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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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Mitchell-Hay R, Ahearn T, Murray A, Waiter G. Phantom study investigating the repeatability of radiomic features with alteration of image acquisition parameters in magnetic resonance imaging. J Med Imaging Radiat Sci 2024; 55:19-28. [PMID: 37932212 DOI: 10.1016/j.jmir.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 11/08/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) has many different alterable parameters that affect how an image appears. This is relevant in radiomics which produces quantitative features through analysis of medical images. One significant acknowledged limitation of radiomics is repeatability. This phantom study aims to further investigate the repeatability of radiomic features (RaF), within MRI, across a range of different echo (TE) and repetition times (TR). METHODS A phantom was scanned 10 times under identical conditions on a 3T scanner using head coil over 4 months. The TE ranged from 80 to 110 ms while the TR from 3000 to 5000 ms. Radiomics analysis was performed on the same segmented section of the phantom across all TE and TR combinations. Intraclass Correlation Coefficient (ICC) was calculated across the different TE and TR ranges to investigate the repeatability of RaF. RESULTS Of 1596 features calculated, 187 features had ICC >0.9 across the range of TE, while 82 features had an ICC >0.9 across a range of TR. 664 had ICC >0.75 across the range of TEs, with 541 across the range of TR values. There was an overlap of 51 features with ICC >0.9. CONCLUSION Repeatability of RaF in MRI is dependent on imaging parameters and careful consideration of these, in combination with variable selection, is required when applying radiomics to MRI.
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Affiliation(s)
- Rosalind Mitchell-Hay
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, Scotland; Radiology Department, NHS Grampian, Aberdeen, Scotland.
| | - Trevor Ahearn
- Radiology Department, NHS Grampian, Aberdeen, Scotland
| | - Alison Murray
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, Scotland
| | - Gordon Waiter
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, Scotland
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Lee DY, Shin J, Kim S, Baek SE, Lee S, Son NH, Park MS. Radiomics model versus 2017 revised international consensus guidelines for predicting malignant intraductal papillary mucinous neoplasms. Eur Radiol 2024; 34:1222-1231. [PMID: 37615762 DOI: 10.1007/s00330-023-10158-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/05/2023] [Accepted: 07/20/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVES To evaluate a CT-based radiomics model for identifying malignant pancreatic intraductal papillary mucinous neoplasms (IPMNs) and compare its performance with the 2017 international consensus guidelines (ICGs). MATERIALS AND METHODS We retrospectively included 194 consecutive patients who underwent surgical resection of pancreatic IPMNs between January 2008 and December 2020. Surgical histopathology was the reference standard for diagnosing malignancy. Using radiomics features from preoperative contrast-enhanced CT, a radiomics model was built with the least absolute shrinkage and selection operator by a five-fold cross-validation. CT and MR images were independently reviewed based on the 2017 ICGs by two abdominal radiologists, and the performances of the 2017 ICGs and radiomics model were compared. The areas under the curve (AUCs) were compared using the DeLong method. RESULTS A total of 194 patients with pancreatic IPMNs (benign, 83 [43%]; malignant, 111 [57%]) were chronologically divided into training (n = 141; age, 65 ± 8.6 years; 88 males) and validation sets (n = 53; age, 66 ± 9.7 years; 31 males). There was no statistically significant difference in the diagnostic performance of the 2017 ICGs between CT and MRI (AUC, 0.71 vs. 0.71; p = 0.93) with excellent intermodality agreement (k = 0.86). In the validation set, the CT radiomics model had higher AUC (0.85 vs. 0.71; p = 0.038), specificity (84.6% vs. 61.5%; p = 0.041), and positive predictive value (84.0% vs. 66.7%; p = 0.044) than the 2017 ICGs. CONCLUSION The CT radiomics model exhibited better diagnostic performance than the 2017 ICGs in classifying malignant IPMNs. CLINICAL RELEVANCE STATEMENT Compared with the radiologists' evaluation based on the 2017 international consensus guidelines, the CT radiomics model exhibited better diagnostic performance in classifying malignant intraductal papillary mucinous neoplasms. KEY POINTS • There is a paucity of comparisons between the 2017 international consensus guidelines (ICGs) and radiomics models for malignant intraductal papillary mucinous neoplasms (IPMNs). • The CT radiomics model developed in this study exhibited better diagnostic performance than the 2017 ICGs in classifying malignant IPMNs. • The radiomics model may serve as a valuable complementary tool to the 2017 ICGs, potentially allowing a more quantitative assessment of IPMNs.
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Affiliation(s)
- Doo Young Lee
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jaeseung Shin
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University College of Medicine, 81 Irwon-Ro, Kangnam-Gu, Seoul, 06351, Republic of Korea.
| | - Sungwon Kim
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Song-Ee Baek
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Suji Lee
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Nak-Hoon Son
- Department of Statistics, Keimyung University, Daegu, Korea
| | - Mi-Suk Park
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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Jin Y, Yin H, Zhang H, Wang Y, Liu S, Yang L, Song B. Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features. Insights Imaging 2023; 14:221. [PMID: 38117396 PMCID: PMC10733230 DOI: 10.1186/s13244-023-01564-w] [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: 06/15/2023] [Accepted: 11/05/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Tumor deposits (TDs) are associated with poor prognosis in rectal cancer (RC). This study aims to develop and validate a deep learning (DL) model incorporating T2-MR image and clinical factors for the preoperative prediction of TDs in RC patients. METHODS AND METHODS A total of 327 RC patients with pathologically confirmed TDs status from January 2016 to December 2019 were retrospectively recruited, and the T2-MR images and clinical variables were collected. Patients were randomly split into a development dataset (n = 246) and an independent testing dataset (n = 81). A single-channel DL model, a multi-channel DL model, a hybrid DL model, and a clinical model were constructed. The performance of these predictive models was assessed by using receiver operating characteristics (ROC) analysis and decision curve analysis (DCA). RESULTS The areas under the curves (AUCs) of the clinical, single-DL, multi-DL, and hybrid-DL models were 0.734 (95% CI, 0.674-0.788), 0.710 (95% CI, 0.649-0.766), 0.767 (95% CI, 0.710-0.819), and 0.857 (95% CI, 0.807-0.898) in the development dataset. The AUC of the hybrid-DL model was significantly higher than the single-DL and multi-DL models (both p < 0.001) in the development dataset, and the single-DL model (p = 0.028) in the testing dataset. Decision curve analysis demonstrated the hybrid-DL model had higher net benefit than other models across the majority range of threshold probabilities. CONCLUSIONS The proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer. CRITICAL RELEVANCE STATEMENT The proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer. KEY POINTS • Preoperative non-invasive identification of TDs is of great clinical significance. • The combined hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer. • A preoperative nomogram provides gastroenterologist with an accurate and effective tool.
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Affiliation(s)
- Yumei Jin
- Department of Medical Imaging Center, Qujing First People's Hospital, Qujing, 655000, Yunnan Province, China.
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan Province, China.
| | - Hongkun Yin
- Beijing Infervision Technology Co.Ltd, Beijing, China
| | - Huiling Zhang
- Beijing Infervision Technology Co.Ltd, Beijing, China
| | - Yewu Wang
- Department of Joint and Sports Medicine, Qujing First People's Hospital, Qujing, 655000, Yunnan Province, China
| | - Shengmei Liu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Ling Yang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan Province, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan Province, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan Province, 572000, China.
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Wichtmann BD, Harder FN, Weiss K, Schönberg SO, Attenberger UI, Alkadhi H, Pinto Dos Santos D, Baeßler B. Influence of Image Processing on Radiomic Features From Magnetic Resonance Imaging. Invest Radiol 2023; 58:199-208. [PMID: 36070524 DOI: 10.1097/rli.0000000000000921] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Before implementing radiomics in routine clinical practice, comprehensive knowledge about the repeatability and reproducibility of radiomic features is required. The aim of this study was to systematically investigate the influence of image processing parameters on radiomic features from magnetic resonance imaging (MRI) in terms of feature values as well as test-retest repeatability. MATERIALS AND METHODS Utilizing a phantom consisting of 4 onions, 4 limes, 4 kiwifruits, and 4 apples, we acquired a test-retest dataset featuring 3 of the most commonly used MRI sequences on a 3 T scanner, namely, a T1-weighted, a T2-weighted, and a fluid-attenuated inversion recovery sequence, each at high and low resolution. After semiautomatic image segmentation, image processing with systematic variation of image processing parameters was performed, including spatial resampling, intensity discretization, and intensity rescaling. For each respective image processing setting, a total of 45 radiomic features were extracted, corresponding to the following 7 matrices/feature classes: conventional indices, histogram matrix, shape matrix, gray-level zone length matrix, gray-level run length matrix, neighboring gray-level dependence matrix, and gray-level cooccurrence matrix. Systematic differences of individual features between different resampling steps were assessed using 1-way analysis of variance with Tukey-type post hoc comparisons to adjust for multiple testing. Test-retest repeatability of radiomic features was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient. RESULTS Image processing influenced radiological feature values. Regardless of the acquired sequence and feature class, significant differences ( P < 0.05) in feature values were found when the size of the resampled voxels was too large, that is, bigger than 3 mm. Almost all higher-order features depended strongly on intensity discretization. The effects of intensity rescaling were negligible except for some features derived from T1-weighted sequences. For all sequences, the percentage of repeatable features (concordance correlation coefficient and dynamic range ≥ 0.9) varied considerably depending on the image processing settings. The optimal image processing setting to achieve the highest percentage of stable features varied per sequence. Irrespective of image processing, the fluid-attenuated inversion recovery sequence in high-resolution overall yielded the highest number of stable features in comparison with the other sequences (89% vs 64%-78% for the respective optimal image processing settings). Across all sequences, the most repeatable features were generally obtained for a spatial resampling close to the originally acquired voxel size and an intensity discretization to at least 32 bins. CONCLUSION Variation of image processing parameters has a significant impact on the values of radiomic features as well as their repeatability. Furthermore, the optimal image processing parameters differ for each MRI sequence. Therefore, it is recommended that these processing parameters be determined in corresponding test-retest scans before clinical application. Extensive repeatability, reproducibility, and validation studies as well as standardization are required before quantitative image analysis and radiomics can be reliably translated into routine clinical care.
