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Ma Z, Zhang J, Liu X, Teng X, Huang YH, Zhang X, Li J, Pan Y, Sun J, Dong Y, Li T, Chan LWC, Chang ATY, Siu SWK, Cheung ALY, Yang R, Cai J. Comparative Analysis of Repeatability in CT Radiomics and Dosiomics Features under Image Perturbation: A Study in Cervical Cancer Patients. Cancers (Basel) 2024; 16:2872. [PMID: 39199643 DOI: 10.3390/cancers16162872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 08/12/2024] [Accepted: 08/16/2024] [Indexed: 09/01/2024] Open
Abstract
This study aims to evaluate the repeatability of radiomics and dosiomics features via image perturbation of patients with cervical cancer. A total of 304 cervical cancer patients with planning CT images and dose maps were retrospectively included. Random translation, rotation, and contour randomization were applied to CT images and dose maps before radiomics feature extraction. The repeatability of radiomics and dosiomics features was assessed using intra-class correlation of coefficient (ICC). Pearson correlation coefficient (r) was adopted to quantify the correlation between the image characteristics and feature repeatability. In general, the repeatability of dosiomics features was lower compared with CT radiomics features, especially after small-sigma Laplacian-of-Gaussian (LoG) and wavelet filtering. More repeatable features (ICC > 0.9) were observed when extracted from the original, Large-sigma LoG filtered, and LLL-/LLH-wavelet filtered images. Positive correlations were found between image entropy and high-repeatable feature number in both CT and dose (r = 0.56, 0.68). Radiomics features showed higher repeatability compared to dosiomics features. These findings highlight the potential of radiomics features for robust quantitative imaging analysis in cervical cancer patients, while suggesting the need for further refinement of dosiomics approaches to enhance their repeatability.
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Affiliation(s)
- Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xi Liu
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China
- School of Physics, Beihang University, Beijing 102206, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xile Zhang
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China
| | - Jun Li
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China
| | - Yuxi Pan
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China
| | - Jiachen Sun
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yanjing Dong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Lawrence Wing Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Amy Tien Yee Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | | | - Andy Lai-Yin Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Clinical Oncology, St. Paul's Hospital, Hong Kong, China
| | - Ruijie Yang
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Kawaguchi M, Kato H, Koie T, Noda Y, Hyodo F, Miyazaki T, Matsuo M. CT and MRI findings of small cell neuroendocrine carcinoma of the urinary bladder: comparison with urothelial carcinoma. Abdom Radiol (NY) 2024; 49:2672-2682. [PMID: 38584191 PMCID: PMC11300546 DOI: 10.1007/s00261-024-04274-z] [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: 12/20/2023] [Revised: 02/27/2024] [Accepted: 03/01/2024] [Indexed: 04/09/2024]
Abstract
OBJECTIVE This study aimed to evaluate the efficacy of CT and MRI findings to differentiate small cell neuroendocrine carcinoma (SCNEC) from urothelial carcinoma (UC) of the urinary bladder. MATERIALS AND METHODS This study included 90 patients with histopathologically confirmed bladder cancer (10 SCNECs and 80 UCs). Eight patients with bladder SCNEC and 80 with UC underwent CT and MRI, whereas the remaining two patients with SCNEC underwent CT alone before treatment. CT and MRI findings were retrospectively evaluated and compared between the two pathologies. RESULTS The maximum diameter (36.5 mm vs. 19.0 mm, p < 0.01) and height (22.0 mm vs. 14.0 mm, p < 0.01) of the tumor in bladder SCNEC were higher than in UC. The pedunculated configuration (20% vs. 61%, p < 0.05) and irregular tumor margins (20% vs. 76%, p < 0.01) in bladder SCNEC were less common than in UC. The CT attenuation of the solid component in unenhanced CT images was higher in bladder SCNEC than in UC (37 Hounsfield unit [HU] vs. 34 HU, p < 0.01). The apparent diffusion coefficient (ADC) of the solid component in bladder SCNEC was lower than in UC (0.49 × 10-3 mm2/s vs. 1.02 × 10-3 mm2/s, p < 0.01). CONCLUSION In comparison with UC, bladder SCNEC was larger, had higher unenhanced CT attenuation, and had a lower ADC value. The pedunculated configuration and irregular tumor margins were typical of bladder UC.
