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Zhang K, Yu B, Tang M, Li Y, Wu M, Lv F. Endovaginal coil for pelvic high-resolution magnetic resonance imaging of cervical cancer: a preliminary parameter optimization study. Quant Imaging Med Surg 2024; 14:3851-3862. [PMID: 38846274 PMCID: PMC11151224 DOI: 10.21037/qims-23-1718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/03/2024] [Indexed: 06/09/2024]
Abstract
Background The diagnosis of early-stage cervical cancer through conventional magnetic resonance imaging (MRI) remains challenging, highlighting a greater need for pelvic high-resolution MRI (HR MRI). This study used our research team's endovaginal coil imaging to optimize scanning parameters and aimed to achieve HR MRI of the pelvis and determine its clinical value. Methods Fifty participants were recruited prospectively for this cross-sectional study conducted at the First Affiliated Hospital of Chongqing Medical University from January 2023 to November 2023. Initially, 10 volunteers requiring pelvic imaging diagnosis underwent pelvic MRI with the endovaginal coil combined with a conventional external array coil to test and optimize the scanning parameters. Subsequently, 40 patients who were highly suspected or diagnosed with cervical cancer were randomly assigned to undergo an initial pelvic scan with an external array coil with subsequent examinations of both the conventional coil and the endovaginal coil. Two experienced radiologists performed quantitative analyses, measuring signals and calculating the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and contrast (C). They also conducted qualitative analyses, evaluating imaging artifacts, anatomical structures, and overall image quality. The paired sample t-test and Wilcoxon rank-sum test were conducted to compare the statistical differences between the two sets of images, while the intraclass correlation coefficient (ICC) and Kappa consistency tests were used to assess the measurement and scoring consistency between the two radiologists. Results The optimized endovaginal images had higher mean SNR, CNR, and C values (18.62±7.85, 16.04±7.72, and 0.73±0.11, respectively) compared to the conventional images (6.77±2.36, 4.47±2.05, and 0.47±0.12, respectively). Additionally, the ratings for imaging artifacts, anatomical structures, and overall quality of the endovaginal images were all 4 [interquartile range (IQR) 4, 4]; meanwhile, the conventional images scored lower with ratings of 4 (IQR 3, 4), 3 (IQR 3, 3), and 3 (IQR 3, 3) for SNR, CNR, and C, respectively. All analysis results underwent paired-sample t-tests or Wilcoxon rank-sum tests between the two groups, yielding a P value <0.001. The optimized endovaginal images also showed improved resolution with a reconstructed voxel size of 0.11 mm3, and HR MRI was successfully achieved. The ICC values for the measurements were 0.914, 0.947, and 0.912, respectively, and for the ratings, the measurement was 0.923, indicating excellent consistency between the two physicians (ICC/Kappa value between 0.85 and 1.00). Conclusions Endovaginal technology, which provides precise clinical information for the diagnosis of cervical cancer, provides straightforward operation and exceptional imaging quality, making it highly suitable for expanded clinical use.
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Affiliation(s)
- Ke Zhang
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Bin Yu
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingmei Tang
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, China
| | - Yingyuan Li
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
- Department of Radiology, Liuzhou Maternity and Child Healthcare Hospital, Liuzhou, China
| | - Meixian Wu
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
- Department of Radiology, Liuzhou Maternity and Child Healthcare Hospital, Liuzhou, China
| | - Fajin Lv
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Halle MK, Hodneland E, Wagner-Larsen KS, Lura NG, Fasmer KE, Berg HF, Stokowy T, Srivastava A, Forsse D, Hoivik EA, Woie K, Bertelsen BI, Krakstad C, Haldorsen IS. Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer. Sci Rep 2024; 14:11339. [PMID: 38760387 PMCID: PMC11101482 DOI: 10.1038/s41598-024-61271-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/03/2024] [Indexed: 05/19/2024] Open
Abstract
Cervical cancer (CC) is a major global health problem with 570,000 new cases and 266,000 deaths annually. Prognosis is poor for advanced stage disease, and few effective treatments exist. Preoperative diagnostic imaging is common in high-income countries and MRI measured tumor size routinely guides treatment allocation of cervical cancer patients. Recently, the role of MRI radiomics has been recognized. However, its potential to independently predict survival and treatment response requires further clarification. This retrospective cohort study demonstrates how non-invasive, preoperative, MRI radiomic profiling may improve prognostication and tailoring of treatments and follow-ups for cervical cancer patients. By unsupervised clustering based on 293 radiomic features from 132 patients, we identify three distinct clusters comprising patients with significantly different risk profiles, also when adjusting for FIGO stage and age. By linking their radiomic profiles to genomic alterations, we identify putative treatment targets for the different patient clusters (e.g., immunotherapy, CDK4/6 and YAP-TEAD inhibitors and p53 pathway targeting treatments).
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Affiliation(s)
- Mari Kyllesø Halle
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Erlend Hodneland
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Mathematics, University of Bergen, Bergen, Norway
| | - Kari S Wagner-Larsen
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Njål G Lura
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Kristine E Fasmer
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Hege F Berg
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Tomasz Stokowy
- Genomics Core Facility, Department of Clinical Science, University of Bergen, Bergen, Norway
- Section of Bioinformatics, Clinical Laboratory, Haukeland University Hospital, Bergen, Norway
| | - Aashish Srivastava
- Genomics Core Facility, Department of Clinical Science, University of Bergen, Bergen, Norway
- Section of Bioinformatics, Clinical Laboratory, Haukeland University Hospital, Bergen, Norway
| | - David Forsse
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Erling A Hoivik
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Kathrine Woie
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
| | - Bjørn I Bertelsen
- Department of Pathology, Haukeland University Hospital, Bergen, Norway
| | - Camilla Krakstad
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway.
| | - Ingfrid S Haldorsen
- Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Section of Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway.
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Feng Y. An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators. J Ovarian Res 2024; 17:45. [PMID: 38378582 PMCID: PMC10877874 DOI: 10.1186/s13048-024-01365-9] [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] [Received: 08/31/2023] [Accepted: 02/01/2024] [Indexed: 02/22/2024] Open
Abstract
OBJECTIVE To construct a machine learning diagnostic model integrating feature dimensionality reduction techniques and artificial neural network classifiers to develop the value of clinical routine blood indexes for the auxiliary diagnosis of ovarian cancer. METHODS Patients with ovarian cancer clearly diagnosed in our hospital were collected as a case group (n = 185), and three groups of patients with other malignant otolaryngology tumors (n = 138), patients with benign otolaryngology diseases (n = 339) and those with normal physical examination (n = 92) were used as an overall control group. In this paper, a fully automated segmentation network for magnetic resonance images of ovarian cancer is proposed to improve the reproducibility of tumor segmentation results while effectively reducing the burden on radiologists. A pre-trained Res Net50 is used to the three edge output modules are fused to obtain the final segmentation results. The segmentation results of the proposed network architecture are compared with the segmentation results of the U-net based network architecture and the effect of different loss functions and region of interest sizes on the segmentation performance of the proposed network is analyzed. RESULTS The average Dice similarity coefficient, average sensitivity, average specificity (specificity) and average hausdorff distance of the proposed network segmentation results reached 83.62%, 89.11%, 96.37% and 8.50, respectively, which were better than the U-net based segmentation method. For ROIs containing tumor tissue, the smaller the size, the better the segmentation effect. Several loss functions do not differ much. The area under the ROC curve of the machine learning diagnostic model reached 0.948, with a sensitivity of 91.9% and a specificity of 86.9%, and its diagnostic efficacy was significantly better than that of the traditional way of detecting CA125 alone. The model was able to accurately diagnose ovarian cancer of different disease stages and showed certain discriminative ability for ovarian cancer in all three control subgroups. CONCLUSION Using machine learning to integrate multiple conventional test indicators can effectively improve the diagnostic efficacy of ovarian cancer, which provides a new idea for the intelligent auxiliary diagnosis of ovarian cancer.
