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Kisohara M, Hiwatashi A. Editorial for "MR Diffusion Kurtosis Imaging (DKI) of the Normal Human Uterus in Vivo During the Menstrual Cycle". J Magn Reson Imaging 2024; 60:481-482. [PMID: 38014864 DOI: 10.1002/jmri.29151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 11/29/2023] Open
Affiliation(s)
- Masaya Kisohara
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Akio Hiwatashi
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
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Li Y, Chen Y, Fu C, Li Q, Liu H, Zhang Q. MR Diffusion Kurtosis Imaging (DKI) of the Normal Human Uterus in Vivo During the Menstrual Cycle. J Magn Reson Imaging 2024; 60:471-480. [PMID: 37994206 DOI: 10.1002/jmri.29153] [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: 08/02/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 11/24/2023] Open
Abstract
BACKGROUND The uterus undergoes dynamic changes throughout the menstrual cycle. Diffusion kurtosis imaging (DKI) is based on the non-Gaussian distribution of water molecules and can perhaps represent the changes of uterine microstructure. PURPOSE To investigate the temporal changes in DKI-parameters of the normal uterine corpus and cervix during the menstrual cycle. STUDY TYPE Prospective. POPULATION 21 healthy female volunteers (26.64 ± 4.72 years) with regular menstrual cycles (28 ± 7 days). FIELD STRENGTH/SEQUENCE Readout segmentation of long variable echo-trains (RESOLVE)-based DKI and fast spin-echo T2-weighted sequences at 3.0T. ASSESSMENT Each volunteer was scanned during the menstrual phase, ovulatory phase, and luteal phase. Regions of interest (ROI) were manually delineated in the endometrium, junctional zone, and myometrium of the uterine body, and in the mucosal layer, fibrous stroma layer, and loose stroma layer of the cervix. The mean Kapp (diffusion kurtosis coefficient), Dapp (diffusion coefficient), and ADC (apparent diffusion coefficient) values were measured in the ROI. STATISTICAL TESTS ANOVA with Bonferroni or Tamhane correction. Intraclass correlation coefficient (ICC) for assessing agreement. P < 0.05 was considered statistically significant. RESULTS During the menstrual cycle, the highest Kapp (0.848 ± 0.184) and lowest Dapp (1.263 ± 0.283 *10-3 mm2/sec) values were found in the endometrium during the menstrual phase. The Dapp values for the myometrium were significantly higher than those of the endometrium and the junctional zone in every phase. Meanwhile, the Dapp values for the three zonal structures of the cervix during ovulation were significantly higher than those during the luteal phase. However, there was no significant difference in the ADC values of the loose stroma between ovulation and the luteal phase (P = 0.568). The reproducibility of DKI parameters was good (ICC, 0.857-0.944). DATA CONCLUSION DKI can show dynamic changes of the normal uterus during the menstrual cycle. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yajie Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Ye Chen
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Qing Li
- MR Collaborations, Siemens Healthineers Digital Technology (Shanghai) Co., Ltd., Shanghai, China
| | - Hanqiu Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Qi Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
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Zhong S, Ai C, Ding Y, Tan J, Jin Y, Wang H, Zhang H, Li M, Zhu R, Gu S, Zhang Y. Combining multimodal diffusion-weighted imaging and morphological parameters for detecting lymph node metastasis in cervical cancer. Abdom Radiol (NY) 2024:10.1007/s00261-024-04494-3. [PMID: 38990301 DOI: 10.1007/s00261-024-04494-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 07/05/2024] [Accepted: 07/06/2024] [Indexed: 07/12/2024]
Abstract
BACKGROUND Accurate detection of lymph node metastasis (LNM) is crucial for determining the tumor stage, selecting optimal treatment, and estimating the prognosis for cervical cancer. This study aimed to assess the diagnostic efficacy of multimodal diffusion-weighted imaging (DWI) and morphological parameters alone or in combination, for detecting LNM in cervical cancer. METHODS In this prospective study, we enrolled consecutive cervical cancer patients who received multimodal DWI (conventional DWI, intravoxel incoherent motion DWI, and diffusion kurtosis imaging) before treatment from June 2022 to June 2023. The largest lymph node (LN) observed on each side on imaging was matched with that detected on pathology to improve the accuracy of LN matching. Comparison of the diffusion and morphological parameters of LNs and the primary tumor between the positive and negative LN groups. A combined diagnostic model was constructed using multivariate logistic regression, and the diagnostic performance was evaluated using receiver operating characteristic curves. RESULTS A total of 93 cervical cancer patients were enrolled: 35 with LNM (48 positive LNs were collected), and 58 without LNM (116 negative LNs were collected). The area under the curve (AUC) values for the apparent diffusion coefficient, diffusion coefficient, mean diffusivity, mean kurtosis, long-axis diameter, short-axis diameter of LNs, and the largest primary tumor diameter were 0.716, 0.720, 0.716, 0.723, 0.726, 0.798, and 0.744, respectively. Independent risk factors included the diffusion coefficient, mean kurtosis, short-axis diameter of LNs, and the largest primary tumor diameter. The AUC value of the combined model based on the independent risk factors was 0.920, superior to the AUC values of all the parameters mentioned above. CONCLUSION Combining multimodal DWI and morphological parameters improved the diagnostic efficacy for detecting cervical cancer LNM than using either alone.
