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Yang Y, Xu Z, Cai Z, Zhao H, Zhu C, Hong J, Lu R, Lai X, Guo L, Hu Q, Xu Z. Novel deep learning radiomics nomogram-based multiparametric MRI for predicting the lymph node metastasis in rectal cancer: A dual-center study. J Cancer Res Clin Oncol 2024; 150:450. [PMID: 39379733 PMCID: PMC11461781 DOI: 10.1007/s00432-024-05986-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: 06/02/2024] [Accepted: 10/03/2024] [Indexed: 10/10/2024]
Abstract
PURPOSE To develop and evaluate a nomogram that integrates clinical parameters with deep learning radiomics (DLR) extracted from Magnetic Resonance Imaging (MRI) data to enhance the predictive accuracy for preoperative lymph node (LN) metastasis in rectal cancer. METHODS A retrospective analysis was conducted on 356 patients diagnosed with rectal cancer. Of these, 286 patients were allocated to the training set, and 70 patients comprised the external validation cohort. Preprocessed T2-weighted and diffusion-weighted imaging performed preoperatively facilitated the extraction of DLR features. Five machine learning algorithms-k-nearest neighbor, light gradient boosting machine, logistic regression, random forest, and support vector machine-were utilized to develop DLR models. The most effective algorithm was identified and used to establish a clinical DLR (CDLR) nomogram specifically designed to predict LN metastasis in rectal cancer. The performance of the nomogram was evaluated using receiver operating characteristic curve analysis. RESULTS The logistic regression classifier demonstrated significant predictive accuracy using the DLR signature, achieving an Area Under the Curve (AUC) of 0.919 in the training cohort and 0.778 in the external validation cohort. The integrated CDLR nomogram exhibited robust predictive performance across both datasets, with AUC values of 0.921 in the training cohort and 0.818 in the external validation cohort. Notably, it outperformed both the clinical model, which had AUC values of 0.770 and 0.723 in the training and external validation cohorts, respectively, and the stand-alone DLR model. CONCLUSION The nomogram derived from multiparametric MRI data, referred to as the CDLR model, demonstrates strong predictive efficacy in forecasting LN metastasis in rectal cancer.
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Affiliation(s)
- Yunjun Yang
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Zhenyu Xu
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Zhiping Cai
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China
| | - Hai Zhao
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Cuiling Zhu
- Department of Radiology, Foshan Hospital of Traditional Chinese Medicine, Guangzhou University of Traditional Chinese Medicine, Foshan, China
| | - Julu Hong
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Ruiliang Lu
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Xiaoyu Lai
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China
| | - Li Guo
- Department of Institute of Translational Medicine, The First People's Hospital of Foshan, Foshan, China
| | - Qiugen Hu
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China
| | - Zhifeng Xu
- Department of Radiology, The First People's Hospital of Foshan, No. 81 North Lingnan Avenue, Foshan, 528010, China.
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Alves VDPV, Mahalingam N, Tkach JA, Towbin AJ, Imbus R, Denson LA, Dillman JR. Prospective characterization of intestinal MRI intravoxel incoherent motion in pediatric and young adult patients with newly diagnosed small bowel Crohn's disease. Abdom Radiol (NY) 2024; 49:3325-3336. [PMID: 38724774 DOI: 10.1007/s00261-024-04318-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 03/28/2024] [Accepted: 03/29/2024] [Indexed: 09/13/2024]
Abstract
BACKGROUND MRI diffusion-weighted imaging (DWI) is commonly used in MR enterography protocols for assessment of intestinal inflammation in patients with Crohn's disease. The intravoxel incoherent motion (IVIM) approach to DWI has been proposed as a more objective approach, providing quantitative parameters that reflect water diffusivity (D), blood flow (D*), and perfusion fraction (f). PURPOSE We aimed to determine if DWI-IVIM metrics from the terminal ileum in patients with newly diagnosed Crohn's disease differ from healthy participants and change in response to biologic medical therapy. METHODS In this prospective case-control study, 20 consecutive pediatric patients (mean age = 14 years ± 2 [SD]; eight females) with newly diagnosed ileal Crohn's disease and 15 pediatric healthy participants (mean age = 18 years ± 4 [SD]; eight females) underwent research MRI examinations of the small bowel between 12/2018 and 10/2021. Participants with Crohn's disease underwent MR studies at baseline, 6 weeks, and 6 months following initiation of anti-TNF-alpha therapy, whereas control participants underwent one research MRI examination. The MRI protocol included a DWI-IVIM sequence with nine b-values and the IVIM parameters (D, D*, and f) were extracted. Unpaired t-tests and mixed-effects models were used for analyses. RESULTS Mean IVIM D (P < 0.001), D* (P = 0.004), and f (P = 0.001) metrics were lower for Crohn's patients at the time of diagnosis compared to healthy participants. Mean IVIM f value increased over time in response to medical therapy (mean f at baseline, 22% ± 6%; 6 weeks, 25% ± 7%; 6 months, 29% ± 10%; P = 0.016). Mean IVIM D* value increased over time in response to treatment (mean D* at baseline, 10.9 ± 3.0 × 10-3 mm2/s; 6 weeks, 11.8 ± 2.8 × 10-3 mm2/s; 6 months, 13.3 ± 3.3 × 10-3 mm2/s; P = 0.047), while there was no significant change in mean IVIM D value (P = 0.10). CONCLUSION MRI DWI-IVIM metrics in patients with ileal Crohn's disease change over time in response to biological therapy and help discriminate these patients from healthy participants.
