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Ma S, Lu H, Jing G, Li Z, Zhang Q, Ma X, Chen F, Shao C, Lu Y, Wang H, Shen F. Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study. Front Med (Lausanne) 2023; 10:1276672. [PMID: 38105891 PMCID: PMC10722265 DOI: 10.3389/fmed.2023.1276672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023] Open
Abstract
Background Precise preoperative evaluation of lymph node metastasis (LNM) is crucial for ensuring effective treatment for rectal cancer (RC). This research aims to develop a clinical-radiomics nomogram based on deep learning techniques, preoperative magnetic resonance imaging (MRI) and clinical characteristics, enabling the accurate prediction of LNM in RC. Materials and methods Between January 2017 and May 2023, a total of 519 rectal cancer cases confirmed by pathological examination were retrospectively recruited from two tertiary hospitals. A total of 253 consecutive individuals were selected from Center I to create an automated MRI segmentation technique utilizing deep learning algorithms. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, two external validation cohorts were established: one comprising 178 patients from center I (EVC1) and another consisting of 88 patients from center II (EVC2). The automatic segmentation provided radiomics features, which were then used to create a Radscore. A predictive nomogram integrating the Radscore and clinical parameters was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were employed to evaluate the discrimination capabilities of the Radscore, nomogram, and subjective evaluation model, respectively. Results The mean DSC, HD95 and ASD were 0.857 ± 0.041, 2.186 ± 0.956, and 0.562 ± 0.194 mm, respectively. The nomogram, which incorporates MR T-stage, CEA, CA19-9, and Radscore, exhibited a higher area under the ROC curve (AUC) compared to the Radscore and subjective evaluation in the training set (0.921 vs. 0.903 vs. 0.662). Similarly, in both external validation sets, the nomogram demonstrated a higher AUC than the Radscore and subjective evaluation (0.908 vs. 0.735 vs. 0.640, and 0.884 vs. 0.802 vs. 0.734). Conclusion The application of the deep learning method enables efficient automatic segmentation. The clinical-radiomics nomogram, utilizing preoperative MRI and automatic segmentation, proves to be an accurate method for assessing LNM in RC. This approach has the potential to enhance clinical decision-making and improve patient care. Research registration unique identifying number UIN Research registry, identifier 9158, https://www.researchregistry.com/browse-the-registry#home/registrationdetails/648e813efffa4e0028022796/.
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Affiliation(s)
- Shiyu Ma
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Haidi Lu
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Guodong Jing
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Zhihui Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qianwen Zhang
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Xiaolu Ma
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Fangying Chen
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Yong Lu
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Hao Wang
- Department of Colorectal Surgery, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Fu Shen
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
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Tian C, Ma X, Lu H, Wang Q, Shao C, Yuan Y, Shen F. Deep learning models for preoperative T-stage assessment in rectal cancer using MRI: exploring the impact of rectal filling. Front Med (Lausanne) 2023; 10:1326324. [PMID: 38105894 PMCID: PMC10722089 DOI: 10.3389/fmed.2023.1326324] [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: 10/23/2023] [Accepted: 11/14/2023] [Indexed: 12/19/2023] Open
Abstract
Background The objective of this study was twofold: firstly, to develop a convolutional neural network (CNN) for automatic segmentation of rectal cancer (RC) lesions, and secondly, to construct classification models to differentiate between different T-stages of RC. Additionally, it was attempted to investigate the potential benefits of rectal filling in improving the performance of deep learning (DL) models. Methods A retrospective study was conducted, including 317 consecutive patients with RC who underwent MRI scans. The datasets were randomly divided into a training set (n = 265) and a test set (n = 52). Initially, an automatic segmentation model based on T2-weighted imaging (T2WI) was constructed using nn-UNet. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, three types of DL-models were constructed: Model 1 trained on the total training dataset, Model 2 trained on the rectal-filling dataset, and Model 3 trained on the non-filling dataset. The diagnostic values were evaluated and compared using receiver operating characteristic (ROC) curve analysis, confusion matrix, net reclassification index (NRI), and decision curve analysis (DCA). Results The automatic segmentation showed excellent performance. The rectal-filling dataset exhibited superior results in terms of DSC and ASD (p = 0.006 and 0.017). The DL-models demonstrated significantly superior classification performance to the subjective evaluation in predicting T-stages for all test datasets (all p < 0.05). Among the models, Model 1 showcased the highest overall performance, with an area under the curve (AUC) of 0.958 and an accuracy of 0.962 in the filling test dataset. Conclusion This study highlighted the utility of DL-based automatic segmentation and classification models for preoperative T-stage assessment of RC on T2WI, particularly in the rectal-filling dataset. Compared with subjective evaluation, the models exhibited superior performance, suggesting their noticeable potential for enhancing clinical diagnosis and treatment practices.
