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Deng L, Yang J, Zhang M, Zhu K, Jing M, Zhang Y, Zhang B, Han T, Zhou J. Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas. Diagn Interv Imaging 2024; 105:165-173. [PMID: 38072730 DOI: 10.1016/j.diii.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/30/2023] [Accepted: 12/02/2023] [Indexed: 05/05/2024]
Abstract
PURPOSE The purpose of this study was to evaluate and compare the performances of whole-lesion iodine map histogram analysis to those of single-slice spectral computed tomography (CT) parameters in discriminating between low-to-moderate grade invasive non-mucinous pulmonary adenocarcinoma (INMA) and high-grade INMA according to the novel International Association for the Study of Lung Cancer grading system of INMA. MATERIALS AND METHODS Sixty-one patients with INMA (34 with low-to-moderate grade [i.e., grade I and grade II] and 27 with high grade [i.e., grade III]) were evaluated with spectral CT. There were 28 men and 33 women, with a mean age of 56.4 ± 10.5 (standard deviation) years (range: 29-78 years). The whole-lesion iodine map histogram parameters (mean, standard deviation, variance, skewness, kurtosis, entropy, and 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile) were measured for each INMA. In other sessions, by placing regions of interest at representative levels of the tumor and normalizing them, spectral CT parameters (iodine concentration and normalized iodine concentration) were obtained. Discriminating capabilities of spectral CT and histogram parameters were assessed and compared using area under the ROC curve (AUC) and logistic regression models. RESULTS The 1st, 10th, and 25th percentiles of the iodine map histogram analysis, and iodine concentration and normalized iodine concentration of single-slice spectral CT parameters were significantly different between high-grade and low-to-moderate grade INMAs (P < 0.001 to P = 0.002). The 1st percentile of histogram parameters (AUC, 0.84; 95% confidence interval [CI]: 0.73-0.92) and iodine concentration (AUC, 0.78; 95% CI: 0.66-0.88) from single-slice spectral CT parameters had the best performance for discriminating between high-grade and low-to-moderate grade INMAs. At ROC curve analysis no significant differences in AUC were found between histogram parameters (AUC = 0.86; 95% CI: 0.74-0.93) and spectral CT parameters (AUC = 0.81; 95% CI: 0.74-0.93) (P = 0.60). CONCLUSION Both whole-lesion iodine map histogram analysis and single-slice spectral CT parameters help discriminate between low-to-moderate grade and high-grade INMAs according to the novel International Association for the Study of Lung Cancer grading system, with no differences in diagnostic performances.
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Affiliation(s)
- Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Jingjing Yang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Mingtao Zhang
- Second Clinical School, Lanzhou University, Lanzhou 730000, China; Department of Orthopedics, Lanzhou University Second Hospital, 730000, China
| | - Kaibo Zhu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730000, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou 730000, China; Second Clinical School, Lanzhou University, Lanzhou 730000, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730000, China.
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Wu Q, Yi Y, Lai B, Li J, Lian Y, Chen J, Wu Y, Wang X, Cao W. Texture analysis of apparent diffusion coefficient maps: can it identify nonresponse to neoadjuvant chemotherapy for additional radiation therapy in rectal cancer patients? Gastroenterol Rep (Oxf) 2024; 12:goae035. [PMID: 38651169 PMCID: PMC11035003 DOI: 10.1093/gastro/goae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/15/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Background Neoadjuvant chemotherapy (NCT) alone can achieve comparable treatment outcomes to chemoradiotherapy in locally advanced rectal cancer (LARC) patients. This study aimed to investigate the value of texture analysis (TA) in apparent diffusion coefficient (ADC) maps for identifying non-responders to NCT. Methods This retrospective study included patients with LARC after NCT, and they were categorized into nonresponse group (pTRG 3) and response group (pTRG 0-2) based on pathological tumor regression grade (pTRG). Predictive texture features were extracted from pre- and post-treatment ADC maps to construct a TA model using RandomForest. The ADC model was developed by manually measuring pre- and post-treatment ADC values and calculating their changes. Simultaneously, subjective evaluations based on magnetic resonance imaging assessment of TRG were performed by two experienced radiologists. Model performance was compared using the area under the curve (AUC) and DeLong test. Results A total of 299 patients from two centers were divided into three cohorts: the primary cohort (center A; n = 194, with 36 non-responders and 158 responders), the internal validation cohort (center A; n = 49, with 9 non-responders) and external validation cohort (center B; n = 56, with 33 non-responders). The TA model was constructed by post_mean, mean_change, post_skewness, post_entropy, and entropy_change, which outperformed both the ADC model and subjective evaluations with an impressive AUC of 0.997 (95% confidence interval [CI], 0.975-1.000) in the primary cohort. Robust performances were observed in internal and external validation cohorts, with AUCs of 0.919 (95% CI, 0.805-0.978) and 0.938 (95% CI, 0.840-0.985), respectively. Conclusions The TA model has the potential to serve as an imaging biomarker for identifying nonresponse to NCT in LARC patients, providing a valuable reference for these patients considering additional radiation therapy.
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Affiliation(s)
- Qianyu Wu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Yongju Yi
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Department of Information Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Bingjia Lai
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Jiao Li
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Yanbang Lian
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, P. R. China
| | - Junhong Chen
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, P. R. China
| | - Yue Wu
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Xinhua Wang
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
| | - Wuteng Cao
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, P. R. China
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Cheng Y, Yang L, Wang Y, Kuang L, Pan X, Chen L, Cao X, Xu Y. Development and validation of a radiomics model based on T2-weighted imaging for predicting the efficacy of high intensity focused ultrasound ablation in uterine fibroids. Quant Imaging Med Surg 2024; 14:1803-1819. [PMID: 38415139 PMCID: PMC10895146 DOI: 10.21037/qims-23-916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/06/2023] [Indexed: 02/29/2024]
Abstract
Background The heterogeneity of uterine fibroids in magnetic resonance imaging (MRI) is complex for a subjective visual evaluation, therefore it is difficult for an accurate prediction of the efficacy of high intensity focused ultrasound (HIFU) ablation in fibroids before the treatment. The purpose of this study was to set up a radiomics model based on MRI T2-weighted imaging (T2WI) for predicting the efficacy of HIFU ablation in uterine fibroids, and it would be used in preoperative screening of the fibroids for achieving high non-perfused volume ratio (NPVR). Methods A total of 178 patients with uterine fibroids were consecutively enrolled and treated with ultrasound-guided HIFU under conscious sedation between February 2017 and December 2021. Among them, 96 patients with 108 uterine fibroids with high ablation efficacy (NPVR ≥80%, h_NPVR) and 82 patients with 92 fibroids with lower ablation efficacy (NPVR <80%, l_NPVR) were retrospectively analyzed. The transverse T2WI images of fibroids were selected, and the fibroids were delineated slice by slice using ITK-SNAP software. The radiomics analysis was performed to find the imaging biomarker for the construction of a predicting model for the evaluation of the ablation efficacy, including the feature extraction, feature selection and model construction. The prediction model was built by logistic regression and assessed by receiver operating characteristic (ROC) curve, and the prediction efficiency of the two models was compared by Delong test. The ratio of the training set to the testing set was 8:2. Results The logistic regression model showed that the mean area under the curve (AUC) of the training set was 0.817 [95% confidence interval (CI): 0.755-0.882], and the testing set was 0.805 (95% CI: 0.670-0.941), respectively, which indicated a strong classification ability. The Delong test showed that there was no significant difference in the area under the ROC curve between the training set and testing set (P>0.05). Conclusions The radiomics model based on T2WI is feasible and effective for predicting the efficacy of HIFU ablation in treatment of uterine fibroids.
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Affiliation(s)
- Yu Cheng
- Department of Imaging and Interventional Radiology, Shanghai Xuhui Central Hospital, Shanghai, China
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
| | - Lixia Yang
- Department of Imaging and Interventional Radiology, Shanghai Xuhui Central Hospital, Shanghai, China
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
| | - Yiran Wang
- Department of Imaging and Interventional Radiology, Shanghai Xuhui Central Hospital, Shanghai, China
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
| | - Lanqiong Kuang
- Department of Imaging and Interventional Radiology, Shanghai Xuhui Central Hospital, Shanghai, China
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
| | - Xianpan Pan
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiaohuan Cao
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yonghua Xu
- Department of Imaging and Interventional Radiology, Shanghai Xuhui Central Hospital, Shanghai, China
- Department of Imaging and Interventional Radiology, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, China
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Han Q, Lu Y, Wang D, Li X, Ruan Z, Mei N, Ji X, Geng D, Yin B. Glioblastomas with and without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity present morphological and microstructural differences on conventional MR images. Eur Radiol 2023; 33:9139-9151. [PMID: 37495706 DOI: 10.1007/s00330-023-09924-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 05/04/2023] [Accepted: 05/14/2023] [Indexed: 07/28/2023]
Abstract
OBJECTIVES Glioblastoma (GB) without peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity is atypical and its characteristics are barely known. The aim of this study was to explore the differences in pathological and MRI-based intrinsic features (including morphologic and first-order features) between GBs with peritumoral FLAIR hyperintensity (PFH-bearing GBs) and GBs without peritumoral FLAIR hyperintensity (PFH-free GBs). METHODS In total, 155 patients with pathologically diagnosed GBs were retrospectively collected, which included 110 PFH-bearing GBs and 45 PFH-free GBs. The pathological and imaging data were collected. The Visually AcceSAble Rembrandt Images (VASARI) features were carefully evaluated. The first-order radiomics features from the tumor region were extracted from FLAIR, apparent diffusion coefficient (ADC), and T1CE (T1-contrast enhanced) images. All parameters were compared between the two groups of GBs. RESULTS The pathological data showed more alpha thalassemia/mental retardation syndrome X-linked (ATRX)-loss in PFH-free GBs compared to PFH-bearing ones (p < 0.001). Based on VASARI evaluation, PFH-free GBs had larger intra-tumoral enhancing proportion and smaller necrotic proportion (both, p < 0.001), more common non-enhancing tumor (p < 0.001), mild/minimal enhancement (p = 0.003), expansive T1/FLAIR ratio (p < 0.001) and solid enhancement (p = 0.009), and less pial invasion (p = 0.010). Moreover, multiple ADC- and T1CE-based first-order radiomics features demonstrated differences, especially the lower intensity heterogeneity in PFH-free GBs (for all, adjusted p < 0.05). CONCLUSIONS Compared to PFH-bearing GBs, PFH-free ones demonstrated less immature neovascularization and lower intra-tumoral heterogeneity, which would be helpful in clinical treatment stratification. CLINICAL RELEVANCE STATEMENT Glioblastomas without peritumoral FLAIR hyperintensity show less immature neovascularization and lower heterogeneity leading to potential higher treatment benefits due to less drug resistance and treatment failure. KEY POINTS • The study explored the differences between glioblastomas with and without peritumoral FLAIR hyperintensity. • Glioblastomas without peritumoral FLAIR hyperintensity showed less necrosis and contrast enhancement and lower intensity heterogeneity. • Glioblastomas without peritumoral FLAIR hyperintensity had less immature neovascularization and lower tumor heterogeneity.
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Affiliation(s)
- Qiuyue Han
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Yiping Lu
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Dongdong Wang
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Zhuoying Ruan
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Nan Mei
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China
| | - Xiong Ji
- Department of Pathology, Huashan Hospital, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China.
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Shanghai, China.
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, 200040, Shanghai, China.
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Amintas S, Giraud N, Fernandez B, Dupin C, Denost Q, Garant A, Frulio N, Smith D, Rullier A, Rullier E, Vuong T, Dabernat S, Vendrely V. The Crying Need for a Better Response Assessment in Rectal Cancer. Curr Treat Options Oncol 2023; 24:1507-1523. [PMID: 37702885 PMCID: PMC10643426 DOI: 10.1007/s11864-023-01125-9] [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] [Accepted: 07/09/2023] [Indexed: 09/14/2023]
Abstract
OPINION STATEMENT Since total neoadjuvant treatment achieves almost 30% pathologic complete response, organ preservation has been increasingly debated for good responders after neoadjuvant treatment for patients diagnosed with rectal cancer. Two organ preservation strategies are available: a watch and wait strategy and a local excision strategy including patients with a near clinical complete response. A major issue is the selection of patients according to the initial tumor staging or the response assessment. Despite modern imaging improvement, identifying complete response remains challenging. A better selection could be possible by radiomics analyses, exploiting numerous image features to feed data characterization algorithms. The subsequent step is to include baseline and/or pre-therapeutic MRI, PET-CT, and CT radiomics added to the patients' clinicopathological data, inside machine learning (ML) prediction models, with predictive or prognostic purposes. These models could be further improved by the addition of new biomarkers such as circulating tumor biomarkers, molecular profiling, or pathological immune biomarkers.