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Affiliation(s)
- Barbara D Wichtmann
- From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Felix N Harder
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany
| | | | - Stefan O Schönberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Germany
| | - Ulrike I Attenberger
- From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Hatem Alkadhi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
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Zhong J, Xia Y, Chen Y, Li J, Lu W, Shi X, Feng J, Yan F, Yao W, Zhang H. Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study. Eur Radiol 2023; 33:812-824. [PMID: 36197579 DOI: 10.1007/s00330-022-09119-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/26/2022] [Accepted: 08/17/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness. METHODS A phantom with clinical-relevant densities was imaged on seven DECT scanners with the same voxel size using typical abdominal-pelvis examination protocols. On one DECT scanner, raw data were reconstructed using both conventional IR (adaptive statistical iterative reconstruction-V, ASIR-V) and DLIR. Nine sets of corresponding images were generated on other six DECT scanners using scanner-equipped conventional IR. Regions of interest were delineated through rigid registrations. Image quality was compared. Pyradiomics platform was used for radiomics feature extraction. Test-retest repeatability was assessed by Bland-Altman analysis for repeated scans. Inter-reconstruction algorithm reproducibility between conventional IR and DLIR was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Robust features were identified. RESULTS DLIR significantly improved image quality. Ninety-four radiomics features were extracted and nine features were considered as robust. 93.87% features were repeatable between repeated scans. ASIR-V images showed higher reproducibility to other conventional IR than DLIR (ICC mean, 0.603 vs 0.558, p = 0.001; CCC mean, 0.554 vs 0.510, p = 0.004). 7.45% and 26.83% features were reproducible among scanners evaluated by CV and QCD, respectively. CONCLUSIONS DLIR improves quality of DECT images but may alter radiomics features compared to conventional IR. Nine robust DECT radiomics features were identified. KEY POINTS • DLIR improves DECT image quality in terms of signal-to-noise ratio and contrast-to-noise ratio compared with ASIR-V and showed the highest noise reduction rate and lowest peak frequency shift. • Most of radiomics features are repeatable between repeated DECT scans, while inter-reconstruction algorithm reproducibility between conventional IR and DLIR, and inter-scanner reproducibility, are low. • Although DLIR may alter radiomics features compared to IR algorithms, nine radiomics features survived repeatability and reproducibility analysis among DECT scanners and reconstruction algorithms, which allows further validation and clinical-relevant analysis.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yihan Xia
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Jianxing Feng
- Haohua Technology Co., Ltd., Shanghai, 201100, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Hu L, Fu C, Song X, Grimm R, von Busch H, Benkert T, Kamen A, Lou B, Huisman H, Tong A, Penzkofer T, Choi MH, Shabunin I, Winkel D, Xing P, Szolar D, Coakley F, Shea S, Szurowska E, Guo JY, Li L, Li YH, Zhao JG. Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique. Cancer Imaging 2023; 23:6. [PMID: 36647150 PMCID: PMC9843860 DOI: 10.1186/s40644-023-00527-0] [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: 11/16/2022] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency. METHODS This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant. RESULTS DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUCpatient: 0.89 vs. 0.86; AUClesion: 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (ORrectal susceptibility artifact = 5.46; ORdiameter, = 1.12; ORADC = 0.998; all P < .001) and false negatives (ORrectal susceptibility artifact = 3.31; ORdiameter = 0.82; ORADC = 1.007; all P ≤ .03) of DL-CAD. CONCLUSIONS Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD. TRIAL REGISTRATION ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021.
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Affiliation(s)
- Lei Hu
- grid.16821.3c0000 0004 0368 8293Department of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen magnetic Resonance Ltd., Shenzhen, China
| | - Xinyang Song
- grid.443573.20000 0004 1799 2448Department of Radiology, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, 441000 China
| | - Robert Grimm
- grid.5406.7000000012178835XMR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Heinrich von Busch
- grid.5406.7000000012178835XInnovation Owner Artificial Intelligence for Oncology, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- grid.5406.7000000012178835XMR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Ali Kamen
- grid.415886.60000 0004 0546 1113Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ USA
| | - Bin Lou
- grid.415886.60000 0004 0546 1113Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ USA
| | - Henkjan Huisman
- grid.10417.330000 0004 0444 9382Radboud University Medical Center, Nijmegen, Netherlands
| | - Angela Tong
- grid.137628.90000 0004 1936 8753New York University, New York City, NY USA
| | - Tobias Penzkofer
- grid.6363.00000 0001 2218 4662Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Moon Hyung Choi
- grid.411947.e0000 0004 0470 4224Eunpyeong St. Mary’s Hospital, Catholic University of Korea, Seoul, Republic of Korea
| | | | - David Winkel
- grid.410567.1Universitätsspital Basel, Basel, Switzerland
| | - Pengyi Xing
- grid.411525.60000 0004 0369 1599Changhai Hospital, Shanghai, China
| | | | - Fergus Coakley
- grid.5288.70000 0000 9758 5690Oregon Health and Science University, Portland, OR USA
| | - Steven Shea
- grid.411451.40000 0001 2215 0876Loyola University Medical Center, Maywood, IL USA
| | - Edyta Szurowska
- grid.11451.300000 0001 0531 3426Medical University of Gdansk, Gdansk, Poland
| | - Jing-yi Guo
- grid.16821.3c0000 0004 0368 8293Clinical Research Center, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233 China
| | - Liang Li
- grid.412632.00000 0004 1758 2270Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060 China
| | - Yue-hua Li
- grid.16821.3c0000 0004 0368 8293Department of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
| | - Jun-gong Zhao
- grid.16821.3c0000 0004 0368 8293Department of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
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Siow TY, Yeh CH, Lin G, Lin CY, Wang HM, Liao CT, Toh CH, Chan SC, Lin CP, Ng SH. MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy. Cancers (Basel) 2022; 14:cancers14246119. [PMID: 36551604 PMCID: PMC9775984 DOI: 10.3390/cancers14246119] [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: 11/21/2022] [Revised: 12/06/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
A reliable prognostic stratification of patients with locally advanced hypopharyngeal cancer who had been treated with concurrent chemoradiotherapy (CCRT) is crucial for informing tailored management strategies. The purpose of this retrospective study was to develop robust and objective magnetic resonance imaging (MRI) radiomics-based models for predicting overall survival (OS) and progression-free survival (PFS) in this patient population. The study participants included 198 patients (median age: 52.25 years (interquartile range = 46.88-59.53 years); 95.96% men) who were randomly divided into a training cohort (n = 132) and a testing cohort (n = 66). Radiomic parameters were extracted from post-contrast T1-weighted MR images. Radiomic features for model construction were selected from the training cohort using least absolute shrinkage and selection operator-Cox regression models. Prognostic performances were assessed by calculating the integrated area under the receiver operating characteristic curve (iAUC). The ability of radiomic models to predict OS (iAUC = 0.580, 95% confidence interval (CI): 0.558-0.591) and PFS (iAUC = 0.625, 95% CI = 0.600-0.633) was validated in the testing cohort. The combination of radiomic signatures with traditional clinical parameters outperformed clinical variables alone in the prediction of survival outcomes (observed iAUC increments = 0.279 [95% CI = 0.225-0.334] and 0.293 [95% CI = 0.232-0.351] for OS and PFS, respectively). In summary, MRI radiomics has value for predicting survival outcomes in patients with hypopharyngeal cancer treated with CCRT, especially when combined with clinical prognostic variables.
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Affiliation(s)
- Tiing Yee Siow
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Chih-Hua Yeh
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
| | - Chien-Yu Lin
- Department of Radiation Oncology and Proton Therapy Center, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
| | - Hung-Ming Wang
- Division of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
| | - Chun-Ta Liao
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan 333423, Taiwan
| | - Cheng-Hong Toh
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
| | - Sheng-Chieh Chan
- Department of Nuclear Medicine, Hualien Tzu Chi Hospital, Tzu Chi University School of Medicine, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan
| | - Ching-Po Lin
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
- Correspondence: (C.-P.L.); (S.-H.N.)
| | - Shu-Hang Ng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung University College of Medicine, Taoyuan 333423, Taiwan
- Correspondence: (C.-P.L.); (S.-H.N.)
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9
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Qin X, Wang C, Gong G, Wang L, Su Y, Yin Y. Functional MRI radiomics-based assessment of pelvic bone marrow changes after concurrent chemoradiotherapy for cervical cancer. BMC Cancer 2022; 22:1149. [PMID: 36348290 PMCID: PMC9644624 DOI: 10.1186/s12885-022-10254-7] [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: 08/01/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022] Open
Abstract
Objectives To quantify the dose-response relationship of changes in pelvic bone marrow (PBM) functional MR radiomic features (RF) during concurrent chemoradiotherapy (CCRT) for patients with cervical cancer and establish the correlation with hematologic toxicity to provide a basis for PBM sparing. Methods A total of 54 cervical cancer patients who received CCRT were studied retrospectively. Patients underwent MRI IDEAL IQ and T2 fat suppression (T2fs) scanning pre- and post-CCRT. The PBM RFs were extracted from each region of interest at dose gradients of 5–10 Gy, 10–15 Gy, 15–20 Gy, 20–30 Gy, 30–40 Gy, 40–50 Gy, and > 50 Gy, and changes in peripheral blood cell (PBC) counts during radiotherapy were assessed. The dose-response relationship of RF changes and their correlation with PBC changes were investigated. Results White blood cell, neutrophils (ANC) and lymphocyte counts during treatment were decreased by 49.4%, 41.4%, and 76.3%, respectively. Most firstorder features exhibited a significant dose-response relationship, particularly FatFrac IDEAL IQ, which had a maximum dose-response curve slope of 10.09, and WATER IDEAL IQ had a slope of − 7.93. The firstorder-Range in FAT IDEAL IQ and firstorder-10Percentile in T2fs, showed a significant correlation between the changes in ANC counts under the low dose gradient of 5–10 Gy (r = 0.744, -0.654, respectively, p < 0.05). Conclusion Functional MR radiomics can detect microscopic changes in PBM at various dose gradients and provide an objective reference for bone marrow sparing and dose limitation in cervical cancer CCRT. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10254-7.