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Affiliation(s)
- Masaya Kawaguchi
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
- Department of Radiology, Ogaki Municipal Hospital, 4-86 Minaminokawacho, Ogaki, 503-0864, Japan.
| | - Hiroki Kato
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Takuya Koie
- Department of Urology, Gifu University, Gifu, Japan
| | - Yoshifumi Noda
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Fuminori Hyodo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
- Center for One Medicine Innovative Translational Research (COMIT), Institute for Advanced Study, Gifu University, Gifu, Japan
| | | | - Masayuki Matsuo
- Department of Radiology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan
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Wu L, Li S, Li S, Lin Y, Wei D. Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis. Front Oncol 2024; 14:1416378. [PMID: 39026971 PMCID: PMC11254676 DOI: 10.3389/fonc.2024.1416378] [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: 04/12/2024] [Accepted: 05/27/2024] [Indexed: 07/20/2024] Open
Abstract
Background The purpose of this systematic review and meta-analysis is to evaluate the potential significance of radiomics, derived from preoperative magnetic resonance imaging (MRI), in detecting deep stromal invasion (DOI), lymphatic vascular space invasion (LVSI) and lymph node metastasis (LNM) in cervical cancer (CC). Methods A rigorous and systematic evaluation was conducted on radiomics studies pertaining to CC, published in the PubMed database prior to March 2024. The area under the curve (AUC), sensitivity, and specificity of each study were separately extracted to evaluate the performance of preoperative MRI radiomics in predicting DOI, LVSI, and LNM of CC. Results A total of 4, 7, and 12 studies were included in the meta-analysis of DOI, LVSI, and LNM, respectively. The overall AUC, sensitivity, and specificity of preoperative MRI models in predicting DOI, LVSI, and LNM were 0.90, 0.83 (95% confidence interval [CI], 0.75-0.89) and 0.83 (95% CI, 0.74-0.90); 0.85, 0.80 (95% CI, 0.73-0.86) and 0.75 (95% CI, 0.66-0.82); 0.86, 0.79 (95% CI, 0.74-0.83) and 0.80 (95% CI, 0.77-0.83), respectively. Conclusion MRI radiomics has demonstrated considerable potential in predicting DOI, LVSI, and LNM in CC, positioning it as a valuable tool for preoperative precision evaluation in CC patients.
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Affiliation(s)
| | | | | | | | - Dayou Wei
- Department of Medical Ultrasound, Maoming People’s Hospital, Maoming, Guangdong, China
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Aouadi S, Torfeh T, Bouhali O, Yoganathan SA, Paloor S, Chandramouli S, Hammoud R, Al-Hammadi N. Prediction of cervix cancer stage and grade from diffusion weighted imaging using EfficientNet. Biomed Phys Eng Express 2024; 10:045042. [PMID: 38815562 DOI: 10.1088/2057-1976/ad5207] [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: 01/15/2024] [Accepted: 05/30/2024] [Indexed: 06/01/2024]
Abstract
Purpose. This study aims to introduce an innovative noninvasive method that leverages a single image for both grading and staging prediction. The grade and the stage of cervix cancer (CC) are determined from diffusion-weighted imaging (DWI) in particular apparent diffusion coefficient (ADC) maps using deep convolutional neural networks (DCNN).Methods. datasets composed of 85 patients having annotated tumor stage (I, II, III, and IV), out of this, 66 were with grade (II and III) and the remaining patients with no reported grade were retrospectively collected. The study was IRB approved. For each patient, sagittal and axial slices containing the gross tumor volume (GTV) were extracted from ADC maps. These were computed using the mono exponential model from diffusion weighted images (b-values = 0, 100, 1000) that were acquired prior to radiotherapy treatment. Balanced training sets were created using the Synthetic Minority Oversampling Technique (SMOTE) and fed to the DCNN. EfficientNetB0 and EfficientNetB3 were transferred from the ImageNet application to binary and four-class classification tasks. Five-fold stratified cross validation was performed for the assessment of the networks. Multiple evaluation metrics were computed including the area under the receiver operating characteristic curve (AUC). Comparisons with Resnet50, Xception, and radiomic analysis were performed.Results. for grade prediction, EfficientNetB3 gave the best performance with AUC = 0.924. For stage prediction, EfficientNetB0 was the best with AUC = 0.931. The difference between both models was, however, small and not statistically significant EfficientNetB0-B3 outperformed ResNet50 (AUC = 0.71) and Xception (AUC = 0.89) in stage prediction, and demonstrated comparable results in grade classification, where AUCs of 0.89 and 0.90 were achieved by ResNet50 and Xception, respectively. DCNN outperformed radiomic analysis that gave AUC = 0.67 (grade) and AUC = 0.66 (stage).Conclusion.the prediction of CC grade and stage from ADC maps is feasible by adapting EfficientNet approaches to the medical context.