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Affiliation(s)
- Samuel Amintas
- Tumor Biology and Tumor Bank Laboratory, CHU Bordeaux, F-33600, Pessac, France.
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France.
| | - Nicolas Giraud
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
| | | | - Charles Dupin
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
| | - Quentin Denost
- Bordeaux Colorectal Institute, F-33000, Bordeaux, France
| | - Aurelie Garant
- UT Southwestern Department of Radiation Oncology, Dallas, USA
| | - Nora Frulio
- Radiology Department, CHU Bordeaux, F-33600, Pessac, France
| | - Denis Smith
- Department of Digestive Oncology, CHU Bordeaux, F-33600, Pessac, France
| | - Anne Rullier
- Histology Department, CHU Bordeaux, F-33000, Bordeaux, France
| | - Eric Rullier
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Surgery Department, CHU Bordeaux, F-33600, Pessac, France
| | - Te Vuong
- Department of Radiation Oncology, McGill University, Jewish General Hospital, Montreal, Canada
| | - Sandrine Dabernat
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Biochemistry Department, CHU Bordeaux, F-33000, Bordeaux, France
| | - Véronique Vendrely
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
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Kawashima Y, Miyakoshi M, Kawabata Y, Indo H. Efficacy of texture analysis of ultrasonographic images in the differentiation of metastatic and non-metastatic cervical lymph nodes in patients with squamous cell carcinoma of the tongue. Oral Surg Oral Med Oral Pathol Oral Radiol 2023:S2212-4403(23)00439-X. [PMID: 37353468 DOI: 10.1016/j.oooo.2023.04.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/13/2023] [Accepted: 04/23/2023] [Indexed: 06/25/2023]
Abstract
OBJECTIVE We investigated the efficacy of using texture analysis of ultrasonographic images of the cervical lymph nodes of patients with squamous cell carcinoma of the tongue to differentiate between metastatic and non-metastatic lymph nodes. STUDY DESIGN We analyzed 32 metastatic and 28 non-metastatic lymph nodes diagnosed by histopathologic examination on presurgical US images. Using the LIFEx texture analysis program, we extracted 36 texture features from the images and calculated the statistical significance of differences in texture features between metastatic and non-metastatic lymph nodes using the t test. To assess the diagnostic ability of the significantly different texture features to discriminate between metastatic and non-metastatic nodes, we performed receiver operating characteristic curve analysis and calculated the area under the curve. We set the cutoff points that maximized the sensitivity and specificity for each curve according to the Youden J statistic. RESULTS We found that 20 texture features significantly differed between metastatic and non-metastatic lymph nodes. Among them, only the gray-level run length matrix feature of run length non-uniformity and the gray-level zone length matrix features of gray-level non-uniformity and zone length non-uniformity showed an excellent ability to discriminate between metastatic and non-metastatic lymph nodes as indicated by the area under the curve and the sum of sensitivity and specificity. CONCLUSIONS Analysis of the texture features of run length non-uniformity, gray-level non-uniformity, and zone length non-uniformity values allows for differentiation between metastatic and non-metastatic lymph nodes, with the use of gray-level non-uniformity appearing to be the best means of predicting metastatic lymph nodes.
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Affiliation(s)
- Yusuke Kawashima
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan.
| | - Masaaki Miyakoshi
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan
| | - Yoshihiro Kawabata
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan
| | - Hiroko Indo
- Department of Maxillofacial Radiology, Kagoshima University Graduate School of Medical and Dental Sciences Field of Oncology, Kagoshima, Japan
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Feasibility and intra-and interobserver reproducibility of quantitative susceptibility mapping with radiomic features for intracranial dissecting intramural hematomas and atherosclerotic calcifications. Sci Rep 2023; 13:3651. [PMID: 36871117 PMCID: PMC9985647 DOI: 10.1038/s41598-023-30745-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) for 61 patients with dissecting intramural hematomas (n = 36) or atherosclerotic calcifications (n = 25) in intracranial vertebral arteries were collected to assess intra- and interobserver reproducibility in a 3.0-T MR system between January 2015 and December 2017. Two independent observers each segmented regions of interest for lesions twice. The reproducibility was evaluated using intra-class correlation coefficients (ICC) and within-subject coefficients of variation (wCV) for means and concordance correlation coefficients (CCC) and ICC for radiomic features (CCC and ICC > 0.85) were used. Mean QSM values were 0.277 ± 0.092 ppm for dissecting intramural hematomas and - 0.208 ± 0.078 ppm for atherosclerotic calcifications. ICCs and wCVs were 0.885-0.969 and 6.5-13.7% in atherosclerotic calcifications and 0.712-0.865 and 12.4-18.7% in dissecting intramural hematomas, respectively. A total of 9 and 19 reproducible radiomic features were observed in dissecting intramural hematomas and atherosclerotic calcifications, respectively. QSM measurements in dissecting intramural hematomas and atherosclerotic calcifications were feasible and reproducible between intra- and interobserver comparisons, and some reproducible radiomic features were demonstrated.
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Histogram analysis of synthetic magnetic resonance imaging: Correlations with histopathological factors in head and neck squamous cell carcinoma. Eur J Radiol 2023; 160:110715. [PMID: 36753947 DOI: 10.1016/j.ejrad.2023.110715] [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: 12/31/2022] [Accepted: 01/24/2023] [Indexed: 01/30/2023]
Abstract
PURPOSE To analyse the association between histogram parameters derived from synthetic MRI (SyMRI) and different histopathological factors in head and neck squamous cell carcinoma (HNSCC). METHOD Sixty-one patients with histologically proven primary HNSCC were prospectively enrolled. The correlations between histogram parameters of SyMRI (T1, T2 and proton density (PD) maps) and histopathological factors were analysed using Spearman analysis. The Mann-Whitney U test or Student's t test was utilized to differentiate histological grades and human papillomavirus (HPV) status. The ROC curves and leave-one-out cross-validation (LOOCV) were used to evaluate the differentiation performance. Bootstrapping was applied to avoid overfitting. RESULTS Several histogram parameters were associated with histological grade: T1 map (r = 0.291) and PD map (r = 0.294 - 0.382/-0.343), and PD_75th Percentile showed the highest differentiation performance (AUC: 0.721 (ROC) and 0.719 (LOOCV)). Moderately negative correlations were found between p16 status and the histogram parameters: T1 map (r = -0.587 - -0.390), T2 map (r = -0.649 - -0.357) and PD map (r = -0.537 - -0.338). In differentiating HPV infection, Entropy was the most discriminative parameter in each map and T2_Entropy showed the highest diagnostic performance (AUC: 0.851 [ROC] and 0.851 [LOOCV]). Additionally, several histogram parameters were correlated with Ki-67 (r = -0.379/-0.397), epidermal growth factor receptor (EGFR) (r = 0.318/0.322) status and p53 (r = 0.452 - 0.665/-0.607) status. CONCLUSIONS Histogram parameters derived from SyMRI may serve as a potential biomarker for discriminating relevant histopathological features, including histological differentiation grade, HPV infection, Ki-67, EGFR and p53 statuses.
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Yang F, Li Y, Li X, Yu X, Zhao Y, Li L, Xie L, Lin M. The utility of texture analysis based on quantitative synthetic magnetic resonance imaging in nasopharyngeal carcinoma: a preliminary study. BMC Med Imaging 2023; 23:15. [PMID: 36698156 PMCID: PMC9875491 DOI: 10.1186/s12880-023-00968-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/13/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI) is commonly used for the diagnosis of nasopharyngeal carcinoma (NPC) and occipital clivus (OC) invasion, but a proportion of lesions may be missed using non-enhanced MRI. The purpose of this study is to investigate the diagnostic performance of synthetic magnetic resonance imaging (SyMRI) in differentiating NPC from nasopharyngeal hyperplasia (NPH), as well as evaluating OC invasion. METHODS Fifty-nine patients with NPC and 48 volunteers who underwent SyMRI examination were prospectively enrolled. Eighteen first-order features were extracted from VOIs (primary tumours, benign mucosa, and OC). Statistical comparisons were conducted between groups using the independent-samples t-test and the Mann-Whitney U test to select significant parameters. Multiple diagnostic models were then constructed using multivariate logistic analysis. The diagnostic performance of the models was calculated by receiver operating characteristics (ROC) curve analysis and compared using the DeLong test. Bootstrap and 5-folds cross-validation were applied to avoid overfitting. RESULTS The T1, T2 and PD map-derived models had excellent diagnostic performance in the discrimination between NPC and NPH in volunteers, with area under the curves (AUCs) of 0.975, 0.972 and 0.986, respectively. Besides, SyMRI models also showed excellent performance in distinguishing OC invasion from non-invasion (AUC: 0.913-0.997). Notably, the T1 map-derived model showed the highest diagnostic performance with an AUC, sensitivity, specificity, and accuracy of 0.997, 96.9%, 97.9% and 97.5%, respectively. By using 5-folds cross-validation, the bias-corrected AUCs were 0.965-0.984 in discriminating NPC from NPH and 0.889-0.975 in discriminating OC invasion from OC non-invasion. CONCLUSIONS SyMRI combined with first-order parameters showed excellent performance in differentiating NPC from NPH, as well as discriminating OC invasion from non-invasion.
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Affiliation(s)
- Fan Yang
- grid.506261.60000 0001 0706 7839Department 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, 100021 China
| | - Yujie Li
- grid.506261.60000 0001 0706 7839Department 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, 100021 China
| | - Xiaolu Li
- grid.506261.60000 0001 0706 7839Department 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, 100021 China
| | - Xiaoduo Yu
- grid.506261.60000 0001 0706 7839Department 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, 100021 China
| | - Yanfeng Zhao
- grid.506261.60000 0001 0706 7839Department 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, 100021 China
| | - Lin Li
- grid.506261.60000 0001 0706 7839Department 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, 100021 China
| | - Lizhi Xie
- MR Research China, GE Healthcare, Beijing, China
| | - Meng Lin
- grid.506261.60000 0001 0706 7839Department 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, 100021 China
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Zhu F, Xiao Y, Tao B, Gao Z, Gao X, Zhao Q, Zhang Q, Tang B, Zhang X, Zhao Y, Bishop JR, Sweeney JA, Lui S. Radiomic features of gray matter in never-treated first-episode schizophrenia. Cereb Cortex 2022; 33:5957-5967. [PMID: 36513368 DOI: 10.1093/cercor/bhac474] [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: 08/12/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 12/15/2022] Open
Abstract
Alterations of radiomic features (RFs) in gray matter are observed in schizophrenia, of which the results may be limited by small study samples and confounding effects of drug therapies. We tested for RFs alterations of gray matter in never-treated first-episode schizophrenia (NT-FES) patients and examined their associations with known gene expression profiles. RFs were examined in the first sample with 197 NT-FES and 178 healthy controls (HCs) and validated in the second independent sample (90 NT-FES and 74 HCs). One-year follow-up data were available from 87 patients to determine whether RFs were associated with treatment outcomes. Associations between identified RFs in NT-FES and gene expression profiles were evaluated. NT-FES exhibited alterations of 30 RFs, with the greatest involvement of microstructural heterogeneity followed by measures of brain region shape. The identified RFs were mainly located in the central executive network, frontal-temporal network, and limbic system. Two baseline RFs with the involvement of microstructural heterogeneity predicted treatment response with moderate accuracy (78% for the first sample, 70% for the second sample). Exploratory analyses indicated that RF alterations were spatially related to the expression of schizophrenia risk genes. In summary, the present findings link brain abnormalities in schizophrenia with molecular features and treatment response.
