People’s Republic of China
| | - Shuai Zhao
- Department of Galactophore, Fujian Maternity and Child Health Hospital, Fuzhou, Fujian Province, 350001, People’s Republic of China
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Li Z, Wu Y, Liu M, Miao D, Zhang D, Ding W. A novel quantitative method to evaluate lumbar disc degeneration: MRI histogram analysis. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2024; 33:2420-2429. [PMID: 38705902 DOI: 10.1007/s00586-024-08279-6] [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: 01/24/2024] [Revised: 01/24/2024] [Accepted: 04/16/2024] [Indexed: 05/07/2024]
Abstract
PURPOSE This study aimed to use MRI histogram analysis to routine MRI sequences to evaluate lumbar disc degeneration (LDD), illustrate the correlation between this novel method and the traditional Pfirrmann classification method, and more importantly, perform comprehensive agreement analysis of MRI histogram analysis in various situations to evaluate its objectivity and stability. METHODS Lumbar MRI images from 133 subjects were included in this study. LDD was classified into grades by Pfirrmann classification and was measured as peak separation value by MRI histogram analysis. Correlation analysis between the two methods was performed and cutoff values were determined. In addition, the agreement analysis of peak separation value was performed by intraclass correlation coefficient (ICC) in four scenarios, including inter-resolution, inter-observer, inter-regions of interest (ROI) and inter-slice. RESULTS Peak separation values were strongly correlated with Pfirrmann grades (r = - 0.847). The inter-resolution agreements of peak separation value between original image resolution of 2304 × 2304 and compressed image resolutions (1152 × 1152, 576 × 576, 288 × 288) were good to excellent (ICCs were 0.916, 0.876 and 0.822), except 144 × 144 was moderate (ICC = 533). The agreements of inter-observer (ICC = 0.982) and inter-ROI (ICC = 0.915) were excellent. Compared with the mid-sagittal slice, the inter-slice agreements were good for the first adjacent slices (ICCs were 0.826 and 0.844), and moderate to good for the second adjacent slices (ICC = 0.733 and 0.753). CONCLUSION MRI histogram analysis, used in routine MRI sequences, demonstrated a strong correlation with Pfirrmann classification and good agreements in various scenarios, expanding the range of application and providing an effective, objective and quantitative tool to evaluate LDD.
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Affiliation(s)
- Zhaohui Li
- Department of Spine Surgery, Hebei Medical University Third Hospital, 139 Ziqiang Road, Shijiazhuang, 050051, China
| | - Yurui Wu
- School of Public Health, Hebei Medical University, 361 East Zhongshan Road, Shijiazhuang, 050017, China
| | - Mengyang Liu
- School of Public Health, Hebei Medical University, 361 East Zhongshan Road, Shijiazhuang, 050017, China
| | - Dazhuang Miao
- Department of Spine Surgery, Hebei Medical University Third Hospital, 139 Ziqiang Road, Shijiazhuang, 050051, China
| | - Di Zhang
- Department of Spine Surgery, Hebei Medical University Third Hospital, 139 Ziqiang Road, Shijiazhuang, 050051, China.
| | - Wenyuan Ding
- Department of Spine Surgery, Hebei Medical University Third Hospital, 139 Ziqiang Road, Shijiazhuang, 050051, China.
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Dai X, Zhao B, Zang J, Wang X, Liu Z, Sun T, Yu H, Sui X. Diagnostic Performance of Radiomics and Deep Learning to Identify Benign and Malignant Soft Tissue Tumors: A Systematic Review and Meta-analysis. Acad Radiol 2024:S1076-6332(24)00197-1. [PMID: 38614826 DOI: 10.1016/j.acra.2024.03.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 03/24/2024] [Accepted: 03/24/2024] [Indexed: 04/15/2024]
Abstract
RATIONALE AND OBJECTIVES To systematically evaluate the application value of radiomics and deep learning (DL) in the differential diagnosis of benign and malignant soft tissue tumors (STTs). MATERIALS AND METHODS A systematic review was conducted on studies published up to December 11, 2023, that utilized radiomics and DL methods for the diagnosis of STTs. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS) 2.0 system and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, respectively. A bivariate random-effects model was used to calculate the summarized sensitivity and specificity. To identify factors contributing to heterogeneity, meta-regression and subgroup analyses were performed to assess the following covariates: diagnostic modality, region/volume of interest, imaging examination, study design, and pathology type. The asymmetry of Deeks' funnel plot was used to assess publication bias. RESULTS A total of 21 studies involving 3866 patients were included, with 13 studies using independent test/validation sets included in the quantitative statistical analysis. The average RQS was 21.31, with substantial or near-perfect inter-rater agreement. The combined sensitivity and specificity were 0.84 (95% CI: 0.76-0.89) and 0.88 (95% CI: 0.69-0.96), respectively. Meta-regression and subgroup analyses showed that study design and the region/volume of interest were significant factors affecting study heterogeneity (P < 0.05). No publication bias was observed. CONCLUSION Radiomics and DL can accurately distinguish between benign and malignant STTs. Future research should concentrate on enhancing the rigor of study designs, conducting multicenter prospective validations, amplifying the interpretability of DL models, and integrating multimodal data to elevate the diagnostic accuracy and clinical utility of soft tissue tumor assessments.
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Affiliation(s)
- Xinpeng Dai
- Department of Ultrasound, Hebei Medical University Third Hospital, Hebei, China
| | - Bingxin Zhao
- Department of Ultrasound, Hebei Medical University Third Hospital, Hebei, China
| | - Jiangnan Zang
- Hebei Medical University, Shijiazhuang, Hebei, China
| | - Xinying Wang
- Department of Ultrasound, Hebei Medical University Third Hospital, Hebei, China
| | - Zongjie Liu
- Department of Ultrasound, Hebei Medical University Third Hospital, Hebei, China
| | - Tao Sun
- Department of Orthopaedic Oncology, Hebei Medical University Third Hospital, Hebei, China
| | - Hong Yu
- Department of CT/MR, Hebei Medical University Third Hospital, Hebei, China
| | - Xin Sui
- Department of Ultrasound, Hebei Medical University Third Hospital, No.139 Ziqiang road, Qiaoxi Area, Shijiazhuang, Hebei Province, China.
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Cheong EN, Park JE, Park SY, Jung SC, Kim HS. Achieving imaging and computational reproducibility on multiparametric MRI radiomics features in brain tumor diagnosis: phantom and clinical validation. Eur Radiol 2024; 34:2008-2023. [PMID: 37665391 DOI: 10.1007/s00330-023-10164-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 07/02/2023] [Accepted: 07/06/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES The Image Biomarker Standardization Initiative has helped improve the computational reproducibility of MRI radiomics features. Nonetheless, the MRI sequences and features with high imaging reproducibility are yet to be established. To determine reproducible multiparametric MRI radiomics features across test-retest, multi-scanner, and computational reproducibility comparisons, and to evaluate their clinical value in brain tumor diagnosis. METHODS To assess reproducibility, T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) were acquired from three 3-T MRI scanners using standardized phantom, and radiomics features were extracted using two computational algorithms. Reproducible radiomics features were selected when the concordance correlation coefficient value above 0.9 across multiple sessions, scanners, and computational algorithms. Random forest classifiers were trained with reproducible features (n = 117) and validated in a clinical cohort (n = 50) to evaluate whether features with high reproducibility improved the differentiation of glioblastoma from primary central nervous system lymphomas (PCNSLs). RESULTS Radiomics features from T2WI demonstrated higher repeatability (65-94%) than those from DWI (38-48%) or T1WI (2-92%). Across test-retest, multi-scanner, and computational comparisons, T2WI provided 41 reproducible features, DWI provided six, and T1WI provided two. The performance of the classification model with reproducible features was higher than that using non-reproducible features in both training set (AUC, 0.916 vs. 0.877) and validation set (AUC, 0.957 vs. 0.869). CONCLUSION Radiomics features with high reproducibility across multiple sessions, scanners, and computational algorithms were identified, and they showed higher diagnostic performance than non-reproducible radiomics features in the differentiation of glioblastoma from PCNSL. CLINICAL RELEVANCE STATEMENT By identifying the radiomics features showing higher multi-machine reproducibility, our results also demonstrated higher radiomics diagnostic performance in the differentiation of glioblastoma from PCNSL, paving the way for further research designs and clinical application in neuro-oncology. KEY POINTS • Highly reproducible radiomics features across multiple sessions, scanners, and computational algorithms were identified using phantom and applied to clinical diagnosis. • Radiomics features from T2-weighted imaging were more reproducible than those from T1-weighted and diffusion-weighted imaging. • Radiomics features with good reproducibility had better diagnostic performance for brain tumors than features with poor reproducibility.