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Affiliation(s)
- Vinicius de Padua V Alves
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
| | - Neeraja Mahalingam
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Jean A Tkach
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Alexander J Towbin
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Rebecca Imbus
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Lee A Denson
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children's Hospital, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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Wang J, Hu S, Liang P, Hu X, Shen Y, Peng Y, Kamel I, Li Z. R2* mapping and reduced field-of-view diffusion-weighted imaging for preoperative assessment of nonenlarged lymph node metastasis in rectal cancer. NMR IN BIOMEDICINE 2024; 37:e5174. [PMID: 38712650 DOI: 10.1002/nbm.5174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 04/18/2024] [Accepted: 04/20/2024] [Indexed: 05/08/2024]
Abstract
The aim of the current study is to investigate the diagnostic value of R2* mapping versus reduced field-of-view diffusion-weighted imaging (rDWI) of the primary lesion of rectal cancer for preoperative prediction of nonenlarged lymph node metastasis (NLNM). Eighty-one patients with pathologically confirmed rectal cancer underwent preoperative R2* mapping and rDWI sequences before total mesorectal excisions and accompanying regional lymph node dissections. Two radiologists independently performed whole-tumor measurements of R2* and apparent diffusion coefficient (ADC) parameters on primary lesions of rectal cancer. Patients were divided into positive (NLNM+) and negative (NLNM-) groups based on their pathological analysis. The tumor location, maximum diameter of the tumor, and maximum short diameter of the lymph node were assessed. R2* and ADC, pT stage, tumor grade, status of mesorectal fascia, and extramural vascular invasion were also studied for their potential relationships with NLNM using multivariate logistic regression analysis. The NLNM+ group had significantly higher R2* (43.56 ± 8.43 vs. 33.87 ± 9.57, p < 0.001) and lower ADC (1.00 ± 0.13 vs. 1.06 ± 0.22, p = 0.036) than the NLNM- group. R2* and ADC were correlated to lymph node metastasis (r = 0.510, p < 0.001 for R2*; r = -0.235, p = 0.035 for ADC). R2* and ADC showed good and moderate diagnostic abilities in the assessment of NLNM status with corresponding area-under-the-curve values of 0.795 and 0.636. R2* provided a significantly better diagnostic performance compared with ADC for the prediction of NLNM status (z = 1.962, p = 0.0498). The multivariate logistic regression analysis demonstrated that R2* was a compelling factor of lymph node metastasis (odds ratio = 56.485, 95% confidence interval: 5.759-554.013; p = 0.001). R2* mapping had significantly higher diagnostic performance than rDWI from the primary tumor of rectal cancer in the prediction of NLNM status.
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Affiliation(s)
- Jing Wang
- Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shan Hu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Ping Liang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xuemei Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yaqi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yang Peng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ihab Kamel
- Russell H. Morgan Department of Radiology and Radiological Science, the Johns Hopkins Medical Institutions, Baltimore, Maryland, USA
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Zheng Y, Han N, Huang W, Jiang Y, Zhang J. Evaluating Mediastinal Lymph Node Metastasis of Non-Small Cell Lung Cancer Using Mono-exponential, Bi-exponential, and Stretched-exponential Models of Diffusion-weighted Imaging. J Thorac Imaging 2024; 39:285-292. [PMID: 38153288 DOI: 10.1097/rti.0000000000000771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
PURPOSE To explore and compare the diagnostic values of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) parameters of primary lesions and lymph nodes (LNs) to predict mediastinal LN metastasis in patients with non-small cell lung cancer. PATIENTS AND METHODS Sixty-one patients with non-small cell lung cancer underwent preoperative magnetic resonance imaging, including multiple b -value DWI. The DWI parameters, including apparent diffusion coefficient (ADC) from a mono-exponential model, true diffusion (D) coefficient, pseudo-diffusion (D*) coefficient, and perfusion fraction (f) from a bi-exponential model, distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index (α) from a stretched-exponential model of primary tumors and LNs and the size characteristics of LNs, were measured and compared. Multivariate logistic regression analysis was used to establish models for predicting mediastinal LN metastasis. Receiver operating characteristic analysis was applied to evaluate diagnostic performances. RESULTS The DWI parameters of primary tumors showed no statistical significance between LN metastasis-positive and LN metastasis-negative groups. Nonmetastatic LNs had significantly higher ADC, D, DDC, and α values compared with metastatic LNs (all P < 0.05). The short-dimension, long-dimension, and short-long dimension ratio of metastatic LNs was significantly larger than those of nonmetastatic ones (all P < 0.05). The D value showed the best diagnostic performance among all DWI-derived single parameters, and the short dimension of LNs performed the same among all the size variables. Furthermore, the combination of DWI parameters (ADC and D) and the short dimension of LNs can significantly improve diagnostic efficiency. CONCLUSIONS The ADC, D, DDC, and α from the mono-exponential, bi-exponential, and stretched-exponential models were demonstrated efficient in differentiating benign from metastatic LNs, and the combination of ADC, D, and short dimension of LNs may have a better diagnostic performance than DWI or size-derived parameters either in combination or individually.
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Affiliation(s)
- Yu Zheng
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Na Han
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Wenjing Huang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Yanli Jiang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
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Wang H, Zhang J, Li Y, Wang D, Zhang T, Yang F, Li Y, Zhang Y, Yang L, Li P. Deep-learning features based on F18 fluorodeoxyglucose positron emission tomography/computed tomography ( 18F-FDG PET/CT) to predict preoperative colorectal cancer lymph node metastasis. Clin Radiol 2024; 79:e1152-e1158. [PMID: 38955636 DOI: 10.1016/j.crad.2024.05.017] [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: 10/13/2023] [Revised: 04/04/2024] [Accepted: 05/24/2024] [Indexed: 07/04/2024]
Abstract
AIM The objective of this study was to create and authenticate a prognostic model for lymph node metastasis (LNM) in colorectal cancer (CRC) that integrates clinical, radiomics, and deep transfer learning features. MATERIALS AND METHODS In this study, we analyzed data from 119 CRC patients who underwent F18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scanning. The patient cohort was divided into training and validation subsets in an 8:2 ratio, with an additional 33 external data points for testing. Initially, we conducted univariate analysis to screen clinical parameters. Radiomics features were extracted from manually drawn images using pyradiomics, and deep-learning features, radiomics features, and clinical features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Spearman correlation coefficient. We then constructed a model by training a support vector machine (SVM), and evaluated the performance of the prediction model by comparing the area under the curve (AUC), sensitivity, and specificity. Finally, we developed nomograms combining clinical and radiological features for interpretation and analysis. RESULTS The deep learning radiomics (DLR) nomogram model, which was developed by integrating deep learning, radiomics, and clinical features, exhibited excellent performance. The area under the curve was (AUC = 0.934, 95% confidence interval [CI]: 0.884-0.983) in the training cohort, (AUC = 0.902, 95% CI: 0.769-1.000) in the validation cohort, and (AUC = 0.836, 95% CI: 0.673-0.998) in the test cohort. CONCLUSION We developed a preoperative predictive machine-learning model using deep transfer learning, radiomics, and clinical features to differentiate LNM status in CRC, aiding in treatment decision-making for patients.