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Affiliation(s)
- Fei Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yuan Xiao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Bo Tao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Ziyang Gao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Xin Gao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Qiannan Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Qi Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Biqiu Tang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | | | - Yu Zhao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH 45219, USA
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
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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: 1] [Impact Index Per Article: 0.5] [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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12
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Wan L, Peng W, Zou S, Shi Q, Wu P, Zhao Q, Ye F, Zhao X, Zhang H. Predicting perineural invasion using histogram analysis of zoomed EPI diffusion-weighted imaging in rectal cancer. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3353-3363. [PMID: 35779094 DOI: 10.1007/s00261-022-03579-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 06/06/2022] [Accepted: 06/06/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To investigate the utility of histogram analysis of zoomed EPI diffusion-weighted imaging (DWI) for predicting the perineural invasion (PNI) status of rectal cancer (RC). METHODS This prospective study evaluated 94 patients diagnosed with histopathologically confirmed RC between July 2020 and July 2021. Patients underwent preoperative rectal magnetic resonance imaging (MRI) examinations, including the zoomed EPI DWI sequence. Ten whole-tumor histogram parameters of each patient were derived from zoomed EPI DWI. Reproducibility was evaluated according to the intra-class correlation coefficient (ICC). The association of the clinico-radiological and histogram features with PNI status was assessed using univariable analysis for trend and multivariable logistic regression analysis with β value calculation. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance. RESULTS Forty-two patients exhibited positive PNI. The inter- and intraobserver agreements were excellent for the histogram parameters (all ICCs > 0.80). The maximum (p = 0.001), energy (p = 0.021), entropy (p = 0.021), kurtosis (p < 0.001), and skewness (p < 0.001) were significantly higher in the positive PNI group than in the negative PNI group. Multivariable analysis showed that higher MRI T stage [β = 2.154, 95% confidence interval (CI) 0.932-3.688; p = 0.002] and skewness (β = 0.779, 95% CI 0.255-1.382; p = 0.006) were associated with positive PNI. The model combining skewness and MRI T stage had an area under the ROC curve of 0.811 (95% CI 0.724-0.899) for predicting PNI status. CONCLUSION Histogram parameters in zoomed EPI DWI can help predict the PNI status in RC.
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Affiliation(s)
- Lijuan Wan
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Wenjing Peng
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shuangmei Zou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Qinglei Shi
- MR Scientific Marketing, Siemens Healthineers Ltd., Beijing, 100021, China
| | - Peihua Wu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Qing Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Feng Ye
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Xinming Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Hongmei Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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13
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Bellini D, Carbone I, Rengo M, Vicini S, Panvini N, Caruso D, Iannicelli E, Tombolini V, Laghi A. Performance of Machine Learning and Texture Analysis for Predicting Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer with 3T MRI. Tomography 2022; 8:2059-2072. [PMID: 36006071 PMCID: PMC9416446 DOI: 10.3390/tomography8040173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 11/23/2022] Open
Abstract
Background: To evaluate the diagnostic performance of a Machine Learning (ML) algorithm based on Texture Analysis (TA) parameters in the prediction of Pathological Complete Response (pCR) to Neoadjuvant Chemoradiotherapy (nChRT) in Locally Advanced Rectal Cancer (LARC) patients. Methods: LARC patients were prospectively enrolled to undergo pre- and post-nChRT 3T MRI for initial loco-regional staging. TA was performed on axial T2-Weighted Images (T2-WI) to extract specific parameters, including skewness, kurtosis, entropy, and mean of positive pixels. For the assessment of TA parameter diagnostic performance, all patients underwent complete surgical resection, which served as a reference standard. ROC curve analysis was carried out to determine the discriminatory accuracy of each quantitative TA parameter to predict pCR. A ML-based decisional tree was implemented combining all TA parameters in order to improve diagnostic accuracy. Results: Forty patients were considered for final study population. Entropy, kurtosis and MPP showed statistically significant differences before and after nChRT in patients with pCR; in particular, when patients with Pathological Partial Response (pPR) and/or Pathological Non-Response (pNR) were considered, entropy and skewness showed significant differences before and after nChRT (all p < 0.05). In terms of absolute value changes, pre- and post-nChRT entropy, and kurtosis showed significant differences (0.31 ± 0.35, in pCR, −0.02 ± 1.28 in pPR/pNR, (p = 0.04); 1.87 ± 2.19, in pCR, −0.06 ± 3.78 in pPR/pNR (p = 0.0005); 107.91 ± 274.40, in pCR, −28.33 ± 202.91 in pPR/pNR, (p = 0.004), respectively). According to ROC curve analysis, pre-treatment kurtosis with an optimal cut-off value of ≤3.29 was defined as the best discriminative parameter, resulting in a sensitivity and specificity in predicting pCR of 81.5% and 61.5%, respectively. Conclusions: TA parameters extracted from T2-WI MRI images could play a key role as imaging biomarkers in the prediction of response to nChRT in LARC patients. ML algorithms can be used to efficiently combine all TA parameters in order to improve diagnostic accuracy.
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Affiliation(s)
- Davide Bellini
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Iacopo Carbone
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
- Correspondence: ; Tel.: +39-351836065
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Simone Vicini
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Nicola Panvini
- Department of Radiological Sciences, Oncology and Pathology, “Sapienza” University of Rome—I.C.O.T. Hospital, Via Franco Faggiana, 1668, 04100 Latina, Italy
| | - Damiano Caruso
- Department of Surgical and Medical Sciences and Translational Medicine, “Sapienza” University of Rome—Diagnostic Imaging Unit, Sant′Andrea University Hospital, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Elsa Iannicelli
- Department of Surgical and Medical Sciences and Translational Medicine, “Sapienza” University of Rome—Diagnostic Imaging Unit, Sant′Andrea University Hospital, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Vincenzo Tombolini
- Department of Radiotherapy, Policlinico Umberto I, “Sapienza” University of Rome, 00161 Rome, Italy
| | - Andrea Laghi
- Department of Surgical and Medical Sciences and Translational Medicine, “Sapienza” University of Rome—Diagnostic Imaging Unit, Sant′Andrea University Hospital, Via di Grottarossa 1035, 00189 Rome, Italy
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Rai R, Barton MB, Chlap P, Liney G, Brink C, Vinod S, Heinke M, Trada Y, Holloway LC. Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer. J Med Imaging (Bellingham) 2022; 9:044005. [PMID: 35992729 PMCID: PMC9386367 DOI: 10.1117/1.jmi.9.4.044005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 08/09/2022] [Indexed: 08/20/2023] Open
Abstract
Purpose: Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer. Approach: An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of 10 patients with test-retest imaging among two observers. Results: We found that many radiomic features, particularly from texture classes, were highly sensitive to changes in image resolution and noise. About 49% of features had coefficient of variations ≤ 10 % in longitudinal phantom measurements. About 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8 . We saw excellent interobserver agreement with mean Dice similarity coefficient of 0.95 ± 0.04 for test and retest scans. Conclusions: The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.
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Affiliation(s)
- Robba Rai
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Michael B. Barton
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Phillip Chlap
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Gary Liney
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Carsten Brink
- Odense University Hospital, Laboratory of Radiation Physics, Department of Oncology, Odense, Denmark
- University of Southern Denmark, Department of Clinical Research, Odense, Denmark
| | - Shalini Vinod
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | | | - Yuvnik Trada
- Calvary Mater Newcastle, Department of Radiation Oncology, Newcastle, New South Wales, Australia
| | - Lois C. Holloway
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- University of Wollongong, Centre of Radiation Physics, Wollongong, New South Wales, Australia
- University of Sydney, Institute of Medical Physics, School of Physics, Sydney, New South Wales, Australia
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Borgheresi A, De Muzio F, Agostini A, Ottaviani L, Bruno A, Granata V, Fusco R, Danti G, Flammia F, Grassi R, Grassi F, Bruno F, Palumbo P, Barile A, Miele V, Giovagnoni A. Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective. J Clin Med 2022; 11:jcm11092599. [PMID: 35566723 PMCID: PMC9104021 DOI: 10.3390/jcm11092599] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.
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Affiliation(s)
- Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
| | - Letizia Ottaviani
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale IRCCS di Napoli, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
- Correspondence:
| | - Ginevra Danti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Federica Flammia
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Francesca Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Abruzzo Health Unit 1, Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, 67100 L’Aquila, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
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16
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Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2022; 49:2820-2835. [PMID: 34455593 PMCID: PMC8882689 DOI: 10.1002/mp.15195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 01/31/2023] Open
Abstract
Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.
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Affiliation(s)
- Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Jana G. Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, 10993 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Kalina V. Jordanova
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Megan E. Poorman
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Prathyush Chirra
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Akshay S. Chaudhari
- Department of Radiology, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
| | - Bettina Baessler
- University Hospital of Zurich and University of Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
| | - Jessica Winfield
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
| | - Satish E. Viswanath
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Nandita M. deSouza
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
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17
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Li W, Zhang H, Ren L, Zou Y, Tian F, Ji X, Li Q, Wang W, Ma G, Xia S. Radiomics of dual-energy computed tomography for predicting progression-free survival in patients with early glottic cancer. Future Oncol 2022; 18:1873-1884. [PMID: 35293227 DOI: 10.2217/fon-2021-1125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: This study aimed to predict progression-free survival (PFS) in patients with early glottic cancer using radiomic features on dual-energy computed tomography iodine maps. Methods: Radiomic features were extracted from arterial and venous phase iodine maps, and radiomic risk scores were determined by univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator regression with tenfold cross-validation. The Kaplan-Meier method was used to evaluate the association between radiomic risk scores and PFS. Results: Patients were stratified into low-risk and high-risk groups using radiomics, the PFS corresponding rates with statistical significance between the two groups. The high-risk group showed better survival, benefiting from laryngectomy. Conclusion: Radiomics could provide a promising biomarker for predicting the PFS of early glottic cancer patients.
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Affiliation(s)
- Wenfei Li
- Department of Radiology, The First Central Clinical School, Tianjin Medical University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China.,Department of Radiology, The First Hospital of Qinhuangdao, No. 258 Wenhua Road, Haigang District, Qinhuangdao, 066000, China
| | - Huanlei Zhang
- Department of Radiology, The First Central Clinical School, Tianjin Medical University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Lei Ren
- Department of Radiology, The First Central Clinical School, Tianjin Medical University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China.,Department of Medical Imaging, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, No. 88 Changling Road, Xiqing District, Tianjin, 300381, China
| | - Ying Zou
- Department of Medical Imaging, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, No. 88 Changling Road, Xiqing District, Tianjin, 300381, China
| | - Fengyue Tian
- Department of Radiology, Affiliated Hospital of Nankai University (Tianjin No. 4 Hospital), No. 4 Weishan Road, Hexi District, Tianjin, 300222, China
| | - Xiaodong Ji
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Qing Li
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Wei Wang
- Department of Otolaryngology, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
| | - Shuang Xia
- Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, No. 24 Fukang Road, Nankai District, Tianjin, 300192, China
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18
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Boca (Petresc) B, Caraiani C, Popa L, Lebovici A, Feier DS, Bodale C, Buruian MM. The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study. BIOLOGY 2022; 11:biology11030452. [PMID: 35336825 PMCID: PMC8945327 DOI: 10.3390/biology11030452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/04/2022] [Accepted: 03/14/2022] [Indexed: 11/16/2022]
Abstract
Simple Summary Metachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up. Early identification of patients with higher risk of developing distant metachronous metastases would help to improve therapeutic protocols and could allow for a more accurate, personalized management. Apparent diffusion coefficient (ADC) represents an MRI quantitative biomarker, which can assess the diffusion characteristics of tissues, depending on the microscopic mobility of water, showing information related to tissue cellularity. First-order histogram-based features statistics describe the frequency distribution of intensity values within a region of interest, revealing microstructural alterations. In our study, we demonstrated that whole-tumor ADC first-order features may provide useful information for the assessment of rectal cancer prognosis, regarding the occurrence of metachronous metastases. Abstract This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.
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Affiliation(s)
- Bianca Boca (Petresc)
- Department of Radiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (B.B.); (M.M.B.)
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania; (A.L.); (D.S.F.)
- Department of Medical Imaging, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Cosmin Caraiani
- Department of Medical Imaging, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Radiology, Regional Institute of Gastroenterology and Hepatology “Prof. Dr. Octavian Fodor”, 400158 Cluj-Napoca, Romania
- Correspondence: (C.C.); (L.P.)
| | - Loredana Popa
- Department of Medical Imaging, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Correspondence: (C.C.); (L.P.)
| | - Andrei Lebovici
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania; (A.L.); (D.S.F.)