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Affiliation(s)
- Yiwen Feng
- Departments of Obstetrics and Gynecology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200080, P.R. China.
- Jiuquan Hospital, Shanghai General Hospital, 200003, Shanghai, China.
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Wu F, Zhang R, Li F, Qin X, Xing H, Lv H, Li L, Ai T. Radiomics analysis based on multiparametric magnetic resonance imaging for differentiating early stage of cervical cancer. Front Med (Lausanne) 2024; 11:1336640. [PMID: 38371508 PMCID: PMC10869616 DOI: 10.3389/fmed.2024.1336640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Objective To investigate the performance of multiparametric magnetic resonance imaging (MRI)-based radiomics models in differentiating early stage of cervical cancer (Stage I-IIa vs. IIb-IV). Methods One hundred patients with cervical cancer who underwent preoperative MRI between June 2020 and March 2022 were retrospectively enrolled. Training (n = 70) and testing cohorts (n = 30) were assigned by stratified random sampling. The clinical and pathological features, including age, histological subtypes, tumor grades, and node status, were compared between the two cohorts by t-test or chi-square test. Radiomics features were extracted from each volume of interest (VOI) on T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps. The data balance of the training cohort was resampled by synthesizing minority oversampling techniques. Subsequently, the adiomics signatures were constructed by the least absolute shrinkage and selection operator algorithm and minimum-redundancy maximum-relevance with 10-fold cross-validation. Logistic regression was applied to predict the cervical cancer stages (low [I-IIa]) and (high [IIb-IV] FIGO stages). The receiver operating characteristic curve (area under the curve [AUC]) and decision curve analysis were used to assess the performance of the radiomics model. Results The characteristics of age, histological subtypes, tumor grades, and node status were not significantly different between the low [I-IIa] and high [IIb-IV] FIGO stages (p > 0.05 for both the training and test cohorts). Three models based on T2WI, ADC maps, and the combined were developed based on six radiomics features from T2WI and three radiomics features from ADC maps, with AUCs of 0.855 (95% confidence interval [CI], 0.777-0.934) and 0.823 (95% CI, 0.727-0.919), 0.861 (95% CI, 0.785-0.936) and 0.81 (95% CI, 0.701-0.918), 0.934 (95% CI, 0.884-0.984) and 0.902 (95% CI, 0.832-0.972) in the training and test cohorts. Conclusion The radiomics models combined T2W and ADC maps had good predictive performance in differentiating the early stage from locally advanced cervical cancer.
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Affiliation(s)
- Feng Wu
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Rui Zhang
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Feng Li
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Xiaomin Qin
- Department of Obstetrics and Gynaecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science Xiangyang, China
| | - Hui Xing
- Department of Obstetrics and Gynaecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science Xiangyang, China
| | - Huabing Lv
- Department of Obstetrics and Gynaecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science Xiangyang, China
| | - Lin Li
- Department of Obstetrics and Gynaecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science Xiangyang, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Shakur A, Lee JYJ, Freeman S. An Update on the Role of MRI in Treatment Stratification of Patients with Cervical Cancer. Cancers (Basel) 2023; 15:5105. [PMID: 37894476 PMCID: PMC10605640 DOI: 10.3390/cancers15205105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
Cervical cancer is the fourth most common cancer in women worldwide and the most common gynaecological malignancy. The FIGO staging system is the most commonly utilised classification system for cervical cancer worldwide. Prior to the most recent update in the FIGO staging in 2018, the staging was dependent upon clinical assessment alone. Concordance between the surgical and clinical FIGO staging decreases rapidly as the tumour becomes more advanced. MRI now plays a central role in patients diagnosed with cervical cancer and enables accurate staging, which is essential to determining the most appropriate treatment. MRI is the best imaging option for the assessment of tumour size, location, and parametrial and sidewall invasion. Notably, the presence of parametrial invasion precludes surgical options, and the patient will be triaged to chemoradiotherapy. As imaging is intrinsic to the new 2018 FIGO staging system, nodal metastases have been included within the classification as stage IIIC disease. The presence of lymph node metastases within the pelvis or abdomen is associated with a poorer prognosis, which previously could not be included in the staging classification as these could not be reliably detected on clinical examination. MRI findings corresponding to the 2018 revised FIGO staging of cervical cancers and their impact on treatment selection will be described.
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Affiliation(s)
| | | | - Sue Freeman
- Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK; (A.S.); (J.Y.J.L.)
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Abdul-Latif M, Tharmalingam H, Tsang Y, Hoskin PJ. Functional Magnetic Resonance Imaging in Cervical Cancer Diagnosis and Treatment. Clin Oncol (R Coll Radiol) 2023; 35:598-610. [PMID: 37246040 DOI: 10.1016/j.clon.2023.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 05/12/2023] [Indexed: 05/30/2023]
Abstract
Cervical Cancer is the fourth most common cancer in women worldwide. Treatment with chemoradiotherapy followed by brachytherapy achieves high local control, but recurrence with metastatic disease impacts survival. This highlights the need for predictive and prognostic biomarkers identifying populations at risk of poorer treatment response and survival. Magnetic resonance imaging (MRI) is routinely used in cervical cancer and is a potential source for biomarkers. Functional MRI (fMRI) can characterise tumour beyond anatomical MRI, which is limited to the assessment of morphology. This review summarises fMRI techniques used in cervical cancer and examines the role of fMRI parameters as predictive or prognostic biomarkers. Different techniques characterise different tumour factors, which helps to explain the variation in patient outcomes. These can impact simultaneously on outcomes, making biomarker identification challenging. Most studies are small, focussing on single MRI techniques, which raises the need to investigate combined fMRI approaches for a more holistic characterisation of tumour.
