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Moon HH, Park JE, Kim N, Kim YH, Song SW, Hong CK, Kim JH, Kim HS. Prospective longitudinal analysis of imaging-based spatiotemporal tumor habitats in glioblastoma, IDH-wild type: implication in patient outcome using multiparametric physiologic MRI. BMC Cancer 2024; 24:1197. [PMID: 39334005 PMCID: PMC11438318 DOI: 10.1186/s12885-024-12939-7] [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: 08/27/2023] [Accepted: 09/11/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Physiologic MRI-based tumor habitat analysis has the potential to predict patient outcomes by identifying the spatiotemporal habitats of glioblastoma. This study aims to prospectively validate the cut-off for tumor progression obtained from tumor habitat analysis based on physiologic MRI in ascertaining time-to-progression (TTP) and the site of progression in glioblastoma patients following concurrent chemoradiotherapy (CCRT). METHODS In this prospective study (ClinicalTrials.gov ID: NCT02613988), we will recruit patients with IDH-wild type glioblastoma who underwent CCRT and obtained immediate post-operative and three serial post-CCRT MRI scans within a three-month interval, conducted using diffusion-weighted imaging and dynamic susceptibility contrast imaging. Voxels from cerebral blood volume and apparent diffusion coefficient maps will be grouped using k-means clustering into three spatial habitats (hypervascular cellular, hypovascular cellular, and nonviable tissue). The spatiotemporal habitats of the tumor will be evaluated by comparing changes in each habitat between the serial MRI scans (post-operative and post-CCRT #1, #2, and #3). Associations between spatiotemporal habitats and TTP will be analyzed using cox proportional hazard modeling. The site of progression will be matched with spatiotemporal habitats. DISCUSSION The perfusion- and diffusion-derived tumor habitat in glioblastoma is expected to stratify TTP and may serve as an early predictor for tumor progression in patients with IDH wild-type glioblastoma. TRIAL REGISTRATION ClinicalTrials.gov ID: NCT02613988.
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Affiliation(s)
- Hye Hyeon Moon
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, Republic of Korea.
| | | | - Young-Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Woo Song
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Chang Ki Hong
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jeong Hoon Kim
- Department of Neurosurgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, Seoul, 05505, Republic of Korea
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Boutry S, Laurent S, Burtea C. Editorial for: "Treatment Response Assessment in Multiple Myeloma: Histogram Analysis of Total Tumor Apparent Diffusion Coefficient based on Whole-body Diffusion-weighted MR Imaging". J Magn Reson Imaging 2024; 60:1061-1062. [PMID: 38091345 DOI: 10.1002/jmri.29158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 11/06/2023] [Indexed: 08/02/2024] Open
Affiliation(s)
- Sebastien Boutry
- Unit of General, Organic and Biomedical Chemistry, University of Mons (UMONS), Mons, Belgium
- Center for Microscopy and Molecular Imaging, Charleroi, Belgium
| | - Sophie Laurent
- Unit of General, Organic and Biomedical Chemistry, University of Mons (UMONS), Mons, Belgium
- Center for Microscopy and Molecular Imaging, Charleroi, Belgium
| | - Carmen Burtea
- Unit of General, Organic and Biomedical Chemistry, University of Mons (UMONS), Mons, Belgium
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Yang D, Ren Y, Wang C. Histogram analysis of intravoxel incoherent motion imaging: Correlation with molecular prognostic factors and combined subtypes of breast cancer. Magn Reson Imaging 2024; 111:210-216. [PMID: 38777242 DOI: 10.1016/j.mri.2024.05.010] [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: 03/20/2024] [Revised: 05/18/2024] [Accepted: 05/18/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE To look for links between diffusion and IVIM parameters and different molecular subtypes and prognostic factors through histogram analysis. MATERIALS AND METHODS A total of 139 patients with breast cancer who had pre-operative MRI examinations were enrolled in this retrospective study. Histograms of the diffusion and IVIM parameters were analyzed for the whole tumor, and an association was investigated between the parameters and the different molecular prognostic factors and subtypes using the nonparametric test, Spearman's rank correlation, and receiver operating characteristic (ROC) curve. RESULTS The histogram metrics of the diffusion and IVIM parameters were significantly different for molecular prognostic factors such as human epidermal receptor factor-2 (HER2), progesterone receptor, estrogen receptor, and ki-67. All histogram metrics displayed a poor correlation with all groups (r = -0.28-0.29). There were significant differences in the histogram metrics for the Luminal B-HER2 (-) vs. HER2-positive (non-luminal) subtypes in the mean and 10th percentile D, with the area under the curves (AUCs) of 0.742 and 0.700, respectively, and for the Luminal A and HER2-positive (non-luminal) subtypes in the 90th percentile and entropy of D*, with AUCs of 0.769 and 0.727, respectively. CONCLUSION The histogram metrics of IVIM parameters exhibited links with breast cancer prognosis factors and combined subtypes.
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Affiliation(s)
- Dan Yang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China.
| | - Yike Ren
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
| | - Chunhong Wang
- Department of Radiology, Xinyang Central Hospital, No. 01 Xinyang Siyi Road, Xinyang 464000, Henan, China
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Leonhardi J, Sabanov A, Höhn A, Sucher R, Seehofer D, Mehdorn M, Schnarkowski B, Ebel S, Denecke T, Meyer H. CT Texture Analysis of Perihilar Cholangiocarcinoma-Associations With Tumor Grading, Tumor Markers and Clinical Outcome. Cancer Rep (Hoboken) 2024; 7:e2132. [PMID: 39307946 PMCID: PMC11417006 DOI: 10.1002/cnr2.2132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 05/30/2024] [Accepted: 06/30/2024] [Indexed: 09/26/2024] Open
Abstract
BACKGROUND Texture analysis derived from computed tomography (CT) may provide clinically relevant imaging biomarkers associated with tumor histopathology. Perihilar cholangiocarcinoma is a malignant disease with an overall poor prognosis. AIMS The present study sought to elucidate possible associations between texture features derived from CT images with grading, tumor markers, and survival in extrahepatic, perihilar cholangiocarcinomas tumors. METHODS This retrospective study included 22 patients (10 females, 45%) with a mean age of 71.8 ± 8.7 years. Texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. Survival and clinical parameters were used as primary study outcomes. RESULTS In discrimination analysis, "S(1,1)SumVarnc" was statistically significantly different between patients with long-term survival and nonlong-term survival (mean 275.8 ± 32.6 vs. 239.7 ± 26.0, p = 0.01). The first-order parameter "skewness" was associated with the tumor marker "carcinoembryonic antigen" (CEA) (r = -0.7, p = 0.01). A statistically significant correlation of the texture parameter "S(5,0)SumVarnc" with tumor grading was identified (r = -0.6, p < 0.01). Several other texture features correlated with tumor markers CA-19-9 and AFP, as well as with T and N stage of tumors. CONCLUSION Several texture features derived from CT images were associated with tumor characteristics and survival in patients with perihilar cholangiocarcinomas. CT texture features could be used as valuable novel imaging markers in clinical routine.
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Affiliation(s)
- Jakob Leonhardi
- Department of Diagnostic and Interventional RadiologyUniversity of Leipzig Medical CenterLeipzigGermany
| | - Arsen Sabanov
- Department of SurgeryUniversity of Leipzig Medical CenterLeipzigGermany
| | - Anne Kathrin Höhn
- Department of PathologyUniversity of Leipzig Medical CenterLeipzigGermany
| | - Robert Sucher
- Department of SurgeryUniversity of Leipzig Medical CenterLeipzigGermany
- Department of Surgery, Division of General, Visceral and Transplant SurgeryMedical University of GrazGrazAustria
| | - Daniel Seehofer
- Department of SurgeryUniversity of Leipzig Medical CenterLeipzigGermany
| | - Matthias Mehdorn
- Department of SurgeryUniversity of Leipzig Medical CenterLeipzigGermany
| | - Benedikt Schnarkowski
- Department of Diagnostic and Interventional RadiologyUniversity of Leipzig Medical CenterLeipzigGermany
| | - Sebastian Ebel
- Department of Diagnostic and Interventional RadiologyUniversity of Leipzig Medical CenterLeipzigGermany
| | - Timm Denecke
- Department of Diagnostic and Interventional RadiologyUniversity of Leipzig Medical CenterLeipzigGermany
| | - Hans‐Jonas Meyer
- Department of Diagnostic and Interventional RadiologyUniversity of Leipzig Medical CenterLeipzigGermany
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Chen C, Xiong L, Lin Y, Li M, Song Z, Su J, Cao W. Super-resolution reconstruction for early cervical cancer magnetic resonance imaging based on deep learning. Biomed Eng Online 2024; 23:84. [PMID: 39175006 PMCID: PMC11342621 DOI: 10.1186/s12938-024-01281-5] [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: 12/29/2023] [Accepted: 08/08/2024] [Indexed: 08/24/2024] Open
Abstract
This study aims to develop a super-resolution (SR) algorithm tailored specifically for enhancing the image quality and resolution of early cervical cancer (CC) magnetic resonance imaging (MRI) images. The proposed method is subjected to both qualitative and quantitative analyses, thoroughly investigating its performance across various upscaling factors and assessing its impact on medical image segmentation tasks. The innovative SR algorithm employed for reconstructing early CC MRI images integrates complex architectures and deep convolutional kernels. Training is conducted on matched pairs of input images through a multi-input model. The research findings highlight the significant advantages of the proposed SR method on two distinct datasets at different upscaling factors. Specifically, at a 2× upscaling factor, the sagittal test set outperforms the state-of-the-art methods in the PSNR index evaluation, second only to the hybrid attention transformer, while the axial test set outperforms the state-of-the-art methods in both PSNR and SSIM index evaluation. At a 4× upscaling factor, both the sagittal test set and the axial test set achieve the best results in the evaluation of PNSR and SSIM indicators. This method not only effectively enhances image quality, but also exhibits superior performance in medical segmentation tasks, thereby providing a more reliable foundation for clinical diagnosis and image analysis.
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Affiliation(s)
- Chunxia Chen
- Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, No.18 Daoshan Road, Gulou District, Fuzhou, 350001, Fujian, China
| | - Liu Xiong
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China
| | - Yongping Lin
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China.
| | - Ming Li
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China
| | - Zhiyu Song
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China
| | - Jialin Su
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China
| | - Wenting Cao
- Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, No.18 Daoshan Road, Gulou District, Fuzhou, 350001, Fujian, China
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Leonhardi J, Dahms U, Schnarkowski B, Struck MF, Höhn AK, Krämer S, Ebel S, Prasse G, Frille A, Denecke T, Meyer HJ. Impact of radiomics features, pulmonary emphysema score and muscle mass on the rate of pneumothorax and chest tube insertion in CT-guided lung biopsies. Respir Res 2024; 25:320. [PMID: 39174980 PMCID: PMC11340115 DOI: 10.1186/s12931-024-02936-6] [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: 01/02/2024] [Accepted: 08/01/2024] [Indexed: 08/24/2024] Open
Abstract
Iatrogenic pneumothorax is a relevant complication of computed tomography (CT)-guided percutaneous lung biopsy. The aim of the present study was to analyze the prognostic significance of texture analysis, emphysema score and muscle mass derived from CT-imaging to predict postinterventional pneumothorax after CT-guided lung biopsy. Consecutive patients undergoing CT-guided percutaneous lung biopsy between 2012 and 2021 were analyzed. Multivariate logistic regression analysis included clinical risk factors and CT-imaging features to detect associations with pneumothorax development. Overall, 479 patients (178 females, mean age 65 ± 11.7 years) underwent CT-guided percutaneous lung biopsy of which 180 patients (37.5%) developed pneumothorax including 55 patients (11.5%) requiring chest tube placement. Risk factors associated with pneumothorax were chronic-obstructive pulmonary disease (COPD) (p = 0.03), age (p = 0.02), total lung capacity (p < 0.01) and residual volume (p = 0.01) as well as interventional parameters needle length inside the lung (p < 0.001), target lesion attached to pleura (p = 0.04), and intervention duration (p < 0.001). The combined model demonstrated a prediction accuracy of the occurrence of pneumothorax with an AUC of 0.78 [95%CI: 0.70-0.86] with a resulting sensitivity 0.80 and a specificity of 0.66. In conclusion, radiomics features of the target lesion and the lung lobe CT-emphysema score are predictive for the occurrence of pneumothorax and need for chest insertion after CT-guided lung biopsy.
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Affiliation(s)
- Jakob Leonhardi
- Department of Diagnostic and Interventional Radiology, University of Leipzig, 49341/9717400, Leipzig, Germany
| | - Ulrike Dahms
- Department of Diagnostic and Interventional Radiology, University of Leipzig, 49341/9717400, Leipzig, Germany
| | - Benedikt Schnarkowski
- Department of Diagnostic and Interventional Radiology, University of Leipzig, 49341/9717400, Leipzig, Germany
| | - Manuel Florian Struck
- Department of Anesthesiology and Intensive Care Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Anne-Kathrin Höhn
- Department of Pathology, University Hospital Leipzig, University of Leipzig, Leipzig, Germany
| | - Sebastian Krämer
- Department of Thoracic Surgery, University Hospital Leipzig, University of Leipzig, Leipzig, Germany
| | - Sebastian Ebel
- Department of Diagnostic and Interventional Radiology, University of Leipzig, 49341/9717400, Leipzig, Germany
| | - Gordian Prasse
- Department of Diagnostic and Interventional Radiology, University of Leipzig, 49341/9717400, Leipzig, Germany
| | - Armin Frille
- Department of Respiratory Medicine, University Hospital Leipzig, University of Leipzig, Leipzig, Germany
| | - Timm Denecke
- Department of Diagnostic and Interventional Radiology, University of Leipzig, 49341/9717400, Leipzig, Germany
| | - Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, 49341/9717400, Leipzig, Germany.