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Affiliation(s)
- H Wang
- Department of PET/CT, The Second Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, Heilongjiang Province, China.
| | - J Zhang
- Department of PET/CT, The Second Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, Heilongjiang Province, China.
| | - Y Li
- Department of PET/CT, The Second Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, Heilongjiang Province, China.
| | - D Wang
- Department of PET/CT, The Second Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, Heilongjiang Province, China.
| | - T Zhang
- Department of PET/CT, The Second Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, Heilongjiang Province, China.
| | - F Yang
- Department of PET/CT, The Second Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, Heilongjiang Province, China.
| | - Y Li
- Department of PET/CT, The Second Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, Heilongjiang Province, China.
| | - Y Zhang
- Department of PET/CT, The Second Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, Heilongjiang Province, China.
| | - L Yang
- PET/MR Department, Harbin Medical University Cancer Hospital, Haping Road, Nangang District, Harbin, Heilongjiang Province, China.
| | - P Li
- Department of PET/CT, The Second Affiliated Hospital of Harbin Medical University, Baojian Road, Nangang District, Harbin, Heilongjiang Province, China.
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Liu W, Yang Y, Wang X, Li C, Liu C, Li X, Wen J, Lin X, Qin J. A Comprehensive Model Outperformed the Single Radiomics Model in Noninvasively Predicting the HER2 Status in Patients with Breast Cancer. Acad Radiol 2024:S1076-6332(24)00481-1. [PMID: 39122586 DOI: 10.1016/j.acra.2024.07.048] [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: 06/02/2024] [Revised: 07/23/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024]
Abstract
RATIONALE AND OBJECTIVES This study aimed to develop predictive models based on conventional magnetic resonance imaging (cMRI) and radiomics features for predicting human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC) and compare their performance. MATERIALS AND METHODS A total of 287 patients with invasive BC in our hospital were retrospectively analyzed. All patients underwent preoperative breast MRI consisting of fat-suppressed T2-weighted imaging, axial dynamic contrast-enhanced MRI, and diffusion-weighted imaging sequences. From these sequences, radiomics features were derived. Three distinct models were established utilizing cMRI features, radiomics features, and a comprehensive model that amalgamated both. The predictive capabilities of these models were assessed using the receiver operating characteristic curve analysis. The comparative performance was then determined through the DeLong test and net reclassification improvement (NRI). RESULTS In a randomized split, the 287 patients with BC were allotted to either training (234; 46 HER2-zero, 107 HER2-low, 81 HER2-positive) or test (53; 8 HER2-zero, 27 HER2-low, 18 HER2-positive) at an 8:2 ratio. The mean area under the curve (AUCs) for cMRI, radiomics, and comprehensive models predicting HER2 status were 0.705, 0.819, and 0.859 in training set and 0.639, 0.797, and 0.842 in test set, respectively. DeLong's test indicated that the combined model's AUC surpassed the radiomics model significantly (p < 0.05). NRI analysis verified superiority of the combined model over the radiomics for BC HER2 prediction (NRI 25.0) in the test set. CONCLUSION The comprehensive model based on the combination of cMRI and radiomics features outperformed the single radiomics model in noninvasively predicting the three-tiered HER2 status in patients with BC.
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Affiliation(s)
- Weimin Liu
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Yiqing Yang
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Xiaohong Wang
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Chao Li
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Chen Liu
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Xiaolei Li
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Junzhe Wen
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Xue Lin
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China
| | - Jie Qin
- Department of Radiology, the Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, Guangdong 510630, P.R. China.
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Fan L, Wu H, Wu Y, Wu S, Zhao J, Zhu X. Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3. BMC Gastroenterol 2024; 24:247. [PMID: 39103772 DOI: 10.1186/s12876-024-03316-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 07/04/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND This study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately preoperatively differentiate between stage T2 and T3 rectal cancer. METHODS We included 361 patients with pathologically confirmed stage T2 or T3 rectal cancer, divided into a training set (252 patients) and a test set (109 patients) at a 7:3 ratio. The study utilized features derived from deep transfer learning and radiomics, with Spearman rank correlation and the Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques to reduce feature redundancy. Predictive models were developed using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM), selecting the best-performing model for a comprehensive predictive framework incorporating clinical data. RESULTS After removing redundant features, 24 key features were identified. In the training set, the area under the curve (AUC)values for LR, RF, DT, and SVM were 0.867, 0.834, 0.900, and 0.944, respectively; in the test set, they were 0.847, 0.803, 0.842, and 0.910, respectively. The combined model, using SVM and clinical variables, achieved AUCs of 0.946 in the trainingset and 0.920 in the test set. CONCLUSION The study confirms the utility of a combined model of MRI deep transfer learning, radiomic features, and clinical factors for preoperative classification of stage T2 vs. T3 rectal cancer, offering significant technological support for precise diagnosis and potential clinical application.
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Affiliation(s)
- Lifang Fan
- School of Medical Imageology, Wannan Medical College, Wuhu, 241002, Anhui, China
- Anhui Province Key Laboratory of Cancer Translational Medicine, Bengbu Medical University, 2600 Donghai Avenue, Bengbu, Anhui, 233030, China
| | - Huazhang Wu
- Anhui Province Key Laboratory of Cancer Translational Medicine, Bengbu Medical University, 2600 Donghai Avenue, Bengbu, Anhui, 233030, China
| | - Yimin Wu
- Department of Ultrasound, The Second People's Hospital, WuHu Hospital, East China Normal University, Wuhu, Anhui, 241001, China
| | - Shujian Wu
- Department of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu, Anhui, China
| | - Jinsong Zhao
- School of Medical Imageology, Wannan Medical College, Wuhu, 241002, Anhui, China.
| | - Xiangming Zhu
- Department of Ultrasound, Yijishan Hospital of Wannan Medical College, No.2 Zheshan West Road, Jinghu District, Wuhu, Anhui Province, 241001, China.
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Wang Y, Zhao H, Fu P, Tian L, Su Y, Lyu Z, Gu W, Wang Y, Liu S, Wang X, Zheng H, Du J, Zhang R. Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis. Med Phys 2024; 51:5214-5225. [PMID: 38801340 DOI: 10.1002/mp.17193] [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: 11/09/2023] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC). PURPOSE Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC. METHODS The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model. RESULTS The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility. CONCLUSIONS 18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.
