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He Y, Wang M, Yi S, Lu Y, Ren J, Zhou P, Xu K. Diffusion-weighted imaging in the assessment of cervical cancer: comparison of reduced field-of-view diffusion-weighted imaging and conventional techniques. Acta Radiol 2023; 64:2485-2491. [PMID: 37545177 DOI: 10.1177/02841851231183870] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
BACKGROUND Cervical cancer (CC) is the second most common cancer in women worldwide. Diffusion-weighted imaging (DWI) plays an important role in the diagnosis of CC, but the conventional techniques are affected by many factors. PURPOSE To compare reduced-field-of-view (r-FOV) and full-field-of-view (f-FOV) DWI in the diagnosis of CC. MATERIAL AND METHODS Preoperative magnetic resonance imaging (MRI) with r-FOV and f-FOV DWI images were collected. Two radiologists reviewed the images using a subjective 4-point scale for anatomical features, magnetic susceptibility artifacts, visual distortion, and overall diagnostic confidence for r-FOV and f-FOV DWI. The objective features included the region of interest (ROI) signal intensity of the cervical lesion (SIlesion) and gluteus maximus muscle (SIgluteus), standard deviation of the background noise (SDbackground), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The differences of measured apparent diffusion coefficient (ADC) values between the two examinations in pathological grades and FIGO tumor stages were compared. RESULTS A total of 200 patients were included (170 with squamous cell carcinoma and 30 with adenocarcinoma). The scores of anatomical features, magnetic susceptibility artifacts, visual distortion, and overall diagnostic confidence for r-FOV DWI were significantly higher than those for f-FOV DWI. There was no difference in SNR and CNR between r-FOV DWI and f-FOV DWI. There were significant differences in ADC values between the two groups in all comparisons (P < 0.05). CONCLUSION Compared with f-FOV DWI, r-FOV DWI might provide clearer imaging, fewer artifacts, less distortion, and higher image quality for the diagnosis of CC and might assist in the detection of CC.
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Affiliation(s)
- Yakun He
- Department of radiology, Sichuan Cancer Hospital, Chengdu, PR China
| | - Min Wang
- Department of radiology, Sichuan Cancer Hospital, Chengdu, PR China
| | - Siqi Yi
- Department of radiology, Sichuan Cancer Hospital, Chengdu, PR China
| | - Yujie Lu
- Department of radiology, Sichuan Cancer Hospital, Chengdu, PR China
| | - Jing Ren
- Department of radiology, Sichuan Cancer Hospital, Chengdu, PR China
| | - Peng Zhou
- Department of radiology, Sichuan Cancer Hospital, Chengdu, PR China
| | - Ke Xu
- Department of Otolaryngology-Head & Neck Surgery, West China Hospital, Sichuan University, Chengdu, PR China
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Huang Q, Deng B, Wang Y, Shen Y, Hu X, Feng C, Li Z. Reduced field-of-view DWI‑derived clinical-radiomics model for the prediction of stage in cervical cancer. Insights Imaging 2023; 14:18. [PMID: 36701003 PMCID: PMC9880109 DOI: 10.1186/s13244-022-01346-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 12/08/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Pretreatment prediction of stage in patients with cervical cancer (CC) is vital for tailoring treatment strategy. This study aimed to explore the feasibility of a model combining reduced field-of-view (rFOV) diffusion-weighted imaging (DWI)-derived radiomics with clinical features in staging CC. METHODS Patients with pathologically proven CC were enrolled in this retrospective study. The rFOV DWI with b values of 0 and 800 s/mm2 was acquired and the clinical characteristics of each patient were collected. Radiomics features were extracted from the apparent diffusion coefficient maps and key features were selected subsequently. A clinical-radiomics model combining radiomics with clinical features was constructed. The receiver operating characteristic curve was introduced to evaluate the predictive efficacy of the model, followed by comparisons with the MR-based subjective stage assessment (radiological model). RESULTS Ninety-four patients were analyzed and divided into training (n = 61) and testing (n = 33) cohorts. In the training cohort, the area under the curve (AUC) of clinical-radiomics model (AUC = 0.877) for staging CC was similar to that of radiomics model (AUC = 0.867), but significantly higher than that of clinical model (AUC = 0.673). In the testing cohort, the clinical-radiomics model yielded the highest predictive performance (AUC = 0.887) of staging CC, even without a statistically significant difference when compared with the clinical model (AUC = 0.793), radiomics model (AUC = 0.846), or radiological model (AUC = 0.823). CONCLUSIONS The rFOV DWI-derived clinical-radiomics model has the potential for staging CC, thereby facilitating clinical decision-making.