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Affiliation(s)
| | | | - Y Tsang
- Mount Vernon Cancer Centre, Northwood, UK; Radiation Medicine Programme, Princess Margaret Cancer Centre, Toronto, Canada
| | - P J Hoskin
- Mount Vernon Cancer Centre, Northwood, UK; Division of Cancer Sciences, University of Manchester, Manchester, UK
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Meng X, Tian S, Zhang Q, Chen L, Lin L, Li J, Shen Z, Wang J, Zhang Y, Song Q, Liu A. Improved differentiation between endometrial carcinoma and endometrial polyp with combination of APTw and IVIM MR imaging. Magn Reson Imaging 2023; 102:43-48. [PMID: 37054801 DOI: 10.1016/j.mri.2023.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 04/08/2023] [Accepted: 04/08/2023] [Indexed: 04/15/2023]
Abstract
PURPOSE To assess the value of amide proton transfer weighted (APTw) combined with intra-voxel-incoherent-motion (IVIM) imaging in differential diagnosis of stage I-II endometrial carcinoma (EC) and endometrial polyp (EP). METHODS A total of 53 female patients (37 cases with EC and 16 cases with EP) confirmed by surgical resection or biopsy from June 2019 to Jan. 2022 were retrospectively reviewed. All patients underwent 3.0 T magnetic resonance imaging (MRI) examination including diffusion weighted imaging (DWI), APTw and IVIM scans. The pure diffusion coefficient (D), pseudo-diffusion coefficient (D⁎), perfusion fraction (f), apparent diffusion coefficient (ADC) and APT values were independently measured by two observers. Intra-class correlation coefficients (ICC) were used to test the consistency of measurements by the two observers. Mann-Whitney U test was performed to analyze the difference of each parameter between EC and EP groups. Receiver operator characteristic (ROC) analysis was performed, and the Delong test was used for ROC curve comparison. Pearson's correlation analysis was used to assess the correlation between APTw and IVIM parameters. RESULTS There was no significant difference in clinical manifestations between the two groups (P > 0.05). APT and D⁎ values of the EC group were significantly higher than those of the EP group [APT: 2.64 ± 0.50% vs. 2.05 ± 0.58%; and D⁎: (54.06 ± 36.06) × 10-3 mm2/s vs. (30.54 ± 16.67) × 10-3 mm2/s]. D, f and ADC values of EC group were significantly lower than those of EP group [D: 0.62(0.53,0.76) × 10-3 mm2/s vs. (1.45 ± 0.48) × 10-3 mm2/s; f: 22.18 ± 8.08% vs. 30.80 ± 8.92%; and ADC: (0.88 ± 0.16) × 10-3 mm2/s vs. (1.57 ± 0.43) × 10-3 mm2/s]. The area under ROC curves were observed as: AUC (IVIM+APT) > AUC (D) > AUC (ADC) > AUC (APT) > AUC (f) > AUC (D⁎). Delong test suggested statistical significance between AUC by APT and D, D and D⁎, D and f, D⁎ and ADC, APT and com(IVIM+APT), D⁎ and com(IVIM+APT), as well as f and com(IVIM+APT). No significant correlation between the APT and IVIM parameters was observed in either EC or EP group. CONCLUSION Both APT and IVIM parameters showed statistical differences between EC and EP. With combination of APT and IVIM parameters, the diagnostic accuracy between EC and EP can be significantly improved.
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Affiliation(s)
- Xing Meng
- Dalian Women and Children's Medical Group, Dalian, Liaoning 116033, China
| | - Shifeng Tian
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, China
| | - Qinhe Zhang
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, China
| | - Lihua Chen
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, China; Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, China
| | - Liangjie Lin
- Advanced Technical Support, Philips Healthcare, Beijing 100600, China.
| | - Jin Li
- Advanced Technical Support, Philips Healthcare, Beijing 100600, China.
| | - Zhiwei Shen
- Advanced Technical Support, Philips Healthcare, Beijing 100600, China; Advanced Technical Support, Philips Healthcare, Beijing 100600, China.
| | - Jiazheng Wang
- Advanced Technical Support, Philips Healthcare, Beijing 100600, China; Advanced Technical Support, Philips Healthcare, Beijing 100600, China.
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang 310000, China.
| | - Qingwei Song
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, China
| | - Ailian Liu
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, China.
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Xiao ML, Wei Y, Zhang J, Jian JM, Song Y, Lin ZJ, Qian L, Zhang GF, Qiang JW. MRI Texture Analysis for Preoperative Prediction of Lymph Node Metastasis in Patients with Nonsquamous Cell Cervical Carcinoma. Acad Radiol 2022; 29:1661-1671. [PMID: 35151550 DOI: 10.1016/j.acra.2022.01.005] [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: 11/16/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 11/30/2022]
Abstract
RATIONALE AND OBJECTIVES To preoperatively predict lymph node metastasis (LNM) in patients with cervical nonsquamous cell carcinoma (non-SCC) based on magnetic resonance imaging (MRI) texture analysis. MATERIALS AND METHODS This retrospective study included 104 consecutive patients (mean age of 47.2 ± 11.3 years) with stage IB-IIA cervical non-SCC. According to the ratio of 7:3, 72, and 32 patients were randomly divided into the training and testing cohorts. A total of 272 original features were extracted. In the process of feature selection, features with intraclass correlation coefficients (ICCs) less than 0.8 were eliminated. The Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) were applied to reduce redundancy, overfitting, and selection biases. Further, a support vector machine (SVM) with linear kernel function was applied to select the optimal feature set with a high discrimination power. RESULTS The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI + DWI + LNS-MRI (LN status on MRI)-based SVM models yielded an AUC and accuracy of 0.78 and 0.79; 0.79 and 0.69; 0.79 and 0.81 for predicting LNM in the training cohort, and 0.82 and 0.78; 0.82 and 0.69; 0.79 and 0.72 in the testing cohort. The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI + DWI + LNS-MRI-based SVM models performed better than morphologic criteria of LNS-MRI and yield similar discrimination abilities in predicting LNM in the training and testing cohorts (all p-value > 0.05). In addition, the T2WI + DWI-based and T2WI + DWI + LNS-MRI-based SVM models showed robust performance in the AC and ASC subgroups (all p-value > 0.05). CONCLUSION The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI+DWI+LNS-MRI-based SVM models showed similar good discrimination ability and performed better than the morphologic criteria of LNS-MRI in predicting LNM in patients with cervical non-SCC. The inclusion of the CE-T1WI sequence and morphologic criteria of LNS-MRI did not significantly improve the performance of the T2WI + DWI-based model. The T2WI + DWI-based and T2WI + DWI + LNS-MRI-based SVM models showed robust performance in the subgroup analysis.
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Affiliation(s)
- Mei Ling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China; Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | - Yan Wei
- Department of Automation, Zhejiang University of Technology, Hangzhou, China
| | - Jing Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Jun Ming Jian
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers, Shanghai, China
| | - Zi Jing Lin
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Lan Qian
- Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | - Guo Fu Zhang
- Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | - Jin Wei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
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Dong H, Yin L, Chen L, Wang Q, Pan X, Li Y, Ye X, Zeng M. Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm. Front Oncol 2022; 12:964322. [PMID: 36185244 PMCID: PMC9522474 DOI: 10.3389/fonc.2022.964322] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/26/2022] [Indexed: 11/13/2022] Open
Abstract
Objective We aimed to develop a Radiological-Radiomics (R-R) based model for predicting the high-grade pattern (HGP) of lung adenocarcinoma and evaluate its predictive performance. Methods The clinical, pathological, and imaging data of 374 patients pathologically confirmed with lung adenocarcinoma (374 lesions in total) were retrospectively analyzed. The 374 lesions were assigned to HGP (n = 81) and non-high-grade pattern (n-HGP, n = 293) groups depending on the presence or absence of high-grade components in pathological findings. The least absolute shrinkage and selection operator (LASSO) method was utilized to screen features on the United Imaging artificial intelligence scientific research platform, and logistic regression models for predicting HGP were constructed, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve (ROC) curves were plotted on the platform, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. Using the platform, nomograms for R-R models were also provided, and calibration curves and decision curves were drawn to evaluate the performance and clinical utility of the model. The statistical differences in the performance of the models were compared by the DeLong test. Results The R-R model for HGP prediction achieved an AUC value of 0.923 (95% CI: 0.891-0.948), a sensitivity of 87.0%, a specificity of 83.4%, and an accuracy of 84.2% in the training set. In the validation set, this model exhibited an AUC value of 0.920 (95% CI: 0.887-0.945), a sensitivity of 87.5%, a specificity of 83.3%, and an accuracy of 84.2%. The DeLong test demonstrated optimal performance of the R-R model among the three models, and decision curves validated the clinical utility of the R-R model. Conclusion In this study, we developed a fusion model using radiomic features combined with radiological features to predict the high-grade pattern of lung adenocarcinoma, and this model shows excellent diagnostic performance. The R-R model can provide certain guidance for clinical diagnosis and surgical treatment plans, contributing to improving the prognosis of patients.