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Affiliation(s)
- Yan Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Hongyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Peng Fu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Lin Tian
- Department of Pathology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Yexin Su
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhehao Lyu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Yang Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Shan Liu
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xi Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Han Zheng
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jingjing Du
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Rui Zhang
- Department of Magnetic Resonance, The First Hospital of Qiqihar, Qiqihar, Heilongjiang, China
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Hao Y, Zheng J, Li W, Zhao W, Zheng J, Wang H, Ren J, Zhang G, Zhang J. Ultra-high b-value DWI in rectal cancer: image quality assessment and regional lymph node prediction based on radiomics. Eur Radiol 2024:10.1007/s00330-024-10958-3. [PMID: 38992110 DOI: 10.1007/s00330-024-10958-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 05/06/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
OBJECTIVES This study aims to evaluate image quality and regional lymph node metastasis (LNM) in patients with rectal cancer (RC) on multi-b-value diffusion-weighted imaging (DWI). METHODS This retrospective study included 199 patients with RC who had undergone multi-b-value DWI. Subjective (five-point Likert scale) and objective assessments of quality images were performed on DWIb1000, DWIb2000, and DWIb3000. Patients were randomly divided into a training (n = 140) or validation cohort (n = 59). Radiomics features were extracted within the whole volume tumor on ADC maps (b = 0, 1000 s/mm2), DWIb1000, DWIb2000, and DWIb3000, respectively. Five prediction models based on selected features were developed using logistic regression analysis. The performance of radiomics models was evaluated with a receiver operating characteristic curve, calibration, and decision curve analysis (DCA). RESULTS The mean signal intensity of the tumor (SItumor), signal-to-noise ratio (SNR), and artifact and anatomic differentiability score gradually were decreased as the b-value increased. However, the contrast-to-noise (CNR) on DWIb2000 was superior to those of DWIb1000 and DWIb3000 (4.58 ± 0.86, 3.82 ± 0.77, 4.18 ± 0.84, p < 0.001, respectively). The overall image quality score of DWIb2000 was higher than that of DWIb3000 (p < 0.001) and showed no significant difference between DWIb1000 and DWIb2000 (p = 0.059). The area under curve (AUC) value of the radiomics model based on DWIb2000 (0.728) was higher than conventional ADC maps (0.690), DWIb1000 (0.699), and DWIb3000 (0.707), but inferior to multi-b-value DWI (0.739) in predicting LNM. CONCLUSION DWIb2000 provides better lesion conspicuity and LNM prediction than DWIb1000 and DWIb3000 in RC. CLINICAL RELEVANCE STATEMENT DWIb2000 offers satisfactory visualization of lesions. Radiomics features based on DWIb2000 can be applied for preoperatively predicting regional lymph node metastasis in rectal cancer, thereby benefiting the stratified treatment strategy. KEY POINTS Lymph node staging is required to determine the best treatment plan for rectal cancer. DWIb2000 provides superior contrast-to-noise ratio and lesion conspicuity and its derived radiomics best predict lymph node metastasis. DWIb2000 may be recommended as the optimal b-value in rectal MRI protocol.
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Affiliation(s)
- Yongfei Hao
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Jianyong Zheng
- Department of Gastrointestinal Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Wanqing Li
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Wanting Zhao
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Jianmin Zheng
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Hong Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Jialiang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, China
| | - Guangwen Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
| | - Jinsong Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
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Wei Q, Chen L, Hou X, Lin Y, Xie R, Yu X, Zhang H, Wen Z, Wu Y, Liu X, Chen W. Multiparametric MRI-based radiomic model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Insights Imaging 2024; 15:163. [PMID: 38922456 PMCID: PMC11208366 DOI: 10.1186/s13244-024-01726-4] [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: 10/06/2023] [Accepted: 05/16/2024] [Indexed: 06/27/2024] Open
Abstract
OBJECTIVES To construct and validate multiparametric MR-based radiomic models based on primary tumors for predicting lymph node metastasis (LNM) following neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients. METHODS A total of 150 LARC patients from two independent centers were enrolled. The training cohort comprised 100 patients from center A. Fifty patients from center B were included in the external validation cohort. Radiomic features were extracted from the manually segmented volume of interests of the primary tumor before and after nCRT. Feature selection was performed using multivariate logistic regression analysis. The clinical risk factors were selected via the least absolute shrinkage and selection operator method. The radiologist's assessment of LNM was performed. Eight models were constructed using random forest classifiers, including four single-sequence models, three combined-sequence models, and a clinical model. The models' discriminative performance was assessed via receiver operating characteristic curve analysis quantified by the area under the curve (AUC). RESULTS The AUCs of the radiologist's assessment, the clinical model, and the single-sequence models ranged from 0.556 to 0.756 in the external validation cohort. Among the single-sequence models, modelpost_DWI exhibited superior predictive power, with an AUC of 0.756 in the external validation set. In combined-sequence models, modelpre_T2_DWI_post had the best diagnostic performance in predicting LNM after nCRT, with a significantly higher AUC (0.831) than those of the clinical model, modelpre_T2_DWI, and the single-sequence models (all p < 0.05). CONCLUSIONS A multiparametric model that incorporates MR radiomic features before and after nCRT is optimal for predicting LNM after nCRT in LARC. CRITICAL RELEVANCE STATEMENT This study enrolled 150 LARC patients from two independent centers and constructed multiparametric MR-based radiomic models based on primary tumors for predicting LNM following nCRT, which aims to guide therapeutic decisions and predict prognosis for LARC patients. KEY POINTS The biological characteristics of primary tumors and metastatic LNs are similar in rectal cancer. Radiomics features and clinical data before and after nCRT provide complementary tumor information. Preoperative prediction of LN status after nCRT contributes to clinical decision-making.
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Affiliation(s)
- Qiurong Wei
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Ling Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiaoyan Hou
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yunying Lin
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Renlong Xie
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiayu Yu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Hanliang Zhang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yuankui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xian Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
| | - Weicui Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
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Abbaspour E, Karimzadhagh S, Monsef A, Joukar F, Mansour-Ghanaei F, Hassanipour S. Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis. Int J Surg 2024; 110:3795-3813. [PMID: 38935817 PMCID: PMC11175807 DOI: 10.1097/js9.0000000000001239] [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: 12/27/2023] [Accepted: 02/19/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data. METHODS Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models' diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted. RESULTS Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022-2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78-0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001). CONCLUSION Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.