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Affiliation(s)
- E-Nae Cheong
- Department of Medical Science and Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
| | - Seo Young Park
- Department of Statistics and Data Science, Korea National Open University, Seoul, Republic of Korea
| | - Seung Chai Jung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
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11
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Chen X, Wang T, Ji Z, Luo J, Lv W, Wang H, Zhao Y, Duan C, Yu X, Li Q, Zhang J, Chen J, Zhang X, Huang M, Zhou S, Lu L, Huang M, Fu S. 3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study. Hepatol Int 2023; 17:1545-1556. [PMID: 37531069 PMCID: PMC10661776 DOI: 10.1007/s12072-023-10570-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 07/07/2023] [Indexed: 08/03/2023]
Abstract
BACKGROUND Overt hepatic encephalopathy (HE) should be predicted preoperatively to identify suitable candidates for transjugular intrahepatic portosystemic shunt (TIPS) instead of first-line treatment. This study aimed to construct a 3D assessment-based model to predict post-TIPS overt HE. METHODS In this multi-center cohort study, 487 patients who underwent TIPS were subdivided into a training dataset (390 cases from three hospitals) and an external validation dataset (97 cases from another two hospitals). Candidate factors included clinical, vascular, and 2D and 3D data. Combining the least absolute shrinkage and operator method, support vector machine, and probability calibration by isotonic regression, we constructed four predictive models: clinical, 2D, 3D, and combined models. Their discrimination and calibration were compared to identify the optimal model, with subgroup analysis performed. RESULTS The 3D model showed better discrimination than did the 2D model (training: 0.719 vs. 0.691; validation: 0.730 vs. 0.622). The model combining clinical and 3D factors outperformed the clinical and 3D models (training: 0.802 vs. 0.735 vs. 0.719; validation: 0.816 vs. 0.723 vs. 0.730; all p < 0.050). Moreover, the combined model had the best calibration. The performance of the best model was not affected by the total bilirubin level, Child-Pugh score, ammonia level, or the indication for TIPS. CONCLUSION 3D assessment of the liver and the spleen provided additional information to predict overt HE, improving the chance of TIPS for suitable patients. 3D assessment could also be used in similar studies related to cirrhosis.
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Affiliation(s)
- Xiaoqiong Chen
- Zhuhai Interventional Medical Centre, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, China
- Zhuhai Engineering Technology Research Center of Intelligent Medical Imaging, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), Zhuhai, China
| | - Tao Wang
- School of Biomedical Engineering, Southern Medical University, No. 1023-1063 Shatai Road, Guangzhou, 510515, Guangdong, China
| | - Zhonghua Ji
- Department of Anesthesia, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Junyang Luo
- Department of Interventional Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Weifu Lv
- Interventional Radiology Department, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Haifang Wang
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yujie Zhao
- Zhuhai Interventional Medical Centre, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, China
- Zhuhai Engineering Technology Research Center of Intelligent Medical Imaging, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), Zhuhai, China
| | - Chongyang Duan
- Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Xiangrong Yu
- Department of Radiology, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), Zhuhai, China
| | - Qiyang Li
- Department of Radiology, Shenzhen People's Hospital, Shenzhen, China
| | - Jiawei Zhang
- School of Biomedical Engineering, Southern Medical University, No. 1023-1063 Shatai Road, Guangzhou, 510515, Guangdong, China
| | - Jinqiang Chen
- Zhuhai Interventional Medical Centre, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, China
- Zhuhai Engineering Technology Research Center of Intelligent Medical Imaging, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), Zhuhai, China
| | - Xiaoling Zhang
- School of Biomedical Engineering, Southern Medical University, No. 1023-1063 Shatai Road, Guangzhou, 510515, Guangdong, China
| | - Mingsheng Huang
- Department of Interventional Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shuoling Zhou
- School of Biomedical Engineering, Southern Medical University, No. 1023-1063 Shatai Road, Guangzhou, 510515, Guangdong, China
| | - Ligong Lu
- Zhuhai Interventional Medical Centre, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, China.
| | - Meiyan Huang
- School of Biomedical Engineering, Southern Medical University, No. 1023-1063 Shatai Road, Guangzhou, 510515, Guangdong, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
| | - Sirui Fu
- Zhuhai Interventional Medical Centre, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, China.
- Zhuhai Engineering Technology Research Center of Intelligent Medical Imaging, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), Zhuhai, China.
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12
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Lin CH, Yan JL, Yap WK, Kang CJ, Chang YC, Tsai TY, Chang KP, Liao CT, Hsu CL, Chou WC, Wang HM, Huang PW, Fan KH, Huang BS, Tung-Chieh Chang J, Tu SJ, Lin CY. Prognostic value of interim CT-based peritumoral and intratumoral radiomics in laryngeal and hypopharyngeal cancer patients undergoing definitive radiotherapy. Radiother Oncol 2023; 189:109938. [PMID: 37806562 DOI: 10.1016/j.radonc.2023.109938] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 09/20/2023] [Accepted: 10/02/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND AND PURPOSE We aimed to investigate the prognostic value of peritumoral and intratumoral computed tomography (CT)-based radiomics during the course of radiotherapy (RT) in patients with laryngeal and hypopharyngeal cancer (LHC). MATERIALS AND METHODS A total of 92 eligible patients were 1:1 randomly assigned into training and validation cohorts. Pre-RT and mid-RT radiomic features were extracted from pre-treatment and interim CT. LASSO-Cox regression was used for feature selection and model construction. Time-dependent area under the receiver operating curve (AUC) analysis was applied to evaluate the models' prognostic performances. Risk stratification ability on overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method and Cox regression. The associations between radiomics and clinical parameters as well as circulating lymphocyte counts were also evaluated. RESULTS The mid-RT peritumoral (AUC: 0.77) and intratumoral (AUC: 0.79) radiomic models yielded better performance for predicting OS than the pre-RT intratumoral model (AUC: 0.62) in validation cohort. This was confirmed by Kaplan-Meier analysis, in which risk stratification depended on the mid-RT peritumoral (p = 0.009) and intratumoral (p = 0.003) radiomics could be improved for OS, in comparison to the pre-RT intratumoral radiomics (p = 0.199). Multivariate analysis identified mid-RT peritumoral and intratumoral radiomic models as independent prognostic factors for both OS and PFS. Mid-RT peritumoral and intratumoral radiomics were correlated with treatment-related lymphopenia. CONCLUSION Mid-RT peritumoral and intratumoral radiomic models are promising image biomarkers that could have clinical utility for predicting OS and PFS in patients with LHC treated with RT.
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Affiliation(s)
- Chia-Hsin Lin
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
| | - Jiun-Lin Yan
- Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan; School of Traditional Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Wing-Keen Yap
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
| | - Chung-Jan Kang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Yun-Chen Chang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Tsung-You Tsai
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Kai-Ping Chang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Chun-Ta Liao
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Cheng-Lung Hsu
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Wen-Chi Chou
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Hung-Ming Wang
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Pei-Wei Huang
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Kang-Hsing Fan
- Department of Radiation Oncology, New Taipei Municipal Tucheng Hospital, New Taipei City, Taiwan.
| | - Bing-Shen Huang
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Science, Chang Gung University, Taoyuan, Taiwan.
| | - Joseph Tung-Chieh Chang
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan; Department of Radiation Oncology, Xiamen Chang Gung Memorial Hospital, Xiamen, Fujian, China.
| | - Shu-Ju Tu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.
| | - Chien-Yu Lin
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
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13
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Han Q, Lu Y, Wang D, Li X, Ruan Z, Mei N, Ji X, Geng D, Yin B. Glioblastomas with and without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity present morphological and microstructural differences on conventional MR images. Eur Radiol 2023; 33:9139-9151. [PMID: 37495706 DOI: 10.1007/s00330-023-09924-2] [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: 06/03/2022] [Revised: 05/04/2023] [Accepted: 05/14/2023] [Indexed: 07/28/2023]
Abstract
OBJECTIVES Glioblastoma (GB) without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity is atypical and its characteristics are barely known. The aim of this study was to explore the differences in pathological and MRI-based intrinsic features (including morphologic and first-order features) between GBs with peritumoral FLAIR hyperintensity (PFH-bearing GBs) and GBs without peritumoral FLAIR hyperintensity (PFH-free GBs). METHODS In total, 155 patients with pathologically diagnosed GBs were retrospectively collected, which included 110 PFH-bearing GBs and 45 PFH-free GBs. The pathological and imaging data were collected. The Visually AcceSAble Rembrandt Images (VASARI) features were carefully evaluated. The first-order radiomics features from the tumor region were extracted from FLAIR, apparent diffusion coefficient (ADC), and T1CE (T1-contrast enhanced) images. All parameters were compared between the two groups of GBs. RESULTS The pathological data showed more alpha thalassemia/mental retardation syndrome X-linked (ATRX)-loss in PFH-free GBs compared to PFH-bearing ones (p < 0.001). Based on VASARI evaluation, PFH-free GBs had larger intra-tumoral enhancing proportion and smaller necrotic proportion (both, p < 0.001), more common non-enhancing tumor (p < 0.001), mild/minimal enhancement (p = 0.003), expansive T1/FLAIR ratio (p < 0.001) and solid enhancement (p = 0.009), and less pial invasion (p = 0.010). Moreover, multiple ADC- and T1CE-based first-order radiomics features demonstrated differences, especially the lower intensity heterogeneity in PFH-free GBs (for all, adjusted p < 0.05). CONCLUSIONS Compared to PFH-bearing GBs, PFH-free ones demonstrated less immature neovascularization and lower intra-tumoral heterogeneity, which would be helpful in clinical treatment stratification. CLINICAL RELEVANCE STATEMENT Glioblastomas without peritumoral FLAIR hyperintensity show less immature neovascularization and lower heterogeneity leading to potential higher treatment benefits due to less drug resistance and treatment failure. KEY POINTS • The study explored the differences between glioblastomas with and without peritumoral FLAIR hyperintensity. • Glioblastomas without peritumoral FLAIR hyperintensity showed less necrosis and contrast enhancement and lower intensity heterogeneity. • Glioblastomas without peritumoral FLAIR hyperintensity had less immature neovascularization and lower tumor heterogeneity.
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Affiliation(s)
- Qiuyue Han
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Yiping Lu
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Dongdong Wang
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Zhuoying Ruan
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Nan Mei
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Xiong Ji
- Department of Pathology, Huashan Hospital, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China.
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Shanghai, China.
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China.
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14
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Fiscone C, Rundo L, Lugaresi A, Manners DN, Allinson K, Baldin E, Vornetti G, Lodi R, Tonon C, Testa C, Castelli M, Zaccagna F. Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis. Sci Rep 2023; 13:16239. [PMID: 37758804 PMCID: PMC10533494 DOI: 10.1038/s41598-023-42914-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference (p > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications.