- Department of Radiology, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Diana Sorina Feier
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania; (A.L.); (D.S.F.)
- Department of Radiology, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Carmen Bodale
- Department of Oncology, Amethyst Radiotherapy Center Cluj, 407280 Florești, Romania;
- Department of Medical Oncology and Radiotherapy, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Mircea Marian Buruian
- Department of Radiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (B.B.); (M.M.B.)
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19
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Schurink NW, van Kranen SR, Roberti S, van Griethuysen JJM, Bogveradze N, Castagnoli F, El Khababi N, Bakers FCH, de Bie SH, Bosma GPT, Cappendijk VC, Geenen RWF, Neijenhuis PA, Peterson GM, Veeken CJ, Vliegen RFA, Beets-Tan RGH, Lambregts DMJ. Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility. Eur Radiol 2022; 32:1506-1516. [PMID: 34655313 PMCID: PMC8831294 DOI: 10.1007/s00330-021-08251-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/23/2021] [Accepted: 08/06/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVES To investigate sources of variation in a multicenter rectal cancer MRI dataset focusing on hardware and image acquisition, segmentation methodology, and radiomics feature extraction software. METHODS T2W and DWI/ADC MRIs from 649 rectal cancer patients were retrospectively acquired in 9 centers. Fifty-two imaging features (14 first-order/6 shape/32 higher-order) were extracted from each scan using whole-volume (expert/non-expert) and single-slice segmentations using two different software packages (PyRadiomics/CapTk). Influence of hardware, acquisition, and patient-intrinsic factors (age/gender/cTN-stage) on ADC was assessed using linear regression. Feature reproducibility was assessed between segmentation methods and software packages using the intraclass correlation coefficient. RESULTS Image features differed significantly (p < 0.001) between centers with more substantial variations in ADC compared to T2W-MRI. In total, 64.3% of the variation in mean ADC was explained by differences in hardware and acquisition, compared to 0.4% by patient-intrinsic factors. Feature reproducibility between expert and non-expert segmentations was good to excellent (median ICC 0.89-0.90). Reproducibility for single-slice versus whole-volume segmentations was substantially poorer (median ICC 0.40-0.58). Between software packages, reproducibility was good to excellent (median ICC 0.99) for most features (first-order/shape/GLCM/GLRLM) but poor for higher-order (GLSZM/NGTDM) features (median ICC 0.00-0.41). CONCLUSIONS Significant variations are present in multicenter MRI data, particularly related to differences in hardware and acquisition, which will likely negatively influence subsequent analysis if not corrected for. Segmentation variations had a minor impact when using whole volume segmentations. Between software packages, higher-order features were less reproducible and caution is warranted when implementing these in prediction models. KEY POINTS • Features derived from T2W-MRI and in particular ADC differ significantly between centers when performing multicenter data analysis. • Variations in ADC are mainly (> 60%) caused by hardware and image acquisition differences and less so (< 1%) by patient- or tumor-intrinsic variations. • Features derived using different image segmentations (expert/non-expert) were reproducible, provided that whole-volume segmentations were used. When using different feature extraction software packages with similar settings, higher-order features were less reproducible.
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Affiliation(s)
- Niels W Schurink
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Simon R van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Sander Roberti
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joost J M van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Radiology, Acad. F. Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia
| | - Francesca Castagnoli
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
| | - Najim El Khababi
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Frans C H Bakers
- Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Shira H de Bie
- Department of Radiology, Deventer Ziekenhuis, Deventer, The Netherlands
| | - Gerlof P T Bosma
- Department of Interventional Radiology, Elisabeth Tweesteden Hospital, Tilburg, The Netherlands
| | - Vincent C Cappendijk
- Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - Remy W F Geenen
- Department of Radiology, Northwest Clinics, Alkmaar, The Netherlands
| | | | | | - Cornelis J Veeken
- Department of Radiology, IJsselland Hospital, Capelle Aan Den IJssel, The Netherlands
| | - Roy F A Vliegen
- Department of Radiology, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands.
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands.
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, POB 90203, 1006 BE, Amsterdam, The Netherlands.
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20
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Zhao L, Liang M, Yang Y, Xie L, Zhang H, Zhao X. The added value of full and reduced field-of-view apparent diffusion coefficient maps for the evaluation of extramural venous invasion in rectal cancer. Abdom Radiol (NY) 2022; 47:48-55. [PMID: 34665287 DOI: 10.1007/s00261-021-03319-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/07/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To investigate the added value of the quantitative analysis of full and reduced field-of-view apparent diffusion coefficient (fADC and rADC) maps for evaluating extramural venous invasion (EMVI) in rectal cancer. MATERIALS AND METHODS A total of 94 rectal cancer patients who underwent direct surgical resection were enrolled in this prospective study. The EMVI status of each patient was evaluated on T2-weighted imaging. The mean values of fADC and rADC within the whole tumor were obtained, and histogram parameters were also extracted. Multivariate binary logistic regression analysis was used to analyze independent predictors of EMVI and construct combined models. Receiver operating characteristic (ROC) curves were applied to assess the diagnostic performance. RESULTS The energy, skewness, total energy, and kurtosis of fADC map, and the energy and total energy of rADC map were significantly different between the EMVI-positive and EMVI-negative groups (all P < 0.05). Multivariate logistic regression analysis revealed that kurtosis of fADC and circumferential percentage of tumor were independent predictors of EMVI (odds ratio 1.684 and 2.647, P = 0.020 and 0.009). These two parameters combined with subjective evaluation demonstrated the superior diagnostic performance with the area under the ROC curve, sensitivity, specificity, and accuracy of 0.841 (95% CI 0.752-0.909), 0.739, 0.803, and 0.809, respectively. CONCLUSION Whole-tumor histogram analysis of ADC map could potentially provide additional information to improve the diagnostic efficiency for assessing EMVI in rectal cancer, which may be beneficial for treatment decision-making.
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Affiliation(s)
- Li Zhao
- 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, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Meng Liang
- 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, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yang Yang
- 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, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | | | - Hongmei 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, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Xinming Zhao
- 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, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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21
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Kim KE, Kim CK. Magnetic resonance imaging-based texture analysis for the prediction of postoperative clinical outcome in uterine cervical cancer. Abdom Radiol (NY) 2022; 47:352-361. [PMID: 34605967 DOI: 10.1007/s00261-021-03288-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 09/19/2021] [Accepted: 09/20/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVES Magnetic resonance imaging (MRI)-based texture analysis (MRTA) is a novel image analysis tool that offers objective information about the spatial arrangement of MRI signal intensity. We aimed to investigate the value of MRTA in predicting the postoperative clinical outcome of patients with uterine cervical cancer. METHODS This retrospective study included 115 patients with surgically proven cervical cancer who underwent preoperative pelvic 3T-MRI, and MRTA was performed on T2-weighted images (T2), apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted images (CE-T1). Filtration histogram-based texture analysis was used to generate six first-order statistical parameters [mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness, and kurtosis] at five spatial scaling factors (SSFs, 2-6 mm) as well as from unfiltered images. Cox proportional hazard models and time-dependent receiver operating characteristic analyses were used to evaluate the associations between parameters and recurrence-free survival (RFS). RESULTS During a median follow-up of 36 months, tumor recurrence was found in 26 patients (22.6%). Multivariate analysis demonstrated that CE-T1 MPP and T2 kurtosis at SSF3-5, CE-T1 MPP at SSF6, and CE-T1 SD at unfiltered images were independent predictors of RFS (p < 0.05). Regarding the 2-year RFS for CE-T1 MPP and T2 kurtosis at SSF5, and CE-T1 MPP at SSF6, patients with > optimal cutoff values demonstrated significantly worse survival than those with ≤ optimal cutoff values (p < 0.05). CONCLUSION Preoperative MRTA may be useful for predicting postoperative outcome in patients with cervical cancer.
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Affiliation(s)
- Ka Eun Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
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22
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Wang M, Perucho JA, Vardhanabhuti V, Ip P, Ngan HY, Lee EY. Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma. Acad Radiol 2021; 29:1133-1140. [PMID: 34583867 DOI: 10.1016/j.acra.2021.08.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 07/28/2021] [Accepted: 08/12/2021] [Indexed: 01/06/2023]
Abstract
RATIONALE AND OBJECTIVES Clinicopathological characteristics including histological subtypes, tumour grades and International Federation of Gynecology and Obstetrics (FIGO) stages are crucial factors in the clinical decision for cervical carcinoma (CC). The purpose of this study was to evaluate the ability of T2-weighted imaging (T2WI) and diffusion kurtosis imaging (DKI) radiomics in differentiating clinicopathological characteristics of CC. MATERIALS AND METHODS One hundred and seventeen histologically confirmed CC patients (mean age 56.5 ± 14.0 years) with pre-treatment magnetic resonance imaging were retrospectively reviewed. DKI was acquired with 4 b-values (0-1500 s/mm2). Volumes of interest were contoured around the tumours on T2WI and DKI. Radiomic features including shape, first-order and grey-level co-occurrence matrix with wavelet transforms were extracted. Intraclass correlation coeffient between 2 radiologists was used for features reduction. Feature selection was achieved by elastic net and minimum redundancy maximum relevance. Selected features were used to build random forest (RF) models. The performances for differentiating histological subtypes, tumour grades and FIGO stages were assessed by receiver operating characteristic analysis. RESULTS Area under the curves (AUCs) for T2WI-only RF models for discriminating histological subtypes, tumour grades and FIGO stages were 0.762, 0.686, and 0.719. AUCs for DWI-only models were 0.663, 0.645, and 0.868, respectively. AUCs of the combined T2WI and DKI models were 0.823, 0.790, and 0.850, respectively. CONCLUSION T2WI and DKI radiomic features could differentiate the clinicopathological characteristics of CC. A combined model showed excellent diagnostic discrimination for histological subtypes, while a DKI-only model presented the best performance in differentiating FIGO stages.
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23
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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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Association Between Histopathology and Magnetic Resonance Imaging Texture in Grading Gliomas Based on Intraoperative Magnetic Resonance Navigated Stereotactic Biopsy. J Comput Assist Tomogr 2021; 45:728-735. [PMID: 34347700 DOI: 10.1097/rct.0000000000001201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To explore the value of magnetic resonance imaging (MRI) textures and its correlation with histopathological malignancy of gliomas by magnetic resonance (MR) navigated stereotactic biopsy. METHODS A total of 36 diffuse glioma cases and 64 puncture targets were included. All patients underwent a preoperative MR scan and intraoperative MR-navigated stereotactic biopsy. The histopathological diagnosis was grade II or grade III diffuse glioma. Regions of interest consistent with puncture targets were delineated on T1-weighted brain volume with gadolinium contrast enhancement images, and textures were extracted using Omni Kinetics software. Mann-Whitney rank sum test was used to analyze texture differences between grade II and grade III samples. False discovery rate (FDR) correction was applied to correct for multiple comparisons. Receiver operating characteristic curves evaluated the diagnostic value of textural analysis for grading gliomas. Correlation between MRI textures and histopathology was examined by Spearman correlation test. RESULTS Texture features, including max intensity, 95th quantile, range, variance, standard deviation, sum variance, and cluster prominence were higher in grade III glioma targets than grade IIs, grade II gliomas showed increased uniformity and short run low gray-level emphasis values (P and qFDRcorr < 0.05). Area under the curve was 0.887 (95% confidence interval, 0.805-0.969; P < 0.001) with combined textures in glioma grading. The listed first-order and gray-level cooccurrence matrix textures were correlated with Ki-67 labeling index. Gray-level cooccurrence matrix and gray-level run length matrix textures were correlated with isocitrate dehydrogenase 1 mutation. CONCLUSIONS Textures on T1-weighted brain volume with gadolinium contrast enhancement images differ between grade III and II gliomas and are correlated with Ki-67 labeling index and isocitrate dehydrogenase 1 mutation.