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Affiliation(s)
- Elahe Abbaspour
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Sahand Karimzadhagh
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Abbas Monsef
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Farahnaz Joukar
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Fariborz Mansour-Ghanaei
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Soheil Hassanipour
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
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Ye YX, Yang L, Kang Z, Wang MQ, Xie XD, Lou KX, Bao J, Du M, Li ZX. Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer. World J Gastrointest Oncol 2024; 16:1849-1860. [PMID: 38764830 PMCID: PMC11099437 DOI: 10.4251/wjgo.v16.i5.1849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/23/2024] [Accepted: 03/04/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND Lymph node (LN) staging in rectal cancer (RC) affects treatment decisions and patient prognosis. For radiologists, the traditional preoperative assessment of LN metastasis (LNM) using magnetic resonance imaging (MRI) poses a challenge. AIM To explore the value of a nomogram model that combines Conventional MRI and radiomics features from the LNs of RC in assessing the preoperative metastasis of evaluable LNs. METHODS In this retrospective study, 270 LNs (158 nonmetastatic, 112 metastatic) were randomly split into training (n = 189) and validation sets (n = 81). LNs were classified based on pathology-MRI matching. Conventional MRI features [size, shape, margin, T2-weighted imaging (T2WI) appearance, and CE-T1-weighted imaging (T1WI) enhancement] were evaluated. Three radiomics models used 3D features from T1WI and T2WI images. Additionally, a nomogram model combining conventional MRI and radiomics features was developed. The model used univariate analysis and multivariable logistic regression. Evaluation employed the receiver operating characteristic curve, with DeLong test for comparing diagnostic performance. Nomogram performance was assessed using calibration and decision curve analysis. RESULTS The nomogram model outperformed conventional MRI and single radiomics models in evaluating LNM. In the training set, the nomogram model achieved an area under the curve (AUC) of 0.92, which was significantly higher than the AUCs of 0.82 (P < 0.001) and 0.89 (P < 0.001) of the conventional MRI and radiomics models, respectively. In the validation set, the nomogram model achieved an AUC of 0.91, significantly surpassing 0.80 (P < 0.001) and 0.86 (P < 0.001), respectively. CONCLUSION The nomogram model showed the best performance in predicting metastasis of evaluable LNs.
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Affiliation(s)
- Yong-Xia Ye
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Liu Yang
- Department of Colorectal Surgery, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
| | - Zheng Kang
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Mei-Qin Wang
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Xiao-Dong Xie
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Ke-Xin Lou
- Department of Pathology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Jun Bao
- Colorectal Center, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Mei Du
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
| | - Zhe-Xuan Li
- Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
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Mesny E, Leporq B, Chapet O, Beuf O. Intravoxel incoherent motion magnetic resonance imaging to assess early tumor response to radiation therapy: Review and future directions. Magn Reson Imaging 2024; 108:129-137. [PMID: 38354843 DOI: 10.1016/j.mri.2024.02.008] [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: 04/20/2023] [Revised: 02/08/2024] [Accepted: 02/10/2024] [Indexed: 02/16/2024]
Abstract
Early prediction of radiation response by imaging is a dynamic field of research and it can be obtained using a variety of noninvasive magnetic resonance imaging methods. Recently, intravoxel incoherent motion (IVIM) has gained interest in cancer imaging. IVIM carries both diffusion and perfusion information, making it a promising tool to assess tumor response. Here, we briefly introduced the basics of IVIM, reviewed existing studies of IVIM in various type of tumors during radiotherapy in order to show whether IVIM is a useful technique for an early assessment of radiation response. 31/40 studies reported an increase of IVIM parameters during radiotherapy compared to baseline. In 27 studies, this increase was higher in patients with good response to radiotherapy. Future directions including implementation of IVIM on MR-Linac and its limitation are discussed. Obtaining new radiologic biomarkers of radiotherapy response could open the way for a more personalized, biology-guided radiation therapy.
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Affiliation(s)
- Emmanuel Mesny
- Radiation Oncology Department, Center Hospitalier Lyon Sud, Pierre Benite, France; Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France.
| | - Benjamin Leporq
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France
| | - Olivier Chapet
- Radiation Oncology Department, Center Hospitalier Lyon Sud, Pierre Benite, France
| | - Olivier Beuf
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France
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14
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Zhu HB, Zhao B, Li XT, Zhang XY, Yao Q, Sun YS. Value of multiple models of diffusion-weighted imaging to predict hepatic lymph node metastases in colorectal liver metastases patients. World J Gastroenterol 2024; 30:308-317. [PMID: 38313236 PMCID: PMC10835543 DOI: 10.3748/wjg.v30.i4.308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/15/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND About 10%-31% of colorectal liver metastases (CRLM) patients would concomitantly show hepatic lymph node metastases (LNM), which was considered as sign of poor biological behavior and a relative contraindication for liver resection. Up to now, there's still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM, except for pathology examination of lymph node after resection. AIM To compare the ability of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery. METHODS In this retrospective study, 97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging, including DWI with ten b values before and after chemotherapy. Various parameters, such as the apparent diffusion coefficient from the mono-exponential model, and the true diffusion coefficient, the pseudo-diffusion coefficient, and the perfusion fraction derived from the intravoxel incoherent motion model, along with distributed diffusion coefficient (DDC) and α from the stretched-exponential model (SEM), were measured. The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups. A nomogram was constructed to predict the hepatic lymph node status. The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient. RESULTS Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes. A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients, with an area under the curve of 0.873. Furthermore, parameters from SEM showed substantial repeatability. CONCLUSION The developed nomogram, incorporating the pre-treatment DDC and the short axis of the largest lymph node, can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery. This nomogram was proven to be more valuable, exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI. The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.