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Qiao X, Wang Z, Zhang X, Chen W, Wang L, Chen YW. Whole-liver histogram analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging for predicting progression in patients with cirrhosis. Quant Imaging Med Surg 2024; 14:6072-6086. [PMID: 39144000 PMCID: PMC11320508 DOI: 10.21037/qims-24-109] [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: 01/18/2024] [Accepted: 07/01/2024] [Indexed: 08/16/2024]
Abstract
Background Liver cirrhosis, as the terminal phase of chronic liver disease fibrosis, is associated with high morbidity and mortality. Traditional methods for assessing liver function, such as clinical scoring systems, offer only a global evaluation and may not accurately reflect regional liver function variations. This study aimed at evaluating the diagnostic potential of whole-liver histogram analysis of gadobenate dimeglumine (Gd-BOPTA)-enhanced magnetic resonance imaging (MRI) for predicting the progression of cirrhosis. Methods In this retrospective study, 265 consecutive patients with cirrhosis admitted to the Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University from August 2012 to September 2019 were enrolled. After the exclusion criteria were applied, 117 patients (84 males and 33 females) were divided into Child-Pugh A cirrhosis (n=43), Child-Pugh B cirrhosis (n=49), and Child-Pugh C cirrhosis (n=25). After correction for liver signal intensity with the spleen was completed, 19 histogram features of the whole liver were extracted and modeled to evaluate liver function, with the Child-Pugh class being incorporated as a clinical parameter. Receiver operating characteristic (ROC) curves were used to assess the diagnosis capability and determine the optimal cutoffs after a mean follow-up of 42.3±19.1 (range, 8-93) months. The association between significant histogram features and the cumulative incidence of hepatic insufficiency was analyzed with the adjusted Kaplan-Meier curve model. Results Among 117 patients (12%), 14 developed hepatic insufficiency through a period of follow-up. Five features, including the median (P<0.01), 90th percentile (P<0.01), root mean squared (P<0.01), mean (P<0.01), and 10th percentile (P<0.05), were significantly different between the groups with and without hepatic insufficiency according to the Kruskal-Wallis test; in the ROC curve analysis, the area under the curve (AUC) of these features was 0.723 [95% confidence interval (CI): 0.653-0.793], 0.722 (95% CI: 0.652-0.792), 0.722 (95% CI: 0.652-0.792), 0.721 (95% CI: 0.651-0.791), and 0.674 (95% CI: 0.600-0.748) after correction, respectively (all P values <0.05). Median, 90th percentile, root mean squared, and mean were found to be significant factors in predicting liver insufficiency. The adjusted Kaplan-Meier curves revealed that patients with a feature level less than the cutoff, as compared to those with a level above the cutoff, showed a statistically shorter progression-free survival and higher incidences of hepatic insufficiency for significant features of median (cutoff =26.001; 21.28% versus 5.71%; P=0.02), 90th percentile (cutoff =86.263; 20.41% versus 5.88%; P<0.01), root mean squared (cutoff =1,028.477; 19.15% versus 7.14%; P=0.049), and mean (cutoff =27.484; 19.15% versus 7.14%; P=0.049). Patients with a 10th percentile less than -39.811 also showed a higher cumulative incidence of hepatic insufficiency than did those with a value higher than the cutoff (0.18% versus 7.46%; P=0.22). Conclusions Whole-liver histogram analysis of Gd-BOPTA-enhanced MRI may serve as a noninvasive analytical method to predict hepatic insufficiency in patients with cirrhosis.
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Affiliation(s)
- Xu Qiao
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Zirui Wang
- Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, China
| | - Xianru Zhang
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Wei Chen
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai’an, China
| | - Li Wang
- Department of Health Management Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yen-Wei Chen
- Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
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Wermelskirchen S, Leonhardi J, Höhn AK, Osterhoff G, Schopow N, Zimmermann S, Ebel S, Prasse G, Henkelmann J, Denecke T, Meyer HJ. Impact of quantitative CT texture analysis on the outcome of CT-guided bone biopsy. J Bone Oncol 2024; 47:100616. [PMID: 39015297 PMCID: PMC11250887 DOI: 10.1016/j.jbo.2024.100616] [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: 01/14/2024] [Revised: 06/14/2024] [Accepted: 06/14/2024] [Indexed: 07/18/2024] Open
Abstract
Texture analysis can provide new imaging-based biomarkers. Texture analysis derived from computed tomography (CT) might be able to better characterize patients undergoing CT-guided percutaneous bone biopsy. The present study evaluated this and correlated texture features with bioptic outcome in patients undergoing CT-guided bone biopsy. Overall, 123 patients (89 female patients, 72.4 %) were included into the present study. All patients underwent CT-guided percutaneous bone biopsy with an 11 Gauge coaxial needle. Clinical parameters and quantitative imaging features were investigated. Random forest classifier was used to predict a positive biopsy result. Overall, 69 patients had osteolytic metastasis (56.1 %) and 54 had osteoblastic metastasis (43.9 %). The overall positive biopsy rate was 72 %. The developed radiomics model demonstrated a prediction accuracy of a positive biopsy result with an AUC of 0.75 [95 %CI 0.65 - 0.85]. In a subgroup of breast cancer patients, the model achieved an AUC of 0.85 [95 %CI 0.73 - 0.96]. In the subgroup of non-breast cancer patients, the signature achieved an AUC of 0.80 [95 %CI 0.60 - 0.99]. Quantitative CT imaging findings comprised of conventional and texture features can aid to predict the bioptic result of CT-guided bone biopsies. The developed radiomics signature aids in clinical decision-making, and could identify patients at risk for a negative biopsy.
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Affiliation(s)
- Silvio Wermelskirchen
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Jakob Leonhardi
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Anne-Kathrin Höhn
- Department of Pathology, University Hospital Leipzig, University of Leipzig, Germany
| | - Georg Osterhoff
- Department of Department of Orthopaedics, Trauma and Reconstructive Surgery, University Hospital Leipzig, Germany
| | - Nikolas Schopow
- Department of Department of Orthopaedics, Trauma and Reconstructive Surgery, University Hospital Leipzig, Germany
| | - Silke Zimmermann
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
| | - Sebastian Ebel
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Gordian Prasse
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Jeanette Henkelmann
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Timm Denecke
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
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Zhang B, Zhou Q, Xue C, Ke X, Zhang P, Han T, Deng L, Jing M, Zhou J. Nomogram of magnetic resonance imaging (MRI) histogram analysis to predict telomerase reverse transcriptase promoter mutation status in glioblastoma. Quant Imaging Med Surg 2024; 14:4840-4854. [PMID: 39022283 PMCID: PMC11250314 DOI: 10.21037/qims-24-71] [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: 01/13/2024] [Accepted: 06/07/2024] [Indexed: 07/20/2024]
Abstract
Background Telomerase reverse transcriptase promoter (pTERT) status is a strong biomarker to diagnose and predict the prognosis of glioblastoma (GBM). In this study, we explored the predictive value of preoperative magnetic resonance imaging (MRI) histogram analysis in the form of nomogram for evaluating pTERT mutation status in GBM. Methods The clinical and imaging data of 181 patients with GBM at our hospital between November 2018 and April 2023 were retrospectively assessed. We used the molecular sequencing results to classify the datasets into pTERT mutations (C228T and C250T) and pTERT-wildtype groups. FireVoxel software was used to extract preoperative T1-weighted contrast-enhanced (T1C) histogram parameters of GBM patients. The T1C histogram parameters were compared between groups. Univariate and multivariate logistic regression analyses were used to construct the nomogram, and the predictive efficacy of model was evaluated using calibration and decision curves. Receiver operating characteristic curve was used to assess model performance. Results Patient age and percentage of unenhanced tumor area showed statistically significant differences between the pTERT mutation and pTERT-wildtype groups (P<0.001). Among the T1C histogram features, the maximum, standard deviation (SD), variance, coefficient of variation (CV), skewness, 5th, 10th, 25th, 95th and 99th percentiles were statistically significantly different between groups (P=0.000-0.040). Multivariate logistic regression analysis showed that age, percentage of unenhanced tumor area, SD and CV were independent risk factors for predicting pTERT mutation status in GBM patients. The logistic regression model based on these four features showed a better sample predictive performance, and the area under the curve (AUC) [95% confidence interval (CI)], accuracy, sensitivity, specificity were 0.842 (0.767-0.917), 0.796, 0.820, and 0.729, respectively. There were no significant differences in the T1C histogram parameters between the C228T and C250T groups (P=0.055-0.854). Conclusions T1C histogram parameters can be used to evaluate pTERT mutations status in GBM. A nomogram based on conventional MRI features and T1C histogram parameters is a reliable tool for the pTERT mutation status, allowing for non-invasive radiological prediction before surgery.
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Affiliation(s)
- Bin Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Xiaoai Ke
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Peng Zhang
- Department of Pathology, Lanzhou University Second Hospital, Lanzhou, China
| | - Tao Han
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Liangna Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
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Wang L, Liu Y, Lin J, Pan Y, Liu Y, Lv F. The Predictive Effect of Quantitative Analysis of Signal Intensity Heterogeneity on T2-Weighted MR Images for High-intensity Focused Ultrasound Treatment of Uterine Fibroids. Acad Radiol 2024; 31:2848-2858. [PMID: 38704283 DOI: 10.1016/j.acra.2024.04.023] [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: 03/16/2024] [Revised: 04/14/2024] [Accepted: 04/14/2024] [Indexed: 05/06/2024]
Abstract
RATIONALE AND OBJECTIVES To investigate whether the quantitative index of signal intensity (SI) heterogeneity on T2-weighted (T2W) magnetic resonance images can predict the difficulty and efficacy of high-intensity focused ultrasound (HIFU) ablation for uterine fibroids. MATERIALS AND METHODS The standard deviation (SD) of T2W image (T2WI) SI was used to quantify SI heterogeneity. The correlation between SD and the non-perfused volume ratio (NPVR) in 575 patients undergoing HIFU treatment was retrospectively analyzed, and the efficacy of SD in predicting NPVR was discussed. Three classifications were made based on the SD, and the ablation difficulty and ablation effect of different grades were compared. A total of 65 cases from another center were used as an external validation set to verify the classification performance of SD. RESULTS The SD of SI was negatively correlated with NPVR (r = -0.460, p < 0.001). The predictive efficiency of SD for the ablation effect was higher than that of the scaled signal intensity (0.767 vs. 0.701, p = 0.006). Univariate and multivariate logistic regression analyses showed that SD was an independent predictor of ablation effect. Based on SD, the three classifications were divided into SD I: SD < 101.0, SD II: 101.0 ≤ SD < 138.7, and SD III: SD≥ 138.7. The treatment time, sonication time, treatment intensity, and total energy of SD I were lower than those of SD II and III (p < 0.05). CONCLUSION The heterogeneity of T2WI SI of uterine fibroids is negatively correlated with NPVR. The SD of SI can be used to predict the ablation difficulty and ablation effect of HIFU.
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Affiliation(s)
- Lu Wang
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Yixueyuan Road, Yuzhong District, Chongqing 400016, China (L.W., J.L., Y.P., Y.L., F.L.); Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Yixueyuan Road, Yuzhong District, Chongqing 400016, China (L.W., J.L., Y.P., Y.L., F.L.)
| | - Yang Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuanjiagang, Yuzhong District, Chongqing 400016, China (Y.L., F.L.)
| | - Jinfeng Lin
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Yixueyuan Road, Yuzhong District, Chongqing 400016, China (L.W., J.L., Y.P., Y.L., F.L.); Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Yixueyuan Road, Yuzhong District, Chongqing 400016, China (L.W., J.L., Y.P., Y.L., F.L.)
| | - Yuanrui Pan
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Yixueyuan Road, Yuzhong District, Chongqing 400016, China (L.W., J.L., Y.P., Y.L., F.L.); Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Yixueyuan Road, Yuzhong District, Chongqing 400016, China (L.W., J.L., Y.P., Y.L., F.L.)
| | - Yang Liu
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Yixueyuan Road, Yuzhong District, Chongqing 400016, China (L.W., J.L., Y.P., Y.L., F.L.); Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Yixueyuan Road, Yuzhong District, Chongqing 400016, China (L.W., J.L., Y.P., Y.L., F.L.)
| | - Fajin Lv
- State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Yixueyuan Road, Yuzhong District, Chongqing 400016, China (L.W., J.L., Y.P., Y.L., F.L.); Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Yixueyuan Road, Yuzhong District, Chongqing 400016, China (L.W., J.L., Y.P., Y.L., F.L.); Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuanjiagang, Yuzhong District, Chongqing 400016, China (Y.L., F.L.).
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11
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Zheng Y, Tang Y, Yao Y, Ge T, Pan H, Cui J, Rao Y, Tao X, Jia R, Ai S, Song X, Zhuang A. Correlation Analysis of Apparent Diffusion Coefficient Histogram Parameters and Clinicopathologic Features for Prognosis Prediction in Uveal Melanoma. Invest Ophthalmol Vis Sci 2024; 65:3. [PMID: 38953846 PMCID: PMC11221615 DOI: 10.1167/iovs.65.8.3] [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: 01/27/2024] [Accepted: 06/03/2024] [Indexed: 07/04/2024] Open
Abstract
Purpose To investigate the correlation between apparent diffusion coefficient (ADC) histograms and high-risk clinicopathologic features related to uveal melanoma (UM) prognosis. Methods This retrospective study included 53 patients with UM who underwent diffusion-weighted imaging (DWI) between August 2015 and March 2024. Axial DWI was performed with a single-shot spin-echo echo-planar imaging sequence. ADC histogram parameters of ADCmean, ADC50%, interquartile range (IQR), skewness, kurtosis, and entropy were obtained from DWI. The relationships between histogram parameters and high-risk clinicopathological characteristics including tumor size, preoperative retinal detachment, histological subtypes, Ki-67 index, and chromosome status, were analyzed by Spearman correlation analysis, Mann-Whitney U test, or Kruskal-Wallis test. Results A total of 53 patients (mean ± SD age, 55 ± 15 years; 22 men) were evaluated. The largest basal diameter (LBD) was correlated with kurtosis (r = 0.311, P = 0.024). Tumor prominence (TP) was correlated with entropy (r = 0.581, P < 0.001) and kurtosis (r = 0.273, P = 0.048). Additionally, significant correlations were identified between the Ki-67 index and ADCmean (r = -0.444, P = 0.005), ADC50% (r = -0.487, P = 0.002), and skewness (r = 0.394, P = 0.014). Finally, entropy was correlated with monosomy 3 (r = 0.541, P = 0.017). Conclusions The ADC histograms provided valuable insights into high-risk clinicopathologic features of UM and hold promise in the early prediction of UM prognosis.