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Affiliation(s)
- Souha Aouadi
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Tarraf Torfeh
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Othmane Bouhali
- Department of Science, Texas A&M University at Qatar, PO Box 23874, Education City, Doha, Qatar
| | - S A Yoganathan
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Satheesh Paloor
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Suparna Chandramouli
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Rabih Hammoud
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Noora Al-Hammadi
- Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
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Qian WL, Chen Q, Zhang JB, Xu JM, Hu CH. RESOLVE-based radiomics in cervical cancer: improved image quality means better feature reproducibility? Clin Radiol 2023; 78:e469-e476. [PMID: 37029000 DOI: 10.1016/j.crad.2023.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 04/07/2023]
Abstract
AIM To compare the reproducibility of apparent diffusion coefficient (ADC)-based radiomic features between readout-segmented echo-planar diffusion-weighted imaging (RESOLVE) and single-shot echo-planar diffusion-weighted imaging (SS-EPI DWI) in cervical cancer. MATERIALS AND METHODS The RESOLVE and SS-EPI DWI images of 36 patients with histopathologically confirmed cervical cancer were collected retrospectively. Two observers independently delineated the whole tumour on RESOLVE and SS-EPI DWI, and then copied them to the corresponding ADC maps. Shape, first-order, and texture features were extracted from ADC maps in the original and filtered (Laplacian of Gaussian [LoG] and wavelet) images. Thereafter, 1,316 features were generated in each RESOLVE and SS-EPI DWI, respectively. The reproducibility of radiomic features was assessed using intraclass correlation coefficient (ICC). RESULTS In the original images, RESOLVE showed 92.86%, 66.67%, and 86.67% of features with excellent reproducibility in shape, first-order, and texture features, while SS-EPI DWI showed 85.71%, 72.22%, and 60% of features with excellent reproducibility, respectively. In the LoG and wavelet filtered images, RESOLVE had 56.77% and 65.32% of features with excellent reproducibility and SS-EPI DWI had 44.95% and 61.96% of features with excellent reproducibility, respectively. CONCLUSION Compared with SS-EPI DWI, the feature reproducibility of RESOLVE was better in cervical cancer, especially for texture features. The filtered images cannot improve the feature reproducibility compared with the original images for both SS-EPI DWI and RESOLVE.