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Affiliation(s)
- Chang Tian
- School of Information Science and Technology and School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Xiaolu Ma
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Haidi Lu
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Qian Wang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Yuan Yuan
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, China
| | - Fu Shen
- Department of Radiology, Changhai Hospital, The Navy Medical University, Shanghai, 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: 9] [Impact Index Per Article: 9.0] [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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Jing G, Xing P, Li Z, Ma X, Lu H, Shao C, Lu Y, Lu J, Shen F. Prediction of clinically significant prostate cancer with a multimodal MRI-based radiomics nomogram. Front Oncol 2022; 12:918830. [PMID: 35912175 PMCID: PMC9334707 DOI: 10.3389/fonc.2022.918830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveTo develop and validate a multimodal MRI-based radiomics nomogram for predicting clinically significant prostate cancer (CS-PCa).MethodsPatients who underwent radical prostatectomy with pre-biopsy prostate MRI in three different centers were assessed retrospectively. Totally 141 and 60 cases were included in the training and test sets in cohort 1, respectively. Then, 66 and 122 cases were enrolled in cohorts 2 and 3, as external validation sets 1 and 2, respectively. Two different manual segmentation methods were established, including lesion segmentation and whole prostate segmentation on T2WI and DWI scans, respectively. Radiomics features were obtained from the different segmentation methods and selected to construct a radiomics signature. The final nomogram was employed for assessing CS-PCa, combining radiomics signature and PI-RADS. Diagnostic performance was determined by receiver operating characteristic (ROC) curve analysis, net reclassification improvement (NRI) and decision curve analysis (DCA).ResultsTen features associated with CS-PCa were selected from the model integrating whole prostate (T2WI) + lesion (DWI) for radiomics signature development. The nomogram that combined the radiomics signature with PI-RADS outperformed the subjective evaluation alone according to ROC analysis in all datasets (all p<0.05). NRI and DCA confirmed that the developed nomogram had an improved performance in predicting CS-PCa.ConclusionsThe established nomogram combining a biparametric MRI-based radiomics signature and PI-RADS could be utilized for noninvasive and accurate prediction of CS-PCa.
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Affiliation(s)
- Guodong Jing
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Pengyi Xing
- Department of Radiology, 989th Hospital of the joint logistic support force of the Chinese People’s Liberation Army, Luoyang, China
| | - Zhihui Li
- Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xiaolu Ma
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Haidi Lu
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Yong Lu
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- *Correspondence: Yong Lu, ; Jianping Lu, ; Fu Shen,
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Shanghai, China
- *Correspondence: Yong Lu, ; Jianping Lu, ; Fu Shen,
| | - Fu Shen
- Department of Radiology, Changhai Hospital, Shanghai, China
- *Correspondence: Yong Lu, ; Jianping Lu, ; Fu Shen,
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Li T, Liu Y, Guo J, Wang Y. Prediction of the activity of Crohn's disease based on CT radiomics combined with machine learning models. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:1155-1168. [PMID: 35988261 DOI: 10.3233/xst-221224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
PURPOSE To investigate the value of a CT-based radiomics model in identification of Crohn's disease (CD) active phase and remission phase. METHODS CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance.
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Affiliation(s)
- Tingting Li
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yu Liu
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School ofMedicine, Shanghai 200011, China
| | - Jiuhong Guo
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School ofMedicine, Shanghai 200011, China
| | - Yuanjun Wang
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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