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Mitchell‐Hay RN, Ahearn TS, Murray AD, Waiter GD. Investigation of the Inter- and Intrascanner Reproducibility and Repeatability of Radiomics Features in T1-Weighted Brain MRI. J Magn Reson Imaging 2022; 56:1559-1568. [PMID: 35396777 PMCID: PMC9790235 DOI: 10.1002/jmri.28191] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 03/24/2022] [Accepted: 03/24/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Radiomics is the high throughput analysis of medical images using computer algorithms, which specifically assess textural features. It has increasingly been proposed as a tool for the development of imaging biomarkers. However, an important acknowledged limitation of radiomics is the lack of reproducibility of features produced. PURPOSE To assess reproducibility and repeatability of radiomics variables in brain MRI through a multivisit, multicenter study. STUDY TYPE Retrospective. POPULATION Fourteen individuals visiting three institutions twice, 10 males with the mean age of 36.3 years and age range 25-51. FIELD STRENGTH 3D T1W inversion recovery on three 1.5-T General Electric scanners. ASSESSMENT Radiomics analysis by a consultant radiologist performed on the T1W images of the whole brain on all visits. All possible radiomics features were generated. STATISTICAL TEST Concordance correlation coefficient (CCC) and dynamic range (DR) for all variables were calculated to assess the test-retest repeatability. Intraclass correlation coefficients (ICCs) were calculated to investigate the reproducibility of features across centers. RESULTS Of 1596 features generated, 57 from center 1, 15 from center 2, and 22 from center 3 had a CCC > 0.9 and DR > 0.9. Eight variables had CCC > 0.9 and DR > 0.9 in all centers. Forty-one variables had an ICC of >0.9. No variables had CCC > 0.9, DR > 0.9, and ICC > 0.9. DATA CONCLUSION Repeatability and reproducibility of variables is a significant limitation of radiomics analysis in 3DT1W brain MRI. Careful selection of radiomic features is required. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Rosalind Nina Mitchell‐Hay
- Department of Radiology, Aberdeen Royal InfirmaryNHS GrampianAberdeenUK,Aberdeen Biomedical Imaging CentreUniversity of AberdeenAberdeenUK
| | - Trevor S. Ahearn
- Department of Radiology, Aberdeen Royal InfirmaryNHS GrampianAberdeenUK
| | - Alison D. Murray
- Aberdeen Biomedical Imaging CentreUniversity of AberdeenAberdeenUK
| | - Gordon D. Waiter
- Aberdeen Biomedical Imaging CentreUniversity of AberdeenAberdeenUK
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11
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Fatania K, Mohamud F, Clark A, Nix M, Short SC, O'Connor J, Scarsbrook AF, Currie S. Intensity standardization of MRI prior to radiomic feature extraction for artificial intelligence research in glioma-a systematic review. Eur Radiol 2022; 32:7014-7025. [PMID: 35486171 PMCID: PMC9474349 DOI: 10.1007/s00330-022-08807-2] [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: 01/14/2022] [Revised: 03/11/2022] [Accepted: 04/10/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVES Radiomics is a promising avenue in non-invasive characterisation of diffuse glioma. Clinical translation is hampered by lack of reproducibility across centres and difficulty in standardising image intensity in MRI datasets. The study aim was to perform a systematic review of different methods of MRI intensity standardisation prior to radiomic feature extraction. METHODS MEDLINE, EMBASE, and SCOPUS were searched for articles meeting the following eligibility criteria: MRI radiomic studies where one method of intensity normalisation was compared with another or no normalisation, and original research concerning patients diagnosed with diffuse gliomas. Using PRISMA criteria, data were extracted from short-listed studies including number of patients, MRI sequences, validation status, radiomics software, method of segmentation, and intensity standardisation. QUADAS-2 was used for quality appraisal. RESULTS After duplicate removal, 741 results were returned from database and reference searches and, from these, 12 papers were eligible. Due to a lack of common pre-processing and different analyses, a narrative synthesis was sought. Three different intensity standardisation techniques have been studied: histogram matching (5/12), limiting or rescaling signal intensity (8/12), and deep learning (1/12)-only two papers compared different methods. From these studies, histogram matching produced the more reliable features compared to other methods of altering MRI signal intensity. CONCLUSION Multiple methods of intensity standardisation have been described in the literature without clear consensus. Further research that directly compares different methods of intensity standardisation on glioma MRI datasets is required. KEY POINTS • Intensity standardisation is a key pre-processing step in the development of robust radiomic signatures to evaluate diffuse glioma. • A minority of studies compared the impact of two or more methods. • Further research is required to directly compare multiple methods of MRI intensity standardisation on glioma datasets.
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Affiliation(s)
- Kavi Fatania
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK.
- Department of Radiology, Leeds General Infirmary, Great George Street, Leeds, LS1 3EX, UK.
| | | | - Anna Clark
- Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Michael Nix
- Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Susan C Short
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
- Department of Clinical Oncology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - James O'Connor
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
- Department of Radiology, The Christie Hospital, Manchester, UK
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London, UK
| | - Andrew F Scarsbrook
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | - Stuart Currie
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
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12
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Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
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13
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Cui Y, Yin FF. Impact of image quality on radiomics applications. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7fd7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.
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14
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Bao J, Feng X, Ma Y, Wang Y, Qi J, Qin C, Tan X, Tian Y. The latest application progress of radiomics in prediction and diagnosis of liver diseases. Expert Rev Gastroenterol Hepatol 2022; 16:707-719. [PMID: 35880549 DOI: 10.1080/17474124.2022.2104711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Early detection and individualized treatment of patients with liver disease is the key to survival. Radiomics can extract high-throughput quantitative features by multimode imaging, which has good application prospects for the diagnosis, staging and prognosis of benign and malignant liver diseases. Therefore, this paper summarizes the current research status in the field of liver disease, in order to help these patients achieve personalized and precision medical care. AREAS COVERED This paper uses several keywords on the PubMed database to search the references, and reviews the workflow of traditional radiomics, as well as the characteristics and influencing factors of different imaging modes. At the same time, the references on the application of imaging in different benign and malignant liver diseases were also summarized. EXPERT OPINION For patients with liver disease, the traditional imaging evaluation can only provide limited information. Radiomics exploits the characteristics of high-throughput and high-dimensional extraction, enabling liver imaging capabilities far beyond the scope of traditional visual image analysis. Recent studies have demonstrated the prospect of this technology in personalized diagnosis and treatment decision in various fields of the liver. However, further clinical validation is needed in its application and practice.
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Affiliation(s)
- Jiaying Bao
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xiao Feng
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Yan Ma
- Department of Ultrasound, Zibo Central Hospital, Zibo, P.R. China
| | - Yanyan Wang
- Departments of Emergency Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Jianni Qi
- Central Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Chengyong Qin
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xu Tan
- Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Yongmei Tian
- Department of Geriatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
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15
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Thrussell I, Winfield JM, Orton MR, Miah AB, Zaidi SH, Arthur A, Thway K, Strauss DC, Collins DJ, Koh DM, Oelfke U, Huang PH, O’Connor JPB, Messiou C, Blackledge MD. Radiomic Features From Diffusion-Weighted MRI of Retroperitoneal Soft-Tissue Sarcomas Are Repeatable and Exhibit Change After Radiotherapy. Front Oncol 2022; 12:899180. [PMID: 35924167 PMCID: PMC9343063 DOI: 10.3389/fonc.2022.899180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
Background Size-based assessments are inaccurate indicators of tumor response in soft-tissue sarcoma (STS), motivating the requirement for new response imaging biomarkers for this rare and heterogeneous disease. In this study, we assess the test-retest repeatability of radiomic features from MR diffusion-weighted imaging (DWI) and derived maps of apparent diffusion coefficient (ADC) in retroperitoneal STS and compare baseline repeatability with changes in radiomic features following radiotherapy (RT). Materials and Methods Thirty patients with retroperitoneal STS received an MR examination prior to treatment, of whom 23/30 were investigated in our repeatability analysis having received repeat baseline examinations and 14/30 patients were investigated in our post-treatment analysis having received an MR examination after completing pre-operative RT. One hundred and seven radiomic features were extracted from the full manually delineated tumor region using PyRadiomics. Test-retest repeatability was assessed using an intraclass correlation coefficient (baseline ICC), and post-radiotherapy variance analysis (post-RT-IMS) was used to compare the change in radiomic feature value to baseline repeatability. Results For the ADC maps and DWI images, 101 and 102 features demonstrated good baseline repeatability (baseline ICC > 0.85), respectively. Forty-three and 2 features demonstrated both good baseline repeatability and a high post-RT-IMS (>0.85), respectively. Pearson correlation between the baseline ICC and post-RT-IMS was weak (0.432 and 0.133, respectively). Conclusions The ADC-based radiomic analysis shows better test-retest repeatability compared with features derived from DWI images in STS, and some of these features are sensitive to post-treatment change. However, good repeatability at baseline does not imply sensitivity to post-treatment change.
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Affiliation(s)
- Imogen Thrussell
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Jessica M. Winfield
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Matthew R. Orton
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Aisha B. Miah
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Shane H. Zaidi
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Amani Arthur
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Khin Thway
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- Department of Histopathology, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Dirk C. Strauss
- Department of Surgery, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - David J. Collins
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Dow-Mu Koh
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Uwe Oelfke
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Paul H. Huang
- Division of Molecular Pathology, The Institute of Cancer Research, London, United Kingdom
| | - James P. B. O’Connor
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
- Department of Radiology, The Christie Hospital, Manchester, United Kingdom
| | - Christina Messiou
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
| | - Matthew D. Blackledge
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
- Department of Radiology, The Royal Marsden National Health Service (NHS) Foundation Trust, Sutton, United Kingdom
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Chen X, Ma X, Yan X, Luo F, Yang S, Wang Z, Wu R, Wang J, Lu N, Bi N, Yi J, Wang S, Li Y, Dai J, Men K. Personalized auto-segmentation for magnetic resonance imaging guided adaptive radiotherapy of prostate cancer. Med Phys 2022; 49:4971-4979. [PMID: 35670079 DOI: 10.1002/mp.15793] [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: 12/24/2021] [Revised: 05/13/2022] [Accepted: 05/30/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Fast and accurate delineation of organs on treatment-fraction images is critical in magnetic resonance imaging-guided adaptive radiotherapy (MRIgART). This study proposes a personalized auto-segmentation (AS) framework to assist online delineation of prostate cancer using MRIgART. METHODS Image data from 26 patients diagnosed with prostate cancer and treated using hypofractionated MRIgART (5 fractions per patient) were collected retrospectively. Daily pretreatment T2-weighted MRI was performed using a 1.5-T MRI system integrated into a Unity MR-linac. First-fraction image and contour data from 16 patients (80 image-sets) were used to train the population AS model, and the remaining 10 patients composed the test set. The proposed personalized AS framework contained two main steps. First, a convolutional neural network was employed to train the population model using the training set. Second, for each test patient, the population model was progressively fine-tuned with manually checked delineations of the patient's current and previous fractions to obtain a personalized model that was applied to the next fraction. RESULTS Compared with the population model, the personalized models substantially improved the mean Dice similarity coefficient from 0.79 to 0.93 for the prostate clinical target volume (CTV), 0.91 to 0.97 for the bladder, 0.82 to 0.92 for the rectum, 0.91 to 0.93 for the femoral heads, respectively. CONCLUSIONS The proposed method can achieve accurate segmentation and potentially shorten the overall online delineation time of MRIgART. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiangyu Ma
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xuena Yan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Fei Luo
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Siran Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Zekun Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Runye Wu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianyang Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Ningning Lu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Nan Bi
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Junlin Yi
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Shulian Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yexiong Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
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Impact of Parallel Acquisition Technology on the Robustness of Magnetic Resonance Imaging Radiomic Features. J Comput Assist Tomogr 2022; 46:906-913. [PMID: 35675690 DOI: 10.1097/rct.0000000000001344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of this study was to investigate the impact of integrated parallel acquisition technology (iPAT) on the robustness of magnetic resonance imaging radiomic features. METHODS A phantom and 6 healthy volunteers were scanned on a clinical 3-T system using T1-weighted (S1), T1-weighted fluid-attenuated (S2), T2-weighted fluid-attenuated (S3), and T2-weighted (S4); 2 iPAT flavors (generalized autocalibration partially parallel acquisitions and modified sensitivity encoding [mSENSE]) and their different acceleration factors R. Radiomic features were extracted, and their robustness was assessed using coefficient of variation (CV), and differences between sequences and region of interest (ROI) were evaluated using the χ2 test. RESULTS One volunteer was excluded because of movement during imaging acquisition. Generalized autocalibration partially parallel acquisitions provided more radiomic features with excellent robustness than mSENSE. Radiomic features with excellent robustness, unaffected by iPAT across different sequences and ROIs, in 92 radiomic features for phantom and healthy volunteers are 6.5% and 2.2%. For phantom, difference in the robustness degree between 4 sequences/P-ROIs was significant according to χ2 test; S2 and S3 could provide more excellent robust radiomic features than S1 and S4, and P-ROI3 filled with the biggest polystyrene particles could provide the most radiomic features with excellent robustness than the other P-ROIs. For healthy volunteers, only the difference in the degree of robustness between the 4 V-ROIs was significant, and V-ROI3 in white matter region of the left frontal lobe, which was located at periphery in image, could provide the most robust radiomic features compared with other V-ROIs. CONCLUSIONS Integrated parallel acquisition technology had a significant impact on the robustness of radiomic features. Generalized autocalibration partially parallel acquisitions delivered a more robust substrate for radiomic analyses than mSENSE.
