1
|
Ma Y, Xu X, Lin Y, Li J, Yuan H. An integrative clinical and CT-based tumoral/peritumoral radiomics nomogram to predict the microsatellite instability in rectal carcinoma. Abdom Radiol (NY) 2024; 49:783-790. [PMID: 38001326 DOI: 10.1007/s00261-023-04099-2] [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: 07/04/2023] [Revised: 10/14/2023] [Accepted: 10/18/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND Microsatellite instability (MSI) is detected in approximately 15% of colorectal carcinoma (CRC) patients, which has emerged as a predictor of patient response to adjuvant chemotherapy. Rectal carcinoma (RC) is the most common type of CRC. Therefore, prediction of MSI status of RC is significant for personalized medication. The purpose of this article was to develop an integrative model that combines clinical characteristics and computed tomography-based (CT-based) tumoral/peritumoral radiomics to predict the MSI status in RC. METHODS A cohort of 788 RCs with 97 high-MSI status (MSI-H) and 691 microsatellite stable status (MSS) were enrolled between January 2015 and January 2021 in this retrospective study. Clinical characteristics were recorded, and CT-based tumoral/peritumoral radiomic features were calculated after segmenting volume of interests. The patients were randomly divided into training and validation sets in a 7:3 proportion. Logistic models of single tumoral radiomics (LM-tRadio), peritumoral radiomics (LM-ptRadio), and combined tumoral/peritumoral radiomics (LM-Radio) were constructed to distinguish MSI-H from MSS, and a relevant radiomic score was calculated. An integrative nomogram (LM-Nomo) was developed, including significant clinical characteristics and CT-based tumoral/peritumoral radiomics. The area under receiver operator curve (AUC) was calculated to evaluate the efficacy of prediction. RESULTS The AUCs of LM-Radio were 0.785 (95%CI 0.732-0.837) in the training set and were 0.628 (95%CI 0.528-0.723) in the validation set, which were higher than those of LM-tRadio and LM-ptRadio. The AUCs of single LM-ptRadio were slightly higher than those of LM-tRadio (0.724 vs. 0.708 in the training set, 0.613 vs. 0.602 in the validation set). The LM-Nomo containing carcinoembryonic antigen (CEA), hypertension, and CT-based tumoral/peritumoral radiomic score showed the highest AUCs of 0.796 (95%CI 0.748-0.843) in the training set and 0.679 (95%CI 0.588-0.771) in the validation set in predicting the MSI-H status of RC. CONCLUSION The AUCs of LM-ptRadio were slightly higher than LM-tRadio to evaluate the MSI-H status of RC. The LM-Nomo, which includes significant clinical characteristics and CT-based tumoral/peritumoral radiomics score, demonstrated the best performance in predicting MSI-H status of RC.
Collapse
Affiliation(s)
- Yanqing Ma
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China
| | - Xiren Xu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China
| | - Yi Lin
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China
| | - Jie Li
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China
| | - Hang Yuan
- Cancer Center, Department of Colorectal Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China.
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Yang Y, Wei H, Fu F, Wei W, Wu Y, Bai Y, Li Q, Wang M. Preoperative prediction of lymphovascular invasion of colorectal cancer by radiomics based on 18F-FDG PET-CT and clinical factors. FRONTIERS IN RADIOLOGY 2023; 3:1212382. [PMID: 37614530 PMCID: PMC10442652 DOI: 10.3389/fradi.2023.1212382] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/28/2023] [Indexed: 08/25/2023]
Abstract
Purpose The purpose of this study was to investigate the value of a clinical radiomics model based on Positron emission tomography-computed tomography (PET-CT) radiomics features combined with clinical predictors of Lymphovascular invasion (LVI) in predicting preoperative LVI in patients with colorectal cancer (CRC). Methods A total of 95 CRC patients who underwent preoperative 18F-fluorodeoxyglucose (FDG) PET-CT examination were retrospectively enrolled. Univariate and multivariate logistic regression analyses were used to analyse clinical factors and PET metabolic data in the LVI-positive and LVI-negative groups to identify independent predictors of LVI. We constructed four prediction models based on radiomics features and clinical data to predict LVI status. The predictive efficacy of different models was evaluated according to the receiver operating characteristic curve. Then, the nomogram of the best model was constructed, and its performance was evaluated using calibration and clinical decision curves. Results Mean standardized uptake value (SUVmean), maximum tumour diameter and lymph node metastasis were independent predictors of LVI in CRC patients (P < 0.05). The clinical radiomics model obtained the best prediction performance, with an Area Under Curve (AUC) of 0.922 (95%CI 0.820-0.977) and 0.918 (95%CI 0.782-0.982) in the training and validation cohorts, respectively. A nomogram based on the clinical radiomics model was constructed, and the calibration curve fitted well (P > 0.05). Conclusion The clinical radiomics prediction model constructed in this study has high value in the preoperative individualized prediction of LVI in CRC patients.