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Zhao L, Liang M, Yang Y, Zhao X, Zhang H. Histogram models based on intravoxel incoherent motion diffusion-weighted imaging to predict nodal staging of rectal cancer. Eur J Radiol 2021; 142:109869. [PMID: 34303149 DOI: 10.1016/j.ejrad.2021.109869] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/19/2021] [Accepted: 07/14/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop a model based on histogram parameters derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the nodal staging of rectal cancer (RC). MATERIAL AND METHODS A total of 95 RC patients who underwent direct surgical resection were enrolled in this prospective study. The nodal staging on conventional magnetic resonance imaging (MRI) was evaluated according to the short axis diameter and morphological characteristics. Histogram parameters were extracted from apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) maps. Multivariate binary logistic regression analysis was conducted to establish models for predicting nodal staging among all patients and those underestimated on conventional MRI. RESULTS The combined model based on multiple maps demonstrated superior diagnostic performance to single map models, with an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.959, 94.3%, 88.3%, and 90.5%, respectively. The AUC of the combined model was significantly higher than that of the conventional nodal staging (P < 0.001). Additionally, 85.0% of the underestimated patients had suspicious lymph nodes with 5-8 mm short-axis diameter. The histogram model for these subgroups of patients showed good diagnostic efficacy with an AUC, sensitivity, specificity, and accuracy of 0.890, 100%, 75%, and 80.5%. CONCLUSION The histogram model based on IVIM-DWI could improve the diagnostic performance of nodal staging of RC. In addition, histogram parameters of IVIM-DWI may help to reduce the uncertainty of nodal staging in underestimated patients on conventional MRI.
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Affiliation(s)
- Li Zhao
- 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. No.17, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Meng Liang
- 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. No.17, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Yang Yang
- 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. No.17, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Xinming Zhao
- 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. No.17, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
| | - Hongmei 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. No.17, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
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Zhao L, Liang M, Shi Z, Xie L, Zhang H, Zhao X. Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer. Quant Imaging Med Surg 2021; 11:1805-1816. [PMID: 33936966 DOI: 10.21037/qims-20-659] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background An accurate assessment of lymph node (LN) status in patients with rectal cancer is important for treatment planning and an essential factor for predicting local recurrence and overall survival. In this study, we explored the potential value of histogram parameters of synthetic magnetic resonance imaging (SyMRI) in predicting LN metastasis in rectal cancer and compared their predictive performance with traditional morphological characteristics and chemical shift effect (CSE). Methods A total of 70 patients with pathologically proven rectal adenocarcinoma who received direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI, including SyMRI, were performed, and morphological characteristics and CSE of LN were assessed. Histogram parameters were extracted on a T1 map, T2 map, and proton density (PD) map, including mean, variance, maximum, minimum, 10th percentile, median, 90th percentile, energy, kurtosis, entropy, and skewness. Receiver operating characteristic (ROC) curves were used to explore their predictive performance for assessing LN status. Results Significant differences in the energy of the T1, T2, and PD maps were observed between LN-negative and LN-positive groups [all P<0.001; the area under the ROC curve (AUC) was 0.838, 0.858, and 0.823, respectively]. The maximum and kurtosis of the T2 map, maximum, and variance of PD map could also predict LN metastasis with moderate diagnostic power (P=0.032, 0.045, 0.016, and 0.047, respectively). Energy of the T1 map [odds ratio (OR) =1.683, 95% confidence interval (CI): 1.207-2.346, P=0.002] and extramural venous invasion on MRI (mrEMVI) (OR =10.853, 95% CI: 2.339-50.364, P=0.002) were significant predictors of LN metastasis. Moreover, the T1 map energy significantly improved the predictive performance compared to morphological features and CSE (P=0.0002 and 0.0485). Conclusions The histogram parameters derived from SyMRI of the primary tumor were associated with LN metastasis in rectal cancer and could significantly improve the predictive performance compared with morphological features and CSE.
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Affiliation(s)
- Li Zhao
- 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
| | - Meng Liang
- 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
| | - Zhuo Shi
- 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
| | - Lizhi Xie
- GE Healthcare, Magnetic Resonance Research China, Beijing, China
| | - Hongmei 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
| | - Xinming Zhao
- 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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Wen L, Weng S, Yan C, Ye R, Zhu Y, Zhou L, Gao L, Li Y. A Radiomics Nomogram for Preoperative Prediction of Early Recurrence of Small Hepatocellular Carcinoma After Surgical Resection or Radiofrequency Ablation. Front Oncol 2021; 11:657039. [PMID: 34026632 PMCID: PMC8139248 DOI: 10.3389/fonc.2021.657039] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 04/13/2021] [Indexed: 12/14/2022] Open
Abstract
Background Patients with small hepatocellular carcinoma (HCC) (3 cm) still have a poor prognosis. The purpose of this study was to develop a radiomics nomogram to preoperatively predict early recurrence (ER) (2 years) of small HCC. Methods The study population included 111 patients with small HCC who underwent surgical resection (SR) or radiofrequency ablation (RFA) between September 2015 and September 2018 and were followed for at least 2 years. Radiomic features were extracted from the entire tumor by using the MaZda software. The least absolute shrinkage and selection operator (LASS0) method was applied for feature selection, and radiomics signature construction. A rad-score was then calculated. Multivariable logistic regression analysis was used to establish a prediction model including independent clinical risk factors, radiologic features and rad-score, which was ultimately presented as a radiomics nomogram. The predictive ability of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve and internal validation was performed via bootstrap resampling and 5-fold cross-validation method. Results A total of 53 (53/111, 47.7%) patients had confirmed ER according to the final clinical outcomes. In univariate logistic regression analysis, cirrhosis and hepatitis B infection (P=0.015 and 0.083, respectively), hepatobiliary phase hypointensity (P=0.089), Child-Pugh score (P=0.083), the preoperative platelet count (P=0.003), and rad-score (P<0.001) were correlated with ER. However, after multivariate logistic regression analysis, only the preoperative platelet count and rad-score were included as predictors in the final model. The area under ROC curve (AUC) of the radiomics nomogram to predict ER of small HCC was 0.981 (95% CI: 0.957, 1.00), while the AUC verified by bootstrap is 0.980 (95% CI: 0.962, 1.00), indicating the goodness-of-fit of the final model. Conclusions The radiomics nomogram containing the clinical risk factors and rad-score can be used as a quantitative tool to preoperatively predict individual probability of ER of small HCC.
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Affiliation(s)
- Liting Wen
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Shuping Weng
- Department of Radiology, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Chuan Yan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Rongping Ye
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yuemin Zhu
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Lili Zhou
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Lanmei Gao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Yueming Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.,Key Laboratory of Radiation Biology, Fujian Medical University, Fujian Province University, Fuzhou, China
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Liang L, Ding Y, Yu Y, Liu K, Rao S, Ge Y, Zeng M. Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study. BMC Med Imaging 2021; 21:75. [PMID: 33902469 PMCID: PMC8077911 DOI: 10.1186/s12880-021-00605-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 04/14/2021] [Indexed: 01/13/2023] Open
Abstract
Background Multiple guidelines for pancreatic ductal adenocarcinoma (PDAC) suggest that all stages of patients need to receive postoperative adjuvant chemotherapy. S-1 is a recently emerged oral antitumour agent recommended by the guidelines. However, which population would benefit from S-1 needs to be determined, and predictors of chemotherapy response are needed for personalized precision medicine. This pilot study aimed to initially identify whether whole-tumour evaluation with MRI and radiomics features could be used for predicting the efficacy of S-1 and to find potential predictors of the efficacy of S-1 as evidence to assist personalized precision treatment. Methods Forty-six patients with PDAC (31 in the primary cohort and 15 in the validation cohort) who underwent curative resection and subsequently adjuvant chemotherapy with S-1 were included. Pre-operative abdominal contrast-enhanced MRI was performed, and radiomics features of the whole PDAC were extracted from the primary cohort. After univariable analysis and radiomics features selection, a multivariable Cox regression model for survival analysis was subsequently used to select statistically significant factors associated with postoperative disease-free survival (DFS). Predictive capacities of the factors were tested on the validation cohort by using Kaplan–Meier method. Results Multivariable Cox regression analysis identified the probability of T1WI_NGTDM_Strength and tumour location as independent predictors of the efficacy of S-1 for adjuvant chemotherapy of PDAC (p = 0.005 and 0.013) in the primary cohort, with hazard ratios (HRs) of 0.289 and 0.293, respectively. Further survival analysis showed that patients in the low-T1WI_NGTDM_Strength group had shorter DFS (median = 5.1 m) than those in the high-T1WI_NGTDM_Strength group (median = 13.0 m) (p = 0.006), and patients with PDAC on the pancreatic head exhibited shorter DFS (median = 7.0 m) than patients with tumours in other locations (median = 20.0 m) (p = 0.016). In the validation cohort, the difference in DFS between patients with low-T1WI_NGTDM_Strength and high-T1WI_NGTDM_Strength and the difference between patients with PDAC on the pancreatic head and that in other locations were approved, with marginally significant (p = 0.073 and 0.050), respectively. Conclusions Whole-tumour radiomics feature of T1WI_NGTDM_Strength and tumour location were potential predictors of the efficacy of S-1 and for the precision selection of S-1 as adjuvant chemotherapy regimen for PDAC.
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Affiliation(s)
- Liang Liang
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Ying Ding
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Yiyi Yu
- Department of Medical Oncology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Shengxiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Yingqian Ge
- Siemens Healthineers, No. 278 Zhou Zhu Road, Pudong New District, Shanghai, 201318, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
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Zhang T, Zhang Y, Liu X, Xu H, Chen C, Zhou X, Liu Y, Ma X. Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades. Front Oncol 2021; 10:521831. [PMID: 33643890 PMCID: PMC7905094 DOI: 10.3389/fonc.2020.521831] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 12/11/2020] [Indexed: 02/05/2023] Open
Abstract
Purpose To evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics. Materials and Methods A retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model. Result Our analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively. Conclusion In conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.
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Affiliation(s)
- Tao Zhang
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - YueHua Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xinglong Liu
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hanyue Xu
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyue Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Xuan Zhou
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yichun Liu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
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McHugh DJ, Porta N, Little RA, Cheung S, Watson Y, Parker GJM, Jayson GC, O’Connor JPB. Image Contrast, Image Pre-Processing, and T 1 Mapping Affect MRI Radiomic Feature Repeatability in Patients with Colorectal Cancer Liver Metastases. Cancers (Basel) 2021; 13:E240. [PMID: 33440685 PMCID: PMC7826650 DOI: 10.3390/cancers13020240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 01/01/2021] [Accepted: 01/05/2021] [Indexed: 01/25/2023] Open
Abstract
Imaging biomarkers require technical, biological, and clinical validation to be translated into robust tools in research or clinical settings. This study contributes to the technical validation of radiomic features from magnetic resonance imaging (MRI) by evaluating the repeatability of features from four MR sequences: pre-contrast T1- and T2-weighted images, pre-contrast quantitative T1 maps (qT1), and contrast-enhanced T1-weighted images. Fifty-one patients with colorectal cancer liver metastases were scanned twice, up to 7 days apart. Repeatability was quantified using the intraclass correlation coefficient (ICC) and repeatability coefficient (RC), and the impact of non-Gaussian feature distributions and image normalisation was evaluated. Most radiomic features had non-Gaussian distributions, but Box-Cox transformations enabled ICCs and RCs to be calculated appropriately for an average of 97% of features across sequences. ICCs ranged from 0.30 to 0.99, with volume and other shape features tending to be most repeatable; volume ICC > 0.98 for all sequences. 19% of features from non-normalised images exhibited significantly different ICCs in pair-wise sequence comparisons. Normalisation tended to increase ICCs for pre-contrast T1- and T2-weighted images, and decrease ICCs for qT1 maps. RCs tended to vary more between sequences than ICCs, showing that evaluations of feature performance depend on the chosen metric. This work suggests that feature-specific repeatability, from specific combinations of MR sequence and pre-processing steps, should be evaluated to select robust radiomic features as biomarkers in specific studies. In addition, as different repeatability metrics can provide different insights into a specific feature, consideration of the appropriate metric should be taken in a study-specific context.