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Zhang X, Wang Y, Zhang J, Zhang L, Wang S, Chen Y. Development of a MRI-Based Radiomics Nomogram for Prediction of Response of Patients With Muscle-Invasive Bladder Cancer to Neoadjuvant Chemotherapy. Front Oncol 2022; 12:878499. [PMID: 35646654 PMCID: PMC9132152 DOI: 10.3389/fonc.2022.878499] [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: 02/18/2022] [Accepted: 04/14/2022] [Indexed: 12/12/2022] Open
Abstract
Objective To develop and evaluate the performance of a magnetic resonance imaging (MRI)-based radiomics nomogram for prediction of response of patients with muscle-invasive bladder cancer (MIBC) to neoadjuvant chemotherapy (NAC). Methods A total of 70 patients with clinical T2-4aN0M0 MIBC were enrolled in this retrospective study. For each patient, 1316 radiomics features were extracted from T2-weighted images (T2WI), diffusion-weighted images (DWI), and apparent diffusion coefficient (ADC) maps. The variance threshold algorithm and the Student's t-test or the Mann-Whitney U test were applied to select optimal features. Multivariate logistic regression analysis was used to eliminate irrelevant features, and the retained features were incorporated into the final single-modality radiomics model. Combined radiomic models were generated by combining single-modality radiomics models. A radiomics nomogram, incorporating radiomics signatures and independent clinical risk factors, was developed to determine whether the performance of the model in predicting tumor response to NAC could be further improved. Results Based on pathological T stage post-surgery, 36 (51%) patients were classified as good responders (GR) and 34 (49%) patients as non-good responders (non-GR). In addition, 3 single-modality radiomics models and 4 combined radiomics models were established. Among all radiomics models, the combined radiomics model based on T2WI_Score, DWI_Score, and ADC_Score yielded the highest area under the receiver operating characteristics curve (AUC) (0.967, 95% confidence interval (CI): 0.930-0.995). A radiomics nomogram, integrating the clinical T stage and 3 single-modality radiomics models, yielded a higher AUC (0.973, 95%CI: 0.934-0.998) than other combined radiomics models. Conclusion The proposed MRI-based radiomics nomogram has the potential to be used as a non-invasive tool for the quantitatively prediction of tumor response to NAC in patients with MIBC.
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Affiliation(s)
- Xinxin Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yichen Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jin Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lianyu Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare, Beijing, China
| | - Yan Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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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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Fatania K, Clark A, Frood R, Scarsbrook A, Al-Qaisieh B, Currie S, Nix M. Harmonisation of scanner-dependent contrast variations in magnetic resonance imaging for radiation oncology, using style-blind auto-encoders. Phys Imaging Radiat Oncol 2022; 22:115-122. [PMID: 35619643 PMCID: PMC9127401 DOI: 10.1016/j.phro.2022.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 11/20/2022] Open
Abstract
Background and purpose Magnetic Resonance Imaging (MRI) exhibits scanner dependent contrast, which limits generalisability of radiomics and machine-learning for radiation oncology. Current deep-learning harmonisation requires paired data, retraining for new scanners and often suffers from geometry-shift which alters anatomical information. The aim of this study was to investigate style-blind auto-encoders for MRI harmonisation to accommodate unpaired training data, avoid geometry-shift and harmonise data from previously unseen scanners. Materials and methods A style-blind auto-encoder, using adversarial classification on the latent-space, was designed for MRI harmonisation. The public CC359 T1-w MRI brain dataset includes six scanners (three manufacturers, two field strengths), of which five were used for training. MRI from all six (including one unseen) scanner were harmonised to common contrast. Harmonisation extent was quantified via Kolmogorov-Smirnov testing of residual scanner dependence of 3D radiomic features, and compared to WhiteStripe normalisation. Anatomical content preservation was measured through change in structural similarity index on contrast-cycling (δSSIM). Results The percentage of radiomics features showing statistically significant scanner-dependence was reduced from 41% (WhiteStripe) to 16% for white matter and from 39% to 27% for grey matter. δSSIM < 0.0025 on harmonisation and de-harmonisation indicated excellent anatomical content preservation. Conclusions Our method harmonised MRI contrast effectively, preserved critical anatomical details at high fidelity, trained on unpaired data and allowed zero-shot harmonisation. Robust and clinically translatable harmonisation of MRI will enable generalisable radiomic and deep-learning models for a range of applications, including radiation oncology treatment stratification, planning and response monitoring.
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Affiliation(s)
- Kavi Fatania
- Department of Radiology, St James University Hospital Trust, Beckett Street, Leeds LS9 7TF, UK
| | - Anna Clark
- Leeds Cancer Centre, Bexley Wing, St James University Hospital Trust, Beckett Street, Leeds LS9 7TF, UK
| | - Russell Frood
- Department of Radiology, St James University Hospital Trust, Beckett Street, Leeds LS9 7TF, UK
| | - Andrew Scarsbrook
- Department of Radiology, St James University Hospital Trust, Beckett Street, Leeds LS9 7TF, UK
| | - Bashar Al-Qaisieh
- Leeds Cancer Centre, Bexley Wing, St James University Hospital Trust, Beckett Street, Leeds LS9 7TF, UK
| | - Stuart Currie
- Department of Radiology, St James University Hospital Trust, Beckett Street, Leeds LS9 7TF, UK
| | - Michael Nix
- Leeds Cancer Centre, Bexley Wing, St James University Hospital Trust, Beckett Street, Leeds LS9 7TF, UK
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21
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Carbonell G, Kennedy P, Bane O, Kirmani A, El Homsi M, Stocker D, Said D, Mukherjee P, Gevaert O, Lewis S, Hectors S, Taouli B. Precision of MRI radiomics features in the liver and hepatocellular carcinoma. Eur Radiol 2022; 32:2030-2040. [PMID: 34564745 DOI: 10.1007/s00330-021-08282-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 07/12/2021] [Accepted: 08/17/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVES To assess the precision of MRI radiomics features in hepatocellular carcinoma (HCC) tumors and liver parenchyma. METHODS The study population consisted of 55 patients, including 16 with untreated HCCs, who underwent two repeat contrast-enhanced abdominal MRI exams within 1 month to evaluate: (1) test-retest repeatability using the same MRI system (n = 28, 10 HCCs); (2) inter-platform reproducibility between different MRI systems (n = 27, 6 HCCs); (3) inter-observer reproducibility (n = 16, 16 HCCs). Shape and 1st- and 2nd-order radiomics features were quantified on pre-contrast T1-weighted imaging (WI), T1WI portal venous phase (pvp), T2WI, and ADC (apparent diffusion coefficient), on liver regions of interest (ROIs) and HCC volumes of interest (VOIs). Precision was assessed by calculating intraclass correlation coefficient (ICC), concordance correlation coefficient (CCC), and coefficient of variation (CV). RESULTS There was moderate to excellent test-retest repeatability of shape and 1st- and 2nd-order features for all sequences in HCCs (ICC: 0.53-0.99; CV: 3-29%), and moderate to good test-retest repeatability of 1st- and 2nd-order features for T1WI sequences, and 2nd-order features for T2WI in the liver (ICC: 0.53-0.73; CV: 12-19%). There was poor inter-platform reproducibility for all features and sequences, except for shape and 1st-order features on T1WI in HCCs (CCC: 0.58-0.99; CV: 3-15%). Good to excellent inter-observer reproducibility was found for all features and sequences in HCCs (CCC: 0.80-0.99; CV: 4-15%) and moderate to good for liver (CCC: 0.45-0.86; CV: 6-25%). CONCLUSIONS MRI radiomics features have acceptable repeatability in the liver and HCC when using the same MRI system and across readers but have low reproducibility across MR systems, except for shape and 1st-order features on T1WI. Data must be interpreted with caution when performing multiplatform radiomics studies. KEY POINTS • MRI radiomics features have acceptable repeatability when using the same MRI system but less reproducible when using different MRI platforms. • MRI radiomics features extracted from T1 weighted-imaging show greater stability across exams than T2 weighted-imaging and ADC. • Inter-observer reproducibility of MRI radiomics features was found to be good in HCC tumors and acceptable in liver parenchyma.
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Affiliation(s)
- Guillermo Carbonell
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, University Hospital Virgen de La Arrixaca, Murcia, Spain
| | - Paul Kennedy
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Octavia Bane
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ammar Kirmani
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maria El Homsi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Daniela Said
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Universidad de los Andes, Santiago, Chile
| | | | - Olivier Gevaert
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Sara Lewis
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stefanie Hectors
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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22
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AIM in Oncology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_94] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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23
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Madhogarhia R, Haldar D, Bagheri S, Familiar A, Anderson H, Arif S, Vossough A, Storm P, Resnick A, Davatzikos C, Fathi Kazerooni A, Nabavizadeh A. Radiomics and radiogenomics in pediatric neuro-oncology: A review. Neurooncol Adv 2022; 4:vdac083. [PMID: 35795472 PMCID: PMC9252112 DOI: 10.1093/noajnl/vdac083] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The current era of advanced computing has allowed for the development and implementation of the field of radiomics. In pediatric neuro-oncology, radiomics has been applied in determination of tumor histology, identification of disseminated disease, prognostication, and molecular classification of tumors (ie, radiogenomics). The field also comes with many challenges, such as limitations in study sample sizes, class imbalance, generalizability of the methods, and data harmonization across imaging centers. The aim of this review paper is twofold: first, to summarize existing literature in radiomics of pediatric neuro-oncology; second, to distill the themes and challenges of the field and discuss future directions in both a clinical and technical context.
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Affiliation(s)
- Rachel Madhogarhia
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Debanjan Haldar
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sina Bagheri
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ariana Familiar
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Hannah Anderson
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sherjeel Arif
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Arastoo Vossough
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Phillip Storm
- Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Adam Resnick
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Machine Learning-Based Radiomics in Neuro-Oncology. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:139-151. [PMID: 34862538 DOI: 10.1007/978-3-030-85292-4_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
In the last decades, modern medicine has evolved into a data-centered discipline, generating massive amounts of granular high-dimensional data exceeding human comprehension. With improved computational methods, machine learning and artificial intelligence (AI) as tools for data processing and analysis are becoming more and more important. At the forefront of neuro-oncology and AI-research, the field of radiomics has emerged. Non-invasive assessments of quantitative radiological biomarkers mined from complex imaging characteristics across various applications are used to predict survival, discriminate between primary and secondary tumors, as well as between progression and pseudo-progression. In particular, the application of molecular phenotyping, envisioned in the field of radiogenomics, has gained popularity for both primary and secondary brain tumors. Although promising results have been obtained thus far, the lack of workflow standardization and availability of multicenter data remains challenging. The objective of this review is to provide an overview of novel applications of machine learning- and deep learning-based radiomics in primary and secondary brain tumors and their implications for future research in the field.