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Affiliation(s)
- Cristiana Fiscone
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy
| | - Alessandra Lugaresi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - David Neil Manners
- Department for Life Quality Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Kieren Allinson
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Elisa Baldin
- Epidemiology and Statistics Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Gianfranco Vornetti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Claudia Testa
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy.
| | - Mauro Castelli
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal
| | - Fulvio Zaccagna
- Department of Imaging, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Investigative Medicine Division, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
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15
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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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16
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Jensen LJ, Kim D, Elgeti T, Steffen IG, Schaafs LA, Hamm B, Nagel SN. The role of parametric feature maps to correct different volume of interest sizes: an in vivo liver MRI study. Eur Radiol Exp 2023; 7:48. [PMID: 37670193 PMCID: PMC10480134 DOI: 10.1186/s41747-023-00362-9] [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: 04/23/2023] [Accepted: 06/13/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND Different volume of interest (VOI) sizes influence radiomic features. This study examined if translating images into feature maps before feature sampling could compensate for these effects in liver magnetic resonance imaging (MRI). METHODS T1- and T2-weighted sequences from three different scanners (two 3-T scanners, one 1.5-T scanner) of 66 patients with normal abdominal MRI were included retrospectively. Three differently sized VOIs (10, 20, and 30 mm in diameter) were drawn in the liver parenchyma (right lobe), excluding adjacent structures. Ninety-three features were extracted conventionally using PyRadiomics. All images were also converted to 93 parametric feature maps using a pretested software. Agreement between the three VOI sizes was assessed with overall concordance correlation coefficients (OCCCs), while OCCCs > 0.85 were rated reproducible. OCCCs were calculated twice: for the VOI sizes of 10, 20, and 30 mm and for those of 20 and 30 mm. RESULTS When extracted from original images, only 4 out of the 93 features were reproducible across all VOI sizes in T1- and T2-weighted images. When the smallest VOI was excluded, 5 features (T1-weighted) and 7 features (T2-weighted) were reproducible. Extraction from parametric maps increased the number of reproducible features to 9 (T1- and T2-weighted) across all VOIs. Excluding the 10-mm VOI, reproducibility improved to 16 (T1-weighted) and 55 features (T2-weighted). The stability of all other features also increased in feature maps. CONCLUSIONS Translating images into parametric maps before feature extraction improves reproducibility across different VOI sizes in normal liver MRI. RELEVANCE STATEMENT The size of the segmented VOI influences the feature quantity of radiomics, while software-based conversion of images into parametric feature maps before feature sampling improves reproducibility across different VOI sizes in MRI of normal liver tissue. KEY POINTS • Parametric feature maps can compensate for different VOI sizes. • The effect seems dependent on the VOI sizes and the MRI sequence. • Feature maps can visualize features throughout the entire image stack.
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Affiliation(s)
- Laura Jacqueline Jensen
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Damon Kim
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Thomas Elgeti
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Ingo Günter Steffen
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Lars-Arne Schaafs
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Bernd Hamm
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Sebastian Niko Nagel
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
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17
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Monti S, Truppa ME, Albanese S, Mancini M. Radiomics and Radiogenomics in Preclinical Imaging on Murine Models: A Narrative Review. J Pers Med 2023; 13:1204. [PMID: 37623455 PMCID: PMC10455673 DOI: 10.3390/jpm13081204] [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: 06/09/2023] [Revised: 07/18/2023] [Accepted: 07/27/2023] [Indexed: 08/26/2023] Open
Abstract
Over the past decade, medical imaging technologies have become increasingly significant in both clinical and preclinical research, leading to a better understanding of disease processes and the development of new diagnostic and theranostic methods. Radiomic and radiogenomic approaches have furthered this progress by exploring the relationship between imaging characteristics, genomic information, and outcomes that qualitative interpretations may have overlooked, offering valuable insights for personalized medicine. Preclinical research allows for a controlled environment where various aspects of a pathology can be replicated in animal models, providing radiomic and radiogenomic approaches with the unique opportunity to investigate the causal connection between imaging and molecular factors. The aim of this review is to present the current state of the art in the application of radiomics and radiogenomics on murine models. This review will provide a brief description of relevant articles found in the literature with a discussion on the implications and potential translational relevance of these findings.
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Affiliation(s)
| | | | - Sandra Albanese
- National Research Council, Institute of Biostructures and Bioimaging, 80145 Naples, Italy; (S.M.); (M.E.T.); (M.M.)
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18
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Gaudiano C, Mottola M, Bianchi L, Corcioni B, Braccischi L, Tomassoni MT, Cattabriga A, Cocozza MA, Giunchi F, Schiavina R, Fanti S, Fiorentino M, Brunocilla E, Mosconi C, Bevilacqua A. An Apparent Diffusion Coefficient-Based Machine Learning Model Can Improve Prostate Cancer Detection in the Grey Area of the Prostate Imaging Reporting and Data System Category 3: A Single-Centre Experience. Cancers (Basel) 2023; 15:3438. [PMID: 37444548 DOI: 10.3390/cancers15133438] [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: 05/22/2023] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone noninvasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis.
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Affiliation(s)
- Caterina Gaudiano
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Margherita Mottola
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Lorenzo Bianchi
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Beniamino Corcioni
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Lorenzo Braccischi
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | | | - Arrigo Cattabriga
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Maria Adriana Cocozza
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Francesca Giunchi
- Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Riccardo Schiavina
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Stefano Fanti
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Department of Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Michelangelo Fiorentino
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Eugenio Brunocilla
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Cristina Mosconi
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering (DISI), University of Bologna, 40126 Bologna, Italy
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19
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Dinis Fernandes C, Schaap A, Kant J, van Houdt P, Wijkstra H, Bekers E, Linder S, Bergman AM, van der Heide U, Mischi M, Zwart W, Eduati F, Turco S. Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer. Cancers (Basel) 2023; 15:3074. [PMID: 37370685 DOI: 10.3390/cancers15123074] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/11/2023] [Accepted: 05/16/2023] [Indexed: 06/29/2023] Open
Abstract
Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.
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Affiliation(s)
- Catarina Dinis Fernandes
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Annekoos Schaap
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Joan Kant
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Petra van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Hessel Wijkstra
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Department of Urology, Amsterdam University Medical Centers, 1100 DD Amsterdam, The Netherlands
| | - Elise Bekers
- Department of Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Simon Linder
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Andries M Bergman
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- Division of Medical Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Uulke van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Massimo Mischi
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Wilbert Zwart
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Federica Eduati
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Simona Turco
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
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20
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Salmanpour MR, Rezaeijo SM, Hosseinzadeh M, Rahmim A. Deep versus Handcrafted Tensor Radiomics Features: Prediction of Survival in Head and Neck Cancer Using Machine Learning and Fusion Techniques. Diagnostics (Basel) 2023; 13:diagnostics13101696. [PMID: 37238180 DOI: 10.3390/diagnostics13101696] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/22/2023] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Although handcrafted radiomics features (RF) are commonly extracted via radiomics software, employing deep features (DF) extracted from deep learning (DL) algorithms merits significant investigation. Moreover, a "tensor'' radiomics paradigm where various flavours of a given feature are generated and explored can provide added value. We aimed to employ conventional and tensor DFs, and compare their outcome prediction performance to conventional and tensor RFs. METHODS 408 patients with head and neck cancer were selected from TCIA. PET images were first registered to CT, enhanced, normalized, and cropped. We employed 15 image-level fusion techniques (e.g., dual tree complex wavelet transform (DTCWT)) to combine PET and CT images. Subsequently, 215 RFs were extracted from each tumor in 17 images (or flavours) including CT only, PET only, and 15 fused PET-CT images through the standardized-SERA radiomics software. Furthermore, a 3 dimensional autoencoder was used to extract DFs. To predict the binary progression-free-survival-outcome, first, an end-to-end CNN algorithm was employed. Subsequently, we applied conventional and tensor DFs vs. RFs as extracted from each image to three sole classifiers, namely multilayer perceptron (MLP), random-forest, and logistic regression (LR), linked with dimension reduction algorithms. RESULTS DTCWT fusion linked with CNN resulted in accuracies of 75.6 ± 7.0% and 63.4 ± 6.7% in five-fold cross-validation and external-nested-testing, respectively. For the tensor RF-framework, polynomial transform algorithms + analysis of variance feature selector (ANOVA) + LR enabled 76.67 ± 3.3% and 70.6 ± 6.7% in the mentioned tests. For the tensor DF framework, PCA + ANOVA + MLP arrived at 87.0 ± 3.5% and 85.3 ± 5.2% in both tests. CONCLUSIONS This study showed that tensor DF combined with proper machine learning approaches enhanced survival prediction performance compared to conventional DF, tensor and conventional RF, and end-to-end CNN frameworks.