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Affiliation(s)
- Damien J. McHugh
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Quantitative Biomedical Imaging Laboratory, The University of Manchester, Manchester M13 9PL, UK
| | - Nuria Porta
- Clinical Trials and Statistics Unit, Institute of Cancer Research, London SW3 6JB, UK;
| | - Ross A. Little
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Quantitative Biomedical Imaging Laboratory, The University of Manchester, Manchester M13 9PL, UK
| | - Susan Cheung
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Quantitative Biomedical Imaging Laboratory, The University of Manchester, Manchester M13 9PL, UK
| | - Yvonne Watson
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Quantitative Biomedical Imaging Laboratory, The University of Manchester, Manchester M13 9PL, UK
| | - Geoff J. M. Parker
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK;
- Bioxydyn Ltd., Manchester M15 6SZ, UK
| | - Gordon C. Jayson
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Department of Medical Oncology, The Christie Hospital, Manchester M20 4BX, UK
| | - James P. B. O’Connor
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PL, UK; (D.J.M.); (R.A.L.); (S.C.); (Y.W.); (G.C.J.)
- Quantitative Biomedical Imaging Laboratory, The University of Manchester, Manchester M13 9PL, UK
- Department of Radiology, The Christie Hospital, Manchester M20 4BX, UK
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SW3 6JB, UK
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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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Csutak C, Ștefan PA, Lenghel LM, Moroșanu CO, Lupean RA, Șimonca L, Mihu CM, Lebovici A. Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone. Brain Sci 2020; 10:brainsci10090638. [PMID: 32947822 PMCID: PMC7565295 DOI: 10.3390/brainsci10090638] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 09/03/2020] [Accepted: 09/14/2020] [Indexed: 11/16/2022] Open
Abstract
High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.
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Affiliation(s)
- Csaba Csutak
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
| | - Paul-Andrei Ștefan
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Anatomy and Embryology, Morphological Sciences Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Victor Babeș Street, number 8, Cluj-Napoca, 400012 Cluj, Romania
- Correspondence: ; Tel.: +40-743-957-206
| | - Lavinia Manuela Lenghel
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
| | - Cezar Octavian Moroșanu
- Department of Neurosurgery, North Bristol Trust, Southmead Hospital, Southmead Road, Westbury on Trym, Bristol BS2 8BJ, UK;
| | - Roxana-Adelina Lupean
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania;
| | - Larisa Șimonca
- Department of Paediatric Surgery, Bristol Royal Hospital for Children, Upper Maudlin Street, Bristol BS2 8BJ, UK;
| | - Carmen Mihaela Mihu
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania;
| | - Andrei Lebovici
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, Number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (L.M.L.); (C.M.M.); (A.L.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3–5, Cluj-Napoca, 400006 Cluj, Romania
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Lupean RA, Ștefan PA, Feier DS, Csutak C, Ganeshan B, Lebovici A, Petresc B, Mihu CM. Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts. J Pers Med 2020; 10:jpm10030127. [PMID: 32937851 PMCID: PMC7563604 DOI: 10.3390/jpm10030127] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 08/25/2020] [Accepted: 09/09/2020] [Indexed: 12/13/2022] Open
Abstract
The imaging diagnosis of malignant ovarian cysts relies on their morphological features, which are not always specific to malignancy. The histological analysis of these cysts shows specific fluid characteristics, which cannot be assessed by conventional imaging techniques. This study investigates whether the texture-based radiomics analysis (TA) of magnetic resonance (MRI) images of the fluid content within ovarian cysts can function as a noninvasive tool in differentiating between benign and malignant lesions. Twenty-eight patients with benign (n = 15) and malignant (n = 13) ovarian cysts who underwent MRI examinations were retrospectively included. TA of the fluid component was undertaken on an axial T2-weighted sequence. A comparison of resulted parameters between benign and malignant groups was undertaken using univariate, multivariate, multiple regression, and receiver operating characteristics analyses, with the calculation of the area under the curve (AUC). The standard deviation of pixel intensity was identified as an independent predictor of malignant cysts (AUC = 0.738; sensitivity, 61.54%; specificity, 86.67%). The prediction model was able to identify malignant lesions with 84.62% sensitivity and 80% specificity (AUC = 0.841). TA of the fluid contained within the ovarian cysts can differentiate between malignant and benign lesions and potentially act as a noninvasive tool augmenting the imaging diagnosis of ovarian cystic lesions.
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Affiliation(s)
- Roxana-Adelina Lupean
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania; (R.-A.L.); (C.M.M.)
- Obstetrics and Gynecology Clinic “Dominic Stanca”, County Emergency Hospital, 21 Decembrie 1989 Boulevard, number 55, Cluj-Napoca, 400094 Cluj, Romania
| | - Paul-Andrei Ștefan
- Anatomy and Embryology, Morphological Sciences Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Victor Babeș Street, number 8, Cluj-Napoca, 400012 Cluj, Romania
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (A.L.); (B.P.)
- Correspondence: (P.-A.Ș.); (D.S.F.); Tel.: +40-743957206 (P.-A.Ș.); +40-740537872 (D.S.F.); Fax: +40-264596085 (P.-A.Ș.)
| | - Diana Sorina Feier
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (A.L.); (B.P.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3-5, Cluj-Napoca, 400006 Cluj, Romania
- Correspondence: (P.-A.Ș.); (D.S.F.); Tel.: +40-743957206 (P.-A.Ș.); +40-740537872 (D.S.F.); Fax: +40-264596085 (P.-A.Ș.)
| | - Csaba Csutak
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (A.L.); (B.P.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3-5, Cluj-Napoca, 400006 Cluj, Romania
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College London Hospitals NHS Trust, 235 Euston Road, London NW1 2BU, UK;
| | - Andrei Lebovici
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (A.L.); (B.P.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy, Clinicilor Street, number 3-5, Cluj-Napoca, 400006 Cluj, Romania
| | - Bianca Petresc
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (A.L.); (B.P.)
| | - Carmen Mihaela Mihu
- Histology, Morphological Sciences Department, “Iuliu Hațieganu” University of Medicine and Pharmacy, Louis Pasteur Street, number 4, Cluj-Napoca, 400349 Cluj, Romania; (R.-A.L.); (C.M.M.)
- Radiology and Imaging Department, County Emergency Hospital, Cluj-Napoca, Clinicilor Street, number 5, Cluj-Napoca, 400006 Cluj, Romania; (C.C.); (A.L.); (B.P.)
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Shiri I, Hajianfar G, Sohrabi A, Abdollahi H, P Shayesteh S, Geramifar P, Zaidi H, Oveisi M, Rahmim A. Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test-retest and image registration analyses. Med Phys 2020; 47:4265-4280. [PMID: 32615647 DOI: 10.1002/mp.14368] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 06/17/2020] [Accepted: 06/18/2020] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To assess the repeatability of radiomic features in magnetic resonance (MR) imaging of glioblastoma (GBM) tumors with respect to test-retest, different image registration approaches and inhomogeneity bias field correction. METHODS We analyzed MR images of 17 GBM patients including T1- and T2-weighted images (performed within the same imaging unit on two consecutive days). For image segmentation, we used a comprehensive segmentation approach including entire tumor, active area of tumor, necrotic regions in T1-weighted images, and edema regions in T2-weighted images (test studies only; registration to retest studies is discussed next). Analysis included N3, N4 as well as no bias correction performed on raw MR images. We evaluated 20 image registration approaches, generated by cross-combination of four transformation and five cost function methods. In total, 714 images (17 patients × 2 images × ((4 transformations × 5 cost functions) + 1 test image) and 2856 segmentations (714 images × 4 segmentations) were prepared for feature extraction. Various radiomic features were extracted, including the use of preprocessing filters, specifically wavelet (WAV) and Laplacian of Gaussian (LOG), as well as discretization into fixed bin width and fixed bin count (16, 32, 64, 128, and 256), Exponential, Gradient, Logarithm, Square and Square Root scales. Intraclass correlation coefficients (ICC) were calculated to assess the repeatability of MRI radiomic features (high repeatability defined as ICC ≥ 95%). RESULTS In our ICC results, we observed high repeatability (ICC ≥ 95%) with respect to image preprocessing, different image registration algorithms, and test-retest analysis, for example: RLNU and GLNU from GLRLM, GLNU and DNU from GLDM, Coarseness and Busyness from NGTDM, GLNU and ZP from GLSZM, and Energy and RMS from first order. Highest fraction (percent) of repeatable features was observed, among registration techniques, for the method Full Affine transformation with 12 degrees of freedom using Mutual Information cost function (mean 32.4%), and among image processing methods, for the method Laplacian of Gaussian (LOG) with Sigma (2.5-4.5 mm) (mean 78.9%). The trends were relatively consistent for N4, N3, or no bias correction. CONCLUSION Our results showed varying performances in repeatability of MR radiomic features for GBM tumors due to test-retest and image registration. The findings have implications for appropriate usage in diagnostic and predictive models.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ahmad Sohrabi
- Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical Science, Kerman, Iran
| | - Sajad P Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Faculty of Medicine, Alborz University of Medical Sciences, Karaj, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland.,Geneva University Neurocenter, Geneva University, Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mehrdad Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.,Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada.,Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, BC, Canada
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Thomas JV, Abou Elkassem AM, Ganeshan B, Smith AD. MR Imaging Texture Analysis in the Abdomen and Pelvis. Magn Reson Imaging Clin N Am 2020; 28:447-456. [PMID: 32624161 DOI: 10.1016/j.mric.2020.03.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Add "which is a" before "distribution"? Texture analysis (TA) is a form of radiomics that refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MR images of the abdomen and pelvis, with the main strength quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MRTA. Despite these limitations, there is a growing body of literature supporting MRTA. This review discusses application of MRTA to the abdomen and pelvis.
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Affiliation(s)
- John V Thomas
- Body Imaging Section, Department of Radiology, University of Alabama at Birmingham, N355 Jefferson Tower, 619 19th Street South, Birmingham, AL 35249-6830, USA.
| | - Asser M Abou Elkassem
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, Birmingham, AL 35249-6830, USA
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, University College of London, 5th Floor, Tower, 235 Euston Road, London NW1 2BU, UK
| | - Andrew D Smith
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, Birmingham, AL 35249-6830, USA
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MRI texture features differentiate clinicopathological characteristics of cervical carcinoma. Eur Radiol 2020; 30:5384-5391. [PMID: 32382845 DOI: 10.1007/s00330-020-06913-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 04/23/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC). METHODS Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC. RESULTS Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively. CONCLUSIONS Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC. KEY POINTS • First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.
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MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer. Eur Radiol 2020; 30:4201-4211. [PMID: 32270317 DOI: 10.1007/s00330-020-06835-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/05/2020] [Accepted: 03/25/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES This study aimed to evaluate the efficiency of imaging features and texture analysis (TA) based on baseline rectal MRI for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) and tumor recurrence in patients with locally advanced rectal cancer (LARC). METHODS Consecutive patients with LARC who underwent rectal MRI between January 2014 and December 2015 and surgical resection after completing nCRT were retrospectively enrolled. Imaging features were analyzed, and TA parameters were extracted from the tumor volume of interest (VOI) from baseline rectal MRI. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the optimal TA parameter cutoff values to stratify the patients. Logistic and Cox regression analyses were performed to assess the efficacy of each imaging feature and texture parameter in predicting tumor response and disease-free survival. RESULTS In total, 78 consecutive patients were enrolled. In the logistic regression, good treatment response was associated with lower tumor location (OR = 13.284, p = 0.012), low Conv_Min (OR = 0.300, p = 0.013) and high Conv_Std (OR = 3.174, p = 0.016), Shape_Sphericity (OR = 3.170, p = 0.015), and Shape_Compacity (OR = 2.779, p = 0.032). In the Cox regression, a greater risk of tumor recurrence was related to higher cT stage (HR = 5.374, p = 0.044), pelvic side wall lymph node positivity (HR = 2.721, p = 0.013), and gray-level run length matrix_long-run low gray-level emphasis (HR = 2.268, p = 0.046). CONCLUSIONS Imaging features and TA based on baseline rectal MRI could be valuable for predicting the treatment response to nCRT for rectal cancer and tumor recurrence. KEY POINTS • Imaging features and texture parameters of T2-weighted MR images of rectal cancer can help to predict treatment response and the risk for tumor recurrence. • Tumor location as well as conventional and shape indices of texture features can help to predict treatment response for rectal cancer. • Clinical T stage, positive pelvic side wall lymph nodes, and the high-order texture parameter, GLRLM_LRLGE, can help to predict tumor recurrence for rectal cancer.