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Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer. Sci Rep 2021; 11:22737. [PMID: 34815464 PMCID: PMC8610973 DOI: 10.1038/s41598-021-02154-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 11/03/2021] [Indexed: 01/06/2023] Open
Abstract
This study provides a quantitative assessment of the accuracy of a commercially available deformable image registration (DIR) algorithm to automatically generate prostate contours and additionally investigates the robustness of radiomic features to differing contours. Twenty-eight prostate cancer patients enrolled on an institutional review board (IRB) approved protocol were selected. Planning CTs (pCTs) were deformably registered to daily cone-beam CTs (CBCTs) to generate prostate contours (auto contours). The prostate contours were also manually drawn by a physician. Quantitative assessment of deformed versus manually drawn prostate contours on daily CBCT images was performed using Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), difference in center-of-mass position (ΔCM) and difference in volume (ΔVol). Radiomic features from 6 classes were extracted from each contour. Lin’s concordance correlation coefficient (CCC) and mean absolute percent difference in radiomic feature-derived data (mean |%Δ|RF) between auto and manual contours were calculated. The mean (± SD) DSC, MDA, ΔCM and ΔVol between the auto and manual prostate contours were 0.90 ± 0.04, 1.81 ± 0.47 mm, 2.17 ± 1.26 mm and 5.1 ± 4.1% respectively. Of the 1,010 fractions under consideration, 94.8% of DIRs were within TG-132 recommended tolerance. 30 radiomic features had a CCC > 0.90 and 21 had a mean |%∆|RF < 5%. Auto-propagation of prostate contours resulted in nearly 95% of DIRs within tolerance recommendations of TG-132, leading to the majority of features being regarded as acceptably robust. The use of auto contours for radiomic feature analysis is promising but must be done with caution.
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Rich B, Huang J, Yang Y, Jin W, Johnson P, Wang L, Yang F. Radiomics Predicts for Distant Metastasis in Locally Advanced Human Papillomavirus-Positive Oropharyngeal Squamous Cell Carcinoma. Cancers (Basel) 2021; 13:cancers13225689. [PMID: 34830844 PMCID: PMC8616361 DOI: 10.3390/cancers13225689] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/07/2021] [Accepted: 11/11/2021] [Indexed: 01/04/2023] Open
Abstract
Simple Summary There is strong evidence that locally advanced human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) carries a significantly better prognosis than HPV negative OPSCC, suggesting the possibility of treatment de-escalation and, therefore, toxicity reduction in this patient population. The lack of success in clinical trials towards this end presses the need to risk stratify locally advanced HPV+ OPSCC patients who can safely have treatment de-escalated. The present study had recourse to radiomics for this purpose and showed that radiomics has the ability to discriminate patients with locally advanced HPV+ OPSCC who went on to develop distant metastasis after completion of definitive chemoradiation or radiation alone. The implications of this study aid in demonstrating the potential pivotal role of radiomics in predictive risk assessment and personalizing therapy for this patient population. Abstract (1) Background and purpose: clinical trials have unsuccessfully tried to de-escalate treatment in locally advanced human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) with the goal of reducing treatment toxicity. The aim of this study was to explore the role of radiomics for risk stratification in this patient population to guide treatment. (2) Methods: the study population consisted of 225 patients with locally advanced HPV+ OPSCC treated with curative-intent radiation or chemoradiation therapy. Appearance of distant metastasis was used as the endpoint event. Radiomics data were extracted from the gross tumor volumes (GTVs) identified on the planning CT, with gray level being discretized using three different bin widths (8, 16, and 32). The data extracted for the groups with and without distant metastasis were subsequently balanced using three different algorithms including synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and borderline SMOTE. From these different combinations, a total of nine radiomics datasets were derived. Top features that minimized redundancy while maximizing relevance to the endpoint were selected individually and collectively for the nine radiomics datasets to build support vector machine (SVM) based predictive classifiers. Performance of the developed classifiers was evaluated by receiver operating characteristic (ROC) curve analysis. (3) Results: of the 225 locally advanced HPV+ OPSCC patients being studied, 9.3% had developed distant metastases at last follow-up. SVM classifiers built for the nine radiomics dataset using either their own respective top features or the top consensus ones were all able to differentiate the two cohorts at a level of excellence or beyond, with ROC area under curve (AUC) ranging from 0.84 to 0.95 (median = 0.90). ROC comparisons further revealed that the majority of the built classifiers did not distinguish the two cohorts significantly better than each other. (4) Conclusions: radiomics demonstrated discriminative ability in distinguishing patients with locally advanced HPV+ OPSCC who went on to develop distant metastasis after completion of definitive chemoradiation or radiation alone and may serve to risk stratify this patient population with the purpose of guiding the appropriate therapy.
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Affiliation(s)
- Benjamin Rich
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, USA; (B.R.); (W.J.); (L.W.)
| | - Jianfeng Huang
- Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi 214125, China;
| | - Yidong Yang
- Department of Radiation Oncology, The First Affiliated Hospital of University of Science and Technology of China, Hefei 230026, China;
| | - William Jin
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, USA; (B.R.); (W.J.); (L.W.)
| | - Perry Johnson
- Department of Radiation Oncology, University of Florida, Jacksonville, FL 32209, USA;
| | - Lora Wang
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, USA; (B.R.); (W.J.); (L.W.)
| | - Fei Yang
- Department of Radiation Oncology, University of Miami, Miami, FL 33136, USA; (B.R.); (W.J.); (L.W.)
- Correspondence: ; Tel.: +1-(305)-243-4255
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Yi Z, Long L, Zeng Y, Liu Z. Current Advances and Challenges in Radiomics of Brain Tumors. Front Oncol 2021; 11:732196. [PMID: 34722274 PMCID: PMC8551958 DOI: 10.3389/fonc.2021.732196] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics enable the extraction of a large mass of quantitative features from complex clinical imaging arrays, and then transform them into high-dimensional data which can subsequently be mined to find their relevance with the tumor's histological features, which reflect underlying genetic mutations and malignancy, along with grade, progression, therapeutic effect, or even overall survival (OS). Compared to traditional brain imaging, radiomics provides quantitative information linked to meaningful biologic characteristics and application of deep learning which sheds light on the full automation of imaging diagnosis. Recent studies have shown that radiomics' application is broad in identifying primary tumor, differential diagnosis, grading, evaluation of mutation status and aggression, prediction of treatment response and recurrence in pituitary tumors, gliomas, and brain metastases. In this descriptive review, besides establishing a general understanding among protocols, results, and clinical significance of these studies, we further discuss the current limitations along with future development of radiomics.
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Affiliation(s)
- Zhenjie Yi
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,XiangYa School of Medicine, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Lifu Long
- XiangYa School of Medicine, Central South University, Changsha, China
| | - Yu Zeng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhixiong Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
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Kendrick J, Francis R, Hassan GM, Rowshanfarzad P, Jeraj R, Kasisi C, Rusanov B, Ebert M. Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies. Front Oncol 2021; 11:771787. [PMID: 34790581 PMCID: PMC8591174 DOI: 10.3389/fonc.2021.771787] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 10/11/2021] [Indexed: 12/21/2022] Open
Abstract
Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.
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Affiliation(s)
- Jake Kendrick
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Roslyn Francis
- Medical School, University of Western Australia, Crawley, WA, Australia
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Robert Jeraj
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Collin Kasisi
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Branimir Rusanov
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Martin Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, WA, Australia
- 5D Clinics, Claremont, WA, Australia
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Korte JC, Cardenas C, Hardcastle N, Kron T, Wang J, Bahig H, Elgohari B, Ger R, Court L, Fuller CD, Ng SP. Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer. Sci Rep 2021; 11:17633. [PMID: 34480036 PMCID: PMC8417253 DOI: 10.1038/s41598-021-96600-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.
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Affiliation(s)
- James C. Korte
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1008.90000 0001 2179 088XDepartment of Biomedical Engineering, University of Melbourne, Melbourne, Australia
| | - Carlos Cardenas
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Nicholas Hardcastle
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1007.60000 0004 0486 528XCentre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Tomas Kron
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1008.90000 0001 2179 088XSir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Jihong Wang
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Houda Bahig
- grid.410559.c0000 0001 0743 2111Radiation Oncology Department, Centre Hospitalier de l’Université de Montréal, Montreal, Canada
| | - Baher Elgohari
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA ,grid.10251.370000000103426662Clinical Oncology & Nuclear Medicine Department, Mansoura University, Mansoura, Egypt
| | - Rachel Ger
- grid.470142.40000 0004 0443 9766Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ USA
| | - Laurence Court
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Clifton D. Fuller
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Sweet Ping Ng
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA ,grid.1055.10000000403978434Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia ,grid.482637.cDepartment of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Melbourne, Australia
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30
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Eck B, Chirra PV, Muchhala A, Hall S, Bera K, Tiwari P, Madabhushi A, Seiberlich N, Viswanath SE. Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters. J Magn Reson Imaging 2021; 54:1009-1021. [PMID: 33860966 PMCID: PMC8376104 DOI: 10.1002/jmri.27635] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/25/2021] [Accepted: 03/26/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Radiomic descriptors from magnetic resonance imaging (MRI) are promising for disease diagnosis and characterization but may be sensitive to differences in imaging parameters. OBJECTIVE To evaluate the repeatability and robustness of radiomic descriptors within healthy brain tissue regions on prospectively acquired MRI scans; in a test-retest setting, under controlled systematic variations of MRI acquisition parameters, and after postprocessing. STUDY TYPE Prospective. SUBJECTS Fifteen healthy participants. FIELD STRENGTH/SEQUENCE A 3.0 T, axial T2 -weighted 2D turbo spin-echo pulse sequence, 181 scans acquired (2 test/retest reference scans and 12 with systematic variations in contrast weighting, resolution, and acceleration per participant; removing scans with artifacts). ASSESSMENT One hundred and forty-six radiomic descriptors were extracted from a contiguous 2D region of white matter in each scan, before and after postprocessing. STATISTICAL TESTS Repeatability was assessed in a test/retest setting and between manual and automated annotations for the reference scan. Robustness was evaluated between the reference scan and each group of variant scans (contrast weighting, resolution, and acceleration). Both repeatability and robustness were quantified as the proportion of radiomic descriptors that fell into distinct ranges of the concordance correlation coefficient (CCC): excellent (CCC > 0.85), good (0.7 ≤ CCC ≤ 0.85), moderate (0.5 ≤ CCC < 0.7), and poor (CCC < 0.5); for unprocessed and postprocessed scans separately. RESULTS Good to excellent repeatability was observed for 52% of radiomic descriptors between test/retest scans and 48% of descriptors between automated vs. manual annotations, respectively. Contrast weighting (TR/TE) changes were associated with the largest proportion of highly robust radiomic descriptors (21%, after processing). Image resolution changes resulted in the largest proportion of poorly robust radiomic descriptors (97%, before postprocessing). Postprocessing of images with only resolution/acceleration differences resulted in 73% of radiomic descriptors showing poor robustness. DATA CONCLUSIONS Many radiomic descriptors appear to be nonrobust across variations in MR contrast weighting, resolution, and acceleration, as well in test-retest settings, depending on feature formulation and postprocessing. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Brendan Eck
- Dept of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA,Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Prathyush V. Chirra
- Dept of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Avani Muchhala
- Dept of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Sophia Hall
- Dept of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Kaustav Bera
- Dept of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Pallavi Tiwari
- Dept of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Anant Madabhushi
- Dept of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA,Louis Stokes VA Medical Center, Cleveland, OH, USA
| | - Nicole Seiberlich
- Dept of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA,Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Satish E. Viswanath
- Dept of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
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Zhang Z, Xiao J, Wu S, Lv F, Gong J, Jiang L, Yu R, Luo T. Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades. J Digit Imaging 2021; 33:826-837. [PMID: 32040669 DOI: 10.1007/s10278-020-00322-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The grading of glioma has clinical significance in determining a treatment strategy and evaluating prognosis to investigate a novel set of radiomic features extracted from the fractional anisotropy (FA) and mean diffusivity (MD) maps of brain diffusion tensor imaging (DTI) sequences for computer-aided grading of gliomas. This retrospective study included 108 patients who had pathologically confirmed brain gliomas and DTI scanned during 2012-2018. This cohort included 43 low-grade gliomas (LGGs; all grade II) and 65 high-grade gliomas (HGGs; grade III or IV). We extracted a set of radiomic features, including traditional texture, morphological, and novel deep features derived from pre-trained convolutional neural network models, in the manually-delineated tumor regions. We employed support vector machine and these radiomic features for two classification tasks: LGGs vs HGGs, and grade III vs IV. The area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity was reported as the performance metrics using the leave-one-out cross-validation method. When combining FA+MD, AUC = 0.93, accuracy = 0.94, sensitivity = 0.98, and specificity = 0.86 in classifying LGGs from HGGs, while AUC = 0.99, accuracy = 0.98, sensitivity = 0.98, and specificity = 1.00 in classifying grade III from IV. The AUC and accuracy remain close when features were extracted from only the solid tumor or additionally including necrosis, cyst, and peritumoral edema. Still, the effects in terms of sensitivity and specificity are mixed. Deep radiomic features derived from pre-trained convolutional neural networks showed higher prediction ability than the traditional texture and shape features in both classification experiments. Radiomic features extracted on the FA and MD maps of brain DTI images are useful for noninvasively classification/grading of LGGs vs HGGs, and grade III vs IV.