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Affiliation(s)
- Qiuhan Huang
- grid.412793.a0000 0004 1799 5032Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030 China
| | - Baodi Deng
- grid.412793.a0000 0004 1799 5032Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030 China
| | - Yanchun Wang
- grid.412793.a0000 0004 1799 5032Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030 China
| | - Yaqi Shen
- grid.412793.a0000 0004 1799 5032Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030 China
| | - Xuemei Hu
- grid.412793.a0000 0004 1799 5032Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030 China
| | - Cui Feng
- grid.412793.a0000 0004 1799 5032Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030 China
| | - Zhen Li
- grid.412793.a0000 0004 1799 5032Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030 China
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Zhu Q, Zou J, Ye J, Zhu W, Wu J, Chen W. Comparative study of conventional ROI-based and volumetric histogram analysis derived from CT enhancement in differentiating malignant and benign renal tumors. Br J Radiol 2022; 95:20210801. [PMID: 35333594 PMCID: PMC10996318 DOI: 10.1259/bjr.20210801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 02/12/2022] [Accepted: 03/17/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To quantitatively compare the diagnostic values of conventional region of interest (ROI)-based and volumetric histogram analysis derived from CT enhancement in differentiating malignant and benign renal tumors. METHODS A total of 230 patients with pathologically confirmed renal tumors who had undergone CT enhancement were classified into clear cell renal cell carcinoma (ccRCC) (n = 133), non-ccRCC (n = 56), and benign renal tumor(n = 41) group. Parametric CT enhancement of each tumor from volumetric histogram were obtained using in-house software, including 10th percentile, 25th percentile, median, 75th percentile, 90th percentile, mean, standard deviation, as well as skewness, kurtosis and entropy, and histogram metrics among these groups were analyzed. ROI-based enhancement density was also analyzed. RESULTS The entropy and SD values of ccRCCs were higher than those of non-ccRCCs and benign renal tumors (p < 0.05). The 10th percentile, 25th percentile, median, 75th percentile, 90th percentile and mean values of ccRCCs were lower than those of benign renal tumors, however, higher than those of non-ccRCCs (p < 0.05). The ROI-based enhancement density of non-ccRCCs were lower than those of ccRCCs and benign renal tumors(p < 0.05). Receiver operating characteristic (ROC) curve analyses showed that entropy and mean values had the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs and benign renal tumors. ROC curve analyses showed that mean values had the highest diagnostic efficacy in differentiating ccRCCs and non-ccRCCs. In terms of pairwise comparisons of ROC curves and diagnostic efficacy, ROI-based CT enhancement density was worse than volumetric histogram analysis (p < 0.05). CONCLUSION Volumetric histogram analysis parameters can effectively distinguish malignant and benign renal tumors. ADVANCES IN KNOWLEDGE 1. Entropy and mean values had the highest diagnostic efficacy in differentiating ccRCCs/ non-ccRCCs and benign renal tumors.2. Mean values had the highest diagnostic efficacy in differentiating ccRCCs and non-ccRCCs.3.Volumetric histogram analysis had better performance than ROI-based enhancement density.