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Affiliation(s)
- Hao Dong
- Department of Radiology, First People’s Hospital of Xiaoshan District, Hangzhou, China
| | - Lekang Yin
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Lei Chen
- Department of Research, Shanghai United Imaging Intelligence Co. Ltd., Shanghai, China
| | - Qingle Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xianpan Pan
- Department of Research, Shanghai United Imaging Intelligence Co. Ltd., Shanghai, China
| | - Yang Li
- Department of Research, Shanghai United Imaging Intelligence Co. Ltd., Shanghai, China
| | - Xiaodan Ye
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Xiaodan Ye, ; Mengsu Zeng,
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Xiaodan Ye, ; Mengsu Zeng,
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Bonde A, Andreazza Dal Lago E, Foster B, Javadi S, Palmquist S, Bhosale P. Utility of the Diffusion Weighted Sequence in Gynecological Imaging: Review Article. Cancers (Basel) 2022; 14:cancers14184468. [PMID: 36139628 PMCID: PMC9496793 DOI: 10.3390/cancers14184468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/30/2022] [Accepted: 09/08/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Diffusion weighted imaging (DWI) is a magnetic resonance imaging sequence with diverse clinical applications in malignant and nonmalignant gynecological conditions. It provides vital supplemental information in the diagnosis and management of various gynecological conditions. Radiologists should be aware of fundamental concepts, clinical applications and pitfalls of DWI. Additionally we briefly discuss potential scope of newer advanced techniques based on DWI including diffusion tensor imaging and diffusion-weighted whole-body imaging with background signal suppression. Abstract Functional imaging with diffusion-weighted imaging (DWI) is a complementary tool to conventional diagnostic magnetic resonance imaging sequences. It is being increasingly investigated to predict tumor response and assess tumor recurrence. We elucidate the specific technical modifications of DWI preferred for gynecological imaging, including the different b-values and planes for image acquisition. Additionally, we discuss the problems and potential pitfalls encountered during DWI interpretation and ways to overcome them. DWI has a wide range of clinical applications in malignant and non-malignant gynecological conditions. It provides supplemental information helpful in diagnosing and managing tubo-ovarian abscess, uterine fibroids, endometriosis, adnexal torsion, and dermoid. Similarly, DWI has diverse applications in gynecological oncology in diagnosis, staging, detection of recurrent disease, and tumor response assessment. Quantitative evaluation with apparent diffusion coefficient (ADC) measurement is being increasingly evaluated for correlation with various tumor parameters in managing gynecological malignancies aiding in preoperative treatment planning. Newer advanced DWI techniques of diffusion tensor imaging (DTI) and whole body DWI with background suppression (DWIBS) and their potential uses in pelvic nerve mapping, preoperative planning, and fertility-preserving surgeries are briefly discussed.
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Affiliation(s)
- Apurva Bonde
- Department of Radiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Correspondence:
| | | | - Bryan Foster
- Department of Radiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Sanaz Javadi
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sarah Palmquist
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Priya Bhosale
- Department of Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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11
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ADC and kinetic parameter of primary tumor: Surrogate imaging markers for fertility-sparing vaginal radical trachelectomy in patients with stage IB cervical cancer. Eur J Radiol 2022; 155:110467. [PMID: 35970120 DOI: 10.1016/j.ejrad.2022.110467] [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: 03/03/2022] [Revised: 06/23/2022] [Accepted: 08/07/2022] [Indexed: 12/30/2022]
Abstract
PURPOSE To investigate the role of ADC and kinetic parameters derived from DCE-MRI in selecting eligible candidates for fertility-sparing vaginal radical trachelectomy (VRT). METHOD Female patients with FIGO stage IB cervical cancers between March 2019 and January 2022 were retrospectively included. All patients underwent hysterectomy and bilateral lymphadenectomy. According to the surgical pathology, the study population was divided into VRT-eligible group and VRT-ineligible group. ADC, semi-quantitative and quantitative kinetic parameters of the primary tumor were compared between the two groups. Logistic regression analysis was used to determine the independent predictors for VRT eligibility and ROC curve was used to evaluate the predictive performance. RESULTS 19 patients were deemed eligible for VRT and 50 were ineligible. Compared with VRT-eligible group, time to peak and ADC were significantly lower in VRT-ineligible group (P = 0.004 and 0.001, respectively) while volume fraction of plasma (Vp) was higher in VRT ineligible group (P = 0.001). ADC and Vp were independent predictors for VRT eligibility. Combining Vp and ADC yielded the highest area under the ROC curve of 0.853 compared with that of 0.766 for Vp and 0.764 for ADC, though marginal differences were found (P = 0.109 and 0.078, respectively). CONCLUSIONS ADC and the kinetic DCE-MRI parameter Vp can be used as surrogate markers to select eligible candidates for fertility-sparing VRT.
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Zhang X, Zhao J, Zhang Q, Wang S, Zhang J, An J, Xie L, Yu X, Zhao X. MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy. Cancer Imaging 2022; 22:35. [PMID: 35842679 PMCID: PMC9287951 DOI: 10.1186/s40644-022-00474-2] [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: 03/22/2022] [Accepted: 07/06/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To investigate the magnetic resonance imaging (MRI)-based radiomics value in predicting the survival of patients with locally advanced cervical squamous cell cancer (LACSC) treated with concurrent chemoradiotherapy (CCRT). METHODS A total of 185 patients (training group: n = 128; testing group: n = 57) with LACSC treated with CCRT between January 2014 and December 2018 were retrospectively enrolled in this study. A total of 400 radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient map, arterial- and delayed-phase contrast-enhanced MRI. Univariate Cox regression and least absolute shrinkage and selection operator Cox regression was applied to select radiomics features and clinical characteristics that could independently predict progression-free survival (PFS) and overall survival (OS). The predictive capability of the prediction model was evaluated using Harrell's C-index. Nomograms and calibration curves were then generated. Survival curves were generated using the Kaplan-Meier method, and the log-rank test was used for comparison. RESULTS The radiomics score achieved significantly better predictive performance for the estimation of PFS (C-index, 0.764 for training and 0.762 for testing) and OS (C-index, 0.793 for training and 0.750 for testing), compared with the 2018 FIGO staging system (C-index for PFS, 0.657 for training and 0.677 for testing; C-index for OS, 0.665 for training and 0.633 for testing) and clinical-predicting model (C-index for PFS, 0.731 for training and 0.725 for testing; C-index for OS, 0.708 for training and 0.693 for testing) (P < 0.05). The combined model constructed with T stage, lymph node metastasis position, and radiomics score achieved the best performance for the estimation of PFS (C-index, 0.792 for training and 0.809 for testing) and OS (C-index, 0.822 for training and 0.785 for testing), which were significantly higher than those of the radiomics score (P < 0.05). CONCLUSIONS The MRI-based radiomics score could provide effective information in predicting the PFS and OS in patients with LACSC treated with CCRT. The combined model (including MRI-based radiomics score and clinical characteristics) showed the best prediction performance.