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Affiliation(s)
- Suixing Zhong
- Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China
| | - Conghui Ai
- Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China
| | - Yingying Ding
- Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China
| | - Jing Tan
- Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China
| | - Yan Jin
- Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China
| | - Hongbo Wang
- Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China
| | - Huimei Zhang
- Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China
| | - Miaomiao Li
- Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China
| | - Rong Zhu
- Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China
| | - Shangwei Gu
- Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China
| | - Ya Zhang
- Department of Radiology, Yunnan Cancer Hospital, Third Affiliated Hospital of Kunming Medical University, No. 519, Kunzhou Road, Xishan District, Kunming, 650118, China.
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Zheng X, Shen F, Chen W, Ren W, Tang S. Integrated pretreatment diffusion kurtosis imaging and serum squamous cell carcinoma antigen levels: a biomarker strategy for early assessment of radiotherapy outcomes in cervical cancer. Abdom Radiol (NY) 2024; 49:1502-1511. [PMID: 38536425 DOI: 10.1007/s00261-024-04270-3] [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: 12/03/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 05/22/2024]
Abstract
OBJECTIVE This study aims to explore the utility of pretreatment DKI parameters and serum SCC-Ag in evaluating the early therapeutic response of cervical cancer to radiotherapy. MATERIALS AND METHODS A total of 33 patients diagnosed with cervical cancer, including 31 cases of cervical squamous cell carcinoma and two cases of adenosquamous carcinoma, participated in the study. All patients underwent conventional MRI and DKI scans on a 3T magnetic resonance scanner before radiotherapy and after ten sessions of radiotherapy. The therapeutic response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Patients were categorized into a response group (RG), comprising Complete Remission (CR) and Partial Remission (PR), and a non-response group (NRG), comprising Stable Disease (SD) and Progressive Disease (PD). LASSO was employed to select pretreatment DKI parameters, and ROC curves were generated for the selected parameters and serum SCC-Ag. RESULTS Significant differences were observed in pretreatment MD, Da, Dr, MK, Ka, Kr, and SCC-Ag between the RG and NRG groups (P < 0.01). However, no significant differences were noted for FA and FAK (P = 0.441&0.928). The two selected parameters (MD and MK) demonstrated area under the curve (AUC), sensitivity, and specificity of 0.810, 0.769, 0.850 and 0.827, 0.846, 0.750, respectively. The combination of MD and MK exhibited an improved AUC of 0.901, sensitivity of 0.692, and specificity of 1.000, with a higher Youden index compared to the individual parameters. Conversely, the AUC, sensitivity, and specificity of the combination of MD, MK, and SCC-Ag were 0.852, 0.615, and 1.000, with a Youden index of 0.615. CONCLUSION Pretreatment MD, MK, and SCC-Ag demonstrate potential clinical utility, with the combined application of MD and MK showing enhanced efficacy in assessing the early therapeutic response of cervical cancer to radiotherapy. The addition of SCC-Ag did not contribute further to the assessment efficacy.
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Affiliation(s)
- Xiang Zheng
- Department of Radiologic Diagnosis, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420 Fuma Road, Fuzhou, 350014, Fujian, China.