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Affiliation(s)
- Qingqiang Zhu
- Department of Medical Imaging, Clinical Medical College,
Yangzhou University, Yangzhou,
China
| | - Jinzhao Zou
- Department of Medical Imaging, Clinical Medical College,
Yangzhou University, Yangzhou,
China
| | - Jing Ye
- Department of Medical Imaging, Clinical Medical College,
Yangzhou University, Yangzhou,
China
| | - Wenrong Zhu
- Department of Medical Imaging, Clinical Medical College,
Yangzhou University, Yangzhou,
China
| | - Jingtao Wu
- Department of Medical Imaging, Clinical Medical College,
Yangzhou University, Yangzhou,
China
| | - Wenxin Chen
- Department of Medical Imaging, Clinical Medical College,
Yangzhou University, Yangzhou,
China
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Manganaro L, Nicolino GM, Dolciami M, Martorana F, Stathis A, Colombo I, Rizzo S. Radiomics in cervical and endometrial cancer. Br J Radiol 2021; 94:20201314. [PMID: 34233456 PMCID: PMC9327743 DOI: 10.1259/bjr.20201314] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Radiomics is an emerging field of research that aims to find associations between quantitative information extracted from imaging examinations and clinical data to support the best clinical decision. In the last few years, some papers have been evaluating the role of radiomics in gynecological malignancies, mainly focusing on ovarian cancer. Nonetheless, cervical cancer is the most frequent gynecological malignancy in developing countries and endometrial cancer is the most common in western countries. The purpose of this narrative review is to give an overview of the latest published papers evaluating the role of radiomics in cervical and endometrial cancer, mostly evaluating association with tumor prognostic factors, with response to therapy and with prediction of recurrence and distant metastasis.
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Affiliation(s)
- Lucia Manganaro
- Department of Radiological, Oncological and Pathological Sciences; University of Rome Sapienza (IT), Rome, Italy
| | - Gabriele Maria Nicolino
- Post-graduate School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, Milan, Italy
| | - Miriam Dolciami
- Department of Radiological, Oncological and Pathological Sciences; University of Rome Sapienza (IT), Rome, Italy
| | - Federica Martorana
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland
| | - Anastasios Stathis
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland.,Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via Buffi 13, 6900, Lugano (CH), Switzerland
| | - Ilaria Colombo
- Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500 Bellinzona, (CH), Switzerland
| | - Stefania Rizzo
- Facoltà di Scienze biomediche, Università della Svizzera italiana (USI), Via Buffi 13, 6900, Lugano (CH), Switzerland.,Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale, Via Tesserete 46, Lugano (CH), Switzerland
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Li Z, Dai H, Liu Y, Pan F, Yang Y, Zhang M. Radiomics Analysis of Multi-Sequence MR Images For Predicting Microsatellite Instability Status Preoperatively in Rectal Cancer. Front Oncol 2021; 11:697497. [PMID: 34307164 PMCID: PMC8293900 DOI: 10.3389/fonc.2021.697497] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 06/21/2021] [Indexed: 12/21/2022] Open
Abstract
Background Immunotherapy, adjuvant chemotherapy, and prognosis of colorectal cancer are associated with MSI. Biopsy pathology cannot fully reflect the MSI status and heterogeneity of rectal cancer. Purpose To develop a radiomic-based model to preoperatively predict MSI status in rectal cancer on MRI. Assessment The patients were divided into two cohorts (training and testing) at a 7:3 ratio. Radiomics features, including intensity, texture, and shape, were extracted from the segmented volumes of interest based on T2-weighted and ADC imaging. Statistical Tests Independent sample t test, Mann-Whitney test, the chi-squared test, Receiver operating characteristic curves, calibration curves, decision curve analysis and multi-variate logistic regression analysis Results The radiomics models were significantly associated with MSI status. The T2-based model showed an area under the curve of 0.870 with 95% CI: 0.794–0.945 (accuracy, 0.845; specificity, 0.714; sensitivity, 0.976) in training set and 0.895 with 95% CI, 0.777–1.000 (accuracy, 0.778; specificity, 0.887; sensitivity, 0.772) in testing set. The ADC-based model had an AUC of 0.790 with 95% CI: 0.794–0.945 (accuracy, 0.774; specificity, 0.714; sensitivity, 0.976) in training set and 0.796 with 95% CI, 0.777–1.000 (accuracy, 0.778; specificity, 0.889; sensitivity, 0.772) in testing set. The combined model integrating T2 and ADC features showed an AUC of 0.908 with 95% CI: 0.845–0.971 (accuracy, 0.857; specificity, 0.762; sensitivity, 0.952) in training set and 0.926 with 95% CI: 0.813-1.000 (accuracy, 0.852; specificity, 1.000; sensitivity, 0.778) in testing set. Calibration curve showed that the combined score had a good calibration degree, and the decision curve demonstrated that the combined score was of benefit for clinical use. Data Conclusion Radiomics analysis of T2W and ADC images showed significant relevance in the prediction of microsatellite status, and the accuracy of combined model of ADC and T2W features was better than either alone.