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Affiliation(s)
- Mohammad R Salmanpour
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Technological Virtual Collaboration (TECVICO CORP.), Vancouver, BC V5E 3J7, Canada
| | - Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 6135715794, Iran
| | - Mahdi Hosseinzadeh
- Technological Virtual Collaboration (TECVICO CORP.), Vancouver, BC V5E 3J7, Canada
- Department of Electrical & Computer Engineering, University of Tarbiat Modares, Tehran 14115111, Iran
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Department of Physics & Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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21
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Zhang H. The National Cancer Institute's Co-Clinical Quantitative Imaging Research Resources for Precision Medicine in Preclinical and Clinical Settings. Tomography 2023; 9:931-941. [PMID: 37218936 DOI: 10.3390/tomography9030076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/31/2023] [Accepted: 04/27/2023] [Indexed: 05/24/2023] Open
Abstract
Genetically engineered mouse models (GEMMs) and patient-derived xenograft mouse models (PDXs) can recapitulate important biological features of cancer. They are often part of precision medicine studies in a co-clinical setting, in which therapeutic investigations are conducted in patients and in parallel (or sequentially) in cohorts of GEMMs or PDXs. Employing radiology-based quantitative imaging in these studies allows in vivo assessment of disease response in real time, providing an important opportunity to bridge precision medicine from the bench to the bedside. The Co-Clinical Imaging Research Resource Program (CIRP) of the National Cancer Institute focuses on the optimization of quantitative imaging methods to improve co-clinical trials. The CIRP supports 10 different co-clinical trial projects, spanning diverse tumor types, therapeutic interventions, and imaging modalities. Each CIRP project is tasked to deliver a unique web resource to support the cancer community with the necessary methods and tools to conduct co-clinical quantitative imaging studies. This review provides an update of the CIRP web resources, network consensus, technology advances, and a perspective on the future of the CIRP. The presentations in this special issue of Tomography were contributed by the CIRP working groups, teams, and associate members.
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Affiliation(s)
- Huiming Zhang
- Cancer Imaging Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, NIH, Bethesda, MD 20892, USA
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22
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Liu Y, Wu W, Cai C, Zhang H, Shen H, Han Y. Patient-derived xenograft models in cancer therapy: technologies and applications. Signal Transduct Target Ther 2023; 8:160. [PMID: 37045827 PMCID: PMC10097874 DOI: 10.1038/s41392-023-01419-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/21/2023] [Indexed: 04/14/2023] Open
Abstract
Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.
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Affiliation(s)
- Yihan Liu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Wantao Wu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Changjing Cai
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Hao Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Hong Shen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China.
| | - Ying Han
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China.
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23
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Brown KH, Payan N, Osman S, Ghita M, Walls GM, Patallo IS, Schettino G, Prise KM, McGarry CK, Butterworth KT. Development and optimisation of a preclinical cone beam computed tomography-based radiomics workflow for radiation oncology research. Phys Imaging Radiat Oncol 2023; 26:100446. [PMID: 37252250 PMCID: PMC10213103 DOI: 10.1016/j.phro.2023.100446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/03/2023] [Accepted: 05/08/2023] [Indexed: 05/31/2023] Open
Abstract
Background and purpose Radiomics features derived from medical images have the potential to act as imaging biomarkers to improve diagnosis and predict treatment response in oncology. However, the complex relationships between radiomics features and the biological characteristics of tumours are yet to be fully determined. In this study, we developed a preclinical cone beam computed tomography (CBCT) radiomics workflow with the aim to use in vivo models to further develop radiomics signatures. Materials and methods CBCT scans of a mouse phantom were acquired using onboard imaging from a small animal radiotherapy research platform (SARRP, Xstrahl). The repeatability and reproducibility of radiomics outputs were compared across different imaging protocols, segmentation sizes, pre-processing parameters and materials. Robust features were identified and used to compare scans of two xenograft mouse tumour models (A549 and H460). Results Changes to the radiomics workflow significantly impact feature robustness. Preclinical CBCT radiomics analysis is feasible with 119 stable features identified from scans imaged at 60 kV, 25 bin width and 0.26 mm slice thickness. Large variation in segmentation volumes reduced the number of reliable radiomics features for analysis. Standardization in imaging and analysis parameters is essential in preclinical radiomics analysis to improve accuracy of outputs, leading to more consistent and reproducible findings. Conclusions We present the first optimised workflow for preclinical CBCT radiomics to identify imaging biomarkers. Preclinical radiomics has the potential to maximise the quantity of data captured in in vivo experiments and could provide key information supporting the wider application of radiomics.
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Affiliation(s)
- Kathryn H. Brown
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Neree Payan
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Sarah Osman
- University College London Hospitals NHS Foundation Trust Department of Radiotherapy, London, UK
| | - Mihaela Ghita
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Gerard M. Walls
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
- Cancer Centre, Belfast Health & Social Care Trust, Lisburn Road, Belfast BT9 7AB, Northern Ireland, UK
| | | | | | - Kevin M. Prise
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Conor K. McGarry
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
- Cancer Centre, Belfast Health & Social Care Trust, Lisburn Road, Belfast BT9 7AB, Northern Ireland, UK
| | - Karl T. Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
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Lévi-Strauss T, Tortorici B, Lopez O, Viau P, Ouizeman DJ, Schall B, Adhoute X, Humbert O, Chevallier P, Gual P, Fillatre L, Anty R. Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma. Diagnostics (Basel) 2023; 13:diagnostics13071303. [PMID: 37046521 PMCID: PMC10093101 DOI: 10.3390/diagnostics13071303] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/23/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
Radiomics is a discipline that involves studying medical images through their digital data. Using “artificial intelligence” algorithms, radiomics utilizes quantitative and high-throughput analysis of an image’s textural richness to obtain relevant information for clinicians, from diagnosis assistance to therapeutic guidance. Exploitation of these data could allow for a more detailed characterization of each phenotype, for each patient, making radiomics a new biomarker of interest, highly promising in the era of precision medicine. Moreover, radiomics is non-invasive, cost-effective, and easily reproducible in time. In the field of oncology, it performs an analysis of the entire tumor, which is impossible with a single biopsy but is essential for understanding the tumor’s heterogeneity and is known to be closely related to prognosis. However, current results are sometimes less accurate than expected and often require the addition of non-radiomics data to create a performing model. To highlight the strengths and weaknesses of this new technology, we take the example of hepatocellular carcinoma and show how radiomics could facilitate its diagnosis in difficult cases, predict certain histological features, and estimate treatment response, whether medical or surgical.
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Affiliation(s)
- Thomas Lévi-Strauss
- Hepatology Unit, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France; (T.L.-S.)
| | - Bettina Tortorici
- Department of Diagnosis and Interventional Imaging, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Olivier Lopez
- Department of Diagnosis and Interventional Imaging, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Philippe Viau
- Department of Nuclear Medicine, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Dann J. Ouizeman
- Hepatology Unit, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France; (T.L.-S.)
| | | | - Xavier Adhoute
- Saint Joseph Hospital, 26 Bd de Louvain, 13008 Marseille, France
| | - Olivier Humbert
- Centre Antoine-Lacassagne, Department of Nuclear Medicine, 33 Av. de Valombrose, 06100 Nice, France
- TIRO-UMR E 4320, Université Côte d’Azur, 06000 Nice, France
| | - Patrick Chevallier
- Department of Diagnosis and Interventional Imaging, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Philippe Gual
- INSERM, U1065, C3M, Université Côte d’Azur, 06000 Nice, France
- Correspondence: (P.G.); (R.A.)
| | | | - Rodolphe Anty
- Hepatology Unit, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France; (T.L.-S.)
- INSERM, U1065, C3M, Université Côte d’Azur, 06000 Nice, France
- Correspondence: (P.G.); (R.A.)
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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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Peehl DM, Badea CT, Chenevert TL, Daldrup-Link HE, Ding L, Dobrolecki LE, Houghton AM, Kinahan PE, Kurhanewicz J, Lewis MT, Li S, Luker GD, Ma CX, Manning HC, Mowery YM, O’Dwyer PJ, Pautler RG, Rosen MA, Roudi R, Ross BD, Shoghi KI, Sriram R, Talpaz M, Wahl RL, Zhou R. Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials. Tomography 2023; 9:657-680. [PMID: 36961012 PMCID: PMC10037611 DOI: 10.3390/tomography9020053] [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: 01/26/2023] [Revised: 03/08/2023] [Accepted: 03/08/2023] [Indexed: 03/19/2023] Open
Abstract
The availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on patients that are carried out parallel to or sequentially with animal models of cancer that mirror the biology of the patients' tumors. Patient-derived xenografts (PDX) and genetically engineered mouse models (GEMM) are considered to be the models that best represent human disease and have high translational value. Notably, one element of co-clinical trials that still needs significant optimization is quantitative imaging. The National Cancer Institute has organized a Co-Clinical Imaging Resource Program (CIRP) network to establish best practices for co-clinical imaging and to optimize translational quantitative imaging methodologies. This overview describes the ten co-clinical trials of investigators from eleven institutions who are currently supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group. Each team describes their corresponding clinical trial, type of cancer targeted, rationale for choice of animal models, therapy, and imaging modalities. The strengths and weaknesses of the co-clinical trial design and the challenges encountered are considered. The rich research resources generated by the members of the AMCT Working Group will benefit the broad research community and improve the quality and translational impact of imaging in co-clinical trials.
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Affiliation(s)
- Donna M. Peehl
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA; (J.K.); (R.S.)
| | - Cristian T. Badea
- Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA;
| | - Thomas L. Chenevert
- Department of Radiology and the Center for Molecular Imaging, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA; (T.L.C.); (G.D.L.); (B.D.R.)
| | - Heike E. Daldrup-Link
- Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Stanford, CA 94305, USA; (H.E.D.-L.); (R.R.)
| | - Li Ding
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; (L.D.); (S.L.); (C.X.M.)
| | - Lacey E. Dobrolecki
- Advanced Technology Cores, Baylor College of Medicine, Houston, TX 77030, USA;
| | | | - Paul E. Kinahan
- Department of Radiology, University of Washington, Seattle, WA 98105, USA;
| | - John Kurhanewicz
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA; (J.K.); (R.S.)
| | - Michael T. Lewis
- Departments of Molecular and Cellular Biology and Radiology, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Shunqiang Li
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; (L.D.); (S.L.); (C.X.M.)
| | - Gary D. Luker
- Department of Radiology and the Center for Molecular Imaging, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA; (T.L.C.); (G.D.L.); (B.D.R.)