| | - Fangmin Shen
- Department of Radiologic Diagnosis, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420 Fuma Road, Fuzhou, 350014, Fujian, China
| | - Wenjuan Chen
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Wang Ren
- Department of Radiologic Diagnosis, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No. 420 Fuma Road, Fuzhou, 350014, Fujian, China
| | - Shaoliang Tang
- School of Medical Imaging, Fujian Medical University, Fuzhou, 350122, China
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Xu Q, Song Q, Wang Y, Lin L, Tian S, Wang N, Wang J, Liu A. Amide proton transfer weighted combined with diffusion kurtosis imaging for predicting lymph node metastasis in cervical cancer. Magn Reson Imaging 2024; 106:85-90. [PMID: 38101652 DOI: 10.1016/j.mri.2023.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/07/2023] [Accepted: 12/10/2023] [Indexed: 12/17/2023]
Abstract
OBJECTIVE To investigate the value of amide proton transfer weighted (APTw) combined with diffusion kurtosis imaging (DKI) in quantitative prediction of lymph node metastasis (LNM) in cervical carcinoma (CC). METHODS Data of 19 LNM(+) and 50 LNM(-) patients with CC were retrospectively analyzed. 3.0 T MRI scan was performed before the operation, including APTw and DKI. After post-processing, quantitative magnetization transfer ratio asymmetric at 3.5 ppm [MTRasym (3.5 ppm)], mean kurtosis (MK), and mean diffusivity (MD) maps were obtained. The MTRasym(3.5 ppm), MK, and MD values were respectively measured by two observers, and intra-class correlation coefficients (ICC) were used to test the consistency of the results. The independent samples t-test or Mann-Whitney U test was used to compare the differences in the values of each parameter. The ROC curve was used to analyze the predictive performance of parameters with significant differences and their combination parameter. RESULTS The two observers had good agreement in the measurement of each data (ICC > 0.75). The MTRasym(3.5 ppm) and MK values of the LNM(+) group(3.260 ± 0.538% and 0.531 ± 0.202) were higher than those of the LNM(-) group(2.698 ± 0.597% and 0.401 ± 0.148) (P < 0.05), while there was no significant difference in MD values between the two groups(P > 0.05). The area under the curves (AUCs) of MTRasym(3.5 ppm), MK value, and MTRasym(3.5 ppm) + MK value were 0.763, 0.716, and 0.813, respectively, when predicting LNM status of CC. CONCLUSION APTw and DKI can quantitatively predict LNM status of CC, which is of importance in clinical diagnosis and treatment.
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Affiliation(s)
- Qihao Xu
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian,China
| | - Qingling Song
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian,China
| | - Yue Wang
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian,China
| | - Liangjie Lin
- Clinical and Technical Support, Philips Healthcare, Beijing, China
| | - Shifeng Tian
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian,China
| | - Nan Wang
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian,China
| | - Jiazheng Wang
- Clinical and Technical Support, Philips Healthcare, Beijing, China
| | - Ailian Liu
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian,China; Dalian Medical Imaging Artificial Intelligence Engineering Technology Research Center, Dalian, China.
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Zhang Z, Liu J, Zhang Y, Qu F, Grimm R, Cheng J, Wang W, Zhu J, Li S. T1 mapping as a quantitative imaging biomarker for diagnosing cervical cancer: a comparison with diffusion kurtosis imaging. BMC Med Imaging 2024; 24:16. [PMID: 38200447 PMCID: PMC10782683 DOI: 10.1186/s12880-024-01191-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 01/01/2024] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND T1 mapping can potentially quantitatively assess the intrinsic properties of tumors. This study was conducted to explore the ability of T1 mapping in distinguishing cervical cancer type, grade, and stage and compare the diagnostic performance of T1 mapping with diffusion kurtosis imaging (DKI). METHODS One hundred fifty-seven patients with pathologically confirmed cervical cancer were enrolled in this prospectively study. T1 mapping and DKI were performed. The native T1, difference between native and postcontrast T1 (T1diff), mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were calculated. Cervical squamous cell carcinoma (CSCC) and adenocarcinoma (CAC), low- and high-grade carcinomas, and early- and advanced-stage groups were compared using area under the receiver operating characteristic (AUROC) curves. RESULTS The native T1 and MK were higher, and the MD and ADC were lower for CSCC than for CAC (all p < 0.05). Compared with low-grade CSCC, high-grade CSCC had decreased T1diff, MD, ADC, and increased MK (p < 0.05). Compared with low-grade CAC, high-grade CAC had decreased T1diff and increased MK (p < 0.05). Native T1 was significantly higher in the advanced-stage group than in the early-stage group (p < 0.05). The AUROC curves of native T1, MK, ADC and MD were 0,772, 0.731, 0.715, and 0.627, respectively, for distinguishing CSCC from CAC. The AUROC values were 0.762 between high- and low-grade CSCC and 0.835 between high- and low-grade CAC, with T1diff and MK showing the best discriminative values, respectively. For distinguishing between advanced-stage and early-stage cervical cancer, only the AUROC of native T1 was statistically significant (AUROC = 0.651, p = 0.002). CONCLUSIONS Compared with DKI-derived parameters, native T1 exhibits better efficacy for identifying cervical cancer subtype and stage, and T1diff exhibits comparable discriminative value for cervical cancer grade.
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Affiliation(s)
- Zanxia Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, 450052, Zhengzhou, Henan, China
| | - Jie Liu
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, 450052, Zhengzhou, Henan, China
| | - Yong Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, 450052, Zhengzhou, Henan, China
| | - Feifei Qu
- MR Collaboration, Siemens Healthcare Ltd, Beijing, China
| | - Robert Grimm
- MR Application, Siemens Healthcare GmbH, Predevelopment, Erlangen, Germany
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, 450052, Zhengzhou, Henan, China
| | - Weijian Wang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, 450052, Zhengzhou, Henan, China
| | - Jinxia Zhu
- MR Collaboration, Siemens Healthcare Ltd, Beijing, China
| | - Shujian Li
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Dong Road, 450052, Zhengzhou, Henan, China.