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Affiliation(s)
- Zongbao Li
- China-Japan Union Hospital of Jilin University, Changchun, China
| | - Hui Dai
- China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yunxia Liu
- China-Japan Union Hospital of Jilin University, Changchun, China
| | - Feng Pan
- China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yanyan Yang
- China-Japan Union Hospital of Jilin University, Changchun, China
| | - Mengchao Zhang
- China-Japan Union Hospital of Jilin University, Changchun, China
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Li XX, Lin TT, Liu B, Wei W. Diagnosis of Cervical Cancer With Parametrial Invasion on Whole-Tumor Dynamic Contrast-Enhanced Magnetic Resonance Imaging Combined With Whole-Lesion Texture Analysis Based on T2- Weighted Images. Front Bioeng Biotechnol 2020; 8:590. [PMID: 32596230 PMCID: PMC7300256 DOI: 10.3389/fbioe.2020.00590] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 05/14/2020] [Indexed: 12/17/2022] Open
Abstract
Purpose: To evaluate the diagnostic value of the combination of whole-tumor dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and whole-lesion texture features based on T2-weighted images for cervical cancer with parametrial invasion. Materials and Methods: Sixty-two patients with cervical cancer (27 with parametrial invasion and 35 without invasion) preoperatively underwent routine MRI and DCE-MRI examinations. DCE-MRI parameters (Ktrans, Kep, and Ve) and texture features (mean, skewness, kurtosis, uniformity, energy, and entropy) based on T2-weighted images were acquired by two observers. All parameters of parametrial invasion and non-invasion were analyzed by one-way analysis of variance. The diagnostic efficiency of significant variables was assessed using receiver operating characteristic analysis. Results: The invasion group of cervical cancer demonstrated significantly higher Ktrans (0.335 ± 0.050 vs. 0.269 ± 0.079; p < 0.001), lower energy values (0.503 ± 0.093 vs. 0.602 ± 0.087; p < 0.001), and higher entropy values (1.391 ± 0.193 vs. 1.24 ± 0.129; p < 0.001) than those in the non-invasion group. Optimal diagnostic performance [area under curve [AUC], 0.925; sensitivity, 0.935; specificity, 0.829] could be obtained by the combination of Ktrans, energy, and entropy values. The AUC values of Ktrans (0.788), energy (0.761), entropy (0.749), the combination of Ktrans and energy (0.814), the combination of Ktrans and entropy (0.727), and the combination of energy and entropy (0.619) were lower than those of the combination of Ktrans, energy, and entropy values. Conclusion: The combination of DCE-MRI and texture analysis is a promising method for diagnosis cervical cancer with parametrial infiltration. Moreover, the combination of Ktrans, energy, and entropy is more valuable than any one alone, especially in improving diagnostic sensitivity.
