1
|
O’Sullivan NJ, Temperley HC, Horan MT, Corr A, Mehigan BJ, Larkin JO, McCormick PH, Kavanagh DO, Meaney JFM, Kelly ME. Radiogenomics: Contemporary Applications in the Management of Rectal Cancer. Cancers (Basel) 2023; 15:5816. [PMID: 38136361 PMCID: PMC10741704 DOI: 10.3390/cancers15245816] [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: 11/08/2023] [Revised: 12/05/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
Radiogenomics, a sub-domain of radiomics, refers to the prediction of underlying tumour biology using non-invasive imaging markers. This novel technology intends to reduce the high costs, workload and invasiveness associated with traditional genetic testing via the development of 'imaging biomarkers' that have the potential to serve as an alternative 'liquid-biopsy' in the determination of tumour biological characteristics. Radiogenomics also harnesses the potential to unlock aspects of tumour biology which are not possible to assess by conventional biopsy-based methods, such as full tumour burden, intra-/inter-lesion heterogeneity and the possibility of providing the information of tumour biology longitudinally. Several studies have shown the feasibility of developing a radiogenomic-based signature to predict treatment outcomes and tumour characteristics; however, many lack prospective, external validation. We performed a systematic review of the current literature surrounding the use of radiogenomics in rectal cancer to predict underlying tumour biology.
Collapse
Affiliation(s)
- Niall J. O’Sullivan
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James’s Hospital, D08 NHY1 Dublin, Ireland
| | - Hugo C. Temperley
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - Michelle T. Horan
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James’s Hospital, D08 NHY1 Dublin, Ireland
| | - Alison Corr
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
| | - Brian J. Mehigan
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - John O. Larkin
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - Paul H. McCormick
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
| | - Dara O. Kavanagh
- Department of Surgery, Tallaght University Hospital, D24 NR0A Dublin, Ireland
- Department of Surgery, Royal College of Surgeons, D02 YN77 Dublin, Ireland
| | - James F. M. Meaney
- Department of Radiology, St. James’s Hospital, D08 NHY1 Dublin, Ireland; (M.T.H.)
- The National Centre for Advanced Medical Imaging (CAMI), St. James’s Hospital, D08 NHY1 Dublin, Ireland
| | - Michael E. Kelly
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
- Department of Surgery, St. James’s Hospital, D08 NHY1 Dublin, Ireland;
- Trinity St. James’s Cancer Institute (TSJCI), D08 NHY1 Dublin, Ireland
| |
Collapse
|
2
|
Computed tomography-based radiomics nomogram for the preoperative prediction of perineural invasion in colorectal cancer: a multicentre study. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3251-3263. [PMID: 35960308 DOI: 10.1007/s00261-022-03620-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/10/2022] [Accepted: 07/11/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To develop and validate a computed tomography (CT) radiomics nomogram from multicentre datasets for preoperative prediction of perineural invasion (PNI) in colorectal cancer. METHODS A total of 299 patients with histologically confirmed colorectal cancer from three hospitals were enrolled in this retrospective study. Radiomic features were extracted from the whole tumour volume. The least absolute shrinkage and selection operator logistic regression was applied for feature selection and radiomics signature construction. Finally, a radiomics nomogram combining the radiomics score and clinical predictors was established. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomics nomogram in the training cohort, internal validation and external validation cohorts. RESULTS Twelve radiomics features extracted from the whole tumour volume were used to construct the radiomics model. The area under the curve (AUC) values of the radiomics model in the training cohort, internal validation cohort, external validation cohort 1, and external validation cohort 2 were 0.82 (0.75-0.90), 0.77 (0.62-0.92), 0.71 (0.56-0.85), and 0.73 (0.60-0.85), respectively. The nomogram, which combined the radiomics score with T category and N category by CT, yielded better performance in the training cohort (AUC = 0.88), internal validation cohort (AUC = 0.80), external validation cohort 1 (AUC = 0.75), and external validation cohort 2 (AUC = 0.76). DCA confirmed the clinical utility of the nomogram. CONCLUSIONS The CT-based radiomics nomogram has the potential to accurately predict PNI in patients with colorectal cancer.