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Amide Proton Transfer-Weighted Imaging Combined with ZOOMit Diffusion Kurtosis Imaging in Predicting Lymph Node Metastasis of Cervical Cancer. Bioengineering (Basel) 2023; 10:bioengineering10030331. [PMID: 36978722 PMCID: PMC10045132 DOI: 10.3390/bioengineering10030331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/23/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Background: The aim of this study is to investigate the feasibility of amide proton transfer-weighted (APTw) imaging combined with ZOOMit diffusion kurtosis imaging (DKI) in predicting lymph node metastasis (LNM) in cervical cancer (CC). Materials and Methods: Sixty-one participants with pathologically confirmed CC were included in this retrospective study. The APTw MRI and ZOOMit diffusion-weighted imaging (DWI) were acquired. The mean values of APTw and DKI parameters including mean kurtosis (MK) and mean diffusivity (MD) of the primary tumors were calculated. The parameters were compared between the LNM and non-LNM groups using the Student’s t-test or Mann–Whitney U test. Binary logistic regression analysis was performed to determine the association between the LNM status and the risk factors. The diagnostic performance of these quantitative parameters and their combinations for predicting the LNM was assessed with receiver operating characteristic (ROC) curve analysis. Results: Patients were divided into the LNM group (n = 17) and the non-LNM group (n = 44). The LNM group presented significantly higher APTw (3.7 ± 1.1% vs. 2.4 ± 1.0%, p < 0.001), MK (1.065 ± 0.185 vs. 0.909 ± 0.189, p = 0.005) and lower MD (0.989 ± 0.195 × 10−3 mm2/s vs. 1.193 ± 0.337 ×10−3 mm2/s, p = 0.035) than the non-LNM group. APTw was an independent predictor (OR = 3.115, p = 0.039) for evaluating the lymph node status through multivariate analysis. The area under the curve (AUC) of APTw (0.807) was higher than those of MK (AUC, 0.715) and MD (AUC, 0.675) for discriminating LNM from non-LNM, but the differences were not significant (all p > 0.05). Moreover, the combination of APTw, MK, and MD yielded the highest AUC (0.864), with the corresponding sensitivity of 76.5% and specificity of 88.6%. Conclusion: APTw and ZOOMit DKI parameters may serve as potential noninvasive biomarkers in predicting LNM of CC.
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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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Wang M, Perucho JA, Vardhanabhuti V, Ip P, Ngan HY, Lee EY. Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma. Acad Radiol 2021; 29:1133-1140. [PMID: 34583867 DOI: 10.1016/j.acra.2021.08.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 07/28/2021] [Accepted: 08/12/2021] [Indexed: 01/06/2023]
Abstract
RATIONALE AND OBJECTIVES Clinicopathological characteristics including histological subtypes, tumour grades and International Federation of Gynecology and Obstetrics (FIGO) stages are crucial factors in the clinical decision for cervical carcinoma (CC). The purpose of this study was to evaluate the ability of T2-weighted imaging (T2WI) and diffusion kurtosis imaging (DKI) radiomics in differentiating clinicopathological characteristics of CC. MATERIALS AND METHODS One hundred and seventeen histologically confirmed CC patients (mean age 56.5 ± 14.0 years) with pre-treatment magnetic resonance imaging were retrospectively reviewed. DKI was acquired with 4 b-values (0-1500 s/mm2). Volumes of interest were contoured around the tumours on T2WI and DKI. Radiomic features including shape, first-order and grey-level co-occurrence matrix with wavelet transforms were extracted. Intraclass correlation coeffient between 2 radiologists was used for features reduction. Feature selection was achieved by elastic net and minimum redundancy maximum relevance. Selected features were used to build random forest (RF) models. The performances for differentiating histological subtypes, tumour grades and FIGO stages were assessed by receiver operating characteristic analysis. RESULTS Area under the curves (AUCs) for T2WI-only RF models for discriminating histological subtypes, tumour grades and FIGO stages were 0.762, 0.686, and 0.719. AUCs for DWI-only models were 0.663, 0.645, and 0.868, respectively. AUCs of the combined T2WI and DKI models were 0.823, 0.790, and 0.850, respectively. CONCLUSION T2WI and DKI radiomic features could differentiate the clinicopathological characteristics of CC. A combined model showed excellent diagnostic discrimination for histological subtypes, while a DKI-only model presented the best performance in differentiating FIGO stages.
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