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Affiliation(s)
- Yue Zheng
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Yan Tang
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiran Yao
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Tongxin Ge
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Hui Pan
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Junqi Cui
- Department of Pathology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yamin Rao
- Department of Pathology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaofeng Tao
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Renbing Jia
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Songtao Ai
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xin Song
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Ai Zhuang
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
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12
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Tanaka F, Maeda M, Nakayama R, Inoue K, Kishi S, Kogue R, Umino M, Kitano Y, Obara M, Sakuma H. A Combination of Amide Proton Transfer, Tumor Blood Flow, and Apparent Diffusion Coefficient Histogram Analysis Is Useful for Differentiating Malignant from Benign Intracranial Tumors in Young Patients: A Preliminary Study. Diagnostics (Basel) 2024; 14:1236. [PMID: 38928651 PMCID: PMC11202847 DOI: 10.3390/diagnostics14121236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/06/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024] Open
Abstract
PURPOSE To evaluate the amide proton transfer (APT), tumor blood flow (TBF), and apparent diffusion coefficient (ADC) combined diagnostic value for differentiating intracranial malignant tumors (MTs) from benign tumors (BTs) in young patients, as defined by the 2021 World Health Organization classification of central nervous system tumors. METHODS Fifteen patients with intracranial MTs and 10 patients with BTs aged 0-30 years underwent MRI with APT, pseudocontinuous arterial spin labeling (pCASL), and diffusion-weighted imaging. All tumors were evaluated through the use of histogram analysis and the Mann-Whitney U test to compare 10 parameters for each sequence between the groups. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS The APT maximum, mean, 10th, 25th, 50th, 75th, and 90th percentiles were significantly higher in MTs than in BTs; the TBF minimum (min) was significantly lower in MTs than in BTs; TBF kurtosis was significantly higher in MTs than in BTs; the ADC min, 10th, and 25th percentiles were significantly lower in MTs than in BTs (all p < 0.05). The APT 50th percentile (0.900), TBF min (0.813), and ADC min (0.900) had the highest area under the curve (AUC) values of the parameters in each sequence. The AUC for the combination of these three parameters was 0.933. CONCLUSIONS The combination of APT, TBF, and ADC evaluated through histogram analysis may be useful for differentiating intracranial MTs from BTs in young patients.
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Affiliation(s)
- Fumine Tanaka
- Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Masayuki Maeda
- Department of Neuroradiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Ryohei Nakayama
- Department of Electronic and Computer Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu 5250058, Shiga, Japan
| | - Katsuhiro Inoue
- Department of Radiology, Mie University Hospital, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Seiya Kishi
- Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Ryota Kogue
- Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Maki Umino
- Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Yotaro Kitano
- Department of Neurosurgery, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
| | - Makoto Obara
- MR Clinical Science, Philips Japan, 2-13-37 Konan, Minato 1088507, Tokyo, Japan
| | - Hajime Sakuma
- Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu 5148507, Mie, Japan
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Chen LL, Lauwers I, Verduijn G, Philippens M, Gahrmann R, Capala ME, Petit S. MRI for Differentiation between HPV-Positive and HPV-Negative Oropharyngeal Squamous Cell Carcinoma: A Systematic Review. Cancers (Basel) 2024; 16:2105. [PMID: 38893224 PMCID: PMC11171338 DOI: 10.3390/cancers16112105] [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: 04/22/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Human papillomavirus (HPV) is an important risk factor for oropharyngeal squamous cell carcinoma (OPSCC). HPV-positive (HPV+) cases are associated with a different pathophysiology, microstructure, and prognosis compared to HPV-negative (HPV-) cases. This review aimed to investigate the potential of magnetic resonance imaging (MRI) to discriminate between HPV+ and HPV- tumours and predict HPV status in OPSCC patients. A systematic literature search was performed on 15 December 2022 on EMBASE, MEDLINE ALL, Web of Science, and Cochrane according to PRISMA guidelines. Twenty-eight studies (n = 2634 patients) were included. Five, nineteen, and seven studies investigated structural MRI (e.g., T1, T2-weighted), diffusion-weighted MRI, and other sequences, respectively. Three out of four studies found that HPV+ tumours were significantly smaller in size, and their lymph node metastases were more cystic in structure than HPV- ones. Eleven out of thirteen studies found that the mean apparent diffusion coefficient was significantly higher in HPV- than HPV+ primary tumours. Other sequences need further investigation. Fourteen studies used MRI to predict HPV status using clinical, radiological, and radiomics features. The reported areas under the curve (AUC) values ranged between 0.697 and 0.944. MRI can potentially be used to find differences between HPV+ and HPV- OPSCC patients and predict HPV status with reasonable accuracy. Larger studies with external model validation using independent datasets are needed before clinical implementation.
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Affiliation(s)
- Linda L. Chen
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Iris Lauwers
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Gerda Verduijn
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Marielle Philippens
- Department of Radiotherapy, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands;
| | - Marta E. Capala
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
| | - Steven Petit
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, 3015 GD Rotterdam, The Netherlands (G.V.); (M.E.C.)
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14
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Zheng D, Grandgenett PM, Zhang Q, Baine M, Shi Y, Du Q, Liang X, Wong J, Iqbal S, Preuss K, Kamal A, Yu H, Du H, Hollingsworth MA, Zhang C. radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer. Sci Rep 2024; 14:12316. [PMID: 38811597 PMCID: PMC11137018 DOI: 10.1038/s41598-024-62741-5] [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: 11/17/2023] [Accepted: 05/21/2024] [Indexed: 05/31/2024] Open
Abstract
Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, medical images, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection of patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomic feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomic features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomic features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomic feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the association analysis has revealed potential gene mutations and radiomic feature candidates that warrant further investigation in future research endeavors.
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Affiliation(s)
- Dandan Zheng
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA.
| | - Paul M Grandgenett
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
| | - Qi Zhang
- Department of Mathematics and Statistics, University of New Hampshire, Durham, NH, USA
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Yu Shi
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Qian Du
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL, USA
| | - Jeffrey Wong
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Subhan Iqbal
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Kiersten Preuss
- Department of Nutrition and Health Sciences, University of Nebraska, Lincoln, NE, USA
| | - Ahsan Kamal
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Hongfeng Yu
- School of Computing, University of Nebraska, Lincoln, NE, USA
| | - Huijing Du
- Department of Mathematics, University of Nebraska, Lincoln, NE, USA
| | - Michael A Hollingsworth
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA.
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA.
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15
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Liu J, Dong L, Zhang X, Wu Q, Yang Z, Zhang Y, Xu C, Wu Q, Wang M. Radiomics analysis for prediction of lymph node metastasis after neoadjuvant chemotherapy based on pretreatment MRI in patients with locally advanced cervical cancer. Front Oncol 2024; 14:1376640. [PMID: 38779088 PMCID: PMC11109452 DOI: 10.3389/fonc.2024.1376640] [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: 02/15/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
Background This study aims to develop and validate a pretreatment MRI-based radiomics model to predict lymph node metastasis (LNM) following neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods Patients with LACC who underwent NACT from two centers between 2013 and 2022 were enrolled retrospectively. Based on the lymph node (LN) status determined in the pathology reports after radical hysterectomy, patients were categorized as LN positive or negative. The patients from center 1 were assigned as the training set while those from center 2 formed the validation set. Radiomics features were extracted from pretreatment sagittal T2-weighted imaging (Sag-T2WI), axial diffusion-weighted imaging (Ax-DWI), and the delayed phase of dynamic contrast-enhanced sagittal T1-weighted imaging (Sag-T1C) for each patient. The K-best and least absolute shrinkage and selection operator (LASSO) methods were employed to reduce dimensionality, and the radiomics features strongly associated with LNM were selected and used to construct three single-sequence models. Furthermore, clinical variables were incorporated through multivariate regression analysis and fused with the selected radiomics features to construct the clinical-radiomics combined model. The diagnostic performance of the models was assessed using receiver operating characteristic (ROC) curve analysis. The clinical utility of the models was evaluated by the area under the ROC curve (AUC) and decision curve analysis (DCA). Results A total of 282 patients were included, comprising 171 patients in the training set, and 111 patients in the validation set. Compared to the Sag-T2WI model (AUC, 95%CI, training set, 0.797, 0.722-0.782; validation set, 0.648, 0.521-0.776) and the Sag-T1C model (AUC, 95%CI, training set, 0.802, 0.723-0.882; validation set, 0.630, 0.505-0.756), the Ax-DWI model exhibited the highest diagnostic performance with AUCs of 0.855 (95%CI, 0.791-0.919) in training set, and 0.753 (95%CI, 0.638-0.867) in validation set, respectively. The combined model, integrating selected features from three sequences and FIGO stage, surpassed predictive ability compared to the single-sequence models, with AUC of 0.889 (95%CI, 0.833-0.945) and 0.859 (95%CI, 0.781-0.936) in the training and validation sets, respectively. Conclusions The pretreatment MRI-based radiomics model, integrating radiomics features from three sequences and clinical variables, exhibited superior performance in predicting LNM following NACT in patients with LACC.
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Affiliation(s)
- Jinjin Liu
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Linxiao Dong
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Xiaoxian Zhang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging, United Imaging Intelligence Co., Ltd., Beijing, China
| | - Zihan Yang
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Yuejie Zhang
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Chunmiao Xu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Qingxia Wu
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Meiyun Wang
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Science, Zhengzhou, Henan, China
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16
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Dou J, Jiang N, Zeng J, Wang S, Tian S, Shan S, Li Y, Xu Z, Lin X, Jin S, Dong J, Chen H. Novel 3D morphological characteristics for congenital biliary dilatation diagnosis: a case-control study. Int J Surg 2024; 110:2614-2624. [PMID: 38376858 DOI: 10.1097/js9.0000000000001204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 02/04/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND Congenital biliary dilatation (CBD) necessitates the timely removal of dilated bile ducts. Accurate differentiation between CBD and secondary biliary dilatation (SBD) is crucial for treatment decisions, and identification of CBD with intrahepatic involvement is vital for surgical planning and supportive care. This study aimed to develop quantitative models based on bile duct morphology to distinguish CBD from SBD and further identify CBD with intrahepatic involvement. MATERIALS AND METHODS The retrospective study included 131 CBD and 209 SBD patients between December 2014 and December 2021 for model development, internal validation, and testing. A separate cohort of 15 CBD and 34 SBD patients between January 2022 and December 2022 was recruited for temporally-independent validation. Quantitative shape-based (Shape) and diameter-based (Diam) morphological characteristics of bile ducts were extracted to build a CBD diagnosis model to distinguish CBD from SBD and an intrahepatic involvement identification model to classify CBD with/without intrahepatic involvement. The diagnostic performance of the models was compared with that of experienced hepatobiliary surgeons. RESULTS The CBD diagnosis model using clinical, Shape, and Diam characteristics showed good performance with an AUROC of 0.942 (95% CI: 0.890-0.994), AUPRC of 0.917 (0.855-0.979), accuracy of 0.891, sensitivity of 0.950, and F1-score of 0.864. The model outperformed two experienced surgeons in accuracy, sensitivity, and F1-score. The intrahepatic involvement identification model using clinical, Shape, and Diam characteristics yielded outstanding performance with an AUROC of 0.944 (0.879-1.000), AUPRC of 0.982 (0.947-1.000), accuracy of 0.932, sensitivity of 0.971, and F1-score of 0.957. The models demonstrated generalizable performance on the temporally-independent validation cohort. CONCLUSIONS This study developed two robust quantitative models for distinguishing CBD from SBD and identifying CBD with intrahepatic involvement, respectively, based on morphological characteristics of the bile ducts, showing great potential in risk stratification and surgical planning of CBD.
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Affiliation(s)
- Jiaqi Dou
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Nan Jiang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Jianping Zeng
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Siyuan Wang
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Song Tian
- Philips Healthcare, Beijing, People's Republic of China
| | - Siqiao Shan
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Yuze Li
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Ziming Xu
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Xiaoqi Lin
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
| | - Shuo Jin
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Jiahong Dong
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Key Laboratory of Digital Intelligence Hepatology, Ministry of Education, School of Clinical Medicine
- Institute for Precision Medicine, Tsinghua University
- Research Unit of Precision Hepatobiliary Surgery Paradigm, Chinese Academy of Medical Sciences
| | - Huijun Chen
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine
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Zhang X, Ma Y, Wang Y, Liang Z, Zhang X, Chen Y, Wang Q, Qin H, Wang J. Precision Phenotypic Profiling and Capture of Circulating Tumor Cells via a Vertical Laminar Flow-Stacked Microfluidic Chip. MICROMACHINES 2024; 15:542. [PMID: 38675353 PMCID: PMC11051858 DOI: 10.3390/mi15040542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 04/28/2024]
Abstract
The heterogeneity of circulating tumor cells has a significant impact on the diagnosis, treatment, and monitoring of cancer. Research on the subtypes of circulating tumor cells can bring better treatment outcomes for cancer patients. Here, we proposed a microfluidic chip for the magnetic capture of subtypes of circulating tumor cells from the whole blood and phenotypic profiling by stacking laminar flow vertically. Circulating tumor cells were sorted and captured by the three-dimensional regulation of both magnetic fields in the vertical direction and flow fields in the lateral direction. Using EpCAM-magnetic beads, we achieved sorting and sectional capture of target cells in whole blood and analyzed the surface expression levels of the captured cells, confirming the functionality of the microfluidic chip in sorting and capturing subtypes of circulating tumor cells. This microfluidic chip can also aid in the subsequent subtype analysis of other rare cells.
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Affiliation(s)
- Xinping Zhang
- School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
| | - Yuan Ma
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
| | - Yujiao Wang
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
| | - Zhenwei Liang
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
| | - Xuanhe Zhang
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
| | - Yiqing Chen
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
| | - Qingyi Wang
- School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
| | - Hua Qin
- School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
| | - Jiadao Wang
- Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
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Cheung SM, Chan KS, Zhou W, Husain E, Gagliardi T, Masannat Y, He J. Spatial heterogeneity of peri-tumoural lipid composition in postmenopausal patients with oestrogen receptor positive breast cancer. Sci Rep 2024; 14:4699. [PMID: 38409583 PMCID: PMC10897464 DOI: 10.1038/s41598-024-55458-y] [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: 09/23/2023] [Accepted: 02/23/2024] [Indexed: 02/28/2024] Open
Abstract
Deregulation of lipid composition in adipose tissue adjacent to breast tumour is observed in ex vivo and animal models. Novel non-invasive magnetic resonance imaging (MRI) allows rapid lipid mapping of the human whole breast. We set out to elucidate the spatial heterogeneity of peri-tumoural lipid composition in postmenopausal patients with oestrogen receptor positive (ER +) breast cancer. Thirteen participants (mean age, 62 ± [SD] 6 years) with ER + breast cancer and 13 age-matched postmenopausal healthy controls were scanned on MRI. The number of double bonds in triglycerides was computed from MRI images to derive lipid composition maps of monounsaturated, polyunsaturated, and saturated fatty acids (MUFA, PUFA, SFA). The spatial heterogeneity measures (mean, median, skewness, entropy and kurtosis) of lipid composition in the peri-tumoural region and the whole breast of participants and in the whole breast of controls were computed. The Ki-67 proliferative activity marker and CD163 antibody on tumour-associated macrophages were assessed histologically. Mann Whitney U or Wilcoxon tests and Spearman's coefficients were used to assess group differences and correlations, respectively. For comparison against the whole breast in participants, peri-tumoural MUFA had a lower mean (median (IQR), 0.40 (0.02), p < .001), lower median (0.42 (0.02), p < .001), a negative skewness with lower magnitude (- 1.65 (0.77), p = .001), higher entropy (4.35 (0.64), p = .007) and lower kurtosis (5.13 (3.99), p = .001). Peri-tumoural PUFA had a lower mean (p < .001), lower median (p < .001), a positive skewness with higher magnitude (p = .005) and lower entropy (p = .002). Peri-tumoural SFA had a higher mean (p < .001), higher median (p < .001), a positive skewness with lower magnitude (p < .001) and lower entropy (p = .012). For comparison against the whole breast in controls, peri-tumoural MUFA had a negative skewness with lower magnitude (p = .01) and lower kurtosis (p = .009), however there was no difference in PUFA or SFA. CD163 moderately correlated with peri-tumoural MUFA skewness (rs = - .64), PUFA entropy (rs = .63) and SFA skewness (rs = .59). There was a lower MUFA and PUFA while a higher SFA, and a higher heterogeneity of MUFA while a lower heterogeneity of PUFA and SFA, in the peri-tumoural region in comparison with the whole breast tissue. The degree of lipid deregulation was associated with inflammation as indicated by CD163 antibody on macrophages, serving as potential marker for early diagnosis and response to therapy.