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Affiliation(s)
- Zhiwei Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jingjing Xiao
- Department of Medical Engineering, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, 400037, China.,School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK
| | - Shandong Wu
- Departments of Radiology, Biomedical Informatics, Bioengineering, and Intelligent Systems, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Junwei Gong
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Lin Jiang
- Department of Radiology, The Third Affiliated Hospital of Zunyi Medical College, Zunyi, 563000, Guizhou, China
| | - Renqiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Tianyou Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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Spohn SK, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021; 11:8027-8042. [PMID: 34335978 PMCID: PMC8315055 DOI: 10.7150/thno.61207] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/17/2021] [Indexed: 12/14/2022] Open
Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.
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Affiliation(s)
- Simon K.B. Spohn
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
| | - Alisa S. Bettermann
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Michael Mix
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Nils H. Nicolay
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Tobias Fechter
- Department of Radiation Oncology - Division of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Tobias Hölscher
- Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Radu Grosu
- Institute of Computer Engineering, Vienne University of Technology, Vienna, Austria
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Anca L. Grosu
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
- German Oncology Center, European University of Cyprus, Limassol, Cyprus
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33
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Veres G, Vas NF, Lyngby Lassen M, Béresová M, K. Krizsan A, Forgács A, Berényi E, Balkay L. Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups. PLoS One 2021; 16:e0253419. [PMID: 34143830 PMCID: PMC8213143 DOI: 10.1371/journal.pone.0253419] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/04/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose Many studies of MRI radiomics do not include the discretization method used for the analyses, which might indicate that the discretization methods used are considered irrelevant. Our goals were to compare three frequently used discretization methods (lesion relative resampling (LRR), lesion absolute resampling (LAR) and absolute resampling (AR)) applied to the same data set, along with two different lesion segmentation approaches. Methods We analyzed the effects of altering bin widths or bin numbers for the three different sampling methods using 40 texture indices (TIs). The impact was evaluated on brain MRI studies obtained for 71 patients divided into three different disease groups: multiple sclerosis (MS, N = 22), ischemic stroke (IS, N = 22), cancer patients (N = 27). Two different MRI acquisition protocols were considered for all patients, a T2- and a post-contrast 3D T1-weighted MRI sequence. Elliptical and manually drawn VOIs were employed for both imaging series. Three different types of gray-level discretization methods were used: LRR, LAR and AR. Hypothesis tests were done among all diseased and control areas to compare the TI values in these areas. We also did correlation analyses between TI values and lesion volumes. Results In general, no significant differences were reported in the results when employing the AR and LAR discretization methods. It was found that employing 38 TIs introduced variation in the results when the number of bin parameters was altered, suggesting that both the degree and direction of monotonicity between each TI value and binning parameters were characteristic for each TI. Furthermore, while TIs were changing with altering binning values, no changes correlated to neither disease nor the MRI sequence. We found that most indices correlated weakly with the volume, while the correlation coefficients were independent of both diseases analyzed and MR contrast. Several cooccurrence-matrix based texture parameters show a definite higher correlation when employing the LRR discretization method However, with the best correlations obtained for the manually drawn VOI. Hypothesis tests among all disease and control areas (co-lateral hemisphere) revealed that the AR or LAR discretization techniques provide more suitable texture features than LRR. In addition, the manually drawn segmentation gave fewer significantly different TIs than the ellipsoid segmentations. In addition, the amount of TIs with significant differences was increasing with increasing the number of bins, or decreasing bin widths. Conclusion Our findings indicate that the AR discretization method may offer the best texture analysis in MR image assessments. Employing too many bins or too large bin widths might reduce the selection of TIs that can be used for differential diagnosis. In general, more statistically different TIs were observed for elliptical segmentations when compared to the manually drawn VOIs. In the texture analysis of MR studies, studies and publications should report on all important parameters and methods related to data collection, corrections, normalization, discretization, and segmentation.
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Affiliation(s)
- Gergő Veres
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- * E-mail:
| | - Norman Félix Vas
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Martin Lyngby Lassen
- Cedars-Sinai Medical Center, AIM Group, Los Angeles, CA, United States of America
- Department of Clinical Physiology, Nuclear Medicine and PET and Cluster for Molecular Imaging, University of Copenhagen, Copenhagen, Denmark
| | - Monika Béresová
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | | | | | - Ervin Berényi
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - László Balkay
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
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Skawran SM, Kambakamba P, Baessler B, von Spiczak J, Kupka M, Müller PC, Moeckli B, Linecker M, Petrowsky H, Reiner CS. Can magnetic resonance imaging radiomics of the pancreas predict postoperative pancreatic fistula? Eur J Radiol 2021; 140:109733. [PMID: 33945924 DOI: 10.1016/j.ejrad.2021.109733] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 03/25/2021] [Accepted: 04/20/2021] [Indexed: 01/04/2023]
Abstract
OBJECTIVES To evaluate whether a magnetic resonance imaging (MRI) radiomics-based machine learning classifier can predict postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) and to compare its performance to T1 signal intensity ratio (T1 SIratio). METHODS Sixty-two patients who underwent 3 T MRI before PD between 2008 and 2018 were retrospectively analyzed. POPF was graded and split into clinically relevant POPF (CR-POPF) vs. biochemical leak or no POPF. On T1- and T2-weighted images, 2 regions of interest were placed in the pancreatic corpus and cauda. 173 radiomics features were extracted using pyRadiomics. Additionally, the pancreas-to-muscle T1 SIratio was measured. The dataset was augmented and split into training (70 %) and test sets (30 %). A Boruta algorithm was used for feature reduction. For prediction of CR-POPF models were built using a gradient-boosted tree (GBT) and logistic regression from the radiomics features, T1 SIratio and a combination of the two. Diagnostic accuracy of the models was compared using areas under the receiver operating characteristics curve (AUCs). RESULTS Five most important radiomics features were identified for prediction of CR-POPF. A GBT using these features achieved an AUC of 0.82 (95 % Confidence Interval [CI]: 0.74 - 0.89) when applied on the original (non-augmented) dataset. Using T1 SIratio, a GBT model resulted in an AUC of 0.75 (CI: 0.63 - 0.84) and a logistic regression model delivered an AUC of 0.75 (CI: 0.63 - 0.84). A GBT model combining radiomics features and T1 SIratio resulted in an AUC of 0.90 (CI 0.84 - 0.95). CONCLUSION MRI-radiomics with routine sequences provides promising prediction of CR-POPF.
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Affiliation(s)
- Stephan M Skawran
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland; Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland; University of Zurich, Zurich, Switzerland
| | - Patryk Kambakamba
- University of Zurich, Zurich, Switzerland; Department of Surgery and Transplantation, University Hospital Zurich, Zurich, Switzerland; Department of Hepatobiliary Surgery and Liver Transplantation, St. Vincent's University Hospital, Dublin, Ireland
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland; University of Zurich, Zurich, Switzerland
| | - Jochen von Spiczak
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland; University of Zurich, Zurich, Switzerland
| | - Michael Kupka
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland; University of Zurich, Zurich, Switzerland
| | - Philip C Müller
- University of Zurich, Zurich, Switzerland; Department of Surgery and Transplantation, University Hospital Zurich, Zurich, Switzerland
| | - Beat Moeckli
- University of Zurich, Zurich, Switzerland; Department of Surgery and Transplantation, University Hospital Zurich, Zurich, Switzerland
| | - Michael Linecker
- University of Zurich, Zurich, Switzerland; Department of Surgery and Transplantation, University Hospital Zurich, Zurich, Switzerland; Department of Surgery and Transplantation, University Medical Center, Schleswig-Holstein, Campus Kiel, Germany
| | - Henrik Petrowsky
- University of Zurich, Zurich, Switzerland; Department of Surgery and Transplantation, University Hospital Zurich, Zurich, Switzerland
| | - Caecilia S Reiner
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland; University of Zurich, Zurich, Switzerland.