Collapse
|
3
|
Chen Y, Li B, Jiang Z, Li H, Dang Y, Tang C, Xia Y, Zhang H, Song B, Long L. Multi-parameter diffusion and perfusion magnetic resonance imaging and radiomics nomogram for preoperative evaluation of aquaporin-1 expression in rectal cancer. Abdom Radiol (NY) 2022; 47:1276-1290. [PMID: 35166938 DOI: 10.1007/s00261-021-03397-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/18/2021] [Accepted: 12/20/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE The overexpression of aquaporin-1 (AQP1) is associated with poor prognosis in rectal cancer. This study aimed to explore the value of multi-parameter diffusion and perfusion MRI and radiomics models in predicting AQP1 high expression. METHODS This prospective study was performed from July 2019 to February 2021, which included rectal cancer participants after preoperative rectal MRI, with diffusion-weighted imaging, intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and dynamic contrast-enhanced (DCE) sequences. Radiomic features were extracted from MR images, and immunohistochemical tests assessed AQP1 expression. Selected quantitative MRI and radiomic features were analyzed. Receiver operating characteristic (ROC) curves evaluated the predictive performance. The nomogram performance was evaluated by its calibration, discrimen, and clinical utility. The intraclass correlation coefficient evaluated the interobserver agreement for the MRI features. RESULTS 110 participants with the age of 60.7 ± 12.5 years been enrolled in this study. The apparent diffusion coefficient (ADC), IVIM_D, DKI_diffusivity, and DCE_Ktrans were significantly higher in participants with high AQP1 expression than in those with low expression (P < 0.05). ADC (b = 1000, 2000, and 3000 s/mm2), IVIM_D, DKI_diffusivity, and DCE_Ktrans were positively correlated (r = 0.205, 0.275, 0.37, 0.235, 0.229, and 0.227, respectively; P < 0.05), whereas DKI_Kurtosis was negatively correlated (r = - 0.22, P = 0.021) with AQP1 expression. ADC (b = 3000 s/mm2), IVIM_D, DKI_ diffusivity, DKI_Kurtosis, and DCE_Ktrans had moderate diagnostic efficiencies for high AQP1 expression (AUC = 0.715, 0.636, 0.627, 0.633, and 0.632, respectively; P < 0.05). The radiomic features had excellent predictive efficiency for high AQP1 expression (AUC = 0.967 and 0.917 for training and validation). The model-based nomogram had C-indexes of 0.932 and 0.851 for the training and validation cohorts, which indicated good fitting to the calibration curves (p > 0.05). CONCLUSION Diffusion and perfusion MRI can indicate the aquaporin-1 expression in rectal cancer, and radiomic features can enhance the predictive efficiency for high AQP1 expression. A nomogram for high aquaporin-1 expression will improve clinical decision-making.
Collapse
Affiliation(s)
- Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Basen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Zijian Jiang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Hui Li
- Department of Anus and Intestine Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Yiwu Dang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Cheng Tang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Yuwei Xia
- Huiying Medical Technology, Beijing, 100192, China
| | | | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Liling Long
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China.
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Gaungxi Medical University, Nanning, 530021, China.
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China.
| |
Collapse
|
4
|
Li T, Sun L, Li Q, Luo X, Luo M, Xie H, Wang P. Development and Validation of a Radiomics Nomogram for Predicting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions. Front Oncol 2022; 11:825429. [PMID: 35155214 PMCID: PMC8825569 DOI: 10.3389/fonc.2021.825429] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 12/30/2021] [Indexed: 12/22/2022] Open
Abstract
Purpose To develop and validate a radiomics nomogram for the prediction of clinically significant prostate cancer (CsPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) category 3 lesions. Methods We retrospectively enrolled 306 patients within PI-RADS 3 lesion from January 2015 to July 2020 in institution 1; the enrolled patients were randomly divided into the training group (n = 199) and test group (n = 107). Radiomics features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) imaging, and dynamic contrast-enhanced (DCE) imaging. Synthetic minority oversampling technique (SMOTE) was used to address the class imbalance. The ANOVA and least absolute shrinkage and selection operator (LASSO) regression model were used for feature selection and radiomics signature building. Then, a radiomics score (Rad-score) was acquired. Combined with serum prostate-specific antigen density (PSAD) level, a multivariate logistic regression analysis was used to construct a radiomics nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate radiomics signature and nomogram. The radiomics nomogram calibration and clinical usefulness were estimated through calibration curve and decision curve analysis (DCA). External validation was assessed, and the independent validation cohort contained 65 patients within PI-RADS 3 lesion from January 2020 to July 2021 in institution 2. Results A total of 75 (24.5%) and 16 (24.6%) patients had CsPCa in institution 1 and 2, respectively. The radiomics signature with SMOTE augmentation method had a higher area under the ROC curve (AUC) [0.840 (95% CI, 0.776–0.904)] than that without SMOTE method [0.730 (95% CI, 0.624–0.836), p = 0.08] in the test group and significantly increased in the external validation group [0.834 (95% CI, 0.709–0.959) vs. 0.718 (95% CI, 0.562–0.874), p = 0.017]. The radiomics nomogram showed good discrimination and calibration, with an AUC of 0.939 (95% CI, 0.913–0.965), 0.884 (95% CI, 0.831–0.937), and 0.907 (95% CI, 0.814–1) in the training, test, and external validation groups, respectively. The DCA demonstrated the clinical usefulness of radiomics nomogram. Conclusion The radiomics nomogram that incorporates the MRI-based radiomics signature and PSAD can be conveniently used to individually predict CsPCa in patients within PI-RADS 3 lesion.
