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Wu S, Wang N, Ao W, Hu J, Xu W, Mao G. Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer. Abdom Radiol (NY) 2024; 49:3003-3014. [PMID: 38489038 DOI: 10.1007/s00261-024-04232-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/01/2024] [Accepted: 02/01/2024] [Indexed: 03/17/2024]
Abstract
PURPOSE To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer. METHODS The data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient's mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis. RESULTS The strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001). CONCLUSION The nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer.
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Affiliation(s)
- Sikai Wu
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Neng Wang
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Weiqun Ao
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China
| | - Jinwen Hu
- Department of Radiology, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wenjie Xu
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Guoqun Mao
- Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, Hangzhou, 310012, Zhejiang, China.
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Zheng Y, Chen X, Zhang H, Ning X, Mao Y, Zheng H, Dai G, Liu B, Zhang G, Huang D. Multiparametric MRI-based radiomics nomogram for the preoperative prediction of lymph node metastasis in rectal cancer: A two-center study. Eur J Radiol 2024; 178:111591. [PMID: 39013271 DOI: 10.1016/j.ejrad.2024.111591] [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: 04/14/2024] [Revised: 06/06/2024] [Accepted: 06/24/2024] [Indexed: 07/18/2024]
Abstract
PURPOSE To develop a radiomic nomogram based on multiparametric magnetic resonance imaging for the preoperative prediction of lymph node metastasis (LNM) in rectal cancer. METHODS This retrospective study included 318 patients with pathologically proven rectal adenocarcinoma from two hospitals. Radiomic features were extracted from T2-weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging scans of the training cohort, and the radsore model was then constructed. The combined model was obtained by integrating the Radscore and clinical models. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic effectiveness of each model, and the best-performing model was used to develop the nomogram. RESULTS The Radscore and clinical models exhibited similar diagnostic efficacy (DeLong's test, P > 0.05). The AUC of the combined model was significantly higher than those of the clinical and Radscore models in the training cohort (AUC: 0.837 vs. 0.763 and 0.787, P: 0.02120 and 0.02309) and the external validation cohort (AUC: 0.880 vs. 0.797 and 0.779, P: 0.02310 and 0.02471). However, the diagnostic performance of the three models was comparable in the internal validation cohort (P > 0.05). Thus, among the three models, the combined model exhibited the highest diagnostic efficiency. The calibration curve exhibited satisfactory consistency between the nomogram predictions and the actual results. DCA confirmed the considerable clinical usefulness of the nomogram. CONCLUSION The radiomics nomogram can accurately and noninvasively predict LNM in rectal cancer before surgery, serving as a convenient visualization tool for informing treatment decisions, including the choice of surgical approach and the need for neoadjuvant therapy.
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Affiliation(s)
- Yongfei Zheng
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Xu Chen
- Hangzhou Dianzi University Zhuoyue Honors College, Hangzhou, Zhejiang Province, China
| | - He Zhang
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Xiaoxiang Ning
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Yichuan Mao
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Hailan Zheng
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Guojiao Dai
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Binghui Liu
- Department of Pathology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China
| | - Guohua Zhang
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China.
| | - Danjiang Huang
- Department of Radiology, Huangyan Hospital, Wenzhou Medical University, Taizhou First People's Hospital, Taizhou, Zhejiang Province, China.
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Cui Y, Wang X, Wang Y, Meng N, Wu Y, Shen Y, Roberts N, Bai Y, Song X, Shen G, Guo Y, Guo J, Wang M. Restriction Spectrum Imaging and Diffusion Kurtosis Imaging for Assessing Proliferation Status in Rectal Carcinoma. Acad Radiol 2024:S1076-6332(24)00584-1. [PMID: 39191564 DOI: 10.1016/j.acra.2024.08.021] [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: 06/26/2024] [Revised: 08/04/2024] [Accepted: 08/12/2024] [Indexed: 08/29/2024]
Abstract
OBJECTIVES To investigate the application of the three-compartment restriction spectrum imaging (RSI) model, diffusion kurtosis imaging (DKI), and diffusion-weighted imaging (DWI) in predicting Ki-67 status in rectal carcinoma. METHODS A total of 80 rectal carcinoma patients, including 47 high-proliferation (Ki-67 > 50%) cases and 33 low-proliferation (Ki-67 ≤ 50%) cases, underwent pelvic MRI were enrolled. Parameters derived from RSI (f1, f2, and f3), DKI (MD and MK), and DWI (ADC) were calculated and compared between the two groups. Logistic regression (LR) analysis was conducted to identify independent predictors and assess combined diagnosis. Area under the receiver operating characteristic curve (AUC), DeLong analysis, and calibration curve analyses were performed to evaluate diagnostic performance. RESULTS The patients with high-proliferation rectal carcinoma exhibited significantly higher f1 and MK values and significantly lower ADC, MD, f2, and f3 values than those with low-proliferation rectal carcinoma (P < 0.05). LR analysis showed that MD, MK, and f2 were independent predictors for Ki-67 status in rectal carcinoma. Moreover, the combination of these three parameters achieved an optimal diagnostic efficacy (AUC = 0.877, sensitivity = 80.85%, specificity = 84.85%) that was significantly better than that obtained using ADC (AUC = 0.783, Z = 2.347, P = 0.019), f2 (AUC = 0.732, Z = 2.762, P = 0.006), and f3 (AUC = 0.700, Z = 3.071, P = 0.002). The combined diagnosis also showed good performance (AUC = 0.859) in the internal validation analysis based on 1000 bootstrap samples, while the calibration curve demonstrated that the combined diagnosis provided good stability. CONCLUSION RSI, DKI, and DWI can effectively differentiate between patients with high- and low-proliferation rectal carcinoma. Furthermore, the MD, MK, and f2 imaging parameters may be a novel and promising combination biomarker for examining Ki-67 status in rectal carcinoma.