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Affiliation(s)
- Sai Man Cheung
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.
| | - Kwok-Shing Chan
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Wenshu Zhou
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Ehab Husain
- Department of Pathology, Aberdeen Royal Infirmary, Aberdeen, UK
| | - Tanja Gagliardi
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
- Department of Radiology, Royal Marsden Hospital, London, UK
| | - Yazan Masannat
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
- Broomfield Breast Unit, Broomfield Hospital, Mid and South Essex NHS Trust, Chelmsford, UK
- London Breast Institute, Princess Grace Hospital, London, UK
| | - Jiabao He
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.
- Faculty of Medical Sciences, Newcastle Magnetic Resonance Centre, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
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Li J, Ma Y, Yang C, Qiu G, Chen J, Tan X, Zhao Y. Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy. Front Oncol 2024; 14:1277698. [PMID: 38463221 PMCID: PMC10920317 DOI: 10.3389/fonc.2024.1277698] [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: 08/15/2023] [Accepted: 02/09/2024] [Indexed: 03/12/2024] Open
Abstract
Objectives This study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy. Methods We conducted a retrospective analysis involving 202 patients with surgically confirmed single HCC having undergone preoperative magnetic resonance imaging between 2018 and 2021 at two different institutions. 126 patients from Institution 1 were assigned to the training set, and 76 patients from Institution 2 were assigned to the validation set. A least absolute shrinkage and selection operator (LASSO) regularization was conducted to operate a logistic regression, then features were identified to construct a radiomic score (Rad-score). Uni- and multi-variable tests were used to assess the correlations of clinicopathological features and Rad-score with ER. We then established a combined model encompassing the optimal Rad-score and clinical-pathological risk factors. Additionally, we formulated and validated a predictive nomogram for predicting ER in HCC. The nomogram's discrimination, calibration, and clinical utility were thoroughly evaluated. Results Multivariable logistic regression revealed the Rad-score, microvascular invasion (MVI), and α fetoprotein (AFP) level > 400 ng/mL as significant independent predictors of ER in HCC. We constructed a nomogram based on these significant factors. The areas under the receiver operator characteristic curve of the nomogram and precision-recall curve were 0.901 and 0.753, respectively, with an F1 score of 0.831 in the training set. These values in the validation set were 0.827, 0.659, and 0.808. Conclusion The nomogram that integrates the radiomic score, MVI, and AFP demonstrates high predictive efficacy for estimating the risk of ER in HCC. It facilitates personalized risk classification and therapeutic decision-making for HCC patients.
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Affiliation(s)
- Jia Li
- Department of Oncology, Central People’s Hospital of Zhanjiang, Zhanjiang, China
| | - Yunhui Ma
- Department of Oncology, Central People’s Hospital of Zhanjiang, Zhanjiang, China
| | - Chunyu Yang
- Department of Radiology, The First School of Clinical Medicine, Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Ganbin Qiu
- Imaging Department of Zhaoqing Medical College, Zhaoqing, China
| | - Jingmu Chen
- Department of Radiology, Central People’s Hospital of Zhanjiang, Zhanjiang, China
| | - Xiaoliang Tan
- Department of Radiology, Central People’s Hospital of Zhanjiang, Zhanjiang, China
| | - Yue Zhao
- Department of Radiology, Central People’s Hospital of Zhanjiang, Zhanjiang, China
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Kurokawa R, Hagiwara A, Kurokawa M, Ellingson BM, Baba A, Moritani T. Diffusion histogram profiles predict molecular features of grade 4 in histologically lower-grade adult diffuse gliomas following WHO classification 2021. Eur Radiol 2024; 34:1367-1375. [PMID: 37581661 PMCID: PMC10853353 DOI: 10.1007/s00330-023-10071-x] [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: 03/09/2023] [Revised: 06/30/2023] [Accepted: 07/06/2023] [Indexed: 08/16/2023]
Abstract
OBJECTIVES In the latest World Health Organization classification 2021, grade 4 adult diffuse gliomas can be diagnosed with several molecular features even without histological evidence of necrosis or microvascular proliferation. We aimed to explore whole tumor histogram-derived apparent diffusion coefficient (ADC) histogram profiles for differentiating between the presence (Mol-4) and absence (Mol-2/3) of grade 4 molecular features in histologically lower-grade gliomas. METHODS Between June 2019 and October 2022, 184 adult patients with diffuse gliomas underwent MRI. After excluding 121 patients, 18 (median age, 64.5 [range, 37-84 years]) Mol-4 and 45 (median 40 [range, 18-73] years) Mol-2/3 patients with histologically lower-grade gliomas were enrolled. Whole tumor volume-of-interest-derived ADC histogram profiles were calculated and compared between the two groups. Stepwise logistic regression analysis with Akaike's information criterion using the ADC histogram profiles with p values < 0.01 and age at diagnosis was used to identify independent variables for predicting the Mol-4 group. RESULTS The 90th percentile (p < 0.001), median (p < 0.001), mean (p < 0.001), 10th percentile (p = 0.014), and entropy (p < 0.001) of normalized ADC were lower, and kurtosis (p < 0.001) and skewness (p = 0.046) were higher in the Mol-4 group than in the Mol-2/3 group. Multivariate logistic regression analysis revealed that the entropy of normalized ADC and age at diagnosis were independent predictive parameters for the Mol-4 group with an area under the curve of 0.92. CONCLUSION ADC histogram profiles may be promising preoperative imaging biomarkers to predict molecular grade 4 among histologically lower-grade adult diffuse gliomas. CLINICAL RELEVANCE STATEMENT This study highlighted the diagnostic usefulness of ADC histogram profiles to differentiate histologically lower grade adult diffuse gliomas with the presence of molecular grade 4 features and those without. KEY POINTS • ADC histogram profiles to predict molecular CNS WHO grade 4 status among histologically lower-grade adult diffuse gliomas were evaluated. • Entropy of ADC and age were independent predictive parameters for molecular grade 4 status. • ADC histogram analysis is useful for predicting molecular grade 4 among histologically lower-grade gliomas.
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Affiliation(s)
- Ryo Kurokawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr, Ann Arbor, MI, 48109, USA.
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
| | - Akifumi Hagiwara
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Mariko Kurokawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr, Ann Arbor, MI, 48109, USA
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - Akira Baba
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr, Ann Arbor, MI, 48109, USA
| | - Toshio Moritani
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr, Ann Arbor, MI, 48109, USA
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Li Z, Xue C, Li S, Jing M, Liu S, Sun J, Ren T, Zhou J. Preoperative CT histogram analysis to predict the expression of Ki-67 in solid pseudopapillary tumours of the pancreas. Clin Radiol 2024; 79:e197-e203. [PMID: 38007336 DOI: 10.1016/j.crad.2023.10.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/11/2023] [Accepted: 10/22/2023] [Indexed: 11/27/2023]
Abstract
AIM To explore the value of preoperative computed tomography (CT) histogram features in predicting the expression status of Ki-67 in patients with solid pseudopapillary pancreatic tumours (SPTP). MATERIALS AND METHODS This retrospective study analysed venous phase CT images of 39 patients with SPTP confirmed at surgery and histopathology and measured using the Ki-67 proliferation index from November 2015 to February 2022. According to the Ki-67 proliferation index, they were divided into high expression (Ki-67 ≥ 4%) and low expression (Ki-67 < 4%) groups. The histogram features of quantitative parameters were extracted using MaZda software, and the quantitative parameters of CT histograms were compared between groups. The receiver operating characteristic (ROC) curves of the patients were plotted according to the parameters, with statistically significant differences. The area under the curve (AUC), sensitivity, and specificity were calculated, and the effectiveness of the histogram parameters in predicting Ki-67 expression was analysed and evaluated. RESULTS In total, 27 SPTP patients were enrolled, including 11 with high expression of Ki-67 and 16 with low expression. Comparative analysis of the Ki-67 high- and low-expression groups revealed a statistically significant in necrosis and variance (p<0.05). ROC curve analysis showed that the AUC of necrosis and variance predicting Ki-67 expression status were 0.753 and 0.841, the sensitivities were 81.8% and 81.3%, and the specificities were 68.7% and 81.8%, respectively. CONCLUSION Preoperative CT histogram features help predict Ki-67 expression status in patients with SPTP.
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Affiliation(s)
- Z Li
- Department of Imaging, Shaanxi Provincial People's Hospital, Xi'an, China
| | - C Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - S Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - M Jing
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - S Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - J Sun
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - T Ren
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - J Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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22
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Li L, Liao B, Cai H, Zhang Y, Deng K, Chen Y, Chen M, Zhou X, Tang M, Dong Z, Feng ST. Quantitative assessment of inflammation and evaluation of spatial heterogeneity for non-alcoholic fatty liver disease in mice based on iron-adjustive T1. Quant Imaging Med Surg 2024; 14:219-230. [PMID: 38223091 PMCID: PMC10784091 DOI: 10.21037/qims-23-782] [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: 05/31/2023] [Accepted: 10/12/2023] [Indexed: 01/16/2024]
Abstract
Background A sensitive and non-invasive method is necessary to diagnose non-alcoholic fatty liver disease (NAFLD). We explored the iron-adjustive T1 (aT1) ability to quantify the degree of liver inflammation and evaluate the spatial heterogeneity. Methods Male C57BL/6J mice were randomly categorized as the NAFLD model (n=40), NAFLD-related liver cirrhosis model (n=20), and normal mice (n=10). T1 and T2* maps were acquired using a 3.0T scanner of magnetic resonance imaging (MRI) and aT1 maps through post-processing corrected iron's effect on T1 using T2*. Pathological changes in the left and right liver lobes were assessed using the Non-alcoholic Steatohepatitis-Clinical Research Network scoring system, though hepatic ballooning lesion were rare in models. Spearman's and partial correlation analyses were used to evaluate correlations, and the receiver operating characteristic curve was used to analyze the diagnostic performance. Results aT1 was highly correlated with NAFLD activity score (NAS) (r=0.747, P<0.001) but not with the fibrosis stage when adjusted by NAS (r=-0.135, P=0.147). The area under the curve (AUC) of the aT1 value distinguishing groups with 0< NAS <4 and NAS ≥4 was 0.802. On analyzing the histogram features of aT1, the entropy, interquartile range, range, and variance were significantly different between the groups with 0< NAS <4 and NAS ≥4 (P<0.05). The entropy was the risk factor of NAS ≥4. Conclusions aT1 could help evaluate the inflammatory activity in NAFLD mice unaffected by mild fibrosis, and the higher the degree of inflammation, the higher the heterogeneity of the aT1 map.
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Affiliation(s)
- Lujie Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bing Liao
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huasong Cai
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yinhong Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kan Deng
- Philips Healthcare, Guangzhou, China
| | - Yuying Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Meicheng Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoqi Zhou
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Mimi Tang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhi Dong
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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Alshamrani K, Alshamrani HA. A computational approach for analysis of intratumoral heterogeneity and standardized uptake value in PET/CT images1. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:123-139. [PMID: 37458060 DOI: 10.3233/xst-230095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
BACKGROUND By providing both functional and anatomical information from a single scan, digital imaging technologies like PET/CT and PET/MRI hybrids are gaining popularity in medical imaging industry. In clinical practice, the median value (SUVmed) receives less attention owing to disagreements surrounding what defines a lesion, but the SUVmax value, which is a semi-quantitative statistic used to analyse PET and PET/CT images, is commonly used to evaluate lesions. OBJECTIVE This study aims to build an image processing technique with the purpose of automatically detecting and isolating lesions in PET/CT images, as well as measuring and assessing the SUVmed. METHODS The pictures are separated into their respective lesions using mathematical morphology and the crescent region, which are both part of the image processing method. In this research, a total of 18 different pictures of lesions were evaluated. RESULTS The findings of the study reveal that the threshold is satisfied by both the SUVmax and the SUVmed for most of the lesion types. However, in six instances, the SUVmax and SUVmed values are found to be in different courts. CONCLUSION The new information revealed by this study needs to be further investigated to determine if it has any practical value in diagnosing and monitoring lesions. However, results of this study suggest that SUVmed should receive more attention in the evaluation of lesions in PET and CT images.
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Affiliation(s)
- Khalaf Alshamrani
- Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia
| | - Hassan A Alshamrani
- Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia
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Zheng L, Jiang P, Lin D, Chen X, Zhong T, Zhang R, Chen J, Song Y, Xue Y, Lin L. Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas. Cancer Imaging 2023; 23:117. [PMID: 38053183 DOI: 10.1186/s40644-023-00633-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: 04/10/2023] [Accepted: 11/03/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery. METHODS Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student's t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs. RESULTS DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03). CONCLUSION Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.
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Affiliation(s)
- Lingmin Zheng
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Peirong Jiang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Danjie Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Xiaodan Chen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Tianjin Zhong
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Rufei Zhang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Jing Chen
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Yang Song
- MR Scientific Marketing, Healthineers Ltd, Siemens, Shanghai, China
| | - Yunjing Xue
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
| | - Lin Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
- School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, 350004, China.