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Zhu HB, Xu D, Sun XF, Li XT, Zhang XY, Wang K, Xing BC, Sun YS. Prediction of hepatic lymph node metastases based on magnetic resonance imaging before and after preoperative chemotherapy in patients with colorectal liver metastases underwent surgical resection. Cancer Imaging 2023; 23:18. [PMID: 36810192 PMCID: PMC9942330 DOI: 10.1186/s40644-023-00529-y] [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: 07/16/2022] [Accepted: 01/30/2023] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND Patients with colorectal liver metastases (CRLM) combined with hepatic lymph node (HLN) metastases have a poor prognosis. In this study, we developed and validated a model using clinical and magnetic resonance imaging (MRI) parameters to predict HLN status before surgery. METHODS A total of 104 CRLM patients undergoing hepatic lymphonodectomy with pathologically confirmed HLN status after preoperative chemotherapy were enrolled in this study. The patients were further divided into a training group (n = 52) and a validation group (n = 52). The apparent diffusion coefficient (ADC) values, including ADCmean and ADCmin of the largest HLN before and after treatment, were measured. rADC was calculated referring to the target liver metastases, spleen, and psoas major muscle (rADC-LM, rADC-SP, rADC-m). In addition, ADC change rate (Δ% ADC) was quantitatively calculated. A multivariate logistic regression model for predicting HLN status in CRLM patients was constructed using the training group and further tested in the validation group. RESULTS In the training cohort, post-ADCmean (P = 0.018) and the short diameter of the largest lymph node after treatment (P = 0.001) were independent predictors for metastatic HLN in CRLM patients. The model's AUC was 0.859 (95% CI, 0.757-0.961) and 0.767 (95% CI 0.634-0.900) in the training and validation cohorts, respectively. Patients with metastatic HLN showed significantly worse overall survival (p = 0.035) and recurrence-free survival (p = 0.015) than patients with negative HLN. CONCLUSIONS The developed model using MRI parameters could accurately predict HLN metastases in CRLM patients and could be used to preoperatively assess the HLN status and facilitate surgical treatment decisions in patients with CRLM.
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Affiliation(s)
- Hai-bin Zhu
- grid.412474.00000 0001 0027 0586Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142 China
| | - Da Xu
- grid.412474.00000 0001 0027 0586Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142 China
| | - Xue-Feng Sun
- grid.412474.00000 0001 0027 0586Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142 China
| | - Xiao-Ting Li
- grid.412474.00000 0001 0027 0586Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142 China
| | - Xiao-Yan Zhang
- grid.412474.00000 0001 0027 0586Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142 China
| | - Kun Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
| | - Bao-Cai Xing
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
| | - Ying-Shi Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
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Piao S, Luo X, Bao Y, Hu B, Liu X, Zhu Y, Yang L, Geng D, Li Y. An MRI-based joint model of radiomics and spatial distribution differentiates autoimmune encephalitis from low-grade diffuse astrocytoma. Front Neurol 2022; 13:998279. [PMID: 36408523 PMCID: PMC9669344 DOI: 10.3389/fneur.2022.998279] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/12/2022] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND The differential diagnosis between autoimmune encephalitis and low-grade diffuse astrocytoma remains challenging. We aim to develop a quantitative model integrating radiomics and spatial distribution features derived from MRI for discriminating these two conditions. METHODS In our study, we included 188 patients with confirmed autoimmune encephalitis (n = 81) and WHO grade II diffuse astrocytoma (n = 107). Patients with autoimmune encephalitis (AE, n = 59) and WHO grade II diffuse astrocytoma (AS, n = 79) were divided into training and test sets, using stratified sampling according to MRI scanners. We further included an independent validation set (22 patients with AE and 28 patients with AS). Hyperintensity fluid-attenuated inversion recovery (FLAIR) lesions were segmented for each subject. Ten radiomics and eight spatial distribution features were selected via the least absolute shrinkage and selection operator (LASSO), and joint models were constructed by logistic regression for disease classification. Model performance was measured in the test set using the area under the receiver operating characteristic (ROC) curve (AUC). The discrimination performance of the joint model was compared with neuroradiologists. RESULTS The joint model achieved better performance (AUC 0.957/0.908, accuracy 0.914/0.840 for test and independent validation sets, respectively) than the radiomics and spatial distribution models. The joint model achieved lower performance than a senior neuroradiologist (AUC 0.917/0.875) but higher performance than a junior neuroradiologist (AUC 0.692/0.745) in the test and independent validation sets. CONCLUSION The joint model of radiomics and spatial distribution from a single FLAIR could effectively classify AE and AS, providing clinical decision support for the differential diagnosis between the two conditions.