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Affiliation(s)
- Xiaomiao 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, Beijing, 100021, China
| | - Jingwei 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, Beijing, 100021, China
| | - Qi 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, Beijing, 100021, China
| | | | - Jieying 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, Beijing, 100021, China
| | - Jusheng An
- Department of Gynecologic Oncology, 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
- GE Healthcare, MR Research, Beijing, China
| | - Xiaoduo Yu
- Department of 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.
| | - 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, Beijing, 100021, China.
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13
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Zhang J, Zhang Q, Wang T, Song Y, Yu X, Xie L, Chen Y, Ouyang H. Multimodal MRI-Based Radiomics-Clinical Model for Preoperatively Differentiating Concurrent Endometrial Carcinoma From Atypical Endometrial Hyperplasia. Front Oncol 2022; 12:887546. [PMID: 35692806 PMCID: PMC9186045 DOI: 10.3389/fonc.2022.887546] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/25/2022] [Indexed: 11/24/2022] Open
Abstract
Objectives To develop and validate a radiomics model based on multimodal MRI combining clinical information for preoperative distinguishing concurrent endometrial carcinoma (CEC) from atypical endometrial hyperplasia (AEH). Materials and Methods A total of 122 patients (78 AEH and 44 CEC) who underwent preoperative MRI were enrolled in this retrospective study. Radiomics features were extracted based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. After feature reduction by minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithm, single-modal and multimodal radiomics signatures, clinical model, and radiomics-clinical model were constructed using logistic regression. Receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis were used to assess the models. Results The combined radiomics signature of T2WI, DWI, and ADC maps showed better discrimination ability than either alone. The radiomics-clinical model consisting of multimodal radiomics features, endometrial thickness >11mm, and nulliparity status achieved the highest area under the ROC curve (AUC) of 0.932 (95% confidential interval [CI]: 0.880-0.984), bootstrap corrected AUC of 0.922 in the training set, and AUC of 0.942 (95% CI: 0.852-1.000) in the validation set. Subgroup analysis further revealed that this model performed well for patients with preoperative endometrial biopsy consistent and inconsistent with postoperative pathologic data (consistent group, F1-score = 0.865; inconsistent group, F1-score = 0.900). Conclusions The radiomics model, which incorporates multimodal MRI and clinical information, might be used to preoperatively differentiate CEC from AEH, especially for patients with under- or over-estimated preoperative endometrial biopsy.
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Affiliation(s)
- Jieying 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
| | - Qi 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
| | - Tingting Wang
- Department of Gynecologic Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yan Song
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoduo Yu
- 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
- MR Research China, GE Healthcare, Beijing, China
| | - Yan Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Han Ouyang
- 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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Elkady RM. Radiomics Analysis in Evaluation of Cervical Cancer: A Further Step on the Road. Acad Radiol 2022; 29:1141-1142. [PMID: 35307261 DOI: 10.1016/j.acra.2022.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 02/19/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Reem M Elkady
- Department of radiology, Faculty of medicine, Assiut University, Assiut, Egypt & Department of radiology and medical imaging, College of medicine, Taibah University, Madinah, Saudi Arabia.
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15
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Ren J, Li Y, Yang JJ, Zhao J, Xiang Y, Xia C, Cao Y, Chen B, Guan H, Qi YF, Tang W, Chen K, He YL, Jin ZY, Xue HD. MRI-based radiomics analysis improves preoperative diagnostic performance for the depth of stromal invasion in patients with early stage cervical cancer. Insights Imaging 2022; 13:17. [PMID: 35092505 PMCID: PMC8800977 DOI: 10.1186/s13244-022-01156-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 12/31/2021] [Indexed: 11/10/2022] Open
Abstract
Background The depth of cervical stromal invasion is one of the important prognostic factors affecting decision-making for early stage cervical cancer (CC). This study aimed to develop and validate a T2-weighted imaging (T2WI)-based radiomics model and explore independent risk factors (factors with statistical significance in both univariate and multivariate analyses) of middle or deep stromal invasion in early stage CC. Methods Between March 2017 and March 2021, a total of 234 International Federation of Gynecology and Obstetrics IB1-IIA1 CC patients were enrolled and randomly divided into a training cohort (n = 188) and a validation cohort (n = 46). The radiomics features of each patient were extracted from preoperative sagittal T2WI, and key features were selected. After independent risk factors were identified, a combined model and nomogram incorporating radiomics signature and independent risk factors were developed. Diagnostic accuracy of radiologists was also evaluated. Results The maximal tumor diameter (MTD) on magnetic resonance imaging was identified as an independent risk factor. In the validation cohort, the radiomics model, MTD, and combined model showed areas under the curve of 0.879, 0.844, and 0.886. The radiomics model and combined model showed the same sensitivity and specificity of 87.9% and 84.6%, which were better than radiologists (sensitivity, senior = 75.7%, junior = 63.6%; specificity, senior = 69.2%, junior = 53.8%) and MTD (sensitivity = 69.7%, specificity = 76.9%). Conclusion MRI-based radiomics analysis outperformed radiologists for the preoperative diagnosis of middle or deep stromal invasion in early stage CC, and the probability can be individually evaluated by a nomogram.
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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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Williams TL, Saadat LV, Gonen M, Wei A, Do RKG, Simpson AL. Radiomics in surgical oncology: applications and challenges. Comput Assist Surg (Abingdon) 2021; 26:85-96. [PMID: 34902259 DOI: 10.1080/24699322.2021.1994014] [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: 10/19/2022] Open
Abstract
Surgery is a curative treatment option for many patients with malignant tumors. Increased attention has focused on the combination of surgery with chemotherapy, as multimodality treatment has been associated with promising results in certain cancer types. Despite these data, there remains clinical equipoise on optimal timing and patient selection for neoadjuvant or adjuvant strategies. Radiomics, an emerging field involving the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and contribute to the advance of personalized therapy by helping predict tumor behavior and response to therapy. This review analyzes and summarizes studies that use radiomics with machine learning in patients who have received neoadjuvant and/or adjuvant chemotherapy to predict prognosis, recurrence, survival, and therapeutic response for various cancer types. While studies in both neoadjuvant and adjuvant settings demonstrate above average performance on ability to predict progression-free and overall survival, there remain many challenges and limitations to widespread implementation of this technology. The lack of standardization of common practices to analyze radiomics, limited data sharing, and absence of auto-segmentation have hindered the inclusion and rapid adoption of radiomics in prospective, clinical studies.