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Affiliation(s)
- Hai-Bin Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Bo Zhao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Xiao-Ting Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Xiao-Yan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Qian Yao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital and Institute, Beijing 100142, China
| | - Ying-Shi Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing 100142, China
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15
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Wong C, Liu T, Zhang C, Li M, Zhang H, Wang Q, Fu Y. Preoperative detection of lymphovascular invasion in rectal cancer using intravoxel incoherent motion imaging based on radiomics. Med Phys 2024; 51:179-191. [PMID: 37929807 DOI: 10.1002/mp.16821] [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] [Revised: 10/09/2023] [Accepted: 10/12/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND Lymphovascular invasion (LVI) status plays an important role in treatment decision-making in rectal cancer (RC). Intravoxel incoherent motion (IVIM) imaging has been shown to detect LVI; however, making better use of IVIM data remains an important issue that needs to be discussed. PURPOSE We proposed to explore the best way to use IVIM quantitative parameters and images to construct radiomics models for the noninvasive detection of LVI in RC. METHODS A total of 83 patients (LVI negative (LVI-): LVI positive (LVI+) = 51:32) with postoperative pathology-confirmed LVI status in RC were divided into a training group (n = 58) and a validation group (n = 25). Images were acquired from a 3.0 Tesla machine, including oblique axial T2 weighted imaging (T2WI) and IVIM with 11 b values. The ADC, D, D* and f values were measured on IVIM maps. The ROIs of tumors were delineated on T2WI, DWI, ADCmap , and Dmap images, and three mapping methods were used: ROIs_mapping from DWI, ROIs_mapping from ADCmap , and ROIs_mapping from Dmap . Three-dimensional radiomics features were extracted from the delineated ROIs. Multivariate logistic regression was used for radiomics feature selection. Radiomics models based on different mapping methods were developed. Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were used to evaluate the performance of the models. RESULTS Model B, which was constructed with radiomics features from ADCmap , Dmap and fmap by "ROIs_mapping from DWI" and T2WI (AUC 0.894), performed better than other models based on single sequence (AUC 0.600-0.806) and even better than Model A, which was based on "ROIs_mapping from ADC" and T2WI (AUC 0.838). Furthermore, an integrated model was constructed with Model B and the IVIM parameter (f value) with an AUC of 0.920 (95% CI: 0.820-1.000), which was higher than that of Model B, in the validation group. CONCLUSIONS The integrated model incorporating the radiomics features and IVIM parameters accurately detected LVI of RC. The "ROIs_mapping from DWI" method provided the best results.
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Affiliation(s)
- Chinting Wong
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, China
| | - Tong Liu
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
- Department of Radiology, Zhengzhou University Affiliated Cancer Hospital & Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Chunyu Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Mingyang Li
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
| | - Quan Wang
- Department of Gastrointestinal Surgery, The First Hospital of Jilin University, Changchun, China
| | - Yu Fu
- Department of Radiology, The First Hospital of Jilin University, Changchun, China
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Zheng J, Du PZ, Yang C, Tao YY, Li L, Li ZM, Yang L. DCE-MRI-based radiomics in predicting angiopoietin-2 expression in hepatocellular carcinoma. Abdom Radiol (NY) 2023; 48:3343-3352. [PMID: 37495746 PMCID: PMC10556176 DOI: 10.1007/s00261-023-04007-8] [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: 03/17/2023] [Revised: 07/11/2023] [Accepted: 07/13/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the sixth most common cancer, and the third leading cause of cancer death worldwide. Studies have shown that increased angiopoietin-2 (Ang-2) expression relative to Ang-1 expression in tumors is associated with a poor prognosis.The purpose of this study was to investigate the efficacy of predicting Ang-2 expression in HCC by preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics. METHODS The data of 52 patients with HCC who underwent surgical resection in our hospital were retrospectively analyzed. Ang-2 expression in HCC was analyzed by immunohistochemistry. All patients underwent preoperative upper abdominal DCE-MRI and intravoxel incoherent motion diffusion-weighted imaging scans. Radiomics features were extracted from the early and late arterial and portal phases of axial DCE-MRI. Univariate analysis and least absolute shrinkage and selection operator (LASSO) was performed to select the optimal radiomics features for analysis. A logistic regression analysis was performed to establish a DCE-MRI radiomics model, clinic-radiologic (CR) model and combined model integrating the radiomics score with CR factors. The stability of each model was verified by 10-fold cross-validation. Receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) were employed to evaluate these models. RESULTS Among the 52 HCC patients, high Ang-2 expression was found in 30, and low Ang-2 expression was found in 22. The areas under the ROC curve (AUCs) for the radiomics model, CR model and combined model for predicting Ang-2 expression were 0.800, 0.874, and 0.933, respectively. The DeLong test showed that there was no significant difference in the AUC between the radiomics model and the CR model (p > 0.05) but that the AUC for the combined model was significantly greater than those for the other 2 models (p < 0.05). The DCA results showed that the combined model outperformed the other 2 models and had the highest net benefit. CONCLUSION The DCE-MRI-based radiomics model has the potential to predict Ang-2 expression in HCC patients; the combined model integrating the radiomics score with CR factors can further improve the prediction performance.
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Affiliation(s)
- Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Interventional Medical Center, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Pei-Zhuo Du
- Department of Radiology, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, 637000, China
| | - Cui Yang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Interventional Medical Center, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
- Department of Radiology, Panzhihua Central Hospital, Panzhihua, 617000, China
| | - Yun-Yun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Interventional Medical Center, 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, Department of Radiology, Interventional Medical Center, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
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Cheng Q, Ren A, Xu X, Meng Z, Feng X, Pylypenko D, Dou W, Yu D. Application of DKI and IVIM imaging in evaluating histologic grades and clinical stages of clear cell renal cell carcinoma. Front Oncol 2023; 13:1203922. [PMID: 37954085 PMCID: PMC10637387 DOI: 10.3389/fonc.2023.1203922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 10/09/2023] [Indexed: 11/14/2023] Open
Abstract
Purpose To evaluate the value of quantitative parameters derived from diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in differentiating histologic grades and clinical stages of clear cell renal cell carcinoma (ccRCC). Materials and methods A total of 65 patients who were surgically and pathologically diagnosed as ccRCC were recruited in this study. In addition to routine renal magnetic resonance imaging examination, all patients underwent preoperative IVIM and DKI. The corresponding diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), mean diffusivity (MD), kurtosis anisotropy (KA), and mean kurtosis (MK) values were obtained. Independent-samples t-test or Mann-Whitney U test was used for comparing the differences in IVIM and DKI parameters among different histologic grades and clinical stages. The diagnostic efficacy of IVIM and DKI parameters was evaluated using the receiver operating characteristic (ROC) curve. Spearman's correlation analysis was used to separately analyze the correlation of each parameter with histologic grades and stages of ccRCC. Results The D and MD values were significantly higher in low-grade ccRCC than high-grade ccRCC (all p < 0.001) and in low-stage than high-stage ccRCC (all p < 0.05), and the f value of high-stage ccRCC was lower than that of low-stage ccRCC (p = 0.007). The KA and MK values were significantly higher in low-grade than high-grade ccRCC (p = 0.000 and 0.000, respectively) and in low-stage than high-stage ccRCC (p = 0.000 and 0.000, respectively). The area under the curve (AUC) values of D, D*, f, MD, KA, MK, DKI, and IVIM+DKI values were 0.825, 0.598, 0.626, 0.792, 0.750, 0.754, 0.803, and 0.857, respectively, in grading ccRCC and 0.837, 0.719, 0.710, 0.787, 0.796, 0.784, 0.864, 0.823, and 0.916, respectively, in staging ccRCC. The AUC of IVIM was 0.913 in staging ccRCC. The D, D*, and MD values were negatively correlated with the histologic grades and clinical stages (all p < 0.05), and the KA and MK values showed a positive correlation with histologic grades and clinical stages (all p < 0.05). The f value was also negatively correlated with the ccRCC clinical stage (p = 0.008). Conclusion Both the IVIM and DKI values can be used preoperatively to predict the degree of histologic grades and stages in ccRCC, and the D and MD values have better diagnostic performance in the grading and staging. Also, further slightly enhanced diagnostic efficacy was observed in the model with combined IVIM and DKI parameters.