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Affiliation(s)
- Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yifang Bao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Bin Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xueling Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yuqi Zhu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Liqin Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
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Ren J, Li Y, Liu XY, Zhao J, He YL, Jin ZY, Xue HD. Diagnostic performance of ADC values and MRI-based radiomics analysis for detecting lymph node metastasis in patients with cervical cancer: A systematic review and meta-analysis. Eur J Radiol 2022; 156:110504. [PMID: 36108474 DOI: 10.1016/j.ejrad.2022.110504] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/16/2022] [Accepted: 08/25/2022] [Indexed: 11/21/2022]
Abstract
OBJECTIVE To evaluate and compare the diagnostic performance of apparent diffusion coefficient (ADC) values and MRI-based radiomics analysis for lymph node metastasis (LNM) detection in patients with cervical cancer (CC). METHODS We searched relevant databases for studies on ADC values and MRI-based radiomics analysis for LNM detection in CC between January 2001 and December 2021. Methodological quality assessment of risk of bias using Quality Assessment of Diagnostic Accuracy Studies 2 and radiomics quality score (RQS) of the studies was conducted. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated. Diagnostic performance was compared between the two quantitative analyses using a two-sample Z-test. RESULTS In total, 22 studies including 2314 patients were included. Unclear risk of bias was observed in 4.5-36.4% of the studies. The 8 radiomics studies exhibited a median (interquartile range) RQS of 13.5 (5.5-15.75). The pooled sensitivity, specificity, LR+, LR-, DOR, and AUC of the ADC values vs radiomics analysis were 0.86 vs 0.84, 0.85 vs 0.73, 5.7 vs 3.1, 0.17 vs 0.22, 34 vs 14, and 0.91 vs 0.86, respectively. There was no threshold effect or publication bias, but significant heterogeneity existed among the studies. No significant difference was detected in the diagnostic performance of the two quantitative analyses using the Z-test. CONCLUSION ADC values are more clinically promising because they are more easily accessible and widely applied, and exhibit a non-statistically significant trend to outperform radiomics analysis.
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Affiliation(s)
- Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, PR China.
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Jia Zhao
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
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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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Zhang X, Zhang Q, Xie L, An J, Wang S, Yu X, Zhao X. The Value of Whole-Tumor Texture Analysis of ADC in Predicting the Early Recurrence of Locally Advanced Cervical Squamous Cell Cancer Treated With Concurrent Chemoradiotherapy. Front Oncol 2022; 12:852308. [PMID: 35669419 PMCID: PMC9165468 DOI: 10.3389/fonc.2022.852308] [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: 01/11/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives To investigate the value of whole-tumor texture analysis of apparent diffusion coefficient (ADC) map in predicting the early recurrence of patients with locally advanced cervical squamous cell cancer (LACSC) treated with concurrent chemoradiotherapy (CCRT) and establish a combined prediction model including clinical variables and first-order texture features. Methods In total, 219 patients (training: n = 153; testing: n = 66) with stage IIB-IVA LACSC treated by CCRT between January 2014 and December 2019 were retrospectively enrolled in this study. Clinical variables and 22 first-order texture features extracted from ADC map were collected. The Mann-Whitney U test or independent sample t test, chi-square test or Fisher’s exact were used to analyze statistically significant parameters, logistic regression analysis was used for multivariate analysis, and receiver operating characteristic analysis was used to compare the diagnostic performance. Results In the clinical variables, T stage and lymph node metastasis (LNM) were independent risk factors, and the areas under the curve (AUCs) of the clinical model were 0.697 and 0.667 in the training and testing cohorts, the sensitivity and specificity were 48.8% and 85.5% in the training cohort, and 84.1% and 51.1% in the testing cohort, respectively. In the first-order texture features, mean absolute deviation (MAD) was the independent protective factor, with an AUC of 0.756 in the training cohort and 0.783 in the testing cohort. The sensitivity and specificity were 95.3% and 52.7% in the training cohort and 94.7% and 53.2% in the testing cohort, respectively. The combined model (MAD, T stage, and LNM) was established, it exhibited the highest AUC of 0.804 in the training cohort and 0.821 in the testing cohort, which was significantly higher than the AUC of the clinical prediction model. The sensitivity and specificity were 67.4% and 85.5% in the training cohort and 94.7% and 70.2% in the testing cohort, respectively. Conclusions The first-order texture features of the ADC map could be used along with clinical predictive biomarkers to predict early recurrence in patients with LACSC treated by CCRT.