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Affiliation(s)
- Xin-Xiang Li
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
| | - Ting-Ting Lin
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Bin Liu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Wei Wei
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
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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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Saleh M, Virarkar M, Javadi S, Elsherif SB, de Castro Faria S, Bhosale P. Cervical Cancer: 2018 Revised International Federation of Gynecology and Obstetrics Staging System and the Role of Imaging. AJR Am J Roentgenol 2020; 214:1182-1195. [DOI: 10.2214/ajr.19.21819] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Affiliation(s)
- Mohammed Saleh
- Department of Diagnostic Radiology, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030
| | - Mayur Virarkar
- Department of Diagnostic Radiology, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030
| | - Sanaz Javadi
- Department of Diagnostic Radiology, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030
| | - Sherif B. Elsherif
- Department of Diagnostic Radiology, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030
| | - Silvana de Castro Faria
- Department of Diagnostic Radiology, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030
| | - Priya Bhosale
- Department of Diagnostic Radiology, MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030
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Wormald BW, Doran SJ, Ind TE, D'Arcy J, Petts J, deSouza NM. Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: Prognostic value in low-volume tumors suitable for trachelectomy. Gynecol Oncol 2020; 156:107-114. [PMID: 31685232 PMCID: PMC7001101 DOI: 10.1016/j.ygyno.2019.10.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Revised: 10/07/2019] [Accepted: 10/08/2019] [Indexed: 12/19/2022]
Abstract
BACKGROUND Textural features extracted from MRI potentially provide prognostic information additional to volume for influencing surgical management of cervical cancer. PURPOSE To identify textural features that differ between cervical tumors above and below the volume threshold of eligibility for trachelectomy and determine their value in predicting recurrence in patients with low-volume tumors. METHODS Of 378 patients with Stage1-2 cervical cancer imaged prospectively (3T, endovaginal coil), 125 had well-defined, histologically-confirmed squamous or adenocarcinomas with >100 voxels (>0.07 cm3) suitable for radiomic analysis. Regions-of-interest outlined the whole tumor on T2-W images and apparent diffusion coefficient (ADC) maps. Textural features based on grey-level co-occurrence matrices were compared (Mann-Whitney test with Bonferroni correction) between tumors greater (n = 46) or less (n = 79) than 4.19 cm3. Clustering eliminated correlated variables. Significantly different features were used to predict recurrence (regression modelling) in surgically-treated patients with low-volume tumors and compared with a model using clinico-pathological features. RESULTS Textural features (Dissimilarity, Energy, ClusterProminence, ClusterShade, InverseVariance, Autocorrelation) in 6 of 10 clusters from T2-W and ADC data differed between high-volume (mean ± SD 15.3 ± 11.7 cm3) and low-volume (mean ± SD 1.3 ± 1.2 cm3) tumors. (p < 0.02). In low-volume tumors, predicting recurrence was indicated by: Dissimilarity, Energy (ADC-radiomics, AUC = 0.864); Dissimilarity, ClusterProminence, InverseVariance (T2-W-radiomics, AUC = 0.808); Volume, Depth of Invasion, LymphoVascular Space Invasion (clinico-pathological features, AUC = 0.794). Combining ADC-radiomic (but not T2-radiomic) and clinico-pathological features improved prediction of recurrence compared to the clinico-pathological model (AUC = 0.916, p = 0.006). Findings were supported by bootstrap re-sampling (n = 1000). CONCLUSION Textural features from ADC maps and T2-W images differ between high- and low-volume tumors and potentially predict recurrence in low-volume tumors.
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Affiliation(s)
- Benjamin W Wormald
- MRI Unit, Division of Radiotherapy and Imaging, The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Simon J Doran
- MRI Unit, Division of Radiotherapy and Imaging, The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Thomas Ej Ind
- Department of Gynaecological Oncology, The Royal Marsden NHS Foundation Trust, London, UK; St George's University of London, Tooting, London, UK
| | - James D'Arcy
- MRI Unit, Division of Radiotherapy and Imaging, The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, Sutton, UK
| | - James Petts
- MRI Unit, Division of Radiotherapy and Imaging, The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Nandita M deSouza
- MRI Unit, Division of Radiotherapy and Imaging, The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust, Sutton, UK.