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Affiliation(s)
- Travis L Williams
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lily V Saadat
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mithat Gonen
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alice Wei
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber L Simpson
- School of Computing, Queen's University, Kingston, ON, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
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Siegler E, Goldberg Y, Siegler Y, Shaked-Mishan P, Mazareb S, Kugelman N, Mackuli L, Sabo E, Lavie O, Segev Y. The Association Between Clearance of Human Papillomavirus After Conization for Cervical Cancer and Absence of Cancer. J Low Genit Tract Dis 2021; 25:276-280. [PMID: 34369434 DOI: 10.1097/lgt.0000000000000622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES We assessed the relation between clearance of high-risk human papillomavirus (HR-HPV) after large loop excision of the transformation zone (LLETZ) and absence of residual disease, in women diagnosed with cervical cancer (CC) and adenocarcinoma in situ (AIS). MATERIALS METHODS Data were collected from 92 women diagnosed with CC and AIS who were positive to HR-HPV and had a repeat cervical HPV test 3-12 weeks after LLETZ (in which CC/AIS were diagnosed) and before final surgical treatment. We compared characteristics of women with negative and positive HR-HPV after LLETZ. RESULTS The HR-HPV results after the LLETZ operation were negative in 40 women and positive in 52 women. The HR-HPV-negative group included a significantly higher incidence of AIS: 14 (35%) vs 5 (9.6%, p < .006).In the negative HR-HPV post-LLETZ group, 36 (90%) had normal histology and only 2 (5%) had cancer in the final histological specimen. Among 34 women who underwent radical hysterectomy/trachelectomy after LLETZ, a normal final histology was observed in 75% and 9% of those who were HR-HPV negative and HR-HPV positive, respectively (p < .0005). The positive predictive value for absence of residual cancer, with clearance of HR-HPV after LLETZ, was 95%. CONCLUSIONS Clearance of HR-HPV from the cervix a short time after LLETZ has a high association with the absence of residual cancer in the final outcome, either in the pathology or the follow-up. Testing for HR-HPV a short time after LLETZ might serve as a parameter for risk assessment.
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Affiliation(s)
- Efraim Siegler
- Department of Obstetrics and Gynecology, Carmel Medical Center, Haifa, Israel
| | | | - Yoav Siegler
- Department of Obstetrics and Gynecology, Rambam Medical Center, Haifa, Israel
| | | | - Salam Mazareb
- Department of Pathology, Carmel Medical Center, Haifa, Israel
| | | | - Lena Mackuli
- Department of Obstetrics and Gynecology, Carmel Medical Center, Haifa, Israel
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deSouza NM. Imaging to assist fertility-sparing surgery. Best Pract Res Clin Obstet Gynaecol 2021; 75:23-36. [PMID: 33722497 DOI: 10.1016/j.bpobgyn.2021.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 01/31/2021] [Indexed: 11/23/2022]
Abstract
Cytological screening and human papilloma virus testing has led to diagnosis of cervical cancer in young women at an earlier stage. Defining the full extent of the disease within the cervix with imaging aids the decision on feasibility of fertility-sparing surgical options, such as extended cone biopsy or trachelectomy. High spatial resolution images with maximal contrast between tumour and surrounding background are achieved with T2-weighted and diffusion-weighted (DW) magnetic resonance imaging (MRI) obtained using an endovaginal receiver coil. Tumour size and volume demonstrated in this way correlates between observers and with histology and differences between MRI and histology estimates of normal endocervical canal length are not significant. For planning fertility-sparing surgery, this imaging technique facilitates the best oncological outcome while minimising subsequent obstetric risks. Parametrial invasion may be assessed on large field of view T2-weighted MRI. The fat content of the parametrium limits the utility of DW imaging in this context, because fat typically shows diffusion restriction. The use of contrast-enhanced MRI for assessing the parametrium does not provide additional benefits to the T2-weighted images and the need for an extrinsic contrast agent merely adds additional complexity and cost. For nodal assessment, 18fluorodeoxyglucose positron emission tomography-computerised tomography (18FDG PET-CT) remains the gold standard.
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Affiliation(s)
- N M deSouza
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, 15 Cotswold Road, SM2 5NG, UK.
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Park BK, Kim TJ. Useful MRI Findings for Minimally Invasive Surgery for Early Cervical Cancer. Cancers (Basel) 2021; 13:cancers13164078. [PMID: 34439231 PMCID: PMC8391577 DOI: 10.3390/cancers13164078] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 08/04/2021] [Accepted: 08/08/2021] [Indexed: 01/02/2023] Open
Abstract
Simple Summary Radical hysterectomy and lymph node dissection are extensive procedures with severe post-operative morbidities and should be avoided on patients with low risk of recurrence. Still, due to lack of good prognostic tools, radical surgery is performed on most patients with early stage cervical cancer, leading to overtreatment and unnecessary morbidities. The recent International Federation of Gynecology and Obstetrics (FIGO) staging system accepts the use of magnetic resonance imaging (MRI) in addition to physical examination. Currently, 3 Tesla (3T) MRI is available widely and, due to its high soft tissue contrast, can provide more useful information on precise estimation of tumor size and metastasis than can physical examination in patients with cervical cancer. Therefore, this imaging modality can help gynecologic oncologists to determine whether minimally invasive surgery is necessary and can be used for early detection of small recurrent cancers. Abstract According to the recent International Federation of Gynecology and Obstetrics (FIGO) staging system, Stage III cervical cancer indicates pelvic or paraaortic lymph node metastasis. Accordingly, the new FIGO stage accepts imaging modalities, such as MRI, as part of the FIGO 2018 updated staging. Magnetic resonance imaging (MRI) is the best imaging modality to estimate the size or volume of uterine cancer because of its excellent soft tissue contrast. As a result, MRI is being used increasingly to determine treatment options and follow-up for cervical cancer patients. Increasing availability of cancer screening and vaccination have improved early detection of cervical cancer. However, the incidence of early cervical cancers has increased compared to that of advanced cervical cancer. A few studies have investigated if MRI findings are useful in management of early cervical cancer. MRI can precisely predict tumor burden, allowing conization, trachelectomy, and simple hysterectomy to be considered as minimally invasive treatment options for early cervical cancer. This imaging modality also can be used to determine whether there is recurrent cancer following minimally invasive treatments. The purpose of this review is to highlight useful MRI features for managing women with early cervical cancer.
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Affiliation(s)
- Byung Kwan Park
- Department of Radiology, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul 06351, Korea
- Correspondence: (B.K.P.); (T.-J.K.); Tel.: +82-2-3410-6457 (B.K.P.); +82-2-3410-0630 (T.-J.K.)
| | - Tae-Joong Kim
- Department of Obstetrics & Gynecology, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul 06351, Korea
- Correspondence: (B.K.P.); (T.-J.K.); Tel.: +82-2-3410-6457 (B.K.P.); +82-2-3410-0630 (T.-J.K.)