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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, 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, China
| | - Lizhi Xie
- GE Healthcare, MR Research, Beijing, 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, 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, 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, China
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You J, Yin J. Performances of Whole Tumor Texture Analysis Based on MRI: Predicting Preoperative T Stage of Rectal Carcinomas. Front Oncol 2021; 11:678441. [PMID: 34414105 PMCID: PMC8369414 DOI: 10.3389/fonc.2021.678441] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 07/19/2021] [Indexed: 12/29/2022] Open
Abstract
Objective To determine whether there is a correlation between texture features extracted from high-resolution T2-weighted imaging (HR-T2WI) or apparent diffusion coefficient (ADC) maps and the preoperative T stage (stages T1–2 versus T3–4) in rectal carcinomas. Materials and Methods One hundred and fifty four patients with rectal carcinomas who underwent preoperative HR-T2WI and diffusion-weighted imaging were enrolled. Patients were divided into training (n = 89) and validation (n = 65) cohorts. 3D Slicer was used to segment the entire volume of interest for whole tumors based on HR-T2WI and ADC maps. The least absolute shrinkage and selection operator (LASSO) was performed to select feature. The significantly difference was tested by the independent sample t-test and Mann-Whitney U test. The support vector machine (SVM) model was used to develop classification models. The correlation between features and T stage was assessed by Spearman’s correlation analysis. Multivariate logistic regression analysis was performed to identify independent predictors of tumor invasion. The performance of classifiers was evaluated by the receiver operating characteristic (ROC) curves. Results The wavelet HHH NGTDM strength (RS = -0.364, P < 0.001) from HR-T2WI was an independent predictor of stage T3–4 tumors. The shape maximum 2D diameter column (RS = 0.431, P < 0.001), log σ = 5.0 mm 3D first-order maximum (RS = 0.276, P = 0.009), and log σ = 5.0 mm 3D first-order interquartile range (RS = -0.229, P = 0.032) from ADC maps were independent predictors. In training cohorts, the classification models from HR-T2WI, ADC maps and the combination of two achieved the area under the ROC curves (AUCs) of 0.877, 0.902 and 0.941, with the accuracy of 79.78%, 89.86% and 89.89%, respectively. In validation cohorts, the three models achieved AUCs of 0.845, 0.881 and 0.910, with the accuracy of 78.46%, 83.08% and 87.69%, respectively. Conclusions Texture analysis based on ADC maps shows more potential than HR-T2WI in identifying preoperative T stage in rectal carcinomas. The combined application of HR-T2WI and ADC maps may help to improve the accuracy of preoperative diagnosis of rectal cancer invasion.
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Affiliation(s)
- Jia You
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Reinhold C, Nougaret S. Radiomic ADC Metrics as a Tool to Better Understand Tumor Biology. Radiol Imaging Cancer 2020; 2:e200051. [PMID: 33779655 PMCID: PMC7983656 DOI: 10.1148/rycan.2020200051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 04/28/2020] [Accepted: 04/28/2020] [Indexed: 11/11/2022]
Affiliation(s)
- Caroline Reinhold
- From the Departments of Radiology and Obstetrics and Gynecology, McGill University Health Centre, Montreal, Canada (C.R.); Augmented Intelligence Precision Laboratory, Department of Radiology, MUHC Research Institute, 1001 Decarie Blvd, Montreal, QC, Canada H4A 3J1 (C.R.); Department of Radiology, Montpellier Cancer Institute, Montpellier, France (S.N.); and IRCM, Institut de Recherche en Cancérologie de Montpellier, INSERM, U1194, Montpellier, France (S.N.)
| | - Stephanie Nougaret
- From the Departments of Radiology and Obstetrics and Gynecology, McGill University Health Centre, Montreal, Canada (C.R.); Augmented Intelligence Precision Laboratory, Department of Radiology, MUHC Research Institute, 1001 Decarie Blvd, Montreal, QC, Canada H4A 3J1 (C.R.); Department of Radiology, Montpellier Cancer Institute, Montpellier, France (S.N.); and IRCM, Institut de Recherche en Cancérologie de Montpellier, INSERM, U1194, Montpellier, France (S.N.)
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