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Yang W, Yang P, Li Y, Chen J, Chen J, Cai Y, Zhu K, Zhang H, Li Y, Peng Y, Ge M. Presurgical MRI-Based Radiomics Models for Predicting Cerebellar Mutism Syndrome in Children With Posterior Fossa Tumors. J Magn Reson Imaging 2023; 58:1966-1976. [PMID: 37009777 DOI: 10.1002/jmri.28705] [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: 12/22/2022] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND Current studies have indicated that tumoral morphologic features are associated with cerebellar mutism syndrome (CMS), but the radiomics application in CMS is scarce. PURPOSE To develop a model for CMS discrimination based on multiparametric MRI radiomics in patients with posterior fossa tumors. STUDY TYPE Retrospective. POPULATION A total of 218 patients (males 132, females 86) with posterior fossa tumors, 169 of which were included in the MRI radiomics analysis. The MRI radiomics study cohort (169) was split into training (119) and testing (50) sets with a ratio of 7:3. FIELD/SEQUENCE All the MRI were acquired under 1.5/3.0 T scanners. T2-weighted image (T2W), T1-weighted (T1W), fluid attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI). ASSESSMENT Apparent diffusion coefficient (ADC) maps were generated from DWI. Each MRI dataset generated 1561 radiomics characteristics. Feature selection was performed with univariable logistic analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO) penalized logistic regression. Significant clinical features were selected with multivariable logistic analysis and used to constructed the clinical model. Radiomics models (based on T1W, T2W, FLAIR, DWI, ADC) were constructed with selected radiomics features. The mix model was based on the multiparametric MRI radiomics features. STATISTICAL TEST Multivariable logistic analysis was utilized during clinical features selection. Models' performance was evaluated using the area under the receiver operating characteristic (AUC) curve. Interobserver variability was assessed using Cohen's kappa. Significant threshold was set as P < 0.05. RESULTS Sex (aOR = 3.72), tumor location (aOR = 2.81), hydrocephalus (aOR = 2.14), and tumor texture (aOR = 5.08) were significant features in the multivariable analysis and were used to construct the clinical model (AUC = 0.79); totally, 33 radiomics features were selected to construct radiomics models (AUC = 0.63-0.93). Seven of the 33 radiomics features were selected for the mix model (AUC = 0.93). DATA CONCLUSION Multiparametric MRI radiomics may be better at predicting CMS than single-parameter MRI models and clinical model. EVIDENCE LEVEL 4. TECHNICAL EFFICACY 2.
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Affiliation(s)
- Wei Yang
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Ping Yang
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Yiming Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiahui Chen
- Department of Endocrinology, Genetics and Metabolism, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Jiashu Chen
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Yingjie Cai
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Kaiyi Zhu
- Department of Cardiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Hong Zhang
- Department of Image Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Yanhua Li
- Department of Image Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Yun Peng
- Department of Image Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Ming Ge
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
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Chen L, Chen R, Li T, Huang L, Tang C, Li Y, Zeng Z. MRI radiomics model for predicting TERT promoter mutation status in glioblastoma. Brain Behav 2023; 13:e3324. [PMID: 38054695 PMCID: PMC10726789 DOI: 10.1002/brb3.3324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/05/2023] [Accepted: 10/31/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND AND PURPOSE The presence of TERT promoter mutations has been associated with worse prognosis and resistance to therapy for patients with glioblastoma (GBM). This study aimed to determine whether the combination model of different feature selections and classification algorithms based on multiparameter MRI can be used to predict TERT subtype in GBM patients. METHODS A total of 143 patients were included in our retrospective study, and 2553 features were obtained. The datasets were randomly divided into training and test sets in a ratio of 7:3. The synthetic minority oversampling technique was used to achieve data balance. The Pearson correlation coefficients were used for dimension reduction. Three feature selections and five classification algorithms were used to model the selected features. Finally, 10-fold cross validation was applied to the training dataset. RESULTS A model with eight features generated by recursive feature elimination (RFE) and linear discriminant analysis (LDA) showed the greatest diagnostic performance (area under the curve values for the training, validation, and testing sets: 0.983, 0.964, and 0.926, respectively), followed by relief and random forest (RF), analysis of variance and RF. Furthermore, the relief was the optimal feature selection for separately evaluating those five classification algorithms, and RF was the most preferable algorithm for separately assessing the three feature selectors. ADC entropy was the parameter that made the greatest contribution to the discrimination of TERT mutations. CONCLUSIONS Radiomics model generated by RFE and LDA mainly based on ADC entropy showed good performance in predicting TERT promoter mutations in GBM.
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Affiliation(s)
- Ling Chen
- Department of RadiologyThe First Affiliated Hospital of Guangxi Medical UniversityNanningGuangxiChina
- Department of RadiologyLiuzhou Worker's HospitalThe Fourth Affiliated HospitalGuangxi Medical UniversityNanningGuangxiChina
| | - Runrong Chen
- Department of RadiologyThe First Affiliated Hospital of Guangxi Medical UniversityNanningGuangxiChina
| | - Tao Li
- Department of RadiologyLiuzhou Worker's HospitalThe Fourth Affiliated HospitalGuangxi Medical UniversityNanningGuangxiChina
| | - Lizhao Huang
- Department of RadiologyLiuzhou Worker's HospitalThe Fourth Affiliated HospitalGuangxi Medical UniversityNanningGuangxiChina
| | - Chuyun Tang
- Department of RadiologyThe First Affiliated Hospital of Guangxi Medical UniversityNanningGuangxiChina
| | - Yao Li
- Department of NeurosurgeryLiuzhou Worker's HospitalThe Fourth Affiliated HospitalGuangxi Medical UniversityNanningGuangxiChina
| | - Zisan Zeng
- Department of RadiologyThe First Affiliated Hospital of Guangxi Medical UniversityNanningGuangxiChina
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Mohammed S, Kurtek S, Bharath K, Rao A, Baladandayuthapani V. Tumor radiogenomics in gliomas with Bayesian layered variable selection. Med Image Anal 2023; 90:102964. [PMID: 37797481 PMCID: PMC10653647 DOI: 10.1016/j.media.2023.102964] [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: 11/24/2021] [Revised: 12/09/2022] [Accepted: 07/03/2023] [Indexed: 10/07/2023]
Abstract
We propose a statistical framework to analyze radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor region into concentric spherical layers that mimics the tumor evolution process. MRI data within each layer is represented by voxel-intensity-based probability density functions which capture the complete information about tumor heterogeneity. Under a Riemannian-geometric framework these densities are mapped to a vector of principal component scores which act as imaging phenotypes. Subsequently, we build Bayesian variable selection models for each layer with the imaging phenotypes as the response and the genomic markers as predictors. Our novel hierarchical prior formulation incorporates the interior-to-exterior structure of the layers, and the correlation between the genomic markers. We employ a computationally-efficient Expectation-Maximization-based strategy for estimation. Simulation studies demonstrate the superior performance of our approach compared to other approaches. With a focus on the cancer driver genes in LGG, we discuss some biologically relevant findings. Genes implicated with survival and oncogenesis are identified as being associated with the spherical layers, which could potentially serve as early-stage diagnostic markers for disease monitoring, prior to routine invasive approaches. We provide a R package that can be used to deploy our framework to identify radiogenomic associations.
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Affiliation(s)
- Shariq Mohammed
- Department of Biostatistics, Boston University, 801 Massachusetts Ave, Boston, MA 02118, United States; Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48103, United States; Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, United States.
| | - Sebastian Kurtek
- Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, OH 43210, United States
| | - Karthik Bharath
- School of Mathematical Sciences, University Park, Nottingham, NG7 2RD, United Kingdom
| | - Arvind Rao
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48103, United States; Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, United States; Department of Radiation Oncology, University of Michigan, 1500 E Medical Center Dr, Ann Arbor, MI 48109, United States
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Sun Y, Yang Z, Deng K, Geng Y, Hu X, Song Y, Jiang R. Histogram analysis of quantitative susceptibility mapping and apparent diffusion coefficient for identifying isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas. Quant Imaging Med Surg 2023; 13:8681-8693. [PMID: 38106258 PMCID: PMC10722066 DOI: 10.21037/qims-23-832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 10/19/2023] [Indexed: 12/19/2023]
Abstract
Background Accurate preoperative identification of isocitrate dehydrogenase (IDH) genotypes and tumor subtypes is highly important for proper treatment planning and prognosis evaluation in patients with glioma. This study aimed to differentiate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using histogram features of quantitative susceptibility mapping (QSM) and apparent diffusion coefficient (ADC). Methods This prospective study enrolled patients with suspected gliomas between March 2019 and January 2022 in a random series. Histogram features of QSM and ADC were extracted from the tumor parenchyma. The Mann-Whitney U test was used to compare the difference in histogram features between different IDH genotypes and among tumor subtypes. Receiver operating characteristic (ROC) curves were constructed to assess the corresponding diagnostic performance. Results This study included 47 patients with histopathologically confirmed adult-type diffuse gliomas. Totals of seven QSM features including 10th percentile (P10), 90th percentile (P90), interquartile range (IQR), maximum, mean absolute deviation (MAD), root mean squared (RMS), and variance, and five ADC features including P10, mean, median, RMS, and skewness exhibited significant differences between different IDH genotypes (P<0.05 for all), with the IQR of QSM demonstrating the highest area under curve (AUC) of 0.774 [95% confidence interval (CI): 0.635-0.913]. For separating tumor subtypes, the IQR of QSM also showed the highest AUC of 0.745 (95% CI: 0.566-0.924) for glioblastoma (GBM) versus astrocytoma and 0.848 (95% CI: 0.706-0.989) for GBM versus oligodendroglioma, but none of the features could discriminate astrocytoma from oligodendroglioma. The combination of the IQR of QSM, P10 of ADC, and age achieved the highest AUC of 0.910 (95% CI: 0.826-0.994) for IDH genotypes, and 0.939 (95% CI: 0.859-1.000) and 0.967 (95% CI: 0.904-1.000) for GBM versus astrocytoma and GBM versus oligodendroglioma, respectively. Conclusions QSM and ADC histogram features may serve as potential imaging markers for noninvasively assessing IDH genotypes and tumor subtypes of adult-type diffuse gliomas. Combining significant features may enhance the diagnostic performance substantially.
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Affiliation(s)
- Yifan Sun
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
| | - Zheting Yang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
| | - Kaiji Deng
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
| | - Yingqian Geng
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
| | - Xiaomei Hu
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthcare, Shanghai, China
| | - Rifeng Jiang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- School of Medical Imaging, Fujian Medical University, Fuzhou, China
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Stewart S, Gamble G, Doyle AJ, Son CN, Aati O, Latto K, Horne A, Stamp LK, Dalbeth N. The statistical challenge of analysing changes in dual energy computed tomography (DECT) urate volumes in people with gout. Semin Arthritis Rheum 2023; 63:152303. [PMID: 37939600 DOI: 10.1016/j.semarthrit.2023.152303] [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: 06/30/2023] [Revised: 10/12/2023] [Accepted: 10/31/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Dual energy computed tomography (DECT) allows direct visualization of monosodium urate crystal deposition in gout. However, DECT urate volume data are often highly skewed (mostly small volumes with the remainder considerably larger), making statistical analyses challenging in longitudinal research. The aim of this study was to explore the ability of various analysis methods to normalise DECT urate volume data and determine change in DECT urate volumes over time. METHODS Simulated datasets containing baseline and year 1 DECT urate volumes for 100 people with gout were created from two randomised controlled trials. Five methods were used to transform the DECT urate volume data prior to analysis: log-transformation, Box-Cox transformation, log(X-(min(X)-1)) transformation; inverse hyperbolic sine transformation, and rank order. Linear regression analyses were undertaken to determine the change in DECT urate volume between baseline and year 1. Cohen's d were calculated as a measure of effect size for each data treatment method. These analyses were then tested in a validation clinical trial dataset containing baseline and year 1 DECT urate volumes from 91 people with gout. RESULTS No data treatment method successfully normalised the distribution of DECT urate volumes. For both simulated and validation data sets, significant reductions in DECT urate volumes were observed between baseline and Year 1 across all data treatment methods and there were no significant differences in Cohen's d effect sizes. CONCLUSIONS Normalising highly skewed DECT urate volume data is challenging. Adopting commonly used transformation techniques may not significantly improve the ability to determine differences in measures of central tendency when comparing the change in DECT urate volumes over time.
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Affiliation(s)
- Sarah Stewart
- School of Clinical Sciences, Faculty of Health and Environmental Sciences, Auckland University of Technology, 90 Akoranga Drive, Northcote Auckland 0627, New Zealand; Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand.
| | - Greg Gamble
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand
| | - Anthony J Doyle
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand; Te Whatu Ora Health New Zealand, Te Toka Tumai Auckland, Radiology, Private Bag 92 024, Auckland 1142, New Zealand
| | - Chang-Nam Son
- Department of Rheumatology, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, 712 Dongil-ro, Uijeongbu 11749, South Korea
| | - Opetaia Aati
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand
| | - Kieran Latto
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand
| | - Anne Horne
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand
| | - Lisa K Stamp
- Department of Medicine, University of Otago, Christchurch, 2 Riccarton Avenue, Christchurch 8011, New Zealand
| | - Nicola Dalbeth
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Grafton, Auckland 1023, New Zealand
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Liu N, Wu Y, Tao Y, Zheng J, Huang X, Yang L, Zhang X. Differentiation of Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma through MRI Radiomics. Cancers (Basel) 2023; 15:5373. [PMID: 38001633 PMCID: PMC10670473 DOI: 10.3390/cancers15225373] [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: 08/23/2023] [Revised: 10/25/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
The purpose of this study was to investigate the efficacy of magnetic resonance imaging (MRI) radiomics in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). The clinical and MRI data of 129 pathologically confirmed HCC patients and 48 ICC patients treated at the Affiliated Hospital of North Sichuan Medical College between April 2016 and December 2021 were retrospectively analyzed. The patients were randomly divided at a ratio of 7:3 into a training group of 124 patients (90 with HCC and 34 with ICC) and a validation group of 53 patients (39 with HCC and 14 with ICC). Radiomic features were extracted from axial fat suppression T2-weighted imaging (FS-T2WI) and axial arterial-phase (AP) and portal-venous-phase (PVP) dynamic-contrast-enhanced MRI (DCE-MRI) sequences, and the corresponding datasets were generated. The least absolute shrinkage and selection operator (LASSO) method was used to select the best radiomic features. Logistic regression was used to establish radiomic models for each sequence (FS-T2WI, AP and PVP models), a clinical model for optimal clinical variables (C model) and a joint radiomics model (JR model) integrating the radiomics features of all the sequences as well as a radiomics-clinical model combining optimal radiomic features and clinical risk factors (RC model). The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC). The AUCs of the FS-T2WI, AP, PVP, JR, C and RC models for distinguishing HCC from ICC were 0.693, 0.863, 0.818, 0.914, 0.936 and 0.977 in the training group and 0.690, 0.784, 0.727, 0.802, 0.860 and 0.877 in the validation group, respectively. The results of this study suggest that MRI-based radiomics may help noninvasively differentiate HCC from ICC. The model integrating the radiomics features and clinical risk factors showed a further improvement in performance.