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Affiliation(s)
- Yingying Cui
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China (Y.C., X.W., Y.W., N.M., Y.W., Y.S., Y.B., M.W.)
| | - Xinhui Wang
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China (Y.C., X.W., Y.W., N.M., Y.W., Y.S., Y.B., M.W.)
| | - Ying Wang
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China (Y.C., X.W., Y.W., N.M., Y.W., Y.S., Y.B., M.W.)
| | - Nan Meng
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China (Y.C., X.W., Y.W., N.M., Y.W., Y.S., Y.B., M.W.)
| | - Yaping Wu
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China (Y.C., X.W., Y.W., N.M., Y.W., Y.S., Y.B., M.W.)
| | - Yu Shen
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China (Y.C., X.W., Y.W., N.M., Y.W., Y.S., Y.B., M.W.)
| | - Neil Roberts
- Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK (N.R.); Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou, China (N.R., X.S., M.W.)
| | - Yan Bai
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China (Y.C., X.W., Y.W., N.M., Y.W., Y.S., Y.B., M.W.)
| | - Xiaosheng Song
- Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou, China (N.R., X.S., M.W.)
| | - Guofeng Shen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China (G.S.); Shanghai Shende Green Medical Era Healthcare Technology Co., Ltd., Shanghai, China (G.S.)
| | - Yongjun Guo
- Henan Academy of Innovations in Medical Science, Zhengzhou, China (Y.G.)
| | - Jinxia Guo
- MR Research China, GE Healthcare, Beijing, China (J.G.)
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, China (Y.C., X.W., Y.W., N.M., Y.W., Y.S., Y.B., M.W.); Biomedical Research Institute, Henan Academy of Sciences, Zhengzhou, China (N.R., X.S., M.W.).
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Yao X, Zhu X, Deng S, Zhu S, Mao G, Hu J, Xu W, Wu S, Ao W. MRI-based radiomics for preoperative prediction of recurrence and metastasis in rectal cancer. Abdom Radiol (NY) 2024; 49:1306-1319. [PMID: 38407804 DOI: 10.1007/s00261-024-04205-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 01/09/2024] [Accepted: 01/12/2024] [Indexed: 02/27/2024]
Abstract
OBJECTIVES To explore the value of multi-parametric MRI (mp-MRI) radiomic model for preoperative prediction of recurrence and/or metastasis (RM) as well as survival benefits in patients with rectal cancer. METHODS A retrospective analysis of 234 patients from two centers with histologically confirmed rectal adenocarcinoma was conducted. All patients were divided into three groups: training, internal validation (in-vad) and external validation (ex-vad) sets. In the training set, radiomic features were extracted from T2WI, DWI, and contrast enhancement T1WI (CE-T1) sequence. Radiomic signature (RS) score was then calculated for feature screening to construct a rad-score model. Subsequently, preoperative clinical features with statistical significance were selected to construct a clinical model. Independent predictors from clinical and RS related to RM were selected to build the combined model and nomogram. RESULTS After feature extraction, 26 features were selected to construct the rad-score model. RS (OR = 0.007, p < 0.01), MR-detected T stage (mrT) (OR = 2.92, p = 0.03) and MR-detected circumferential resection margin (mrCRM) (OR = 4.70, p = 0.01) were identified as independent predictors of RM. Then, clinical model and combined model were constructed. ROC curve showed that the AUC, accuracy, sensitivity and specificity of the combined model were higher than that of the other two models in three sets. Kaplan-Meier curves showed that poorer disease-free survival (DFS) time was observed for patients in pT3-4 stages with low RS score (p < 0.001), similar results were also found in pCRM-positive patients (p < 0.05). CONCLUSION The mp-MRI radiomics model can be served as a noninvasive and accurate predictors of RM in rectal cancer that may support clinical decision-making.