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Delgadillo R, Spieler BO, Ford JC, Kwon D, Yang F, Studenski M, Padgett KR, Abramowitz MC, Dal Pra A, Stoyanova R, Pollack A, Dogan N. Repeatability of CBCT radiomic features and their correlation with CT radiomic features for prostate cancer. Med Phys 2021; 48:2386-2399. [PMID: 33598943 DOI: 10.1002/mp.14787] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 12/09/2020] [Accepted: 02/09/2021] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Radiomic features of cone-beam CT (CBCT) images have potential as biomarkers to predict treatment response and prognosis for patients of prostate cancer. Previous studies of radiomic feature analysis for prostate cancer were assessed in a variety of imaging modalities, including MRI, PET, and CT, but usually limited to a pretreatment setting. However, CBCT images may provide an opportunity to capture early morphological changes to the tumor during treatment that could lead to timely treatment adaptation. This work investigated the quality of CBCT-based radiomic features and their relationship with reconstruction methods applied to the CBCT projections and the preprocessing methods used in feature extraction. Moreover, CBCT features were correlated with planning CT (pCT) features to further assess the viability of CBCT radiomic features. METHODS The quality of 42 CBCT-based radiomic features was assessed according to their repeatability and reproducibility. Repeatability was quantified by correlating radiomic features between 20 CBCT scans that also had repeated scans within 15 minutes. Reproducibility was quantified by correlating radiomic features between the planning CT (pCT) and the first fraction CBCT for 20 patients. Concordance correlation coefficients (CCC) of radiomic features were used to estimate the repeatability and reproducibility of radiomic features. The same patient dataset was assessed using different reconstruction methods applied to the CBCT projections. CBCT images were generated using 18 reconstruction methods using iterative (iCBCT) and standard (sCBCT) reconstructions, three convolution filters, and five noise suppression filters. Eighteen preprocessing settings were also considered. RESULTS Overall, CBCT radiomic features were more repeatable than reproducible. Five radiomic features are repeatable in > 97% of the reconstruction and preprocessing methods, and come from the gray-level size zone matrix (GLSZM), neighborhood gray-tone difference matrix (NGTDM), and gray-level-run length matrix (GLRLM) radiomic feature classes. These radiomic features were reproducible in > 9.8% of the reconstruction and preprocessing methods. Noise suppression and convolution filter smoothing increased radiomic features repeatability, but decreased reproducibility. The top-repeatable iCBCT method (iCBCT-Sharp-VeryHigh) is more repeatable than the top-repeatable sCBCT method (sCBCT-Smooth) in 64% of the radiomic features. CONCLUSION Methods for reconstruction and preprocessing that improve CBCT radiomic feature repeatability often decrease reproducibility. The best approach may be to use methods that strike a balance repeatability and reproducibility such as iCBCT-Sharp-VeryLow-1-Lloyd-256 that has 17 repeatable and eight reproducible radiomic features. Previous radiomic studies that only used pCT radiomic features have generated prognostic models of prostate cancer outcome. Since our study indicates that CBCT radiomic features correlated well with a subset of pCT radiomic features, one may expect CBCT radiomics to also generate prognostic models for prostate cancer.
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Affiliation(s)
- Rodrigo Delgadillo
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Benjamin O Spieler
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - John C Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Deukwoo Kwon
- Biostatistics and Bioinformatics Shared Resource, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.,Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Fei Yang
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Matthew Studenski
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Matthew C Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
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Kunimatsu A, Yasaka K, Akai H, Sugawara H, Kunimatsu N, Abe O. Texture Analysis in Brain Tumor MR Imaging. Magn Reson Med Sci 2021; 21:95-109. [PMID: 33692222 PMCID: PMC9199980 DOI: 10.2463/mrms.rev.2020-0159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram- or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors. However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples. To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts.
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Affiliation(s)
- Akira Kunimatsu
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Koichiro Yasaka
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Hiroyuki Akai
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Haruto Sugawara
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo.,Department of Radiology, The University of Tokyo Hospital
| | - Natsuko Kunimatsu
- Department of Radiology, International University of Health and Welfare, Mita Hospital
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo
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Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys 2021; 47:e185-e202. [PMID: 32418336 DOI: 10.1002/mp.13678] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/22/2019] [Accepted: 06/13/2019] [Indexed: 12/12/2022] Open
Abstract
Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three-dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open-source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics-based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, 33081, Italy
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
| | | | - Martin Vallières
- Medical Physics Unit, McGill University, Montreal, QC, Canada.,Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Arvind Rao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48103, USA
| | - Olivier Morin
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Sarah A Mattonen
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
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Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI. Biomolecules 2021; 11:biom11020307. [PMID: 33670596 PMCID: PMC7922315 DOI: 10.3390/biom11020307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/13/2021] [Accepted: 02/17/2021] [Indexed: 12/12/2022] Open
Abstract
Accurate grading of liver fibrosis can effectively assess the severity of liver disease and help doctors make an appropriate diagnosis. This study aimed to perform the automatic staging of hepatic fibrosis on patients with hepatitis B, who underwent gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging with dynamic radiomics analysis. The proposed dynamic radiomics model combined imaging features from multi-phase dynamic contrast-enhanced (DCE) images and time-domain information. Imaging features were extracted from the deep learning-based segmented liver volume, and time-domain features were further explored to analyze the variation in features during contrast enhancement. Model construction and evaluation were based on a 132-case data set. The proposed model achieved remarkable performance in significant fibrosis (fibrosis stage S1 vs. S2–S4; accuracy (ACC) = 0.875, area under the curve (AUC) = 0.867), advanced fibrosis (S1–S2 vs. S3–S4; ACC = 0.825, AUC = 0.874), and cirrhosis (S1–S3 vs. S4; ACC = 0.850, AUC = 0.900) classifications in the test set. It was more dominant compared with the conventional single-phase or multi-phase DCE-based radiomics models, normalized liver enhancement, and some serological indicators. Time-domain features were found to play an important role in the classification models. The dynamic radiomics model can be applied for highly accurate automatic hepatic fibrosis staging.
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Artificial Intelligence and the Medical Physicist: Welcome to the Machine. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041691] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.
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van Houdt PJ, Yang Y, van der Heide UA. Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy. Front Oncol 2021; 10:615643. [PMID: 33585242 PMCID: PMC7878523 DOI: 10.3389/fonc.2020.615643] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 12/08/2020] [Indexed: 12/20/2022] Open
Abstract
MRI-guided radiotherapy systems have the potential to bring two important concepts in modern radiotherapy together: adaptive radiotherapy and biological targeting. Based on frequent anatomical and functional imaging, monitoring the changes that occur in volume, shape as well as biological characteristics, a treatment plan can be updated regularly to accommodate the observed treatment response. For this purpose, quantitative imaging biomarkers need to be identified that show changes early during treatment and predict treatment outcome. This review provides an overview of the current evidence on quantitative MRI measurements during radiotherapy and their potential as an imaging biomarker on MRI-guided radiotherapy systems.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Yingli Yang
- Department of Radiation Oncology, University of California, Los Angeles, CA, United States
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
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Xue C, Zhou Y, Lo GG, Wong OL, Yu SK, Cheung KY, Yuan J. Reliability of radiomics features due to image reconstruction using a standardized T 2 -weighted pulse sequence for MR-guided radiotherapy: An anthropomorphic phantom study. Magn Reson Med 2021; 85:3434-3446. [PMID: 33404129 DOI: 10.1002/mrm.28650] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/24/2020] [Accepted: 11/25/2020] [Indexed: 11/11/2022]
Abstract
PURPOSE To prospectively investigate the impact of image reconstruction on MRI radiomics features. METHODS An anthropomorphic phantom was scanned at 1.5 T using a standardized sequence for MR-guided radiotherapy under SENSE and compressed-SENSE reconstruction settings. A total of 93 first-order and texture radiomics features in 10 volumes of interest were assessed based on (1) accuracy measured by the percentage deviation from the reference, (2) robustness on reconstruction in all volumes of interest measured by the intraclass correlation coefficient, and (3) repeatability measured by the coefficient of variance over the repetitive acquisitions. Finally, reliable and unreliable radiomics features were comprehensively determined based on their accuracy, robustness, and repeatability. RESULTS Better accuracy and robustness of the radiomics features were achieved under SENSE than compressed-SENSE reconstruction. The feature accuracy under SENSE reconstruction was more affected by acceleration factor than direction, whereas under compressed-SENSE reconstruction, accuracy was substantially impacted by the increasing denoising levels. Feature repeatability was dependent more on feature types than on reconstruction. A total of 45 reliable features and 13 unreliable features were finally determined for SENSE, compared with 22 reliable and 26 unreliable features for compressed SENSE. First-order and gray-level co-occurrence matrix features were generally more reliable than other features. CONCLUSION Radiomics features could be substantially affected by MRI reconstruction, so precautions need to be taken regarding their reliability for clinical use. This study helps the guidance of the preselection of reliable radiomics features and the preclusion of unreliable features in MR-guided radiotherapy.
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Affiliation(s)
- Cindy Xue
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong SAR, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong SAR, China
| | - Gladys Goh Lo
- Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong SAR, China
| | - Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong SAR, China
| | - Siu Ki Yu
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong SAR, China
| | - Kin Yin Cheung
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong SAR, China
| | - Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, Hong Kong SAR, China
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42
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AIM in Oncology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_94-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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43
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Simpson G, Ford JC, Llorente R, Portelance L, Yang F, Mellon EA, Dogan N. Impact of quantization algorithm and number of gray level intensities on variability and repeatability of low field strength magnetic resonance image-based radiomics texture features. Phys Med 2020; 80:209-220. [PMID: 33190077 DOI: 10.1016/j.ejmp.2020.10.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/13/2020] [Accepted: 10/29/2020] [Indexed: 02/06/2023] Open
Abstract
PURPOSE The purpose of this work was to investigate the impact of quantization preprocessing parameter selection on variability and repeatability of texture features derived from low field strength magnetic resonance (MR) images. METHODS Texture features were extracted from low field strength images of a daily image QA phantom with four texture inserts. Feature variability over time was quantified using all combinations of three quantization algorithms and four different numbers of gray level intensities. In addition, texture features were extracted using the same combinations from the low field strength MR images of the gross tumor volume (GTV) and left kidney of patients with repeated set up scans. The impact of region of interest (ROI) preprocessing on repeatability was investigated with a test-retest study design. RESULTS The phantom ROIs quantized to 64 Gy level intensities using the histogram equalization method resulted in the greatest number of features with the least variability. There was no clear method that resulted in the highest repeatability in the GTV or left kidney. However, eight texture features extracted from the GTV were repeatable regardless of ROI processing combination. CONCLUSION Low field strength MR images can provide a stable basis for texture analysis with ROIs quantized to 64 Gy levels using histogram equalization, but there is no clear optimal combination for repeatability.
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Affiliation(s)
- Garrett Simpson
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - John C Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Ricardo Llorente
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Lorraine Portelance
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Fei Yang
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Eric A Mellon
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12(th) Ave, Miami, FL 33136, USA.
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Leech M, Osman S, Jain S, Marignol L. Mini review: Personalization of the radiation therapy management of prostate cancer using MRI-based radiomics. Cancer Lett 2020; 498:210-216. [PMID: 33160001 DOI: 10.1016/j.canlet.2020.10.033] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/14/2020] [Accepted: 10/21/2020] [Indexed: 12/21/2022]
Abstract
Decisions on how to treat prostate cancer with radiation therapy are guideline-based but as such guidelines have been developed for populations of patients, this invariably leads to overly aggressive treatment in some patients and insufficient treatment in others. Heterogeneity within prostate tumors and in metastatic sites, even within the same patient, is believed to be a major cause of treatment failure. Radiomics biomarkers, more commonly referred to as radiomics 'features", provide readily available, cost-effective, non-invasive tools for screening, detecting tumors and serial monitoring of patients, including assessments of response to therapy and identification of therapeutic complications. Radiomics offers the potential to analyse whole tumors in 3D, as well as sub-regions or 'habitats' within tumors. Combining quantitative information from imaging with pathology, demographic details and other biomarkers will pave the way for personalised treatment selection and monitoring in prostate cancer. The aim of this review is to consider if MRI-based radiomics can bridge the gap between population-based management and personalised management of prostate cancer.