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Affiliation(s)
- Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
- Hospital of Chengdu Office of People’s Government of Tibetan Autonomous Region (Hospital. C.T.), Chengdu 610041, China
| | - Yaokun Wu
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Yunyun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Xiaohua Huang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Xiaoming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
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Zheng D, Grandgenett PM, Zhang Q, Baine M, Shi Y, Du Q, Liang X, Wong J, Iqbal S, Preuss K, Kamal A, Yu H, Du H, Hollingsworth MA, Zhang C. radioGWAS: link radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.02.23297995. [PMID: 37961101 PMCID: PMC10635263 DOI: 10.1101/2023.11.02.23297995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomics feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomics features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomics features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomics feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the outcome of the association analysis has revealed potential gene mutations and radiomics feature candidates that warrant further investigation in future research endeavors.
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Zheng L, Yang C, Sheng R, Rao S, Wu L, Zeng M, Dai Y. Characterization of Microvascular Invasion in Hepatocellular Carcinoma Using Computational Modeling of Interstitial Fluid Pressure and Velocity. J Magn Reson Imaging 2023; 58:1366-1374. [PMID: 36762823 DOI: 10.1002/jmri.28644] [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: 11/13/2022] [Revised: 01/28/2023] [Accepted: 01/30/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Most solid tumors show increased interstitial fluid pressure (IFP), and this increased IFP is an obstacle to treatment. A noninvasive model for measuring IFP in hepatocellular carcinoma (HCC) is an unresolved issue. PURPOSE To develop a noninvasive model to measure IFP and interstitial fluid velocity (IFV) in HCC and to characterize the microvascular invasion (MVI) status by using this model. STUDY TYPE Retrospective. POPULATION A total of 97 HCC patients (mean age 57.6 ± 10.9 years, 77.3% males), 53 of them with MVI and 44 of them without MVI. FIELD STRENGTH/SEQUENCE A 3-T, three-dimensional spoiled gradient-recalled echo. ASSESSMENT MVI was defined as microscopic vascular invasion of small vessels within the peritumoral liver tissue. The volumes of interest (VOIs) were manually delineated and enclosed the tumor lesion and healthy liver parenchyma, respectively. The extended Tofts model (ETM) was used to estimate permeability parameters from all the VOIs. Subsequently, the continuity partial differential equation (PDE) was implemented and IFP and IFV were acquired. STATISTICAL TESTS Wilcoxon signed-ranks tests, histogram analysis, Mann-Whitney U test, Fisher's exact test, least absolute shrinkage and selection operator (LASSO) logistic regression, receiver operating characteristic (ROC) curve analysis with the area under the curve (AUC), Youden index, DeLong test, and Benjamini-Hochberg correction. A P value <0.05 was considered statistically significant. RESULTS The HCC lesions exhibited elevated IFP and reduced IFV. There were no significant differences in any measured demographic and clinical features between the MVI-positive and MVI-negative groups, except for tumor size. Nine IFP histogram analysis-derived parameters and seven IFV histogram analysis-derived parameters could be used to characterize the MVI status. LASSO regression selected five features: IFP maximum, IFP 10th percentile, IFP 90th percentile, IFV SD, and IFV 10th percentile. The combination of these features showed the highest AUC (0.781) and specificity (77.3%). DATA CONCLUSION A noninvasive IFP and IFV measurement model for HCC was developed. Specific IFP- and IFV-derived parameters exhibited significant association with the MVI status. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Liyun Zheng
- Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ruofan Sheng
- Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Shengxiang Rao
- Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Lifang Wu
- Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mengsu Zeng
- Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
- MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China
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Su GH, Xiao Y, You C, Zheng RC, Zhao S, Sun SY, Zhou JY, Lin LY, Wang H, Shao ZM, Gu YJ, Jiang YZ. Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets. SCIENCE ADVANCES 2023; 9:eadf0837. [PMID: 37801493 PMCID: PMC10558123 DOI: 10.1126/sciadv.adf0837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 09/06/2023] [Indexed: 10/08/2023]
Abstract
Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast-enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.
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Affiliation(s)
- Guan-Hua Su
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ren-Cheng Zheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 201203, China
| | - Shen Zhao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Shi-Yun Sun
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Jia-Yin Zhou
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Lu-Yi Lin
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 201203, China
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ya-Jia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
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Fang S, Yang Y, Tao J, Yin Z, Liu Y, Duan Z, Liu W, Wang S. Intratumoral Heterogeneity of Fibrosarcoma Xenograft Models: Whole-Tumor Histogram Analysis of DWI and IVIM. Acad Radiol 2023; 30:2299-2308. [PMID: 36481126 DOI: 10.1016/j.acra.2022.11.016] [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: 07/27/2022] [Revised: 11/08/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022]
Abstract
RATIONAL AND OBJECTIVE To explore the correlations of histogram parameters from diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) with the heterogeneous features in a nude mouse model of fibrosarcoma. MATERIALS AND METHODS A total of 44 fibrosarcoma xenograft models were established by inoculating HT-1080 cells on the right thigh of mice and subjected tumors to DWI and IVIM imaging with 3.0 T MRI. Whole-tumor histogram parameters were calculated on apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f). Heterogeneous features, including necrosis rate, cell density, Ki-67 labeling index (LI), and microvascular density (MVD) were measured. Intraclass correlation coefficients (ICC), Pearson or Spearman correlation tests, and receiver operating characteristics (ROC) were performed. RESULTS The 90th percentile, skewness and kurtosis of ADC and D histograms showed correlations with necrosis rate, and the highest correlation coefficient was found for D90th (r = 0.485). ADC and D histogram parameters showed correlations with cell density and Ki-67 LI; D90th showed the highest correlation coefficient with cell density (r = -0.504); and Dmedian showed the most significant correlation with Ki-67 LI (r = -0.525). D*skewness, D*kurtosis, D*90th, fmean, and fmedian showed correlations with MVD. ADC90th, ADCskewness, ADCkurtosis, D90th, and Dskewness showed significant differences between the low necrosis and high necrosis groups, and the combination model showed the best diagnostic ability (AUC = 0.882), with 97% sensitivity, and 72.7% specificity. CONCLUSION Whole-tumor histogram parameters of DWI and IVIM were correlated with heterogeneous features in nude murine models of fibrosarcoma.
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Affiliation(s)
- Shaobo Fang
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China
| | - Yanyu Yang
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China
| | - Juan Tao
- Department of Pathology, The Second Hospital, Dalian Medical University, Dalian, China
| | - Zhenzhen Yin
- Department of Radiology, Suzhou Hospital of Anhui Medical University, Anhui, China
| | - Yajie Liu
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China
| | - Zhiqing Duan
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China
| | - Wenyu Liu
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China
| | - Shaowu Wang
- Department of Radiology, The Second Hospital, Dalian Medical University, Dalian 116027, China.
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Akinci O, Turkoglu F, Nalbant MO, Inci E. Differentiating renal cell carcinoma and oncocytoma with volumetric MRI histogram analysis. North Clin Istanb 2023; 10:636-641. [PMID: 37829753 PMCID: PMC10565746 DOI: 10.14744/nci.2023.26122] [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: 07/18/2023] [Accepted: 09/05/2023] [Indexed: 10/14/2023] Open
Abstract
OBJECTIVE In this study, the utility of histogram parameters derived from diffusion-weighted imaging for differentiate renal cell carcinoma (RCC) from oncocytoma was investigated. METHODS This research tracked 126 individuals who were diagnosed with RCC and oncocytoma through histopathological analysis, using magnetic resonance imaging (MRI) assessments from 2015 to 2023. We observed various attributes of these patients, including demographic details, surgical records, pre-surgery MRI results, MRI apparent diffusion coefficient (ADC) histogram analysis, and post-surgery histopathological outcomes. Calculations of ADC measurements such as mean, minimum, and maximum in conjunction with the 5th, 10th, 25th, 50th, 75th, 90th, and 95th quantile points were made. In addition, we also noted the skewness, kurtosis, and variance of these data points. RESULTS The focus group for this investigation consisted of 75 male and 51 female patients. Out of these, 82 were diagnosed with RCC and 44 with oncocytoma. All ADC parameters including ADCmin, ADCmedian, ADCmean, and ADCmax, including the 5th, 10th, 25th, 50th, 75th, 90th, and 95th quantile divisions among the oncocytoma cohort were observed to be higher than the corresponding ones in the RCC group. A statistically meaningful difference was discovered between the minimum ADC value along with the 5th ranking of ADC measurements (p<0.001), in addition to mean of ADC (p=0.050), and the 10th (p=0.002) and 25th (p=0.015) quantiles of ADC data. When considering the region below the curve (AUC) in ROC analysis, the value of ADCmin was recorded as 0.739, with a sensitivity of 75.0%, and specificity of 68.2%. CONCLUSION To distinguish oncocytoma from RCC, it may be useful to conduct a whole-tumor histogram and textural analysis of ADC values.
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Affiliation(s)
- Ozlem Akinci
- Department of Radiology, Bakirkoy Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turkiye
| | - Furkan Turkoglu
- Department of Radiology, Bakirkoy Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turkiye
| | - Mustafa Orhan Nalbant
- Department of Radiology, Bakirkoy Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turkiye
| | - Ercan Inci
- Department of Radiology, Bakirkoy Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turkiye
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Geng Y, Hong R, Cheng Y, Zhang F, Sha Y, Song Y. Whole-tumor histogram analysis of apparent diffusion coefficient maps with machine learning algorithms for predicting histologic grade of sinonasal squamous cell carcinoma: a preliminary study. Eur Arch Otorhinolaryngol 2023; 280:4131-4140. [PMID: 37160465 DOI: 10.1007/s00405-023-07989-9] [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: 01/05/2023] [Accepted: 04/18/2023] [Indexed: 05/11/2023]
Abstract
PURPOSE Accurate histologic grade assessment is helpful for clinical decision making and prognostic assessment of sinonasal squamous cell carcinoma (SNSCC). This research aimed to explore whether whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps with machine learning algorithms can predict histologic grade of SNSCC. METHODS One hundred and forty-seven patients with pathologically diagnosed SNSCC formed this retrospective study. Sixty-six patients were low-grade (grade I/II) and eighty-one patients were high-grade (grade III). Eighteen histogram features were obtained from quantitative ADC maps. Additionally, the mean ADC value and clinical features were analyzed for comparison with histogram features. Machine learning algorithms were applied to build the best diagnostic model for predicting histological grade. The receiver operating characteristic (ROC) curve was used to evaluate the performance of each model prediction, and the area under the ROC curve (AUC) were analyzed. RESULTS The histogram model based on three features (10th Percentile, Mean, and 90th Percentile) with support vector machine (SVM) classifier demonstrated excellent diagnostic performance, with an AUC of 0.947 on the testing dataset. The AUC of the histogram model was similar to that of the mean ADC value model (0.947 vs 0.957; P = 0.7029). The poor diagnostic performance of the clinical model (AUC = 0.692) was improved by the combined model incorporating histogram features or mean ADC value (P < 0.05). CONCLUSION ADC histogram analysis improved the projection of SNSCC histologic grade, compared with clinical model. The complex histogram model had comparable but not better performance than mean ADC value model.
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Affiliation(s)
- Yue Geng
- Department of Radiology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Rujian Hong
- Department of Radiology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Yushu Cheng
- Department of Radiology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Fang Zhang
- Department of Radiology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, China
| | - Yan Sha
- Department of Radiology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, China.
| | - Yang Song
- Scientific Marketing, Siemens Healthineers, Shanghai, 200336, China
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Akıncı Ö, Türkoglu F, Nalbant MO, Öner Ö, İnci E. The Effectiveness of Volumetric MRI Histogram Analysis in Renal Cell Carcinoma. Acad Radiol 2023; 30 Suppl 1:S278-S285. [PMID: 37105802 DOI: 10.1016/j.acra.2023.03.029] [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: 02/16/2023] [Revised: 03/19/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023]
Abstract
RATIONALE AND OBJECTIVES This study investigated the utility of histogram parameters derived from diffusion-weighted imaging (DWI) for evaluating renal cell carcinoma (RCC) grading prior to surgery. MATERIALS AND METHODS This retrospective study included 88 patients who were histopathologically diagnosed with RCC and underwent magnetic resonance imaging (MRI) examinations. The patients were divided into two groups as well-differentiated (Group 1) and poorly differentiated (Group 2). Demographic data, preoperative MRI findings, MRI apparent diffusion coefficient (ADC) histogram analyzes, operation types, postoperative histopathological data and cancer stages of the patients were recorded. The histogram parameters of ADC values, comprising the mean, minimum, maximum, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles, as well as skewness, kurtosis, and variance, were calculated. RESULTS The study included 59 males and 29 women with an average age of 56.21 ± 1.33 years. There were 52 patients in Group 1 and 36 patients in Group 2. The ADCmin, ADCmean, ADCmax, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of ADC values of the poorly differentiated group were all lower than those of the well-differentiated group. ADCmin and the 5th percentile of ADC values, as well as ADCmean and the 10th, 25th, 50th, and 75th percentiles of ADC values, showed a statistically significant difference (p < 0.05). The AUC, sensitivity, and specificity of the ADCmin value were 0.703, 56.3%, and 75.7%, respectively. CONCLUSION The present study indicated that histogram parameters generated from DWI were capable of differentiating between high-grade and low-grade RCC.