- Department of Microbiology and Immunology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Cynthia X. Ma
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; (L.D.); (S.L.); (C.X.M.)
| | - H. Charles Manning
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Yvonne M. Mowery
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708, USA;
- Department of Head and Neck Surgery & Communication Sciences, Duke University School of Medicine, Durham, NC 27708, USA
| | - Peter J. O’Dwyer
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA; (P.J.O.); (M.A.R.); (R.Z.)
| | - Robia G. Pautler
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Mark A. Rosen
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA; (P.J.O.); (M.A.R.); (R.Z.)
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raheleh Roudi
- Molecular Imaging Program at Stanford (MIPS), Department of Radiology, Stanford University, Stanford, CA 94305, USA; (H.E.D.-L.); (R.R.)
| | - Brian D. Ross
- Department of Radiology and the Center for Molecular Imaging, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA; (T.L.C.); (G.D.L.); (B.D.R.)
- Department of Biological Chemistry, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Kooresh I. Shoghi
- Mallinckrodt Institute of Radiology (MIR), Washington University School of Medicine, St. Louis, MO 63110, USA; (K.I.S.); (R.L.W.)
| | - Renuka Sriram
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158, USA; (J.K.); (R.S.)
| | - Moshe Talpaz
- Division of Hematology/Oncology, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA;
- Department of Internal Medicine, University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Richard L. Wahl
- Mallinckrodt Institute of Radiology (MIR), Washington University School of Medicine, St. Louis, MO 63110, USA; (K.I.S.); (R.L.W.)
| | - Rong Zhou
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA; (P.J.O.); (M.A.R.); (R.Z.)
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
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Qian WL, Chen Q, Zhang JB, Xu JM, Hu CH. RESOLVE-based radiomics in cervical cancer: improved image quality means better feature reproducibility? Clin Radiol 2023; 78:e469-e476. [PMID: 37029000 DOI: 10.1016/j.crad.2023.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 04/07/2023]
Abstract
AIM To compare the reproducibility of apparent diffusion coefficient (ADC)-based radiomic features between readout-segmented echo-planar diffusion-weighted imaging (RESOLVE) and single-shot echo-planar diffusion-weighted imaging (SS-EPI DWI) in cervical cancer. MATERIALS AND METHODS The RESOLVE and SS-EPI DWI images of 36 patients with histopathologically confirmed cervical cancer were collected retrospectively. Two observers independently delineated the whole tumour on RESOLVE and SS-EPI DWI, and then copied them to the corresponding ADC maps. Shape, first-order, and texture features were extracted from ADC maps in the original and filtered (Laplacian of Gaussian [LoG] and wavelet) images. Thereafter, 1,316 features were generated in each RESOLVE and SS-EPI DWI, respectively. The reproducibility of radiomic features was assessed using intraclass correlation coefficient (ICC). RESULTS In the original images, RESOLVE showed 92.86%, 66.67%, and 86.67% of features with excellent reproducibility in shape, first-order, and texture features, while SS-EPI DWI showed 85.71%, 72.22%, and 60% of features with excellent reproducibility, respectively. In the LoG and wavelet filtered images, RESOLVE had 56.77% and 65.32% of features with excellent reproducibility and SS-EPI DWI had 44.95% and 61.96% of features with excellent reproducibility, respectively. CONCLUSION Compared with SS-EPI DWI, the feature reproducibility of RESOLVE was better in cervical cancer, especially for texture features. The filtered images cannot improve the feature reproducibility compared with the original images for both SS-EPI DWI and RESOLVE.
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White LM, Atinga A, Naraghi AM, Lajkosz K, Wunder JS, Ferguson P, Tsoi K, Griffin A, Haider M. T2-weighted MRI radiomics in high-grade intramedullary osteosarcoma: predictive accuracy in assessing histologic response to chemotherapy, overall survival, and disease-free survival. Skeletal Radiol 2023; 52:553-564. [PMID: 35778618 DOI: 10.1007/s00256-022-04098-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/16/2022] [Accepted: 06/17/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To analyze radiomic features obtained from pre-treatment T2-weighted MRI acquisitions in patients with histologically proven intramedullary high-grade osteosarcomas and assess the accuracy of radiomic modelling as predictive biomarker of tumor necrosis following neoadjuvant chemotherapy (NAC), overall survival (OS), and disease-free survival (DFS). MATERIALS AND METHODS Pre-treatment MRI exams in 105 consecutive patients who underwent NAC and resection of high-grade intramedullary osteosarcoma were evaluated. Histologic necrosis following NAC, and clinical outcome-survival data was collected for each case. Radiomic features were extracted from segmentations performed by two readers, with poorly reproducible features excluded from further analysis. Cox proportional hazard model and Spearman correlation with multivariable modelling were used for assessing relationships of radiomics features with OS, DFS, and histologic tumor necrosis. RESULTS Study included 74 males, 31 females (mean 32.5yrs, range 15-77 years). Histologic assessment of tumor necrosis following NAC was available in 104 cases, with good response (≥ 90% necrosis) in 41, and poor response in 63. Fifty-three of 105 patients were alive at follow-up (median 40 months, range: 2-213 months). Median OS was 89 months. Excluding 14 patients with metastases at presentation, median DFS was 19 months. Eleven radiomics features were employed in final radiomics model predicting histologic tumor necrosis (mean AUC 0.708 ± 0.046). Thirteen radiomic features were used in model predicting OS (mean concordance index 0.741 ± 0.011), and 12 features retained in predicting DFS (mean concordance index 0.745 ± 0.010). CONCLUSIONS T2-weighted MRI radiomic models demonstrate promising results as potential prognostic biomarkers of prospective tumor response to neoadjuvant chemotherapy and prediction of clinical outcomes in conventional osteosarcoma.
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Affiliation(s)
- Lawrence M White
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. .,Joint Department of Medical Imaging, Mount Sinai Hospital, University Health Network and Women's College Hospital, Rm 562-A, 600 University Ave, Toronto, ON, M5G 1X5, Canada. .,Toronto Sarcoma Program, Mount Sinai Hospital, Toronto, ON, Canada.
| | - Angela Atinga
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Ali M Naraghi
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,Joint Department of Medical Imaging, Mount Sinai Hospital, University Health Network and Women's College Hospital, Rm 562-A, 600 University Ave, Toronto, ON, M5G 1X5, Canada.,Toronto Sarcoma Program, Mount Sinai Hospital, Toronto, ON, Canada
| | - Katherine Lajkosz
- Department of Biostatistics, University Health Network, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Jay S Wunder
- Toronto Sarcoma Program, Mount Sinai Hospital, Toronto, ON, Canada.,Department of Surgery, Division of Orthopedic Surgery, Musculoskeletal Oncology, Mount Sinai Hospital, Toronto, ON, Canada
| | - Peter Ferguson
- Toronto Sarcoma Program, Mount Sinai Hospital, Toronto, ON, Canada.,Department of Surgery, Division of Orthopedic Surgery, Musculoskeletal Oncology, Mount Sinai Hospital, Toronto, ON, Canada
| | - Kim Tsoi
- Toronto Sarcoma Program, Mount Sinai Hospital, Toronto, ON, Canada.,Department of Surgery, Division of Orthopedic Surgery, Musculoskeletal Oncology, Mount Sinai Hospital, Toronto, ON, Canada
| | - Anthony Griffin
- Department of Surgery, Division of Orthopedic Surgery, Musculoskeletal Oncology, Mount Sinai Hospital, Toronto, ON, Canada
| | - Masoom Haider
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,Joint Department of Medical Imaging, Mount Sinai Hospital, University Health Network and Women's College Hospital, Rm 562-A, 600 University Ave, Toronto, ON, M5G 1X5, Canada
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Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics (Basel) 2022; 13:diagnostics13010110. [PMID: 36611402 PMCID: PMC9818323 DOI: 10.3390/diagnostics13010110] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.
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Affiliation(s)
- Kuo Feng Hung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lun M. Wong
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Dion Tik Shun Li
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Correspondence:
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Correlation Analysis of Gene and Radiomic Features in Colorectal Cancer Liver Metastases. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8559011. [PMID: 36593900 PMCID: PMC9805395 DOI: 10.1155/2022/8559011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/27/2022] [Accepted: 11/18/2022] [Indexed: 12/25/2022]
Abstract
Colorectal cancer liver metastasis (CRLM) was one of the cancers with high mortality. Clinically, the target point was determined by invasive detection, which increased the suffering of patients and the cost of treatment. If the target point was found through the relationship between early radiomic information and genetic information, it was expected to assist doctors in diagnosing disease, formulating treatment plans, and reducing the pain and burden of patients. In this study, gene coexpression analysis and hub gene mining were first performed on the gene data; secondly, quantitative radiomic features were extracted from CT-enhanced radiomic data to obtain features highly correlated with CRLM; and finally, we analyzed the relationship between gene features and radiomic feature correlations by establishing a link between early radiomic features and gene sequencing and finding highly correlated expressions. This experiment demonstrated that radiomic features could be used to mine gene attributes. Based on the four previously identified genes (NRAS, KRAS, BRAF, and PIK3CA), we identified two novel genes, MAPK1 and STAT1, highly associated with CRLM. There were specific correlations between these 6 genes and radiomic features (shape_elongation, glcm, glszm, firstorder_10percentile, gradient, exponent_firstorder_Range, and gradient_glszm_SmallAreaLowGrayLevel). Therefore, this paper established the correlation between radiomic features and genes, and through radiomic features, we could find the genes associated with them, which was expected to achieve noninvasive prediction of liver metastasis.