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Affiliation(s)
- QiChao Cheng
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - AnLi Ren
- Department of Radiology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - XingHua Xu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Zhao Meng
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | - Xue Feng
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
| | | | | | - DeXin Yu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China
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Wei T, Wei W, Ma Q, Shen Z, Lu K, Zhu X. Development of a Clinical-Radiomics Nomogram That Used Contrast-Enhanced Ultrasound Images to Anticipate the Occurrence of Preoperative Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Patients. Int J Gen Med 2023; 16:3921-3932. [PMID: 37662506 PMCID: PMC10474867 DOI: 10.2147/ijgm.s424880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/15/2023] [Indexed: 09/05/2023] Open
Abstract
Background and Objectives Papillary thyroid carcinoma (PTC) is a prevalent histological type of thyroid cancer; however, noninvasive assessment of cervical lymph node metastasis (LNM) poses a challenge. This study aims to develop a novel clinical-radiomics nomogram that utilizes ultrasound (US) images to predict the presence of cervical LNM metastasis in patients with PTC. Methods A total of 423 patients with PTC were recruited to participate in this study between January 2020 and December 2022, of which 282 were classified into the training group and 141 patients were classified into the validation set. Contrast-enhanced ultrasound (CEUS) and B-mode ultrasound (BMUS) images were subjected to radiomic analysis, leading to the extraction of 912 radiomic features. Thereafter, a radiomics score (Radscore) was developed to effectively integrate the information derived from BMUS and CEUS modalities. Univariate and multivariate backward stepwise logistic regression analysis techniques were used to construct the clinical and clinical-radiomics models, respectively. Results The findings revealed that the clinical-radiomics nomogram incorporated age, sex, CEUS Radscore, and US-reported LNM as risk factors. The nomogram demonstrated good performance using data from the training (AUC = 0.891) and validation (AUC = 0.870) sets. The decision curve analysis implied that this nomogram exhibited good clinical utility, which was further supported by the results of the calibration curves and Hosmer-Lemeshow test. Conclusion The CEUS Radscore-based clinical radiomics nomogram could serve as a valuable tool for predicting cervical LNM metastasis in patients with PTC, thereby tailoring individualized treatment strategies for them.
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Affiliation(s)
- Tianjun Wei
- School of Continuing Education, Anhui Medical University, Hefei, 230032, People’s Republic of China
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
| | - Wei Wei
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
| | - Qiang Ma
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
| | - Zhongbing Shen
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
| | - Kebing Lu
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
| | - Xiangming Zhu
- School of Continuing Education, Anhui Medical University, Hefei, 230032, People’s Republic of China
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
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19
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Peng W, Qiao H, Mo L, Guo Y. Progress in the diagnosis of lymph node metastasis in rectal cancer: a review. Front Oncol 2023; 13:1167289. [PMID: 37519802 PMCID: PMC10374255 DOI: 10.3389/fonc.2023.1167289] [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: 03/15/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Historically, the chief focus of lymph node metastasis research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen a rapid accumulation of massive omics and imaging data catalyzed by the rapid development of advanced technologies. This rapid increase in data has driven improvements in the accuracy of diagnosis of lymph node metastasis, and its analysis further demands new methods and the opportunity to provide novel insights for basic research. In fact, the combination of omics data, imaging data, clinical medicine, and diagnostic methods has led to notable advances in our basic understanding and transformation of lymph node metastases in rectal cancer. Higher levels of integration will require a concerted effort among data scientists and clinicians. Herein, we review the current state and future challenges to advance the diagnosis of lymph node metastases in rectal cancer.
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Affiliation(s)
- Wei Peng
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Huimin Qiao
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Linfeng Mo
- School of Health and Medicine, Guangzhou Huashang Vocational College, Guangzhou, Guangdong, China
| | - You Guo
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
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Zhu HB, Xu D, Sun XF, Li XT, Zhang XY, Wang K, Xing BC, Sun YS. Prediction of hepatic lymph node metastases based on magnetic resonance imaging before and after preoperative chemotherapy in patients with colorectal liver metastases underwent surgical resection. Cancer Imaging 2023; 23:18. [PMID: 36810192 PMCID: PMC9942330 DOI: 10.1186/s40644-023-00529-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/30/2023] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND Patients with colorectal liver metastases (CRLM) combined with hepatic lymph node (HLN) metastases have a poor prognosis. In this study, we developed and validated a model using clinical and magnetic resonance imaging (MRI) parameters to predict HLN status before surgery. METHODS A total of 104 CRLM patients undergoing hepatic lymphonodectomy with pathologically confirmed HLN status after preoperative chemotherapy were enrolled in this study. The patients were further divided into a training group (n = 52) and a validation group (n = 52). The apparent diffusion coefficient (ADC) values, including ADCmean and ADCmin of the largest HLN before and after treatment, were measured. rADC was calculated referring to the target liver metastases, spleen, and psoas major muscle (rADC-LM, rADC-SP, rADC-m). In addition, ADC change rate (Δ% ADC) was quantitatively calculated. A multivariate logistic regression model for predicting HLN status in CRLM patients was constructed using the training group and further tested in the validation group. RESULTS In the training cohort, post-ADCmean (P = 0.018) and the short diameter of the largest lymph node after treatment (P = 0.001) were independent predictors for metastatic HLN in CRLM patients. The model's AUC was 0.859 (95% CI, 0.757-0.961) and 0.767 (95% CI 0.634-0.900) in the training and validation cohorts, respectively. Patients with metastatic HLN showed significantly worse overall survival (p = 0.035) and recurrence-free survival (p = 0.015) than patients with negative HLN. CONCLUSIONS The developed model using MRI parameters could accurately predict HLN metastases in CRLM patients and could be used to preoperatively assess the HLN status and facilitate surgical treatment decisions in patients with CRLM.