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Cervical Cancer: Associations between Metabolic Parameters and Whole Lesion Histogram Analysis Derived from Simultaneous 18F-FDG-PET/MRI. CONTRAST MEDIA & MOLECULAR IMAGING 2018; 2018:5063285. [PMID: 30154687 PMCID: PMC6098855 DOI: 10.1155/2018/5063285] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 06/12/2018] [Accepted: 06/25/2018] [Indexed: 01/16/2023]
Abstract
Multimodal imaging has been increasingly used in oncology, especially in cervical cancer. By using a simultaneous positron emission (PET) and magnetic resonance imaging (MRI, PET/MRI) approach, PET and MRI can be obtained at the same time which minimizes motion artefacts and allows an exact imaging fusion, which is especially important in anatomically complex regions like the pelvis. The associations between functional parameters from MRI and 18F-FDG-PET reflecting different tumor aspects are complex with inconclusive results in cervical cancer. The present study correlates histogram analysis and 18F-FDG-PET parameters derived from simultaneous FDG-PET/MRI in cervical cancer. Overall, 18 female patients (age range: 32–79 years) with histopathologically confirmed squamous cell cervical carcinoma were retrospectively enrolled. All 18 patients underwent a whole-body simultaneous 18F-FDG-PET/MRI, including diffusion-weighted imaging (DWI) using b-values 0 and 1000 s/mm2. Apparent diffusion coefficient (ADC) histogram parameters included several percentiles, mean, min, max, mode, median, skewness, kurtosis, and entropy. Furthermore, mean and maximum standardized uptake values (SUVmean and SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were estimated. No statistically significant correlations were observed between SUVmax or SUVmean and ADC histogram parameters. TLG correlated inversely with p25 (r=−0.486, P=0.041), p75 (r=−0.490, P=0.039), p90 (r=−0.513, P=0.029), ADC median (r=−0.497, P=0.036), and ADC mode (r=−0.546, P=0.019). MTV also showed significant correlations with several ADC parameters: mean (r=−0.546, P=0.019), p10 (r=−0.473, P=0.047), p25 (r=−0.569, P=0.014), p75 (r=−0.576, P=0.012), p90 (r=−0.585, P=0.011), ADC median (r=−0.577, P=0.012), and ADC mode (r=−0.597, P=0.009). ADC histogram analysis and volume-based metabolic 18F-FDG-PET parameters are related to each other in cervical cancer.
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Weston MJ. Gynaecology virtual special issue. Clin Radiol 2018; 73:837-838. [PMID: 30057331 DOI: 10.1016/j.crad.2018.07.092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 07/02/2018] [Indexed: 11/18/2022]
Affiliation(s)
- M J Weston
- Department of Radiology, St James's University Hospital, Beckett Street, Leeds LS9 7TF, UK.
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Subtype Differentiation of Small (≤ 4 cm) Solid Renal Mass Using Volumetric Histogram Analysis of DWI at 3-T MRI. AJR Am J Roentgenol 2018; 211:614-623. [PMID: 29812980 DOI: 10.2214/ajr.17.19278] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE The purpose of this article is to evaluate the utility of volumetric histogram analysis of apparent diffusion coefficient (ADC) derived from reduced-FOV DWI for small (≤ 4 cm) solid renal mass subtypes at 3-T MRI. MATERIALS AND METHODS This retrospective study included 38 clear cell renal cell carcinomas (RCCs), 16 papillary RCCs, 18 chromophobe RCCs, 13 minimal fat angiomyolipomas (AMLs), and seven oncocytomas evaluated with preoperative MRI. Volumetric ADC maps were generated using all slices of the reduced-FOV DW images to obtain histogram parameters, including mean, median, 10th percentile, 25th percentile, 75th percentile, 90th percentile, and SD ADC values, as well as skewness, kurtosis, and entropy. Comparisons of these parameters were made by one-way ANOVA, t test, and ROC curves analysis. RESULTS ADC histogram parameters differentiated eight of 10 pairs of renal tumors. Three subtype pairs (clear cell RCC vs papillary RCC, clear cell RCC vs chromophobe RCC, and clear cell RCC vs minimal fat AML) were differentiated by mean ADC. However, five other subtype pairs (clear cell RCC vs oncocytoma, papillary RCC vs minimal fat AML, papillary RCC vs oncocytoma, chromophobe RCC vs minimal fat AML, and chromophobe RCC vs oncocytoma) were differentiated by histogram distribution parameters exclusively (all p < 0.05). Mean ADC, median ADC, 75th and 90th percentile ADC, SD ADC, and entropy of malignant tumors were significantly higher than those of benign tumors (all p < 0.05). Combination of mean ADC with histogram parameters yielded the highest AUC (0.851; sensitivity, 80.0%; specificity, 86.1%). CONCLUSION Quantitative volumetric ADC histogram analysis may help differentiate various subtypes of small solid renal tumors, including benign and malignant lesions.
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