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The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis. Diagnostics (Basel) 2022; 13:diagnostics13010045. [PMID: 36611337 PMCID: PMC9818874 DOI: 10.3390/diagnostics13010045] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 12/28/2022] Open
Abstract
Several studies have demonstrated the value of artificial intelligence (AI) applications in breast cancer diagnosis. The systematic review of AI applications in breast cancer diagnosis includes several studies that compare breast cancer diagnosis and AI. However, they lack systematization, and each study appears to be conducted uniquely. The purpose and contributions of this study are to offer elaborative knowledge on the applications of AI in the diagnosis of breast cancer through citation analysis in order to categorize the main area of specialization that attracts the attention of the academic community, as well as thematic issue analysis to identify the species being researched in each category. In this study, a total number of 17,900 studies addressing breast cancer and AI published between 2012 and 2022 were obtained from these databases: IEEE, Embase: Excerpta Medica Database Guide-Ovid, PubMed, Springer, Web of Science, and Google Scholar. We applied inclusion and exclusion criteria to the search; 36 studies were identified. The vast majority of AI applications used classification models for the prediction of breast cancer. Howbeit, accuracy (99%) has the highest number of performance metrics, followed by specificity (98%) and area under the curve (0.95). Additionally, the Convolutional Neural Network (CNN) was the best model of choice in several studies. This study shows that the quantity and caliber of studies that use AI applications in breast cancer diagnosis will continue to rise annually. As a result, AI-based applications are viewed as a supplement to doctors' clinical reasoning, with the ultimate goal of providing quality healthcare that is both affordable and accessible to everyone worldwide.
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Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review. Diagnostics (Basel) 2022; 12:diagnostics12123111. [PMID: 36553119 PMCID: PMC9777253 DOI: 10.3390/diagnostics12123111] [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: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
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Performance sensitivity analysis of brain metastasis stereotactic radiosurgery outcome prediction using MRI radiomics. Sci Rep 2022; 12:20975. [PMID: 36471160 PMCID: PMC9722896 DOI: 10.1038/s41598-022-25389-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/29/2022] [Indexed: 12/09/2022] Open
Abstract
Recent studies have used T1w contrast-enhanced (T1w-CE) magnetic resonance imaging (MRI) radiomic features and machine learning to predict post-stereotactic radiosurgery (SRS) brain metastasis (BM) progression, but have not examined the effects of combining clinical and radiomic features, BM primary cancer, BM volume effects, and using multiple scanner models. To investigate these effects, a dataset of n = 123 BMs from 99 SRS patients with 12 clinical features, 107 pre-treatment T1w-CE radiomic features, and BM progression determined by follow-up MRI was used with a random decision forest model and 250 bootstrapped repetitions. Repeat experiments assessed the relative accuracy across primary cancer sites, BM volume groups, and scanner model pairings. Correction for accuracy imbalances across volume groups was investigated by removing volume-correlated features. We found that using clinical and radiomic features together produced the most accurate model with a bootstrap-corrected area under the receiver operating characteristic curve of 0.77. Accuracy also varied by primary cancer site, BM volume, and scanner model pairings. The effect of BM volume was eliminated by removing features at a volume-correlation coefficient threshold of 0.25. These results show that feature type, primary cancer, volume, and scanner model are all critical factors in the accuracy of radiomics-based prognostic models for BM SRS that must be characterised and controlled for before clinical translation.
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Paquier Z, Chao SL, Bregni G, Sanchez AV, Guiot T, Dhont J, Gulyban A, Levillain H, Sclafani F, Reynaert N, Bali MA. Pre-trial quality assurance of diffusion-weighted MRI for radiomic analysis and the role of harmonisation. Phys Med 2022; 103:138-146. [DOI: 10.1016/j.ejmp.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/30/2022] [Accepted: 10/08/2022] [Indexed: 11/17/2022] Open
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Meena T, Roy S. Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift. Diagnostics (Basel) 2022; 12:diagnostics12102420. [PMID: 36292109 PMCID: PMC9600559 DOI: 10.3390/diagnostics12102420] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 01/16/2023] Open
Abstract
Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging.
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Jiang S, Han L, Liang L, Long L. Development and validation of an MRI-based radiomic model for predicting overall survival in nasopharyngeal carcinoma patients with local residual tumors after intensity-modulated radiotherapy. BMC Med Imaging 2022; 22:174. [PMID: 36195860 PMCID: PMC9533536 DOI: 10.1186/s12880-022-00902-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To investigate the potential value of the pretreatment MRI-based radiomic model in predicting the overall survival (OS) of nasopharyngeal carcinoma (NPC) patients with local residual tumors after intensity-modulated radiotherapy (IMRT). METHODS A total of 218 consecutive nonmetastatic NPC patients with local residual tumors after IMRT [training cohort (n = 173) and validation cohort (n = 45)] were retrospectively included in this study. Clinical and MRI data were obtained. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features from pretreatment MRI. The clinical, radiomic, and combined models for predicting OS were constructed. The models' performances were evaluated using Harrell's concordance index (C-index), calibration curve, and decision curve analysis. RESULTS The C-index of the radiomic model was higher than that of the clinical model, with the C-index of 0.788 (95% CI 0.724-0.852) versus 0.672 (95% CI 0.599-0.745) in the training cohort and 0.753 (95% CI 0.604-0.902) versus 0.634 (95% CI 0.593-0.675) in the validation cohort. Calibration curves showed good agreement between the radiomic model-predicted probability of 2- and 3-year OS and the actual observed probability in the training and validation groups. Decision curve analysis showed that the radiomic model had higher clinical usefulness than the clinical model. The discrimination of the combined model improved significantly in the training cohort (P < 0.01) but not in the validation cohort, with the C-index of 0.834 and 0.734, respectively. The radiomic model divided patients into high- and low-risk groups with a significant difference in OS in both the training and validation cohorts. CONCLUSIONS Pretreatment MRI-based radiomic model may improve OS prediction in NPC patients with local residual tumors after IMRT and may assist in clinical decision-making.
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Affiliation(s)
- Shengping Jiang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, China
| | - Lin Han
- Department of Rehabilitation Medicine, The First People's Hospital of Yulin, No. 495 Jiaoyu Road, Yulin, 537000, China
| | - Leifeng Liang
- Department of Radiation Oncology, The First People's Hospital of Yulin, No. 495 Jiaoyu Road, Yulin, 537000, China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, China.
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning, Guangxi, China.
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Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1100775. [PMID: 36188701 PMCID: PMC9522488 DOI: 10.1155/2022/1100775] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/16/2022] [Accepted: 08/26/2022] [Indexed: 11/17/2022]
Abstract
Breast cancer is the primary health issue that women may face at some point in their lifetime. This may lead to death in severe cases. A mammography procedure is used for finding suspicious masses in the breast. Teleradiology is employed for online treatment and diagnostics processes due to the unavailability and shortage of trained radiologists in backward and remote areas. The availability of online radiologists is uncertain due to inadequate network coverage in rural areas. In such circumstances, the Computer-Aided Diagnosis (CAD) framework is useful for identifying breast abnormalities without expert radiologists. This research presents a decision-making system based on IoMT (Internet of Medical Things) to identify breast anomalies. The proposed technique encompasses the region growing algorithm to segment tumor that extracts suspicious part. Then, texture and shape-based features are employed to characterize breast lesions. The extracted features include first and second-order statistics, center-symmetric local binary pattern (CS-LBP), a histogram of oriented gradients (HOG), and shape-based techniques used to obtain various features from the mammograms. Finally, a fusion of machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA are employed to classify breast cancer using composite feature vectors. The experimental results exhibit the proposed framework's efficacy that separates the cancerous lesions from the benign ones using 10-fold cross-validations. The accuracy, sensitivity, and specificity attained are 96.3%, 94.1%, and 98.2%, respectively, through shape-based features from the MIAS database. Finally, this research contributes a model with the ability for earlier and improved accuracy of breast tumor detection.
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Jensen LJ, Kim D, Elgeti T, Steffen IG, Schaafs LA, Hamm B, Nagel SN. Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study. Eur Radiol Exp 2022; 6:43. [PMID: 36104519 PMCID: PMC9474978 DOI: 10.1186/s41747-022-00297-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In radiomics studies, differences in the volume of interest (VOI) are often inevitable and may confound the extracted features. We aimed to correct this confounding effect of VOI variability by applying parametric maps with a fixed voxel size. METHODS Ten scans of a cup filled with sodium chloride solution were scanned using a multislice computed tomography (CT) unit. Sphere-shaped VOIs with different diameters (4, 8, or 16 mm) were drawn centrally into the phantom. A total of 93 features were extracted conventionally from the original images using PyRadiomics. Using a self-designed and pretested software tool, parametric maps for the same 93 features with a fixed voxel size of 4 mm3 were created. To retrieve the feature values from the maps, VOIs were copied from the original images to preserve the position. Differences in feature quantities between the VOI sizes were tested with the Mann-Whitney U-test and agreement with overall concordance correlation coefficients (OCCC). RESULTS Fifty-five conventionally extracted features were significantly different between the VOI sizes, and none of the features showed excellent agreement in terms of OCCCs. When read from the parametric maps, only 8 features showed significant differences, and 3 features showed an excellent OCCC (≥ 0.85). The OCCCs for 89 features substantially increased using the parametric maps. CONCLUSIONS This phantom study shows that converting CT images into parametric maps resolves the confounding effect of VOI variability and increases feature reproducibility across VOI sizes.