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Affiliation(s)
- Michelle Leech
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, School of Medicine, Trinity St. James's Cancer Institute, Trinity College, Dublin, Ireland.
| | - Sarah Osman
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Lisburn Road, Belfast, BT9 7AE, United Kingdom
| | - Suneil Jain
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Lisburn Road, Belfast, BT9 7AE, United Kingdom
| | - Laure Marignol
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, School of Medicine, Trinity St. James's Cancer Institute, Trinity College, Dublin, Ireland
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Schick U, Lucia F, Bourbonne V, Dissaux G, Pradier O, Jaouen V, Tixier F, Visvikis D, Hatt M. Use of radiomics in the radiation oncology setting: Where do we stand and what do we need? Cancer Radiother 2020; 24:755-761. [DOI: 10.1016/j.canrad.2020.07.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 07/21/2020] [Accepted: 07/23/2020] [Indexed: 12/14/2022]
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Bianchini L, Santinha J, Loução N, Figueiredo M, Botta F, Origgi D, Cremonesi M, Cassano E, Papanikolaou N, Lascialfari A. A multicenter study on radiomic features from T 2 -weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics. Magn Reson Med 2020; 85:1713-1726. [PMID: 32970859 DOI: 10.1002/mrm.28521] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 08/25/2020] [Accepted: 08/26/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features. METHODS T2 -weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features were assessed by the intraclass correlation coefficient and the concordance correlation coefficient, respectively, and by the within-subject coefficient of variation, considering repeated acquisitions with and without phantom repositioning, and with different scanner and acquisition parameters. The features showing intraclass correlation coefficient or concordance correlation coefficient >0.9 were selected, and their dependence on shape information (Spearman's ρ > 0.8) analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities of images. RESULTS From 944 two-dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three-dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo-time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information. CONCLUSION We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.
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Affiliation(s)
- Linda Bianchini
- Department of Physics, Università degli Studi di Milano and INSTM RU, Milan, Italy
| | - João Santinha
- Computational Clinical Imaging Group, Center for the Unknown (CCU), Champalimaud Foundation, Lisbon, Portugal.,Instituto de Telecomunicações, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | | | - Mário Figueiredo
- Instituto de Telecomunicações, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
| | - Francesca Botta
- Medical Physics Unit, IEO, European Institute of Oncology IRCSS, Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO, European Institute of Oncology IRCSS, Milan, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCSS, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO, European Institute of Oncology IRCSS, Milan, Italy
| | - Nikolaos Papanikolaou
- Computational Clinical Imaging Group, Center for the Unknown (CCU), Champalimaud Foundation, Lisbon, Portugal
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Masokano IB, Liu W, Xie S, Marcellin DFH, Pei Y, Li W. The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges. Cancer Imaging 2020; 20:67. [PMID: 32962762 PMCID: PMC7510095 DOI: 10.1186/s40644-020-00341-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 08/31/2020] [Indexed: 02/06/2023] Open
Abstract
Recently, radiomic texture quantification of tumors has received much attention from radiologists, scientists, and stakeholders because several results have shown the feasibility of using the technique to diagnose and manage oncological conditions. In patients with hepatocellular carcinoma, radiomics has been applied in all stages of tumor evaluation, including diagnosis and characterization of the genotypic behavior of the tumor, monitoring of treatment responses and prediction of various clinical endpoints. It is also useful in selecting suitable candidates for specific treatment strategies. However, the clinical validation of hepatocellular carcinoma radiomics is limited by challenges in imaging protocol and data acquisition parameters, challenges in segmentation techniques, dimensionality reduction, and modeling methods. Identification of the best segmentation and optimal modeling methods, as well as texture features most stable to imaging protocol variability would go a long way in harmonizing HCC radiomics for personalized patient care. This article reviews the process of HCC radiomics, its clinical applications, associated challenges, and current optimization strategies.
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Affiliation(s)
- Ismail Bilal Masokano
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Wenguang Liu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | - Simin Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
| | | | - Yigang Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
| | - Wenzheng Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
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Ghosh A, Malla SR, Bhalla AS, Manchanda S, Kandasamy D, Kumar R. Texture analysis of routine T2 weighted fat-saturated images can identify head and neck paragangliomas - A pilot study. Eur J Radiol Open 2020; 7:100248. [PMID: 32984446 PMCID: PMC7498758 DOI: 10.1016/j.ejro.2020.100248] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/14/2020] [Indexed: 01/11/2023] Open
Abstract
PURPOSE To evaluate the role of the first and second-order texture parameters obtained from T2-weighted fat-saturated DIXON images in differentiating paragangliomas from other neck masses, and to develop a statistical model to classify them. METHOD We retrospectively evaluated 38 paragangliomas, 18 nerve-sheath tumours and 14 other miscellaneous neck lesions obtained from an IRB approved study conducted between January 2016 and June 2019; using a composite gold standard of histopathology, cytology and DOTANOC PET CT (A total of 70 lesions in 63 patients). Fat-suppressed T2weighted-DIXON axial images were used. First and second-order texture-parameters were calculated from the original and filtered images. Feature selection using F-statistics and collinearity analysis provided 14 texture parameters for further analysis. Mann-Whitney-U test was used to compare between the groups and p-values were adjusted for multiple comparisons. ROC curve analysis was used to obtain optimal cut-offs. RESULTS A total of ten texture features were found to be significantly different between paragangliomas and non-paraganglioma lesions. Minimum from the histogram of grey levels was lower in paragangliomas with a cut off of ≤113.462 obtaining 62.9 % sensitivity and 77.27 % specificity in differentiating paragangliomas from non-paragangliomas. Logistic regression model was trained (n-49) using forward feature selection, which when evaluated on the validation set(n-21)- obtained an AUC of 0.855(95 %CI, 0.633 to 0.968) with a positive likelihood ratio of 4.545 (95 %CI, 1.298-15.923) in differentiating paragangliomas from non-paragangliomas. CONCLUSION Texture analysis of a routine imaging sequence can identify paragangliomas with high accuracy. Further development of texture analysis would enable better imaging workflow, resource utilisation and imaging cost reductions.
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Key Words
- AUC, area under the curve
- FDG-PET, fluorodeoxy-glucose positron emission tomography
- GLCM, grey level co-occurrence matrix
- Head neck
- ID, inverse difference
- IDM, inverse difference moment
- IDMN, inverse difference moment normalized
- IDN, inverse difference normalized
- IMC1, informational measure of correlation 1
- IMC2, informational measure of correlation 2
- LoG, laplacian of gaussian
- MCC, maximal correlation coefficient
- NST, nerve sheath tumour
- Nerve sheath tumour
- Paraganglioma
- ROC, receiver operator characteristics
- Radiomics
- Schwannoma
- Texture analysis
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Affiliation(s)
- Adarsh Ghosh
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India
| | - Soumya Ranjan Malla
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India
| | - Ashu Seith Bhalla
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India
| | - Smita Manchanda
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India
| | - Devasenathipathy Kandasamy
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India
| | - Rakesh Kumar
- Department of Otorhinolaryngology, Head & Neck Surgery, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110029, India
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49
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Jeon SH, Song C, Chie EK, Kim B, Kim YH, Chang W, Lee YJ, Chung JH, Chung JB, Lee KW, Kang SB, Kim JS. Combining Radiomics and Blood Test Biomarkers to Predict the Response of Locally Advanced Rectal Cancer to Chemoradiation. In Vivo 2020; 34:2955-2965. [PMID: 32871838 DOI: 10.21873/invivo.12126] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 12/29/2022]
Abstract
BACKGROUND/AIM A noninvasive method for predicting a patient's response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer would be useful because this would help determine the subsequent treatment strategy. Two types of noninvasive biomarkers have previously been studied, based on radiomics and based on blood test parameters. We hypothesized that a combination of both types would provide a better predictive power, and this has not previously been investigated. PATIENTS AND METHODS Data from 135 patients with locally advanced rectal cancer who underwent nCRT were retrospectively allocated into training and validation cohorts in a 2:1 ratio. Sixty-five radiomics features were extracted from tumors segmented on T2-weighted magnetic resonance images. An elastic net was applied to generate four models for discerning the patients with good responses to nCRT based on radiomics features (model R), blood biomarkers (model B), both (model RB), and a linear combination of models R and B (model R+B). RESULTS Among 65 radiomics features, 17 were selected as robust features for model development. The AUC values of model R, model B, model RB, and model R+B achieved 0.751, 0.627, 0.785, and 0.711 in the training cohort (n=90), and 0.705, 0.603, 0.679, and 0.705 in validation cohort (n=45), respectively. In the entire cohort, models RB and R+B demonstrated a significantly better performance than model B but not R. There was no correlation between the scores of models R and B (p=0.76). Radiomics features had a greater influence than blood biomarkers on models RB and R+B. CONCLUSION A non-redundancy between radiomics features and blood-based biomarkers was observed. Furthermore, radiomics features are more valuable in terms of predicting response to nCRT. The importance of combining non-invasive biomarkers in future investigations is highlighted.
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Affiliation(s)
- Seung Hyuck Jeon
- Laboratory of Translational Immunology and Vaccinology, Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Changhoon Song
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Eui Kyu Chie
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Bohyoung Kim
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea
| | - Young Hoon Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Won Chang
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Yoon Jin Lee
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Joo-Hyun Chung
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jin Beom Chung
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Keun-Wook Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sung-Bum Kang
- Department of Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jae-Sung Kim
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
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50
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Eun DI, Jang R, Ha WS, Lee H, Jung SC, Kim N. Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches. Sci Rep 2020; 10:13950. [PMID: 32811848 PMCID: PMC7434911 DOI: 10.1038/s41598-020-69932-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 07/14/2020] [Indexed: 01/01/2023] Open
Abstract
While high-resolution proton density-weighted magnetic resonance imaging (MRI) of intracranial vessel walls is significant for a precise diagnosis of intracranial artery disease, its long acquisition time is a clinical burden. Compressed sensing MRI is a prospective technology with acceleration factors that could potentially reduce the scan time. However, high acceleration factors result in degraded image quality. Although recent advances in deep-learning-based image restoration algorithms can alleviate this problem, clinical image pairs used in deep learning training typically do not align pixel-wise. Therefore, in this study, two different deep-learning-based denoising algorithms-self-supervised learning and unsupervised learning-are proposed; these algorithms are applicable to clinical datasets that are not aligned pixel-wise. The two approaches are compared quantitatively and qualitatively. Both methods produced promising results in terms of image denoising and visual grading. While the image noise and signal-to-noise ratio of self-supervised learning were superior to those of unsupervised learning, unsupervised learning was preferable over self-supervised learning in terms of radiomic feature reproducibility.
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Affiliation(s)
- Da-In Eun
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, South Korea
- School of Medicine, Kyunghee University, 26-6, Kyungheedae-ro, Dongdaemun-gu, Seoul, South Korea
| | - Ryoungwoo Jang
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, South Korea
| | - Woo Seok Ha
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, South Korea
- Department of Neurology, Yonsei University College of Medicine, 50, Yonsei-ro, Seodaemun-gu, Seoul, South Korea
| | - Hyunna Lee
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, South Korea
| | - Seung Chai Jung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, South Korea.
| | - Namkug Kim
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, South Korea.
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, South Korea.
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