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Affiliation(s)
- Hai-bin Zhu
- grid.412474.00000 0001 0027 0586Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142 China
| | - Da Xu
- grid.412474.00000 0001 0027 0586Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142 China
| | - Xue-Feng Sun
- grid.412474.00000 0001 0027 0586Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142 China
| | - Xiao-Ting Li
- grid.412474.00000 0001 0027 0586Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142 China
| | - Xiao-Yan Zhang
- grid.412474.00000 0001 0027 0586Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142 China
| | - Kun Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
| | - Bao-Cai Xing
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
| | - Ying-Shi Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
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Wong C, Fu Y, Li M, Mu S, Chu X, Fu J, Lin C, Zhang H. MRI-Based Artificial Intelligence in Rectal Cancer. J Magn Reson Imaging 2023; 57:45-56. [PMID: 35993550 DOI: 10.1002/jmri.28381] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 02/03/2023] Open
Abstract
Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old. Magnetic resonance imaging (MRI) is routinely performed for tumor evaluation. However, the semantic features from images alone remain insufficient to guide treatment decisions. Functional MRIs are useful for revealing microstructural and functional abnormalities and nevertheless have low or modest repeatability and reproducibility. Therefore, during the preoperative evaluation and follow-up treatment of patients with RC, novel noninvasive imaging markers are needed to describe tumor characteristics to guide treatment strategies and achieve individualized diagnosis and treatment. In recent years, the development of artificial intelligence (AI) has created new tools for RC evaluation based on MRI. In this review, we summarize the research progress of AI in the evaluation of staging, prediction of high-risk factors, genotyping, response to therapy, recurrence, metastasis, prognosis, and segmentation with RC. We further discuss the challenges of clinical application, including improvement in imaging, model performance, and the biological meaning of features, which may also be major development directions in the future. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Chinting Wong
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, China
| | - Yu Fu
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
| | - Mingyang Li
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
| | - Shengnan Mu
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
| | - Xiaotong Chu
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
| | - Jiahui Fu
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
| | - Chenghe Lin
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, China
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
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22
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Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters. JOURNAL OF ONCOLOGY 2022; 2022:3335048. [PMID: 35813867 PMCID: PMC9262528 DOI: 10.1155/2022/3335048] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 06/08/2022] [Indexed: 01/17/2023]
Abstract
Objective To investigate the value of apparent diffusion coefficient (ADC) value of endometrial cancer (EC) primary lesion and magnetic resonance imaging (MRI) three-dimensional (3D) radiomics features combined with clinical parameters for preoperative prediction of pelvic lymph node metastasis (PLNM). Methods A total of 136 patients with EC confirmed by postoperative pathology were retrospectively reviewed and analyzed. Patients were randomly divided into training set (n = 95) and test set (n = 41) at a ratio of 7 : 3. Radiomics features based on T2WI, DWI, and contrast-enhanced T1WI (CE-T1WI) sequence were extracted and screened, and then radiomics score (Rads-score) was calculated. Clinical parameters and ADC value of EC primary lesion were measured and collected, and their correlation with PLNM was analyzed. Receiver operating characteristic (ROC) curve was plotted to assess the diagnostic efficacy of the model. A nomogram for PLNM was created based on the multivariate logistic regression model. Results The ADC value of the EC primary lesion showed inverse correlation with PLNM, while CA125 and Rads-score were positively associated with PLNM. A predictive model was proposed based on ADC value, Rads-score, CA125, and MR-reported pelvic lymph node status (PLNS) for PLNM in EC. The area under the curve (AUC) of the model is 0.940; the sensitivity and specificity (87.1% and 90.6%) of the model were significantly higher than that of the MRI morphological signs. Conclusion A combination of ADC value, MRI 3D radiomics features of the EC primary lesion, and clinical parameters generated a prediction model for PLNM in EC and had a good diagnostic performance; it was a useful supplement to MR-reported PLNS based on MRI morphological signs.
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Su R, Wu S, Shen H, Chen Y, Zhu J, Zhang Y, Jia H, Li M, Chen W, He Y, Gao F. Combining Clinicopathology, IVIM-DWI and Texture Parameters for a Nomogram to Predict Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients. Front Oncol 2022; 12:886101. [PMID: 35712519 PMCID: PMC9197196 DOI: 10.3389/fonc.2022.886101] [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: 02/28/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives This study aimed to create a nomogram for the risk prediction of neoadjuvant chemoradiotherapy (nCRT) resistance in locally advanced rectal cancer (LARC). Methods Clinical data in this retrospective study were collected from a total of 135 LARC patients admitted to our hospital from June 2016 to December 2020. After screening by inclusion and exclusion criteria, 62 patients were included in the study. Texture analysis (TA) was performed on T2WI and DWI images. Patients were divided into response group (CR+PR) and no-response group (SD+PD) according to efficacy assessment. Multivariate analysis was performed on clinicopathology, IVIM-DWI and texture parameters for screening of independent predictors. A nomogram was created and model fit and clinical net benefit were assessed. Results Multivariate analysis of clinicopathology parameters showed that the differentiation and T stage were independent predictors (OR values were 14.516 and 11.589, resp.; P<0.05). Multivariate analysis of IVIM-DWI and texture parameters showed that f value and Rads-score were independent predictors (OR values were 0.855, 2.790, resp.; P<0.05). In this study, clinicopathology together with IVIM-DWI and texture parameters showed the best predictive efficacy (AUC=0.979). The nomogram showed good predictive performance and stability in identifying high-risk LARC patients who are resistant to nCRT (C-index=0.979). Decision curve analyses showed that the nomogram had the best clinical net benefit. Ten-fold cross-validation results showed that the average AUC value was 0.967, and the average C-index was 0.966. Conclusions The nomogram combining the differentiation, T stage, f value and Rads-score can effectively estimate the risk of nCRT resistance in patients with LARC.
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Affiliation(s)
- Rixin Su
- Department of Medical Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China
| | - Shusheng Wu
- Department of Medical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Hao Shen
- Department of Medical Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China
| | - Yaolin Chen
- Department of Medical Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China
| | - Jingya Zhu
- Department of Medical Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China
| | - Yu Zhang
- Department of Medical Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China
| | - Haodong Jia
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Mengge Li
- Department of Medical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Wenju Chen
- Department of Medical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Yifu He
- Department of Medical Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, China.,Department of Medical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
| | - Fei Gao
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, China
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