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Affiliation(s)
- Laura J Jensen
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Damon Kim
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Thomas Elgeti
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Ingo G Steffen
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Lars-Arne Schaafs
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Bernd Hamm
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Sebastian N Nagel
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
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Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM). COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7403302. [PMID: 36093488 PMCID: PMC9452941 DOI: 10.1155/2022/7403302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/02/2022] [Accepted: 07/29/2022] [Indexed: 01/07/2023]
Abstract
Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearance. Machine learning techniques are widely employed in imaging analysis as a diagnostic method for breast cancer classification. However, patients cannot take advantage of remote areas as these systems are unavailable on clouds. Thus, breast cancer detection for remote patients is indispensable, which can only be possible through cloud computing. The user is allowed to feed images into the cloud system, which is further investigated through the computer aided diagnosis (CAD) system. Such systems could also be used to track patients, older adults, especially with disabilities, particularly in remote areas of developing countries that do not have medical facilities and paramedic staff. In the proposed CAD system, a fusion of AlexNet architecture and GLCM (gray-level cooccurrence matrix) features are used to extract distinguishable texture features from breast tissues. Finally, to attain higher precision, an ensemble of MK-SVM is used. For testing purposes, the proposed model is applied to the MIAS dataset, a commonly used breast image database, and achieved 96.26% accuracy.
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Crombé A, Lafon M, Nougaret S, Kind M, Cousin S. Ranking the most influential predictors of CT-based radiomics feature values in metastatic lung adenocarcinoma. Eur J Radiol 2022; 155:110472. [PMID: 35985090 DOI: 10.1016/j.ejrad.2022.110472] [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: 01/10/2022] [Revised: 07/09/2022] [Accepted: 08/09/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE To investigate which acquisition, post-processing, tumor, and patient characteristics contribute the most to the value of radiomics features (RFs) in lung adenocarcinoma in order to better understand and order the potential sources of bias in radiomics studies in a multivariate setting. METHODS This single-center retrospective study included all consecutive patients with newly-diagnosed lung adenocarcinoma treated between December 2016 and September 2018 who had pre-treatment contrast-enhanced CT-scan showing ≥ 2 target lesions per response evaluation criteria in solid tumors (RECIST) v1.1. All measurable lesions were manually segmented; 49 RFs were extracted using LIFEx v7.0.0. Afterwards, we reverted the usual radiomics approach (i.e., predicting a clinical outcome base on multiple RFs). To do so, for each RF, random forests and linear regression algorithms were trained using cross-validation to predict the RF value depending on the following variables: patient, mutational status, phase of CT-scan acquisition, discretization (binsize), lesion location, lesion volume, and best response obtained during the first line of treatment (partial response per RECIST vs other). The most important contributors to the value of reproducible RFs (intra-class correlation coefficient > 0.80) according to the best random forests model (selected via R-squared) were ranked. RESULTS 101 patients (median age: 62.3) were included, with a median of 5 target lesions per patient (range: 2-10) providing 466 segmented lesions. Twenty-nine RFs were reproducible. The most important predictors of the reproducible RFs values were, in order: tumor volume, binsize, tumor location, CT-scan phase, KRAS mutation, and treatment response (average importance: 61.7%, 57.4%, 8.1%, 3.3%, 3%, and 2.7%, respectively). The treatment response and KRAS and EGFR/ROS1/ALK mutational status remained independently correlated with the RF value for 64.3%, 32.1%, and 50% reproducible RFs, respectively. CONCLUSION Tumor volume, location, acquisition and post-processing parameters should systematically be incorporated in radiomics-based modeling; however, most reproducible RFs do have significant relationships with mutational status and treatment response.
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Affiliation(s)
- Amandine Crombé
- Department of Oncologic Imaging, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France; Department of Imaging, Pellegrin University Hospital, F-33300 Bordeaux, France; University of Bordeaux, UMR CNRS 5251, INRIA Project team Models in Oncology (Monc), F-33400 Talence, France.
| | - Mathilde Lafon
- Department of Medical Oncology, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France
| | - Stéphanie Nougaret
- Department of Radiology, Institut Régional du Cancer de Montpellier, Montpellier Cancer Research Institute, INSERM U1194, University of Montpellier, F-34295 Montpellier, France
| | - Michèle Kind
- Department of Oncologic Imaging, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France
| | - Sophie Cousin
- Department of Medical Oncology, Institut Bergonié, Regional Comprehensive Cancer, F-33076 Bordeaux, France
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Alsafi H, Munilla J, Rahebi J. An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7276028. [PMID: 35942461 PMCID: PMC9356836 DOI: 10.1155/2022/7276028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 06/02/2022] [Indexed: 11/17/2022]
Abstract
Automatic diagnosis of arrhythmia by electrocardiogram has a significant role to play in preventing and detecting cardiovascular disease at an early stage. In this study, a deep neural network model based on Harris hawks optimization is presented to arrive at a temporal and spatial fusion of information from ECG signals. Compared with the initial model of the multichannel deep neural network mechanism, the proposed model of this research has a flexible input length; the number of parameters is halved and it has a more than 50% reduction in computations in real-time processing. The results of the simulation demonstrate that the approach proposed in this research had a rate of 96.04%, 93.94%, and 95.00% for sensitivity, specificity, and accuracy. Furthermore, the proposed approach has a practical advantage over other similar previous methods.
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Affiliation(s)
- Haedar Alsafi
- Department of Telecommunication Engineering, Malaga University, Malaga, Spain
| | - Jorge Munilla
- Department of Telecommunication Engineering, Malaga University, Malaga, Spain
| | - Javad Rahebi
- Department of Software Engineering, Istanbul Topkapi University, Istanbul, Turkey
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Improvement of the Public Health Service Platform System Based on the Big Data-Driven System. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1476779. [PMID: 35845906 PMCID: PMC9279029 DOI: 10.1155/2022/1476779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/01/2022] [Accepted: 06/13/2022] [Indexed: 11/17/2022]
Abstract
At present, complex discrete dynamic systems are widely used in the field of medicine. The control system in the complex discrete dynamic model is gradually transformed into intelligent control. It has become the main research direction of researchers to improve the medical platform system by adding different modeling strategies. The traditional discrete modeling technology can only be used as the knowledge content of students' textbooks because it can no longer meet the needs of the development of human society. In order to improve the application of a discrete system in the public platform, this paper studies the improvement of the public health service platform system based on the complex discrete dynamic system. Firstly, a time-driven control strategy is proposed to study the output feedback control with random sampling in the platform. Then, the stability of random parameters and the addition of dynamic scheduling strategies are further studied. Compared with the traditional system, the optimized system greatly strengthens the data transmission problem of input and output channels. The results show that by improving the performance of the public health service platform system, the probability of problems in the process of data transmission is greatly reduced. After adding controllable and observable performance to the system, the stability of the whole system is further improved. The improved public health service platform system studied in this paper can store and transmit a large number of user data in the network environment, automatically maintaining the stability of the system and has a good social application value.
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Deep Learning and Transfer Learning for Malaria Detection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2221728. [PMID: 35814548 PMCID: PMC9259269 DOI: 10.1155/2022/2221728] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/23/2022] [Accepted: 05/30/2022] [Indexed: 11/25/2022]
Abstract
Infectious disease malaria is a devastating infectious disease that claims the lives of more than 500,000 people worldwide every year. Most of these deaths occur as a result of a delayed or incorrect diagnosis. At the moment, the manual microscope is considered to be the most effective equipment for diagnosing malaria. It is, on the other hand, time-consuming and prone to human error. Because it is such a serious global health issue, it is important that the evaluation process be automated. The objective of this article is to advocate for the automation of the diagnosis process in order to eliminate the need for human intervention in the process. Convolutional neural networks (CNNs) and other deep-learning technologies, such as image processing, are being utilized to evaluate parasitemia in microscopic blood slides in order to enhance diagnostic accuracy. The approach is based on the intensity characteristics of Plasmodium parasites and erythrocytes, which are both known to be variable. Images of infected and noninfected erythrocytes are gathered and fed into the CNN models ResNet50, ResNet34, VGG-16, and VGG-19, which are all trained on the same dataset. The techniques of transfer learning and fine-tuning are employed, and the outcomes are contrasted. The VGG-19 model obtained the best overall performance given the parameters and dataset that were evaluated.
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Analysis of Vessel Segmentation Based on Various Enhancement Techniques for Improvement of Vessel Intensity Profile. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7086632. [PMID: 35800676 PMCID: PMC9256369 DOI: 10.1155/2022/7086632] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 05/31/2022] [Accepted: 06/07/2022] [Indexed: 11/27/2022]
Abstract
It is vital to develop an appropriate prediction model and link carefully to measurable events such as clinical parameters and patient outcomes to analyze the severity of the disease. Timely identifying retinal diseases is becoming more vital to prevent blindness among young and adults. Investigation of blood vessels delivers preliminary information on the existence and treatment of glaucoma, retinopathy, and so on. During the analysis of diabetic retinopathy, one of the essential steps is to extract the retinal blood vessel accurately. This study presents an improved Gabor filter through various enhancement approaches. The degraded images with the enhancement of certain features can simplify image interpretation both for a human observer and for machine recognition. Thus, in this work, few enhancement approaches such as Gamma corrected adaptively with distributed weight (GCADW), joint equalization of histogram (JEH), homomorphic filter, unsharp masking filter, adaptive unsharp masking filter, and particle swarm optimization (PSO) based unsharp masking filter are taken into consideration. In this paper, an effort has been made to improve the performance of the Gabor filter by combining it with different enhancement methods and to enhance the detection of blood vessels. The performance of all the suggested approaches is assessed on publicly available databases such as DRIVE and CHASE_DB1. The results of all the integrated enhanced techniques are analyzed, discussed, and compared. The best result is delivered by PSO unsharp masking filter combined with the Gabor filter with an accuracy of 0.9593 for the DRIVE database and 0.9685 for the CHASE_DB1 database. The results illustrate the robustness of the recommended model in automatic blood vessel segmentation that makes it possible to be a clinical support decision tool in diabetic retinopathy diagnosis.