Collapse
Affiliation(s)
- Tianping Li
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China.,School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Linna Sun
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Qinghe Li
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Xunrong Luo
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Mingfang Luo
- School of Medical Imaging, Binzhou Medical University, Yantai, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Peiyuan Wang
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| |
Collapse
|
5
|
Chen J, Chen Y, Zheng D, Pang P, Zhang H, Zheng X, Liao J. Pretreatment MR-based radiomics nomogram as potential imaging biomarker for individualized assessment of perineural invasion status in rectal cancer. Abdom Radiol (NY) 2021; 46:847-857. [PMID: 32870349 DOI: 10.1007/s00261-020-02710-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 08/08/2020] [Accepted: 08/15/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To investigate whether pretreatment magnetic resonance (MR)-based radiomics nomogram can individualize prediction of perineural invasion (PNI) status in rectal cancer (RC). MATERIAL AND METHODS A total of 122 RC patients with pathologically confirmed were classified as training cohort (n = 87) and test cohort (n = 35). 180 radiomics features were extracted from all lesions based on oblique axial T2WI TSE images. The dimensionality reduction and feature selection in training cohort were realized by the maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression model. A predictive model combining radiomics features and clinical risk factors (pathological N stage, pathological LVI status) was established by multivariate logistic regression analysis. The performance of the model was assessed based on its receiver operating characteristic (ROC) curve, nomogram, and calibration. RESULTS The developed radiomics nomogram that integrated the radiomics signature and clinical risk factors could provide discrimination in the training and test cohorts. The accuracy and the area under the curve (AUC) for assessing PNI status were 0.82, 0.86, respectively, in the training cohort, while they were 0.71 and 0.85 in the test cohort. The goodness-of-fit of the nomogram was evaluated using the Hosmer-Lemeshow test (p = 0.52 in training cohort and p = 0.24 in test cohort). Decision curve analysis (DCA) showed that the radiomics nomogram was clinically useful. CONCLUSION The developed radiomics nomogram might be helpful in the individualized assessment PNI status in patients with RC. This stratification of RC patients according to their PNI status may provide the basis for individualized adjuvant therapy, especially for stage II patients.
Collapse
Affiliation(s)
- Jiayou Chen
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, 350014, Fujian, China.
| | - Ying Chen
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Dechun Zheng
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, 350014, Fujian, China
| | | | - Hejun Zhang
- Department of Pathology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Xiang Zheng
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Jiang Liao
- Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, 350014, Fujian, China
| |
Collapse
|
6
|
Zhang Y, Chen W, Yue X, Shen J, Gao C, Pang P, Cui F, Xu M. Development of a Novel, Multi-Parametric, MRI-Based Radiomic Nomogram for Differentiating Between Clinically Significant and Insignificant Prostate Cancer. Front Oncol 2020; 10:888. [PMID: 32695660 PMCID: PMC7339043 DOI: 10.3389/fonc.2020.00888] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Accepted: 05/05/2020] [Indexed: 12/15/2022] Open
Abstract
Objectives: To develop and validate a predictive model for discriminating clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa). Methods: This retrospective study was performed with 159 consecutively enrolled pathologically confirmed PCa patients from two medical centers. The dataset was allocated to a training group (n = 54) and an internal validation group (n = 22) from one center along with an external independent validation group (n = 83) from another center. A total of 1,188 radiomic features were extracted from T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images derived from DWI for each patient. Multivariable logistic regression analysis was performed to develop the model, incorporating the radiomic signature, ADC value, and independent clinical risk factors. This was presented using a radiomic nomogram. The receiver operating characteristic (ROC) curve was utilized to assess the predictive efficacy of the radiomic nomogram in both the training and validation groups. The decision curve analysis was used to evaluate which model achieved the most net benefit. Results: The radiomic signature, which was made up of 10 selected features, was significantly associated with csPCa (P < 0.001 for both training and internal validation groups). The area under the curve (AUC) values of discriminating csPCa for the radiomics signature were 0.95 (training group), 0.86 (internal validation group), and 0.81 (external validation group). Multivariate logistic analysis identified the radiomic signature and ADC value as independent parameters of predicting csPCa. Then, the combination nomogram incorporating the radiomic signature and ADC value demonstrated a favorable classification capability with the AUC of 0.95 (training group), 0.93 (internal validation group), and 0.84 (external validation group). Appreciable clinical utility of this model was illustrated using the decision curve analysis for the nomogram. Conclusions: The nomogram, incorporating radiomic signature and ADC value, provided an individualized, potential approach for discriminating csPCa from ciPCa.
Collapse
Affiliation(s)
- Yongsheng Zhang
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Wen Chen
- Department of Radiology, The Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Xianjie Yue
- Department of Radiology, The Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Jianliang Shen
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Chen Gao
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Peipei Pang
- GE Healthcare Life Sciences, Hangzhou, China
| | - Feng Cui
- Department of Radiology, The Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.,Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| |
Collapse
|