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Hwang J, Yoon HM, Jung AY, Lee JS, Cho YA. Comparison of 2-Dimensional Shear Wave Elastographic Measurements Using ElastQ Imaging and SuperSonic Shear Imaging: Phantom Study and Clinical Pilot Study. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2020; 39:311-321. [PMID: 31392769 DOI: 10.1002/jum.15108] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 07/22/2019] [Accepted: 07/25/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVES To compare the feasibility and clinical applicability of ElastQ imaging (Philips Healthcare, Best, the Netherlands) with that of SuperSonic shear imaging (SSI; SuperSonic Imagine, Aix-en-Provence, France) using an elastographic phantom and a pilot study of patients. METHODS Two-dimensional shear wave elastography measurement was performed by ElastQ imaging and SSI by 2 radiologists. An elastographic phantom with 5 target elasticities at 2 acquisition depths was used. The coefficients of variation and intraclass correlation coefficients (ICCs) were evaluated for repeatability and interobserver agreement, respectively. The mean elasticities of the systems at each target were compared. The proportions of measurements that were out of the range of expected values and measurement errors were calculated to determine accuracy. Liver stiffness (LS) was measured by both systems in 27 children and young adult patients with various liver diseases. RESULTS Both systems provided high repeatability in elasticity measurements of phantom targets (coefficients of variation, 0.69%-15.82%), and there was excellent interobserver agreement (ICC, 0.992). Most (90%) mean elasticities of targets were significantly different between the techniques (P ≤ .002) and acquisition depths (P ≤ .004). ElastQ imaging had significantly lower proportions of out-of-range measurements and measurement errors (P ≤ .003) than SSI. In patients with liver disease, LS measurements of the systems were strongly correlated (ρ = 0.955; P < .001) and had excellent agreement (ICC, 0.951; P < .001). CONCLUSIONS ElastQ imaging had comparably good results in terms of repeatability, interobserver agreement, and accuracy in the phantom model compared with SSI. The pilot patient study showed strong correlations in LS values between the systems.
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Affiliation(s)
- Jisun Hwang
- Department of Radiology, Dongtan Sacred Heart Hospital, Hallym University Medical Center, Hwaseong, Korea
| | - Hee Mang Yoon
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Ah Young Jung
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jin Seong Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Young Ah Cho
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements Using Radiomics. J Thorac Imaging 2019; 34:103-115. [PMID: 30664063 DOI: 10.1097/rti.0000000000000390] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics.
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Cattell R, Chen S, Huang C. Robustness of radiomic features in magnetic resonance imaging: review and a phantom study. Vis Comput Ind Biomed Art 2019; 2:19. [PMID: 32240418 PMCID: PMC7099536 DOI: 10.1186/s42492-019-0025-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 10/09/2019] [Indexed: 02/07/2023] Open
Abstract
Radiomic analysis has exponentially increased the amount of quantitative data that can be extracted from a single image. These imaging biomarkers can aid in the generation of prediction models aimed to further personalized medicine. However, the generalizability of the model is dependent on the robustness of these features. The purpose of this study is to review the current literature regarding robustness of radiomic features on magnetic resonance imaging. Additionally, a phantom study is performed to systematically evaluate the behavior of radiomic features under various conditions (signal to noise ratio, region of interest delineation, voxel size change and normalization methods) using intraclass correlation coefficients. The features extracted in this phantom study include first order, shape, gray level cooccurrence matrix and gray level run length matrix. Many features are found to be non-robust to changing parameters. Feature robustness assessment prior to feature selection, especially in the case of combining multi-institutional data, may be warranted. Further investigation is needed in this area of research.
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Affiliation(s)
- Renee Cattell
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Shenglan Chen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Chuan Huang
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA. .,Department of Radiology, Stony Brook Medicine, Stony Brook, NY, 11794, USA. .,Department of Psychiatry, Stony Brook Medicine, Stony Brook, NY, 11794, USA.
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Horvat N, Bates DDB, Petkovska I. Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review. Abdom Radiol (NY) 2019; 44:3764-3774. [PMID: 31055615 DOI: 10.1007/s00261-019-02042-y] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
INTRODUCTION As computational capabilities have advanced, radiologists and their collaborators have looked for novel ways to analyze diagnostic images. This has resulted in the development of radiomics and radiogenomics as new fields in medical imaging. Radiomics and radiogenomics may change the practice of medicine, particularly for patients with colorectal cancer. Radiomics corresponds to the extraction and analysis of numerous quantitative imaging features from conventional imaging modalities in correlation with several endpoints, including the prediction of pathology, genomics, therapeutic response, and clinical outcome. In radiogenomics, qualitative and/or quantitative imaging features are extracted and correlated with genetic profiles of the imaged tissue. Thus far, several studies have evaluated the use of radiomics and radiogenomics in patients with colorectal cancer; however, there are challenges to be overcome before its routine implementation including challenges related to sample size, model design and interpretability, and the lack of robust multicenter validation set. MATERIAL AND METHODS In this article, we will review the concepts of radiomics and radiogenomics and their potential applications in rectal cancer. CONCLUSION Radiologists should be aware of the basic concepts, benefits, pitfalls, and limitations of new radiomic and radiogenomics techniques to achieve a balanced interpretation of the results.
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Affiliation(s)
- Natally Horvat
- Department of Radiology, Hospital Sirio-Libanes, Sao Paulo, SP, Brazil
| | - David D B Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
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Zhang J, Liu X, Zhang H, He X, Liu Y, Zhou J, Guo D. Texture Analysis Based on Preoperative Magnetic Resonance Imaging (MRI) and Conventional MRI Features for Predicting the Early Recurrence of Single Hepatocellular Carcinoma after Hepatectomy. Acad Radiol 2019; 26:1164-1173. [PMID: 30425000 DOI: 10.1016/j.acra.2018.10.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 10/10/2018] [Accepted: 10/12/2018] [Indexed: 12/28/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the value of texture analysis and conventional magnetic resonance imaging (MRI) features for predicting the early recurrence (ER) of single hepatocellular carcinoma (HCC) after hepatectomy. MATERIALS AND METHODS A total of 100 HCC patients were first divided into group A (tumor diameter ≤3 cm) and group B (tumor diameter >3 cm) and then classified into two subgroups with ER or nonearly recurrence. Textural parameters (skewness, kurtosis, uniformity, energy, entropy, and correlation) based on MR images and conventional MRI features were compared between the ER and nonearly recurrence subgroups. Predictive factors for ER were further assessed with multivariate logistic regression analysis. Receiver operating characteristic curve was performed to assess the predictive power. RESULTS There were 53 patients in group A and 47 patients in group B. On arterial phase analysis, tumors with ER displayed significantly lower uniformity and higher entropy in group A, and higher skewness and entropy in group B. On portal venous phase analysis, tumors with ER had significantly lower kurtosis and energy in group A, and higher entropy in group B. Irregular margin in groups A and B, and arterial peritumoral enhancement and capsule presence in group B were associated with ER. In multivariate logistic regression analysis, uniformity and entropy based on arterial phase images and irregular margin in group A, and skewness and entropy based on arterial phase images and arterial peritumoral enhancement in group B were independent predictors for ER. Entropy displayed higher predictive power for ER. CONCLUSION Texture analysis based on preoperative MRI are potential quantitative predictors of ER in HCC patients after hepatectomy, and may provide more information for preoperative treatment decision-making and follow up.
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Accuracy and precision of ultrasound shear wave elasticity measurements according to target elasticity and acquisition depth: A phantom study. PLoS One 2019; 14:e0219621. [PMID: 31295308 PMCID: PMC6622533 DOI: 10.1371/journal.pone.0219621] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 06/27/2019] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE To investigate the accuracy and precision of ultrasound shear wave elasticity measurements as a function of target elasticity and acquisition depth. MATERIALS AND METHODS Using five ultrasound systems (VTQ, VTIQ, EPIQ 5, Aixplorer, and Aplio 500), two operators independently measured shear wave elasticities in two phantoms containing five different target elasticities (8±3, 14±4, 25±6, 45±8, and 80±12 kPa) at depths of 15, 30, 35, and 60 mm. Accuracy was assessed by evaluating measurement errors and the proportions of outliers, while factors affecting accuracy were assessed using logistic regression analysis. Measurement errors were defined as differences between the measured values and 1) the margins of the target elasticity, and 2) the median values of the target elasticity. Outliers were defined as measured values outside the margins of the target elasticity. Precision was assessed by calculating the reproducibility of measurements using the within-subject coefficient of variation (wCV). RESULTS Mean measurement errors and the proportions of outliers were higher for high than for low target elasticities (p<0.001), but did not differ in relation to acquisition depth, either within an elastography system or across the different systems. Logistic regression analysis showed that target elasticity (p<0.001) significantly affected accuracy, whereas acquisition depth (p>0.05) did not. The wCV for the 80±12 kPa target (31.33%) was significantly higher than that for lower elasticity targets (6.96-10.43 kPa; p<0.001). The wCV did not differ across acquisition depths. The individual elastography systems showed consistent results. CONCLUSIONS Targets with high elasticity showed lower accuracy and lower precision than targets with low elasticity, while acquisition depth did not show consistent patterns in either accuracy or precision.
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Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis? Eur Radiol 2019; 29:6469-6476. [PMID: 31278581 DOI: 10.1007/s00330-019-06328-z] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 06/03/2019] [Accepted: 06/13/2019] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To explore if there is a correlation between T2WI histogram features of the primary tumor and the existence of regional lymph node (LN) metastasis in rectal cancer. METHODS Eighty-eight patients with pathologically proven rectal adenocarcinoma, who received direct surgical resection and underwent preoperative rectal MRIs, were enrolled retrospectively. Based on pathological analysis of surgical specimen, patients were classified into negative LN (LN-) and positive LN (LN+) groups. The degree of differentiation and pathological T stage were recorded. Clinical T stage, tumor location, and maximum diameter of tumor were evaluated of each patient. Whole-tumor texture analysis was independently performed by two radiologists on axial T2WI, including skewness, kurtosis, energy, and entropy. RESULTS The interobserver agreement was overall good for texture analysis between two radiologists, with intraclass correlation coefficients (ICCs) ranging from 0.626 to 0.826. The LN- group had a significantly higher skewness (p < 0.001), kurtosis (p < 0.001), and energy (p = 0.004) than the LN+ group, and a lower entropy (p = 0.028). These four parameters showed moderate to good diagnostic power in predicting LN metastasis with respective AUC of 0.750, 0.733, 0.669, and 0.648. In addition, they were both correlated with LN metastasis (rs = - 0.413, - 0.385, - 0.28, and 0.245, respectively). The multivariate analysis showed that lower skewness was an independent risk factor of LN metastasis (odds ratio, OR = 9.832; 95%CI, 1.171-56.295; p = 0.01). CONCLUSIONS Signal intensity histogram parameters of primary tumor on T2WI were associated with regional LN status in rectal cancer, which may help improve the prediction of nodal stage. KEY POINTS • Histogram parameters of tumor on T2WI may help to reduce uncertainty when assessing LN status in rectal cancer. • Histogram parameters of tumor on T2WI showed a significant difference between different regional LN status groups in rectal cancer. • Skewness was an independent risk factor of regional LN metastasis in rectal cancer.