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21
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Fournier L, Costaridou L, Bidaut L, Michoux N, Lecouvet FE, de Geus-Oei LF, Boellaard R, Oprea-Lager DE, Obuchowski NA, Caroli A, Kunz WG, Oei EH, O'Connor JPB, Mayerhoefer ME, Franca M, Alberich-Bayarri A, Deroose CM, Loewe C, Manniesing R, Caramella C, Lopci E, Lassau N, Persson A, Achten R, Rosendahl K, Clement O, Kotter E, Golay X, Smits M, Dewey M, Sullivan DC, van der Lugt A, deSouza NM, European Society Of Radiology. Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers. Eur Radiol 2021; 31:6001-6012. [PMID: 33492473 PMCID: PMC8270834 DOI: 10.1007/s00330-020-07598-8] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/16/2020] [Accepted: 12/03/2020] [Indexed: 02/07/2023]
Abstract
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.
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Affiliation(s)
- Laure Fournier
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
| | - Lena Costaridou
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- School of Medicine, University of Patras, University Campus, Rio, 26 500, Patras, Greece
| | - Luc Bidaut
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- College of Science, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Nicolas Michoux
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Frederic E Lecouvet
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Lioe-Fee de Geus-Oei
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands
| | - Ronald Boellaard
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
| | - Daniela E Oprea-Lager
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
| | - Nancy A Obuchowski
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - Anna Caroli
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - Wolfgang G Kunz
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Edwin H Oei
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - James P B O'Connor
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Marius E Mayerhoefer
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Manuela Franca
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Centro Hospitalar Universitário do Porto, Instituto de Ciências Biomédicas de Abel Salazar, University of Porto, Porto, Portugal
| | - Angel Alberich-Bayarri
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers in Medicine (QUIBIM), Valencia, Spain
| | - Christophe M Deroose
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Christian Loewe
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cardiovascular and Interventional Radiology, Dept. for Bioimaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Rashindra Manniesing
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Caroline Caramella
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Radiology Department, Hôpital Marie Lannelongue, Institut d'Oncologie Thoracique, Université Paris-Saclay, Le Plessis-Robinson, France
| | - Egesta Lopci
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, Humanitas Clinical and Research Hospital - IRCCS, Rozzano, MI, Italy
| | - Nathalie Lassau
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Imaging Department, Gustave Roussy Cancer Campus Grand, Paris, UMR 1281, INSERM, CNRS, CEA, Universite Paris-Saclay, Saint-Aubin, France
| | - Anders Persson
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, and Department of Health, Medicine and Caring Sciences, Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Rik Achten
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Medical Imaging, Ghent University Hospital, Gent, Belgium
| | - Karen Rosendahl
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Hospital of North Norway, Tromsø, Norway
| | - Olivier Clement
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
| | - Elmar Kotter
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Xavier Golay
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Queen Square Institute of Neurology, University College London, London, UK
| | - Marion Smits
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Marc Dewey
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel C Sullivan
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Dept. of Radiology, Duke University, 311 Research Dr, Durham, NC, 27710, USA
| | - Aad van der Lugt
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Nandita M deSouza
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria.
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium.
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA.
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK.
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22
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Laliscia C, Gadducci A, Mattioni R, Orlandi F, Giusti S, Barcellini A, Gabelloni M, Morganti R, Neri E, Paiar F. MRI-based radiomics: promise for locally advanced cervical cancer treated with a tailored integrated therapeutic approach. TUMORI JOURNAL 2021; 108:376-385. [PMID: 34235995 DOI: 10.1177/03008916211014274] [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] [Indexed: 01/04/2023]
Abstract
OBJECTIVE To assess prognostic factors by analyzing clinical and radiomic data of patients with locally advanced cervical cancer (LACC) treated with definitive concurrent cisplatin-based chemoradiotherapy (CCRT) using magnetic resonance imaging (MRI). METHODS We analyzed radiomic features from MRI in 60 women with FIGO (International Federation of Gynecology and Obstetrics) stage IB2-IVA cervical cancer who underwent definitive CCRT 45-50.4 Gy (in 25-28 fractions). Thirty-nine (65.0%) received EBRT sequential boost (4-20 Gy) on primary tumor site and 56 (93.3%) received high-dose-rate brachytherapy boost (6-28 Gy) (daily fractions of 5-7 Gy). Moreover, 71.7% of patients received dose-dense neoadjuvant chemotherapy for 6 cycles. The gross tumor volume was defined on T2-weighted sequences and 29 features were extracted from each MRI performed before and after CCRT, using dedicated software, and their prognostic value was correlated with clinical information. RESULTS In univariate analysis, age ⩾60 years and FIGO stage IB2-IIB had significantly better progression-free survival (PFS) (p = 0.022 and p = 0.009, respectively). There was a trend for significance for worse overall survival (OS) in patients with positive nodes (p = 0.062). In multivariate analysis, only age ⩾60 years and FIGO stage IB2-IIB reached significantly better PFS (p = 0.020 and p = 0.053, respectively). In radiomic dataset, in multivariate analysis, pregray level p75 was significantly associated with PFS (p = 0.047), pre-D3D value with OS (p = 0.049), and preinformation measure of correlation value with local control (p = 0.031). CONCLUSION The combination of clinical and radiomics features can provide information to predict behavior and prognosis of LACC and to make more accurate treatment decisions.
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Affiliation(s)
- Concetta Laliscia
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
| | - Angiolo Gadducci
- Department of Experimental and Clinical Medicine, Division of Gynecology and Obstetrics, University of Pisa, Pisa, Italy
| | - Roberto Mattioni
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
| | - Francesca Orlandi
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
| | - Sabina Giusti
- Department of New Technologies and Translational Research, Division of Radiology, University of Pisa, Pisa, Italy
| | - Amelia Barcellini
- National Center of Oncological Hadrontherapy (Fondazione CNAO), Pavia, Italy
| | - Michela Gabelloni
- Department of New Technologies and Translational Research, Division of Radiology, University of Pisa, Pisa, Italy
| | - Riccardo Morganti
- Department of Clinical and Experimental Medicine, Section of Statistics, University of Pisa, Pisa, Italy
| | - Emanuele Neri
- Department of New Technologies and Translational Research, Division of Radiology, University of Pisa, Pisa, Italy
| | - Fabiola Paiar
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
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23
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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: 15] [Impact Index Per Article: 5.0] [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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24
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Jin J, Wu K, Li X, Yu Y, Wang X, Sun H. Relationship between tumor heterogeneity and volume in cervical cancer: Evidence from integrated fluorodeoxyglucose 18 PET/MR texture analysis. Nucl Med Commun 2021; 42:545-552. [PMID: 33323868 DOI: 10.1097/mnm.0000000000001354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
OBJECTIVE The aim of this study was to evaluate the effect of cervical cancer volume on PET/magnetic resonance (MR) texture heterogeneity. MATERIALS AND METHODS We retrospectively analyzed the PET/MR images of 138 patients with pathologically diagnosed cervical squamous cell carcinoma, including 50 patients undergoing surgery and 88 patients receiving concurrent chemoradiotherapy. Fluorodeoxyglucose 18 (18FDG)-PET/MR examination were performed for each patient before treatment, and the PET and MR texture analysis were undertaken. The texture features of the tumor based on gray-level co-occurrence matrices were extracted, and the correlation between tumor texture features and volume parameters was analyzed using Spearman's rank correlation coefficient. Finally, the variation trend of tumor texture heterogeneity was analyzed as tumor volumes increased. RESULTS PET texture features were highly correlated with metabolic tumor volume (MTV), including entropy-log2, entropy-log10, energy, homogeneity, dissimilarity, contrast, correlation, and the correlation coefficients (rs) were 0.955, 0.955, -0.897, 0.883, -0.881, -0.876, and 0.847 (P < 0.001), respectively. In the range of smaller MTV, the texture heterogeneity of energy, entropy-log2, and entropy-log10 increases with an increase in tumor volume, whereas the texture heterogeneity of homogeneity, dissimilarity, contrast, and correlation decreases with an increase in tumor volume. Only homogeneity, contrast, correlation, and dissimilarity had high correlation with tumor volume on MRI. The correlation coefficients (rs) were 0.76, -0.737, 0.644, and -0.739 (P < 0.001), respectively. The texture heterogeneity of MRI features that are highly correlated with tumor volume decreases with increasing tumor volume. CONCLUSION In the small tumor volume range, the heterogeneity variation trend of PET texture features is inconsistent as the tumor volume increases, but the variation trend of MRI texture heterogeneity is consistent, and MRI texture heterogeneity decreases as tumor volume increases. These results suggest that MRI is a better imaging modality when compared with PET in determining tumor texture heterogeneity in the small tumor volume range.