Collapse
Affiliation(s)
- Yan Yang
- Department of Medical Imaging, People’s Hospital of Zhengzhou University, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Huanhuan Wei
- Department of Medical Imaging, People’s Hospital of Zhengzhou University, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Fangfang Fu
- Henan Key Laboratory of Neurological Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Wei Wei
- Henan Key Laboratory of Neurological Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yaping Wu
- Henan Key Laboratory of Neurological Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yan Bai
- Henan Key Laboratory of Neurological Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Qing Li
- Department of Medical Imaging, People’s Hospital of Zhengzhou University, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Meiyun Wang
- Henan Key Laboratory of Neurological Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| |
Collapse
|
4
|
Li M, Xu G, Chen Q, Xue T, Peng H, Wang Y, Shi H, Duan S, Feng F. Computed Tomography-based Radiomics Nomogram for the Preoperative Prediction of Tumor Deposits and Clinical Outcomes in Colon Cancer: a Multicenter Study. Acad Radiol 2023; 30:1572-1583. [PMID: 36566155 DOI: 10.1016/j.acra.2022.11.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/16/2022] [Accepted: 11/07/2022] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a computed tomography (CT)-based radiomics nomogram for the preoperative prediction of tumor deposits (TDs) and clinical outcomes in patients with colon cancer. MATERIALS AND METHODS This retrospective study included 383 consecutive patients with colon cancer from two centers. Radiomics features were extracted from portal venous phase CT images. Least absolute shrinkage and selection operator regression was applied for feature selection and radiomics signature construction. The multivariate logistic regression model was used to establish a radiomics nomogram. The performance of the nomogram was assessed by using receiver operating characteristic curves, calibration curves and decision curve analysis. Kaplan‒Meier survival analysis was used to assess the difference of the overall survival (OS) in the TDs-positive and TDs-negative groups. RESULTS The radiomics signature was composed of 11 TDs status related features. The AUCs of the radiomics model in the training cohort, internal validation and external validation cohorts were 0.82, 0.78 and 0.78, respectively. The radiomics nomogram that incorporated the radiomics signature and clinical independent predictors (CT-N, CEA and CA199) showed good calibration and discrimination with AUCs of 0.88, 0.80 and 0.81 in the training cohort, internal validation and external validation cohorts, respectively. The radiomics nomogram-predicted high-risk groups had a worse OS than the low-risk groups (p < 0.001). The radiomics nomogram-predicted TDs was an independent preoperative predictor of OS. CONCLUSION The radiomics nomogram based on CT radiomics features and clinical independent predictors could effectively predict the preoperative TDs status and OS of colon cancer. IMPORTANT FINDINGS CT-based radiomics nomogram may be applied in the individual preoperative prediction of TDs status in colon cancer. Additionally, there was a significant difference in OS between the high-risk and low-risk groups defined by the radiomics nomogram, in which patients with high-risk TDs had a significantly worse OS, compared with those with low-risk TDs.
Collapse
Affiliation(s)
- Manman Li
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Guodong Xu
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, Jiangsu, PR China
| | - Qiaoling Chen
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Ting Xue
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Hui Peng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | - Yuwei Wang
- Department of Record room, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China
| | - Hui Shi
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, PR China, 226361.
| |
Collapse
|
5
|
Li M, Xu G, Zhou H, Chen Q, Fan Q, Shi J, Duan S, Cui Y, Feng F. Computed tomography-based radiomics nomogram for the pre-operative prediction of BRAF mutation and clinical outcomes in patients with colorectal cancer: a double-center study. Br J Radiol 2023; 96:20230019. [PMID: 37195006 PMCID: PMC10392655 DOI: 10.1259/bjr.20230019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/10/2023] [Accepted: 04/23/2023] [Indexed: 05/18/2023] Open
Abstract
OBJECTIVE To develop and validate a radiomics nomogram based on CT for the pre-operative prediction of BRAF mutation and clinical outcomes in patients with colorectal cancer (CRC). METHODS A total of 451 CRC patients (training cohort = 190; internal validation cohort = 125; external validation cohort = 136) from 2 centers were retrospectively included. Least absolute shrinkage and selection operator regression was used to select radiomics features and the radiomics score (Radscore) was calculated. Nomogram was constructed by combining Radscore and significant clinical predictors. Receiver operating characteristic curve analysis, calibration curve and decision curve analysis were used to evaluate the predictive performance of the nomogram. Kaplan‒Meier survival curves based on the radiomics nomogram were used to assess overall survival (OS) of the entire cohort. RESULTS The Radscore consisted of nine radiomics features which were the most relevant to BRAF mutation. The radiomics nomogram integrating Radscore and clinical independent predictors (age, tumor location and cN stage) showed good calibration and discrimination with AUCs of 0.86 (95% CI: 0.80-0.91), 0.82 (95% CI: 0.74-0.90) and 0.82 (95% CI: 0.75-0.90) in the training cohort, internal validation and external validation cohorts, respectively. Furthermore,the performance of nomogram was significantly better than that of the clinical model (p < 0.05). The radiomics nomogram-predicted BRAF mutation high-risk group had a worse OS than the low-risk group (p < 0.0001). CONCLUSION The radiomics nomogram showed good performance in predicting BRAF mutation and OS of CRC patients, which could provide valuable information for individualized treatment. ADVANCES IN KNOWLEDGE The radiomics nomogram could effectively predict BRAF mutation and OS in patients with CRC. High-risk BRAF mutation group identified by the radiomics nomogram was independently associated with poor OS.
Collapse
Affiliation(s)
| | - Guodong Xu
- Department of Radiology, Yancheng No. 1 People’s Hospital, Yancheng, Jiangsu Province, China
| | - Hui Zhou
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Qiaoling Chen
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Qi Fan
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Jian Shi
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China
| | | | - Yanfen Cui
- Department of Radiology, Shanxi Cancer Hospital, Shanxi, Shanxi Province, China
| | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China
| |
Collapse
|