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Affiliation(s)
- Özlem Akıncı
- Bakırköy Dr Sadi Konuk Training and Research Hospital, Department of Radiology, Istanbul, Turkey.
| | - Furkan Türkoglu
- Bakırköy Dr Sadi Konuk Training and Research Hospital, Department of Radiology, Istanbul, Turkey
| | - Mustafa Orhan Nalbant
- Bakırköy Dr Sadi Konuk Training and Research Hospital, Department of Radiology, Istanbul, Turkey
| | - Özkan Öner
- Bakırköy Dr Sadi Konuk Training and Research Hospital, Department of Radiology, Istanbul, Turkey
| | - Ercan İnci
- Bakırköy Dr Sadi Konuk Training and Research Hospital, Department of Radiology, Istanbul, Turkey
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Zhou Y, Song L, Xia J, Liu H, Xing J, Gao J. Radiomics model based on contrast-enhanced CT texture features for pretreatment prediction of overall survival in esophageal neuroendocrine carcinoma. Front Oncol 2023; 13:1225180. [PMID: 37664013 PMCID: PMC10473874 DOI: 10.3389/fonc.2023.1225180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/25/2023] [Indexed: 09/05/2023] Open
Abstract
Background Limited studies have observed the prognostic value of CT images for esophageal neuroendocrine carcinoma (NEC) due to rare incidence and low treatment experience in clinical. In this study, the pretreatment enhanced CT texture features and clinical characteristics were investigated to predict the overall survival of esophageal NEC. Methods This retrospective study included 89 patients with esophageal NEC. The training and testing cohorts comprised 61 (70%) and 28 (30%) patients, respectively. A total of 402 radiomics features were extracted from the tumor region that segmented pretreatment venous phase CT images. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to feature dimension reduction, feature selection, and radiomics signature construction. A radiomics nomogram was constructed based on the radiomics signature and clinical risk factors using a multivariable Cox proportional regression. The performance of the nomogram for the pretreatment prediction of overall survival (OS) was evaluated for discrimination and calibration. Results Only the enhancement degree was an independent factor in clinical variable influenced OS. The radiomics signatures demonstrated good predictability for prognostic status discrimination. The radiomics nomogram integrating texture signatures was slightly superior to the nomogram derived from the combined model with a C-index of 0.844 (95%CI: 0.783-0.905) and 0.847 (95% CI: 0.782-0.912) in the training set, and 0.805 (95%CI: 0.707-0.903) and 0.745 (95% CI: 0.639-0.851) in the testing set, respectively. Conclusion The radiomics nomogram based on pretreatment CT radiomics signature had better prognostic power and predictability of the overall survival in patients with esophageal NEC than the model using combined variables.
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Affiliation(s)
- Yue Zhou
- Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lijie Song
- Department of Oncology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jin Xia
- Department of Oncology, Anyang Tumor Hospital, Anyang, China
| | - Huan Liu
- Advanced Analytics Team, GE Healthcare, Shanghai, China
| | - Jingjing Xing
- Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianbo Gao
- Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Sánchez Iglesias Á, Morillo Macías V, Picó Peris A, Fuster-Matanzo A, Nogué Infante A, Muelas Soria R, Bellvís Bataller F, Domingo Pomar M, Casillas Meléndez C, Yébana Huertas R, Ferrer Albiach C. Prostate Region-Wise Imaging Biomarker Profiles for Risk Stratification and Biochemical Recurrence Prediction. Cancers (Basel) 2023; 15:4163. [PMID: 37627191 PMCID: PMC10453281 DOI: 10.3390/cancers15164163] [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: 07/21/2023] [Revised: 08/10/2023] [Accepted: 08/13/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Identifying prostate cancer (PCa) patients with a worse prognosis and a higher risk of biochemical recurrence (BCR) is essential to guide treatment choices. Here, we aimed to identify possible imaging biomarker (perfusion/diffusion + radiomic features) profiles extracted from MRIs that were able to discriminate patients according to their risk or the occurrence of BCR 10 years after diagnosis, as well as to evaluate their predictive value with or without clinical data. METHODS Patients with localized PCa receiving neoadjuvant androgen deprivation therapy and radiotherapy were retrospectively evaluated. Imaging features were extracted from MRIs for each prostate region or for the whole gland. Univariate and multivariate analyses were conducted. RESULTS 128 patients (mean [range] age, 71 [50-83] years) were included. Prostate region-wise imaging biomarker profiles mainly composed of radiomic features allowed discriminating risk groups and patients experiencing BCR. Heterogeneity-related radiomic features were increased in patients with worse prognosis and with BCR. Overall, imaging biomarkers profiles retained good predictive ability (AUC values superior to 0.725 in most cases), which generally improved when clinical data were included (particularly evident for the prediction of the BCR, with AUC values ranging from 0.841 to 0.877 for combined models and sensitivity values above 0.960) and when models were built per prostate region vs. the whole gland. CONCLUSIONS Prostate region-aware imaging profiles enable identification of patients with worse prognosis and with a higher risk of BCR, retaining higher predictive values when combined with clinical variables.
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Affiliation(s)
- Ángel Sánchez Iglesias
- Radiation Oncology Department, Hospital Provincial de Castellón, 12002 Castellón, Spain; (Á.S.I.); (V.M.M.); (R.M.S.)
| | - Virginia Morillo Macías
- Radiation Oncology Department, Hospital Provincial de Castellón, 12002 Castellón, Spain; (Á.S.I.); (V.M.M.); (R.M.S.)
| | - Alfonso Picó Peris
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (A.P.P.); (A.F.-M.); (A.N.I.); (F.B.B.); (M.D.P.); (R.Y.H.)
| | - Almudena Fuster-Matanzo
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (A.P.P.); (A.F.-M.); (A.N.I.); (F.B.B.); (M.D.P.); (R.Y.H.)
| | - Anna Nogué Infante
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (A.P.P.); (A.F.-M.); (A.N.I.); (F.B.B.); (M.D.P.); (R.Y.H.)
| | - Rodrigo Muelas Soria
- Radiation Oncology Department, Hospital Provincial de Castellón, 12002 Castellón, Spain; (Á.S.I.); (V.M.M.); (R.M.S.)
| | - Fuensanta Bellvís Bataller
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (A.P.P.); (A.F.-M.); (A.N.I.); (F.B.B.); (M.D.P.); (R.Y.H.)
| | - Marcos Domingo Pomar
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (A.P.P.); (A.F.-M.); (A.N.I.); (F.B.B.); (M.D.P.); (R.Y.H.)
| | | | - Raúl Yébana Huertas
- Quantitative Imaging Biomarkers in Medicine (Quibim), 46021 Valencia, Spain; (A.P.P.); (A.F.-M.); (A.N.I.); (F.B.B.); (M.D.P.); (R.Y.H.)
| | - Carlos Ferrer Albiach
- Radiation Oncology Department, Hospital Provincial de Castellón, 12002 Castellón, Spain; (Á.S.I.); (V.M.M.); (R.M.S.)
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Park AY, Han MR, Seo BK, Ju HY, Son GS, Lee HY, Chang YW, Choi J, Cho KR, Song SE, Woo OH, Park HS. MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study. Breast Cancer Res 2023; 25:79. [PMID: 37391754 PMCID: PMC10311893 DOI: 10.1186/s13058-023-01668-7] [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: 01/11/2023] [Accepted: 05/31/2023] [Indexed: 07/02/2023] Open
Abstract
BACKGROUND There are few prospective studies on the correlations between MRI features and whole RNA-sequencing data in breast cancer according to molecular subtypes. The purpose of our study was to explore the association between genetic profiles and MRI phenotypes of breast cancer and to identify imaging markers that influences the prognosis and treatment according to subtypes. METHODS From June 2017 to August 2018, MRIs of 95 women with invasive breast cancer were prospectively analyzed, using the breast imaging-reporting and data system and texture analysis. Whole RNA obtained from surgical specimens was analyzed using next-generation sequencing. The association between MRI features and gene expression profiles was analyzed in the entire tumor and subtypes. Gene networks, enriched functions, and canonical pathways were analyzed using Ingenuity Pathway Analysis. The P value for differential expression was obtained using a parametric F test comparing nested linear models and adjusted for multiple testing by reporting Q value. RESULTS In 95 participants (mean age, 53 years ± 11 [standard deviation]), mass lesion type was associated with upregulation of CCL3L1 (sevenfold) and irregular mass shape was associated with downregulation of MIR421 (sixfold). In estrogen receptor-positive cancer with mass lesion type, CCL3L1 (21-fold), SNHG12 (11-fold), and MIR206 (sevenfold) were upregulated, and MIR597 (265-fold), MIR126 (12-fold), and SOX17 (fivefold) were downregulated. In triple-negative breast cancer with increased standard deviation of texture analysis on precontrast T1-weighted imaging, CLEC3A (23-fold), SRGN (13-fold), HSPG2 (sevenfold), KMT2D (fivefold), and VMP1 (fivefold) were upregulated, and IGLC2 (73-fold) and PRDX4 (sevenfold) were downregulated (all, P < 0.05 and Q < 0.1). Gene network and functional analysis showed that mass type estrogen receptor-positive cancers were associated with cell growth, anti-estrogen resistance, and poor survival. CONCLUSION MRI characteristics are associated with the different expressions of genes related to metastasis, anti-drug resistance, and prognosis, depending on the molecular subtypes of breast cancer.
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Affiliation(s)
- Ah Young Park
- Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea
| | - Mi-Ryung Han
- Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea
| | - Bo Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City, Gyeonggi-do, 15355, Republic of Korea.
| | - Hye-Yeon Ju
- Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon, Republic of Korea
| | - Gil Soo Son
- Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Ansan City, Gyeonggi-do, Republic of Korea
| | - Hye Yoon Lee
- Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Ansan City, Gyeonggi-do, Republic of Korea
| | - Young Woo Chang
- Division of Breast and Endocrine Surgery, Department of Surgery, Korea University Ansan Hospital, Korea University College of Medicine, Ansan City, Gyeonggi-do, Republic of Korea
| | - Jungyoon Choi
- Division of Hematology/Oncology, Department of Internal Medicine, Korea University Ansan Hospital, Korea University College of Medicine, Ansan City, Gyeonggi-do, Republic of Korea
| | - Kyu Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sung Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Ok Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Hyun Soo Park
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City, Gyeonggi-do, 15355, Republic of Korea
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Sui Q, Hu Z, Jin X, Bian Y, Liang J, Zhang H, Yang H, Lin Z, Wang Q, Zhan C, Chen Z. The genomic signature of resistance to platinum-containing neoadjuvant therapy based on single-cell data. Cell Biosci 2023; 13:103. [PMID: 37291676 PMCID: PMC10249226 DOI: 10.1186/s13578-023-01061-z] [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: 03/13/2023] [Accepted: 05/26/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND Neoadjuvant chemotherapy (NACT) becomes the first-line option for advanced tumors, while patients who are not sensitive to it may not benefit. Therefore, it is important to screen patients suitable for NACT. METHODS Single-cell data of lung adenocarcinoma (LUAD) and esophageal squamous carcinoma (ESCC) before and after cisplatin-containing (CDDP) NACT and cisplatin IC50 data of tumor cell lines were analyzed to establish a CDDP neoadjuvant chemotherapy score (NCS). Differential analysis, GO, KEGG, GSVA and logistic regression models were performed by R. Survival analysis were applied to public databases. siRNA knockdown in A549, PC9, TE1 cell lines, qRT-PCR, western-blot, cck8 and EdU experiments were used for further verification in vitro. RESULTS 485 genes were expressed differentially in tumor cells before and after neoadjuvant treatment for LUAD and ESCC. After combining the CDDP-associated genes, 12 genes, CAV2, PHLDA1, DUSP23, VDAC3, DSG2, SPINT2, SPATS2L, IGFBP3, CD9, ALCAM, PRSS23, PERP, were obtained and formed the NCS score. The higher the score, the more sensitive the patients were to CDDP-NACT. The NCS divided LUAD and ESCC into two groups. Based on differentially expressed genes, a model was constructed to predict the high and low NCS. CAV2, PHLDA1, ALCAM, CD9, IGBP3 and VDAC3 were significantly associated with prognosis. Finally, we demonstrated that the knockdown of CAV2, PHLDA1 and VDAC3 in A549, PC9 and TE1 significantly increased the sensitivity to cisplatin. CONCLUSIONS NCS scores and related predictive models for CDDP-NACT were developed and validated to assist in selecting patients who might benefit from it.
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Affiliation(s)
- Qihai Sui
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Zhengyang Hu
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Xing Jin
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Yunyi Bian
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Jiaqi Liang
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Huan Zhang
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Huiqiang Yang
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Zongwu Lin
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Qun Wang
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
| | - Cheng Zhan
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
| | - Zhencong Chen
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
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Xu WJ, Zheng BJ, Lu J, Liu SY, Li HL. Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture analysis of dynamic contrast-enhanced MRI. BMC Med Imaging 2023; 23:70. [PMID: 37264313 DOI: 10.1186/s12880-023-01022-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/23/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Triple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC. METHODS This retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (Ktrans, Kep and Ve) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived. RESULTS The binary logistic regression analysis revealed that Kep_Range (p = 0.032) and Ve_SumVariance (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of Kep_Compactness2 (p = 0.049), Kep_SphericalDisproportion (p = 0.049), and Ve_GlcmEntropy (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group. CONCLUSION Histogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.
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Affiliation(s)
- Wen-Juan Xu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Bing-Jie Zheng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Jun Lu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Si-Yun Liu
- GE healthcare (China), Beijing, 100176, China
| | - Hai-Liang Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
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Zhang YP, Zhang XY, Cheng YT, Li B, Teng XZ, Zhang J, Lam S, Zhou T, Ma ZR, Sheng JB, Tam VCW, Lee SWY, Ge H, Cai J. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res 2023; 10:22. [PMID: 37189155 DOI: 10.1186/s40779-023-00458-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 05/17/2023] Open
Abstract
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.
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Affiliation(s)
- Yuan-Peng Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China
| | - Xin-Yun Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Yu-Ting Cheng
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Bing Li
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Xin-Zhi Teng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Saikit Lam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Ta Zhou
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Zong-Rui Ma
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jia-Bao Sheng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Victor C W Tam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Shara W Y Lee
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Hong Ge
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Jing Cai
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China.
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Wang G, Zhou J. The value of whole-volume apparent diffusion coefficient histogram analysis in preoperatively distinguishing intracranial solitary fibrous tumor and transitional meningioma. Front Oncol 2023; 13:1155162. [PMID: 37260978 PMCID: PMC10228830 DOI: 10.3389/fonc.2023.1155162] [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: 01/31/2023] [Accepted: 05/04/2023] [Indexed: 06/02/2023] Open
Abstract
Purpose To investigate the value of whole-volume apparent diffusion coefficient (ADC) histogram analysis in preoperatively distinguishing intracranial solitary fibrous tumors (SFT) from transitional meningiomas (TM), thereby assisting the establishment of the treatment protocol. Methods Preoperative diffusion-weighted imaging datasets of 24 patients with SFT and 28 patients with TM were used to extract whole-volume ADC histogram parameters, including variance, skewness, kurtosis, and mean, as well as 1st (AP1), 10th (AP10), 50th (AP50), 90th (AP90), and 99th (AP99) percentiles of ADC using MaZda software. The independent t-test or Mann-Whitney U test was used to compare the differences between ADC histogram parameters of SFT and TM. Receiver operating characteristic (ROC) curves were generated to evaluate the performance of significant ADC histogram parameters. Spearman's correlation coefficients were calculated to evaluate correlations between these parameters and the Ki-67 expression levels. Results SFT exhibited significantly higher variance, and lower AP1 and AP10 (all P < 0.05) than TM. The best diagnostic performance was obtained by variance, with an area under the ROC curve of 0.848 (0.722-0.933). However, there was no significant difference in skewness, kurtosis, mean, or other percentiles of ADC between the two groups (all P > 0.05). Significant correlations were also observed between the Ki-67 proliferation index and variance (r = 0.519), AP1 (r = -0.425), and AP10 (r = -0.372) (all P < 0.05). Conclusion Whole-volume ADC histogram analysis is a feasible tool for non-invasive preoperative discrimination between intracranial SFT and TM, with variance being the most promising prospective parameter.