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Affiliation(s)
- Xiuzhen Yao
- Department of Ultrasound, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiandi Zhu
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Shuitang Deng
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Sizheng Zhu
- Computer Center, University of Shanghai for Science and Technology, Shanghai, China
| | - Guoqun Mao
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jinwen Hu
- Department of Radiology, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wenjie Xu
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Sikai Wu
- Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Weiqun Ao
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China.
- , No. 234 Gucui Road, Hangzhou, 310012, Zhejiang, China.
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Cai C, Hu T, Rong Z, Gong J, Tong T. Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer. Eur J Radiol 2024; 170:111254. [PMID: 38091662 DOI: 10.1016/j.ejrad.2023.111254] [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: 08/29/2023] [Revised: 11/08/2023] [Accepted: 12/05/2023] [Indexed: 01/16/2024]
Abstract
PURPOSE To develop and validate a radiomics model based on high-resolution T2WI and a clinical-radiomics model for tumour-stroma ratio (TSR) evaluation with a gold standard of TSR evaluated by rectal specimens without therapeutic interference and further apply them in prognosis prediction of locally advanced rectal cancer (LARC) patients who received neoadjuvant chemoradiotherapy. METHODS A total of 178 patients (mean age: 59.35, range 20-85 years; 65 women and 113 men) with rectal cancer who received surgery alone from January 2016 to October 2020 were enrolled and randomly separated at a ratio of 7:3 into training and validation sets. A senior radiologist reviewed after 2 readers manually delineated the whole tumour in consensus on preoperative high-resolution T2WI in the training set. A total of 1046 features were then extracted, and recursive feature elimination embedded with leave-one-out cross validation was applied to select features, with which an MR-TSR evaluation model was built containing 6 filtered features via a support vector machine classifier trained by comparing patients' pathological TSR. Stepwise logistic regression was employed to integrate clinical factors with the radiomics model (Fusion-TSR) in the training set. Later, the MR-TSR and Fusion-TSR models were replicated in the validation set for diagnostic effectiveness evaluation. Subsequently, 243 patients (mean age: 53.74, range 23-74 years; 63 women and 180 men) with LARC from October 2012 to September 2017 who were treated with NCRT prior to surgery and underwent standard pretreatment rectal MR examination were enrolled. The MR-TSR and Fusion-TSR were applied, and the Kaplan-Meier method and log-rank test were used to compare the survival of patients with different MR-TSR and Fusion-TSR. Cox proportional hazards regression was used to calculate the hazard ratio (HR). RESULTS Both the MR-TSR and Fusion-TSR models were validated with favourable diagnostic power: the AUC of the MR-TSR was 0.77 (p = 0.01; accuracy = 69.8 %, sensitivity = 88.9 %, specificity = 65.9 %, PPV = 34.8 %, NPV = 96.7 %), while the AUC of the Fusion-TSR was 0.76 (p = 0.014; accuracy = 67.9 %, sensitivity = 88.9 %, specificity = 63.6 %, PPV = 33.3 %, NPV = 96.6 %), outperforming their effectiveness in the training set: the AUC of the MR-TSR was 0.65 (p = 0.035; accuracy = 66.4 %, sensitivity = 61.9 %, specificity = 67.3 %, PPV = 27.7 %, NPV = 90.0 %), while the AUC of the Fusion-TSR was 0.73 (p = 0.001; accuracy = 73.6 %, sensitivity = 71.4 %, specificity = 74.0 %, PPV = 35.73 %, NPV = 92.8 %). With further prognostic analysis, the MR-TSR was validated as a significant prognostic factor for DFS in LARC patients treated with NCRT (p = 0.020, HR = 1.662, 95 % CI = 1.077-2.565), while the Fusion-TSR was a significant prognostic factor for OS (p = 0.005, HR = 2.373, 95 % CI = 1.281-4.396). CONCLUSIONS We developed and validated a radiomics TSR and a clinical-radiomics TSR model and successfully applied them to better risk stratification for LARC patients receiving NCRT and for better decision making.
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Affiliation(s)
- Chongpeng Cai
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai 200032, China
| | - Tingdan Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai 200032, China
| | - Zening Rong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai 200032, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai 200032, China.
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai 200032, China.
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