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Value of High-Resolution DWI in Combination With Texture Analysis for the Evaluation of Tumor Response After Preoperative Chemoradiotherapy for Locally Advanced Rectal Cancer. AJR Am J Roentgenol 2019; 212:1279-1286. [PMID: 30860889 DOI: 10.2214/ajr.18.20689] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
OBJECTIVE. The purpose of this study is to determine the performance of the apparent diffusion coefficient (ADC) value calculated from high-resolution DWI using readout-segmented echo-planar imaging (rs-EPI) and to assess the texture parameters of T2-weighted MR images in identifying pathologic complete response (pCR) after patients with locally advanced rectal cancer (LARC) undergo preoperative chemoradiotherapy (CRT). MATERIALS AND METHODS. A total of 76 patients with LARC who underwent preoperative CRT and subsequent surgery were enrolled in the study retrospectively. All patients underwent post-CRT MRI, which included acquisition of a DWI sequence with use of the rs-EPI technique. The histopathologic tumor regression grade was the reference standard. Patients were subdivided into pCR and non-pCR groups. Two radiologists independently drew whole-tumor ROIs on DW images and T2-weighted MR images to calculate the mean ADC value and first-order texture parameters. RESULTS. Interobserver agreement was good to excellent (intraclass correlation coefficient [ICC], 0.79-0.993) for imaging analysis. Calculated from high-resolution DWI, the mean post-CRT ADC value was significantly higher in the pCR group (p < 0.001). The pCR group also showed lower uniformity (p < 0.001) of the T2-weighted image. The mean ADC value and uniformity were significantly correlated with the tumor regression grade. The mean ADC value was a good indicator for differentiating pCR from absence of pCR (ROC AUC value, 0.912). Uniformity (ROC AUC value, 0.776) showed a moderate ability to identify pCR. Combining the mean ADC value and uniformity yielded an ROC AUC value comparable to that of the mean ADC value (p = 0.125). CONCLUSION. Mean post-CRT ADC values calculated from high-resolution DWI using rs-EPI could effectively select for patients with LARC who have a pCR after preoperative CRT. First-order texture parameters of T2-weighted MR images could also identify patients with pCR by reflecting tumor heterogeneity, even though they could not significantly improve the diagnostic performance.
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Owczarczyk K, Prezzi D, Cascino M, Kozarski R, Gaya A, Siddique M, Cook GJ, Glynne-Jones R, Goh V. MRI heterogeneity analysis for prediction of recurrence and disease free survival in anal cancer. Radiother Oncol 2019; 134:119-126. [PMID: 31005205 DOI: 10.1016/j.radonc.2019.01.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 01/15/2019] [Accepted: 01/17/2019] [Indexed: 12/15/2022]
Abstract
BACKGROUND The aim of this study was to evaluate the role of image heterogeneity analysis of standard care magnetic resonance imaging (MRI) in patients with anal squamous cell carcinoma (ASCC) to predict chemoradiotherapy (CRT) outcome. The ability to predict disease recurrence following CRT has the potential to inform personalized radiotherapy approaches currently being explored in novel clinical trials. METHODS An IRB waiver was obtained for retrospective analysis of standard care MRIs from ASCC patients presenting between 2010 and 2014. Whole tumor 3D volume-of-interest (VOI) was outlined on T2-weighted (T2w) and diffusion weighted imaging (DWI) of the pre- and post-treatment scans. Independent imaging features most predictive of disease recurrence were added to the baseline clinico-pathological model and the predictive value of respective extended models was calculated using net reclassification improvement (NRI) algorithm. Cross-validation analysis was carried out to determine percentage error reduction with inclusion of imaging features to the baseline model for both endpoints. RESULTS Forty patients who underwent 1.5 T pelvic MRI at baseline and following completion of CRT were included. A combination of two baseline MR heterogeneity features (baseline T2w energy and DWI coefficient of variation) was most predictive of disease recurrence resulting in significant NRI (p = 0 < 0.001). This was confirmed in cross-validation analysis with 34.8% percentage error reduction for the primary endpoint and 18.1% reduction for the secondary endpoint with addition of imaging variables to baseline model. CONCLUSION MRI heterogeneity analysis offers complementary information, in addition to clinical staging, in predicting outcome of CRT in anal SCC, warranting validation in larger datasets.
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Affiliation(s)
- Kasia Owczarczyk
- Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Davide Prezzi
- Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Robert Kozarski
- Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Andrew Gaya
- Department of Oncology, Guy's and St Thomas' Hospital NHS Foundation Trust, London, United Kingdom
| | - Muhammad Siddique
- Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gary J Cook
- Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Vicky Goh
- Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Traverso A, Kazmierski M, Shi Z, Kalendralis P, Welch M, Nissen HD, Jaffray D, Dekker A, Wee L. Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing. Phys Med 2019; 61:44-51. [PMID: 31151578 DOI: 10.1016/j.ejmp.2019.04.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 04/11/2019] [Accepted: 04/12/2019] [Indexed: 12/14/2022] Open
Abstract
Quantitative imaging features (radiomics) extracted from apparent diffusion coefficient (ADC) maps of rectal cancer patients can provide additional information to support treatment decision. Most available radiomic computational packages allow extraction of hundreds to thousands of features. However, two major factors can influence the reproducibility of radiomic features: interobserver variability, and imaging filtering applied prior to features extraction. In this exploratory study we seek to determine to what extent various commonly-used features are reproducible with regards to the mentioned factors using ADC maps from two different clinics (56 patients). Features derived from intensity distribution histograms are less sensitive to manual tumour delineation differences, noise in ADC images, pixel size resampling and intensity discretization. Shape features appear to be strongly affected by delineation quality. On the whole, textural features appear to be poorly or moderately reproducible with respect to the image pre-processing perturbations we reproduced.
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Affiliation(s)
- Alberto Traverso
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands; Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.
| | - Michal Kazmierski
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands
| | - Zhenwei Shi
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands
| | - Petros Kalendralis
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands
| | - Mattea Welch
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada
| | | | - David Jaffray
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands
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Mahon RN, Hugo GD, Weiss E. Repeatability of texture features derived from magnetic resonance and computed tomography imaging and use in predictive models for non-small cell lung cancer outcome. Phys Med Biol 2019; 64:145007. [PMID: 30978707 DOI: 10.1088/1361-6560/ab18d3] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE To evaluate the repeatability of MRI and CT derived texture features and to investigate the feasibility of use in predictive single and multi-modality models for radiotherapy of non-small cell lung cancer.
Methods: Fifty-nine texture features were extracted from unfiltered and wavelet filtered images. Repeatability of test-retest features from helical 4D CT scans, true fast MRI with steady state precession (TRUFISP), and volumetric interpolation breath-hold examination (VIBE) was determined by the concordance correlation coefficient (CCC). A workflow was developed to predict overall survival at 12, 18, and 24 months and tumour response at end of treatment for tumour features, and normal muscle tissue features as a control. Texture features were reduced to repeatable and stable features before clustering. Cluster representative feature selection was performed by univariate or medoid analysis before model selection. P-values were corrected for false discovery rate.
Results: Repeatable (CCC ≥ 0.9) features were found for both tumour and normal muscle tissue: CT: 54.4% for tumour and 78.5% for normal tissue, TRUFISP: 64.4% for tumour and 67.8% for normal tissue, and VIBE: 52.6% for tumour and 72.9% for normal muscle tissue. Muscle tissue control analysis found 7 significant models with 6 of 7 models utilizing the univariate representative feature selection technique. Tumour analysis revealed 12 significant models for overall survival and none for tumour response at end of treatment. The accuracy of significant single modality was about the same for MR and CT. Multi-modality tumour models had comparable performance to single modality models.
Conclusion: MR derived texture features may add value to predictive models and should be investigated in a larger cohort. Control analysis demonstrated that the medoid representative feature selection method may result in more robust models.
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Affiliation(s)
- Rebecca Nichole Mahon
- Radiation Oncology, Virginia Commonwealth University Health System, Richmond, Virginia, UNITED STATES
| | - Geoffrey D Hugo
- Radiation Oncology, Washington University in Saint Louis School of Medicine, Saint Louis, Missouri, UNITED STATES
| | - Elisabeth Weiss
- Radiation Oncology, Virginia Commonwealth University Health System, Richmond, Virginia, UNITED STATES
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Chirra P, Leo P, Yim M, Bloch BN, Rastinehad AR, Purysko A, Rosen M, Madabhushi A, Viswanath SE. Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. J Med Imaging (Bellingham) 2019; 6:024502. [PMID: 31259199 PMCID: PMC6566001 DOI: 10.1117/1.jmi.6.2.024502] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 05/15/2019] [Indexed: 12/18/2022] Open
Abstract
Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple sites and scanners. This may be particularly relevant in the context of radiomic features derived from T1- or T2-weighted magnetic resonance images (MRIs), where signal intensity values are known to lack tissue-specific meaning and vary based on differing acquisition protocols between institutions. We present the first empirical study of benchmarking five different radiomic feature families in terms of both reproducibility and discriminability in a multisite setting, specifically, for identifying prostate tumors in the peripheral zone on MRI. Our cohort comprised 147 patient T2-weighted MRI datasets from four different sites, all of which are first preprocessed to correct for acquisition-related artifacts such as bias field, differing voxel resolutions, and intensity drift (nonstandardness). About 406 three-dimensional voxel-wise radiomic features from five different families (gray, Haralick, gradient, Laws, and Gabor) were evaluated in a cross-site setting to determine (a) how reproducible they are within a relatively homogeneous nontumor tissue region and (b) how well they could discriminate tumor regions from nontumor regions. Our results demonstrate that a majority of the popular Haralick features are reproducible in over 99% of all cross-site comparisons, as well as achieve excellent cross-site discriminability (classification accuracy of ≈ 0.8 ). By contrast, a majority of Laws features are highly variable across sites (reproducible in < 75 % of all cross-site comparisons) as well as resulting in low cross-site classifier accuracies ( < 0.6 ), likely due to a large number of noisy filter responses that can be extracted. These trends suggest that only a subset of radiomic features and associated parameters may be both reproducible and discriminable enough for use within machine learning classifier schemes.
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Affiliation(s)
- Prathyush Chirra
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Patrick Leo
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Michael Yim
- Northeast Ohio Medical University, College of Medicine, Rootstown, Ohio, United States
| | - B. Nicolas Bloch
- Boston University School of Medicine, Department of Radiology, Boston, Massachusetts, United States
| | - Ardeshir R. Rastinehad
- Icahn School of Medicine at Mount Sinai, Department of Urology, New York, New York, United States
| | - Andrei Purysko
- Cleveland Clinic, Department of Radiology, Cleveland, Ohio, United States
| | - Mark Rosen
- Hospital of the University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, United States
| | - Satish E. Viswanath
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
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Rui W, Wu Y, Ma Z, Wang Y, Wang Y, Xu X, Zhang J, Yao Z. MR textural analysis on contrast enhanced 3D-SPACE images in assessment of consistency of pituitary macroadenoma. Eur J Radiol 2018; 110:219-224. [PMID: 30599863 DOI: 10.1016/j.ejrad.2018.12.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 08/26/2018] [Accepted: 12/02/2018] [Indexed: 11/24/2022]
Abstract
OBJECTIVES To explore the value of magnetic resonance textural analysis (MRTA) in assessing consistency of pituitary macroadenoma (PMA) based on contrast enhanced (CE) three-dimensional sampling perfection with application-optimized contrasts by using different flip angle evolution (3D-SPACE) images. MATERIALS AND METHODS Fifty-three patients with PMAs that underwent CE 3D-SPACE scanning by 3.0 T MRI and endoscopic trans-sphenoidal surgery were included in the present study. Consistency levels of PMAs were evaluated intraoperatively by two neurosurgeons. Each resection specimen was stained with H&E and anti-collagen IV. MRTA was conducted and texture features were calculated. An unpaired t-test was used to analyze the differences of texture features between soft and hard PMAs. ROC curves by individual and combined features were used to calculate the diagnostic accuracy of MRTA in predicting PMA consistency. RESULTS First-order energy and second-order correlation negatively correlated with hard PMAs, while first-order entropy and second-order variance, sum variance, and sum entropy positively correlated with stiffness. All showed significant differences between soft and hard PMAs (P < 0.05). Diagnostic accuracy of combined negative features could achieve an AUC of 0.819, sensitivity of 88.9%, specificity of 61.5%, PPV of 70.6%, NPV of 84.2% and positive features could achieve an AUC of 0.836, sensitivity of 85.2%, specificity of 69.2%, PPV of 74.2%, NPV of 81.8% (P < 0.001). CONCLUSION MRTA using CE 3D-SPACE images is helpful for assessing PMA consistency preoperatively and noninvasively.
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Affiliation(s)
- Wenting Rui
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China.
| | - Yue Wu
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China.
| | - Zengyi Ma
- Department of Neurosurgery, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China.
| | - Yongfei Wang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China.
| | - Yin Wang
- Department of Neuropathology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China.
| | - Xiao Xu
- GE Healthcare Life Sciences, GE Chinese Science and Technology Park, Huatuo Road, Shanghai, 201203, PR China.
| | - Junhai Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China.
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, PR China.
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