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Affiliation(s)
- Gang Wang
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
| | - Junlin Zhou
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
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Gong XQ, Liu N, Tao YY, Li L, Li ZM, Yang L, Zhang XM. Radiomics models based on multisequence MRI for predicting PD-1/PD-L1 expression in hepatocellular carcinoma. Sci Rep 2023; 13:7710. [PMID: 37173350 PMCID: PMC10182068 DOI: 10.1038/s41598-023-34763-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 05/07/2023] [Indexed: 05/15/2023] Open
Abstract
The purpose of this study was to explore the effectiveness of radiomics based on multisequence MRI in predicting the expression of PD-1/PD-L1 in hepatocellular carcinoma (HCC). One hundred and eight patients with HCC who underwent contrast-enhanced MRI 2 weeks before surgical resection were enrolled in this retrospective study. Corresponding paraffin sections were collected for immunohistochemistry to detect the expression of PD-1 and PD-L1. All patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Univariate and multivariate analyses were used to select potential clinical characteristics related to PD-1 and PD-L1 expression. Radiomics features were extracted from the axial fat-suppression T2-weighted imaging (FS-T2WI) images and the arterial phase and portal venous phase images from the axial dynamic contrast-enhanced MRI, and the corresponding feature sets were generated. The least absolute shrinkage and selection operator (LASSO) was used to select the optimal radiomics features for analysis. Logistic regression analysis was performed to construct single-sequence and multisequence radiomics and radiomic-clinical models. The predictive performance was judged by the area under the receiver operating characteristic curve (AUC) in the training and validation cohorts. In the whole cohort, PD-1 expression was positive in 43 patients, and PD-L1 expression was positive in 34 patients. The presence of satellite nodules served as an independent predictor of PD-L1 expression. The AUC values of the FS-T2WI, arterial phase, portal venous phase and multisequence models in predicting the expression of PD-1 were 0.696, 0.843, 0.863, and 0.946 in the training group and 0.669, 0.792, 0.800 and 0.815 in the validation group, respectively. The AUC values of the FS-T2WI, arterial phase, portal venous phase, multisequence and radiomic-clinical models in predicting PD-L1 expression were 0.731, 0.800, 0.800, 0.831 and 0.898 in the training group and 0.621, 0.743, 0.771, 0.810 and 0.779 in the validation group, respectively. The combined models showed better predictive performance. The results of this study suggest that a radiomics model based on multisequence MRI has the potential to predict the preoperative expression of PD-1 and PD-L1 in HCC, which could become an imaging biomarker for immune checkpoint inhibitor (ICI)-based treatment.
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Affiliation(s)
- Xue-Qin Gong
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, 400030, China
| | - Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Yun-Yun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Li Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Zu-Mao Li
- Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
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Tanişman Ö, Kiziltepe FT, Yildirim Ç, Coşar ZS. Prediction of prognostic factors in breast cancer: A noninvasive method utilizing histogram parameters derived from Adc maps. Heliyon 2023; 9:e16282. [PMID: 37251865 PMCID: PMC10208937 DOI: 10.1016/j.heliyon.2023.e16282] [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: 11/03/2022] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 05/31/2023] Open
Abstract
Objective The aim of this study is to investigate the relationship between histogram parameters and prognostic factors of breast cancer and to reveal the diagnostic performance of histogram parameters in predicting prognostic factors status. Materials and methods Ninety-two patients with a confirmed histopathological diagnosis of breast cancer were included in the study. Magnetic resonance imaging (MRI) was performed using a 1.5T scanner and two different b values were used for diffusion-weighted imaging (DWI) (b values: 0 s/mm2, b: 800 s/mm2). For 3D histogram analysis, regions of interest (ROI) were drawn each slice of the lesion on apparent diffusion coefficient (ADC) maps. The following data were derived from the histogram analysis data: percentiles, skewness, kurtosis, and entropy. The relationship between prognostic factors and histogram analysis data was investigated using the Kolmogorov-Smirnov test, Shapiro-Wilk test, skewness-kurtosis test, independent t-test, Mann-Whitney U test, and Kruskal-Wallis test. Receiver operator characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the histogram parameters. Results ADCmax, kurtosis, and entropy parameters were statistically significantly correlated with tumor diameter (p = 0.002, p = 0.008, and p = 0.001, respectively). There was a significant difference in ADC90% and ADCmax values, depending on estrogen receptor (ER) and progesterone receptor (PR) status. These values were lower in ER- and PR-positive than ER- and PR-negative patients (p = 0.02 and p = 0.001 vs. p = 0.018, p = 0.008). All ADC percentage values were lower in patients with a positive Ki-67 proliferation index as compared with those with a negative Ki-67 proliferation index (all p = 0.001). The entropy value was high in high-grade lesions and lesions with axillary involvement (p = 0.039 and p = 0.048, respectively). The highest area under the curve (AUC) for ER and PR status was calculated for the ADC90% value with ROC curve analysis. The highest AUC for Ki-67 proliferation index was found for the ADC50%. Conclusion Histogram analysis parameters derived from of ADC maps of whole lesions can reflect histopathological features of the tumors. Based on our study, it was concluded that histogram analysis parameters were related to the prognostic factors of the tumor.
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Affiliation(s)
- Özge Tanişman
- Deparment of Radiology, Oltu State Hospital, Erzurum, Turkey
| | - Fatma Tuba Kiziltepe
- Deparment of Radiology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Çiğdem Yildirim
- Department of Pathology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
| | - Zehra Sumru Coşar
- Deparment of Radiology, University of Health Sciences Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, Turkey
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Paudyal R, Shah AD, Akin O, Do RKG, Konar AS, Hatzoglou V, Mahmood U, Lee N, Wong RJ, Banerjee S, Shin J, Veeraraghavan H, Shukla-Dave A. Artificial Intelligence in CT and MR Imaging for Oncological Applications. Cancers (Basel) 2023; 15:cancers15092573. [PMID: 37174039 PMCID: PMC10177423 DOI: 10.3390/cancers15092573] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Akash D Shah
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Usman Mahmood
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Nancy Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Richard J Wong
- Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | | | | | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA
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Kim HY, Bae MS, Seo BK, Lee JY, Cho KR, Woo OH, Song SE, Cha J. Comparison of CT- and MRI-Based Quantification of Tumor Heterogeneity and Vascularity for Correlations with Prognostic Biomarkers and Survival Outcomes: A Single-Center Prospective Cohort Study. Bioengineering (Basel) 2023; 10:bioengineering10050504. [PMID: 37237574 DOI: 10.3390/bioengineering10050504] [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: 03/20/2023] [Revised: 04/17/2023] [Accepted: 04/20/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Tumor heterogeneity and vascularity can be noninvasively quantified using histogram and perfusion analyses on computed tomography (CT) and magnetic resonance imaging (MRI). We compared the association of histogram and perfusion features with histological prognostic factors and progression-free survival (PFS) in breast cancer patients on low-dose CT and MRI. METHODS This prospective study enrolled 147 women diagnosed with invasive breast cancer who simultaneously underwent contrast-enhanced MRI and CT before treatment. We extracted histogram and perfusion parameters from each tumor on MRI and CT, assessed associations between imaging features and histological biomarkers, and estimated PFS using the Kaplan-Meier analysis. RESULTS Out of 54 histogram and perfusion parameters, entropy on T2- and postcontrast T1-weighted MRI and postcontrast CT, and perfusion (blood flow) on CT were significantly associated with the status of subtypes, hormone receptors, and human epidermal growth factor receptor 2 (p < 0.05). Patients with high entropy on postcontrast CT showed worse PFS than patients with low entropy (p = 0.053) and high entropy on postcontrast CT negatively affected PFS in the Ki67-positive group (p = 0.046). CONCLUSIONS Low-dose CT histogram and perfusion analysis were comparable to MRI, and the entropy of postcontrast CT could be a feasible parameter to predict PFS in breast cancer patients.
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Affiliation(s)
- Hyo-Young Kim
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
| | - Min-Sun Bae
- Department of Radiology, Inha University Hospital, Inha University College of Medicine, Inhang-ro 27, Jung-gu, Incheon 22332, Republic of Korea
| | - Bo-Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
| | - Ji-Young Lee
- Department of Radiology, Ilsan Paik Hospital, Inje University College of Medicine, 170 Juhwa-ro, Ilsanseo-gu, Goyang 10380, Republic of Korea
| | - Kyu-Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Ok-Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul 08308, Republic of Korea
| | - Sung-Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Jaehyung Cha
- Medical Science Research Center, Korea University Ansan Hospital, Korea University College of Medicine, 123 Jeokgeum-ro, Danwon-gu, Ansan City 15355, Republic of Korea
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Ogbonnaya CN, Alsaedi BSO, Alhussaini AJ, Hislop R, Pratt N, Nabi G. Radiogenomics Reveals Correlation between Quantitative Texture Radiomic Features of Biparametric MRI and Hypoxia-Related Gene Expression in Men with Localised Prostate Cancer. J Clin Med 2023; 12:jcm12072605. [PMID: 37048688 PMCID: PMC10095552 DOI: 10.3390/jcm12072605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 04/14/2023] Open
Abstract
OBJECTIVES To perform multiscale correlation analysis between quantitative texture feature phenotypes of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes. MATERIALS AND METHODS Images from pre-biopsy 3T bpMRI scans in clinically localised PCa patients of various risk categories (n = 15) were used to extract textural features. The genomic landscape of hypoxia-related gene expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the important hypoxia-related genes with 28 radiomic texture features. Then, cBioportal was accessed, and a gene-specific query was executed to extract the Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Based on each selected gene profile, correlation analysis using Pearson's coefficients and survival analysis using Kaplan-Meier estimators were performed. RESULTS The quantitative bpMR imaging textural features, including the histogram and grey level co-occurrence matrix (GLCM), correlated with three hypoxia-related genes (ANGPTL4, VEGFA, and P4HA1) based on RNA sequencing using the TempO-Seq method. Further radiogenomic analysis, including data accessed from the cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcomes, with a median survival ratio of 81.11:133.00 months in those with and without alterations in genes, respectively. CONCLUSION This study found that there is a correlation between the radiomic texture features extracted from bpMRI in localised prostate cancer and the hypoxia-related genes that are differentially expressed. The analysis of expression data based on cBioportal revealed that these hypoxia-related genes, which were the focus of the study, are linked to an unfavourable survival outcomes in prostate cancer patients.
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Affiliation(s)
- Chidozie N Ogbonnaya
- Division of Imaging Science and Technology, University of Dundee, Dundee DD1 4HN, UK
- College of Basic Medical Sciences, Abia State University, Uturu 441103, Nigeria
| | - Basim S O Alsaedi
- Statistics Department, University of Tabuk, Tabuk 47512, Saudi Arabia
| | - Abeer J Alhussaini
- Division of Imaging Science and Technology, University of Dundee, Dundee DD1 4HN, UK
- Department of Medical Imaging, Al-Amiri Hospital, Ministry of Health, Sulaibikhat 1300, Kuwait
| | - Robert Hislop
- Cytogenetic, Human Genetics Unit, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
| | - Norman Pratt
- Cytogenetic, Human Genetics Unit, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
| | - Ghulam Nabi
- Division of Imaging Science and Technology, University of Dundee, Dundee DD1 4HN, UK
- School of Medicine, Ninewells Hospital, Dundee DD1 9SY, UK
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An H, Bhatia I, Cao F, Huang Z, Xie C. CT texture analysis in predicting treatment response and survival in patients with hepatocellular carcinoma treated with transarterial chemoembolization using random forest models. BMC Cancer 2023; 23:201. [PMID: 36869284 PMCID: PMC9983241 DOI: 10.1186/s12885-023-10620-z] [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: 09/12/2022] [Accepted: 02/06/2023] [Indexed: 03/05/2023] Open
Abstract
BACKGROUND Using texture features derived from contrast-enhanced computed tomography (CT) combined with general imaging features as well as clinical information to predict treatment response and survival in patients with hepatocellular carcinoma (HCC) who received transarterial chemoembolization (TACE) treatment. METHODS From January 2014 to November 2022, 289 patients with HCC who underwent TACE were retrospectively reviewed. Their clinical information was documented. Their treatment-naïve contrast-enhanced CTs were retrieved and reviewed by two independent radiologists. Four general imaging features were evaluated. Texture features were extracted based on the regions of interest (ROIs) drawn on the slice with the largest axial diameter of all lesions using Pyradiomics v3.0.1. After excluding features with low reproducibility and low predictive value, the remaining features were selected for further analyses. The data were randomly divided in a ratio of 8:2 for model training and testing. Random forest classifiers were built to predict patient response to TACE treatment. Random survival forest models were constructed to predict overall survival (OS) and progress-free survival (PFS). RESULTS We retrospectively evaluated 289 patients (55.4 ± 12.4 years old) with HCC treated with TACE. Twenty features, including 2 clinical features (ALT and AFP levels), 1 general imaging feature (presence or absence of portal vein thrombus) and 17 texture features, were included in model construction. The random forest classifier achieved an area under the curve (AUC) of 0.947 with an accuracy of 89.5% for predicting treatment response. The random survival forest showed good predictive performance with out-of-bag error rate of 0.347 (0.374) and a continuous ranked probability score (CRPS) of 0.170 (0.067) for the prediction of OS (PFS). CONCLUSIONS Random forest algorithm based on texture features combined with general imaging features and clinical information is a robust method for predicting prognosis in patients with HCC treated with TACE, which may help avoid additional examinations and assist in treatment planning.
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Affiliation(s)
- He An
- Diagnostic Imaging Division, Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Inderjeet Bhatia
- Department of Cardiothoracic Surgery, Queen Mary Hospital, Hong Kong, China
| | - Fei Cao
- Minimally Invasive Interventional Division, Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zilin Huang
- Minimally Invasive Interventional Division, Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chuanmiao Xie
- Diagnostic Imaging Division, Department of Medical Imaging and Interventional Radiology, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, 510060, China.
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