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Affiliation(s)
- Junjie Jin
- Department of Radiology, Shengjing Hospital of China Medical University
- Liaoning Provincial Key Laboratory of Medical Imaging
| | - Ke Wu
- Department of Radiology, Shengjing Hospital of China Medical University
| | - Xiaoran Li
- Department of Radiology, Shengjing Hospital of China Medical University
| | - Yang Yu
- Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xinghao Wang
- Department of Radiology, Shengjing Hospital of China Medical University
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University
- Liaoning Provincial Key Laboratory of Medical Imaging
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25
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Piludu F, Marzi S, Ravanelli M, Pellini R, Covello R, Terrenato I, Farina D, Campora R, Ferrazzoli V, Vidiri A. MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation. Front Oncol 2021; 11:656918. [PMID: 33987092 PMCID: PMC8111169 DOI: 10.3389/fonc.2021.656918] [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: 01/21/2021] [Accepted: 04/08/2021] [Indexed: 12/23/2022] Open
Abstract
Background The differentiation between benign and malignant parotid lesions is crucial to defining the treatment plan, which highly depends on the tumor histology. We aimed to evaluate the role of MRI-based radiomics using both T2-weighted (T2-w) images and Apparent Diffusion Coefficient (ADC) maps in the differentiation of parotid lesions, in order to develop predictive models with an external validation cohort. Materials and Methods A sample of 69 untreated parotid lesions was evaluated retrospectively, including 37 benign (of which 13 were Warthin’s tumors) and 32 malignant tumors. The patient population was divided into three groups: benign lesions (24 cases), Warthin’s lesions (13 cases), and malignant lesions (32 cases), which were compared in pairs. First- and second-order features were derived for each lesion. Margins and contrast enhancement patterns (CE) were qualitatively assessed. The model with the final feature set was achieved using the support vector machine binary classification algorithm. Results Models for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. After the feature selection process, four parameters for each model were used, including histogram-based features from ADC and T2-w images, shape-based features and types of margins and/or CE. Comparable accuracies were obtained after validation with the external cohort. Conclusions Radiomic analysis of ADC, T2-w images, and qualitative scores evaluating margins and CE allowed us to obtain good to excellent diagnostic accuracies in differentiating parotid lesions, which were confirmed with an external validation cohort.
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Affiliation(s)
- Francesca Piludu
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Simona Marzi
- Medical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Marco Ravanelli
- Department of Radiology, University of Brescia, Brescia, Italy
| | - Raul Pellini
- Department of Otolaryngology & Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Renato Covello
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Irene Terrenato
- Biostatistics-Scientific Direction, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Davide Farina
- Department of Radiology, University of Brescia, Brescia, Italy
| | | | - Valentina Ferrazzoli
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
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Javadi S, Kundra V. Multiparameter MRI and Clinical Factors for Predicting Early Response to Chemoradiotherapy in Cervical Cancer. Radiol Imaging Cancer 2021; 3:e209038. [PMID: 33778762 DOI: 10.1148/rycan.2021209038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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27
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Kim N, Park W, Cho WK, Bae DS, Kim BG, Lee JW, Kim TJ, Choi CH, Lee YY, Cho YS. Early Metabolic Response Assessed Using 18F-FDG-PET/CT for Image-Guided Intracavitary Brachytherapy Can Better Predict Treatment Outcomes in Patients with Cervical Cancer. Cancer Res Treat 2020; 53:803-812. [PMID: 33321566 PMCID: PMC8291185 DOI: 10.4143/crt.2020.1251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 12/08/2020] [Indexed: 01/10/2023] Open
Abstract
Purpose This study aimed to identify the prognostic value of early metabolic response assessed using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) during radiation therapy (RT) for cervical cancer. Materials and Methods We identified 116 patients treated with definitive RT, including FDG-PET/CT–guided intracavitary brachytherapy, between 2009 and 2018. We calculated parameters including maximum (SUVmax) and mean standardized uptake values (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for baseline FDG-PET/CT (PETbase) and image-guided brachytherapy planning FDG-PET/CT (PETIGBT). Multivariable analyses of disease-free survival (DFS) and overall survival (OS) were performed. Results We observed a time-dependent decrease in PET parameters between PETbase and PETIGBT; ΔSUVmax, ΔSUVmean, ΔMTV, and ΔTLG were 65%, 61%, 78%, and 93%, respectively. With a median follow-up of 59.5 months, the 5-year DFS and OS rates were 66% and 79%, respectively. Multivariable analysis demonstrated that ΔSUVmax ≥ 50% was associated with favorable DFS (hazard ratio [HR], 2.56; 95% confidence interval [CI], 1.14 to 5.77) and OS (HR, 5.14; 95% CI, 1.55 to 17.01). Patients with ΔSUVmax ≥ 50% (n=87) showed better DFS and OS than those with ΔSUVmax < 50% (n=29) (DFS, 76% vs. 35%, p < 0.001; OS, 90% vs. 41%, p < 0.001, respectively). Adenocarcinoma was frequently observed in ΔSUVmax < 50% compared to ΔSUVmax ≥ 50% (27.6% vs. 10.3%, p=0.003). In addition, models incorporating metabolic parameters showed improved accuracy for predicting DFS (p=0.012) and OS (p=0.004) than models with clinicopathologic factors. Conclusion Changes in metabolic parameters, especially those in SUVmax by > 50%, can help improve survival outcome predictions for patients with cervical cancer treated with definitive RT.
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Affiliation(s)
- Nalee Kim
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Won Park
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Won Kyung Cho
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Duk-Soo Bae
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Byoung-Gie Kim
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeong-Won Lee
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Tae-Joong Kim
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Chel Hun Choi
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yoo-Young Lee
- Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Young Seok Cho
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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