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Segmentation for Multimodal Brain Tumor Images Using Dual-Tree Complex Wavelet Transform and Deep Reinforcement Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5369516. [PMID: 35655520 PMCID: PMC9152408 DOI: 10.1155/2022/5369516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/27/2022] [Accepted: 05/04/2022] [Indexed: 11/18/2022]
Abstract
Image segmentation is an effective tool for computer-aided medical treatment, to retain the detailed features and edges of the segmented image and improve the segmentation accuracy. Therefore, a segmentation algorithm using deep reinforcement learning (DRL) and dual-tree complex wavelet transform (DTCWT) for multimodal brain tumor images is proposed. First, the bivariate concept in DTCWT is used to determine whether the image noise points belong to the real or imaginary region, and the noise probability is checked after calculation; second, the wavelet coefficients corresponding to the region where the noise is located are selected to transform the noise into normal pixel points by bivariate; then, the conditional probability of occurrence of marker points in the edge and center regions of the image is calculated with the target points, and the initial segmentation of the image is achieved by the known wavelet coefficients; finally, the segmentation framework is constructed using DRL, and the network is trained by loss function to optimize the segmentation results and achieve accurate image segmentation. The experiment was evaluated on BraTS2018 dataset, CQ500 dataset, and a hospital brain tumor dataset. The results show that the algorithm in this paper can effectively remove multimodal brain tumor image noise, and the segmented image has good retention of detail features and edges, and the segmented image has high similarity with the original image. The highest information loss index of the segmentation results is only 0.18, the image boundary error is only about 0.3, and F-value is high, which indicates that the proposed algorithm is accurate and can operate efficiently, and has practical applicability.
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Stępień I, Oszust M. A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images. J Imaging 2022; 8:160. [PMID: 35735959 PMCID: PMC9224540 DOI: 10.3390/jimaging8060160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/31/2022] [Accepted: 06/01/2022] [Indexed: 02/08/2023] Open
Abstract
No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image. Therefore, due to the lack of pristine images in most medical image acquisition systems, they play a major role in supporting the examination of resulting images and may affect subsequent treatment. Their usage is particularly important in magnetic resonance imaging (MRI) characterized by long acquisition times and a variety of factors that influence the quality of images. In this work, a survey covering recently introduced NR-IQA methods for the assessment of MR images is presented. First, typical distortions are reviewed and then popular NR methods are characterized, taking into account the way in which they describe MR images and create quality models for prediction. The survey also includes protocols used to evaluate the methods and popular benchmark databases. Finally, emerging challenges are outlined along with an indication of the trends towards creating accurate image prediction models.
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Affiliation(s)
- Igor Stępień
- Doctoral School of Engineering and Technical Sciences, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland;
| | - Mariusz Oszust
- Department of Computer and Control Engineering, Rzeszow University of Technology, Wincentego Pola 2, 35-959 Rzeszow, Poland
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Effect and Prognosis Factors of Combining Laparoscopic Radical Resection of Colon Adenocarcinoma with Docetaxel Therapy in Treating Middle and Advanced Colon Adenocarcinoma. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6122261. [PMID: 35669650 PMCID: PMC9167002 DOI: 10.1155/2022/6122261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/24/2022] [Accepted: 05/03/2022] [Indexed: 11/17/2022]
Abstract
Objective. The aim of the study is to explore the clinical efficacy and prognosis factors of joint application of laparoscopic radical resection of colon adenocarcinoma (COAD) and docetaxel therapy in treating COAD of middle and advanced stages. Methods. The clinical data of 103 COAD patients of middle and advanced stages treated in our hospital from July 2016 to July 2018 were selected for the retrospective analysis, all patients received the treatment scheme of combining laparoscopic radical resection of COAD with docetaxel therapy for the observation of short-term efficacy, follow-up was conducted to record their 3-year survival, and relevant factors affecting patient prognosis were analyzed by the logistic regression model. Results. After treatment, the total remission rate of patients was 75.73% (78/103), the total incidence rate of adverse reactions was 16.50% (17/103); patients’ level values of various serum tumor markers after treatment were significantly lower than those before treatment (
); according to the univariate analysis results, for COAD patients with different tumor diameters, differentiated degrees, TNM stages, perineural invasion degrees, pathological types, and depths of invasion, their modality rates were statistically different (
); and the logistic regression analysis showed that tumor diameter ≥5 cm, poor differentiation, TNM stage IV, perineural invasion, pathologically signet-ring cell carcinoma, and T3-invasion were the independent risk factors affecting patient prognosis (
). Conclusion. Combining laparoscopic radical resection of COAD with docetaxel therapy in treating COAD of middle and advanced stages achieves affirmed short-term efficacy, which can reduce patients’ level of serum tumor markers and ensure high safety and good survival prognosis. Tumor diameter, differentiated degree, TNM stage, perineural invasion, pathological type, and T3-invasion are the relevant factors affecting the prognosis of middle and advanced COAD.
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Fully Automatic Whole-Volume Tumor Segmentation in Cervical Cancer. Cancers (Basel) 2022; 14:cancers14102372. [PMID: 35625977 PMCID: PMC9139985 DOI: 10.3390/cancers14102372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/02/2022] [Accepted: 05/05/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Uterine cervical cancer (CC) is a leading cause of cancer-related deaths in women worldwide. Pelvic magnetic resonance imaging (MRI) allows the assessment of local tumor extent and guides the choice of primary treatment. MRI tumor segmentation enables whole-volume radiomic tumor profiling, which is potentially useful for prognostication and individualization of therapy in CC. Manual tumor segmentation is, however, labor intensive and thus not part of routine clinical workflow. In the current work, we trained a deep learning (DL) algorithm to automatically segment the primary tumor in CC patients. Although the achieved segmentation performance of the trained DL algorithm is slightly lower than that for human experts, it is still relatively good. This study suggests that automated MRI primary tumor segmentations by DL algorithms without any human interaction is possible in patients with CC. Abstract Uterine cervical cancer (CC) is the most common gynecologic malignancy worldwide. Whole-volume radiomic profiling from pelvic MRI may yield prognostic markers for tailoring treatment in CC. However, radiomic profiling relies on manual tumor segmentation which is unfeasible in the clinic. We present a fully automatic method for the 3D segmentation of primary CC lesions using state-of-the-art deep learning (DL) techniques. In 131 CC patients, the primary tumor was manually segmented on T2-weighted MRI by two radiologists (R1, R2). Patients were separated into a train/validation (n = 105) and a test- (n = 26) cohort. The segmentation performance of the DL algorithm compared with R1/R2 was assessed with Dice coefficients (DSCs) and Hausdorff distances (HDs) in the test cohort. The trained DL network retrieved whole-volume tumor segmentations yielding median DSCs of 0.60 and 0.58 for DL compared with R1 (DL-R1) and R2 (DL-R2), respectively, whereas DSC for R1-R2 was 0.78. Agreement for primary tumor volumes was excellent between raters (R1-R2: intraclass correlation coefficient (ICC) = 0.93), but lower for the DL algorithm and the raters (DL-R1: ICC = 0.43; DL-R2: ICC = 0.44). The developed DL algorithm enables the automated estimation of tumor size and primary CC tumor segmentation. However, segmentation agreement between raters is better than that between DL algorithm and raters.
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Scalco E, Rizzo G, Mastropietro A. The stability of oncologic MRI radiomic features and the potential role of deep learning: a review. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac60b9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/24/2022] [Indexed: 11/11/2022]
Abstract
Abstract
The use of MRI radiomic models for the diagnosis, prognosis and treatment response prediction of tumors has been increasingly reported in literature. However, its widespread adoption in clinics is hampered by issues related to features stability. In the MRI radiomic workflow, the main factors that affect radiomic features computation can be found in the image acquisition and reconstruction phase, in the image pre-processing steps, and in the segmentation of the region of interest on which radiomic indices are extracted. Deep Neural Networks (DNNs), having shown their potentiality in the medical image processing and analysis field, can be seen as an attractive strategy to partially overcome the issues related to radiomic stability and mitigate their impact. In fact, DNN approaches can be prospectively integrated in the MRI radiomic workflow to improve image quality, obtain accurate and reproducible segmentations and generate standardized images. In this review, DNN methods that can be included in the image processing steps of the radiomic workflow are described and discussed, in the light of a detailed analysis of the literature in the context of MRI radiomic reliability.
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Effect of Matrix Size Reduction on Textural Information in Clinical Magnetic Resonance Imaging. J Clin Med 2022; 11:jcm11092526. [PMID: 35566657 PMCID: PMC9103884 DOI: 10.3390/jcm11092526] [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: 03/02/2022] [Revised: 04/12/2022] [Accepted: 04/26/2022] [Indexed: 12/10/2022] Open
Abstract
The selection of the matrix size is an important element of the magnetic resonance imaging (MRI) process, and has a significant impact on the acquired image quality. Signal to noise ratio, often used to assess MR image quality, has its limitations. Thus, for this purpose we propose a novel approach: the use of texture analysis as an index of the image quality that is sensitive for the change of matrix size. Image texture in biomedical images represents tissue and organ structures visualized via medical imaging modalities such as MRI. The correlation between texture parameters determined for the same tissues visualized in images acquired with different matrix sizes is analyzed to aid in the assessment of the selection of the optimal matrix size. T2-weighted coronal images of shoulders were acquired using five different matrix sizes while maintaining the same field of view; three regions of interest (bone, fat, and muscle) were considered. Lin’s correlation coefficients were calculated for all possible pairs of the 310-element texture feature vectors evaluated for each matrix. The obtained results are discussed considering the image noise and blurring effect visible in images acquired with smaller matrices. Taking these phenomena into account, recommendations for the selection of the matrix size used for the MRI imaging were proposed.
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