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Zhao H, Su Y, Wang Y, Lyu Z, Xu P, Gu W, Tian L, Fu P. Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer. Cancer Imaging 2024; 24:26. [PMID: 38342905 PMCID: PMC10860234 DOI: 10.1186/s40644-024-00670-2] [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: 08/14/2023] [Accepted: 01/29/2024] [Indexed: 02/13/2024] Open
Abstract
BACKGROUND To investigate the association between Kirsten rat sarcoma viral oncogene homolog (KRAS) / neuroblastoma rat sarcoma viral oncogene homolog (NRAS) /v-raf murine sarcoma viral oncogene homolog B (BRAF) mutations and the tumor habitat-derived radiomic features obtained during pretreatment 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in patients with colorectal cancer (CRC). METHODS We retrospectively enrolled 62 patients with CRC who had undergone 18F-FDG PET/computed tomography from January 2017 to July 2022 before the initiation of therapy. The patients were randomly split into training and validation cohorts with a ratio of 6:4. The whole tumor region radiomic features, habitat-derived radiomic features, and metabolic parameters were extracted from 18F-FDG PET images. After reducing the feature dimension and selecting meaningful features, we constructed a hierarchical model of KRAS/NRAS/BRAF mutations by using the support vector machine. The convergence of the model was evaluated by using learning curve, and its performance was assessed based on the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. The SHapley Additive exPlanation was used to interpret the contributions of various features to predictions of the model. RESULTS The model constructed by using habitat-derived radiomic features had adequate predictive power with respect to KRAS/NRAS/BRAF mutations, with an AUC of 0.759 (95% CI: 0.585-0.909) on the training cohort and that of 0.701 (95% CI: 0.468-0.916) on the validation cohort. The model exhibited good convergence, suitable calibration, and clinical application value. The results of the SHapley Additive explanation showed that the peritumoral habitat and a high_metabolism habitat had the greatest impact on predictions of the model. No meaningful whole tumor region radiomic features or metabolic parameters were retained during feature selection. CONCLUSION The habitat-derived radiomic features were found to be helpful in stratifying the status of KRAS/NRAS/BRAF in CRC patients. The approach proposed here has significant implications for adjuvant treatment decisions in patients with CRC, and needs to be further validated on a larger prospective cohort.
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Affiliation(s)
- Hongyue Zhao
- Department of Nuclear Medicine, 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
| | - Yan Wang
- 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
| | - Peng Xu
- 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, Ibaraki, Japan
| | - Lin Tian
- Department of Pathology, 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.
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Lo CM, Jiang JK, Lin CC. Detecting microsatellite instability in colorectal cancer using Transformer-based colonoscopy image classification and retrieval. PLoS One 2024; 19:e0292277. [PMID: 38271352 PMCID: PMC10810505 DOI: 10.1371/journal.pone.0292277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 09/15/2023] [Indexed: 01/27/2024] Open
Abstract
Colorectal cancer (CRC) is a major global health concern, with microsatellite instability-high (MSI-H) being a defining characteristic of hereditary nonpolyposis colorectal cancer syndrome and affecting 15% of sporadic CRCs. Tumors with MSI-H have unique features and better prognosis compared to MSI-L and microsatellite stable (MSS) tumors. This study proposed establishing a MSI prediction model using more available and low-cost colonoscopy images instead of histopathology. The experiment utilized a database of 427 MSI-H and 1590 MSS colonoscopy images and vision Transformer (ViT) with different feature training approaches to establish the MSI prediction model. The accuracy of combining pre-trained ViT features was 84% with an area under the receiver operating characteristic curve of 0.86, which was better than that of DenseNet201 (80%, 0.80) in the experiment with support vector machine. The content-based image retrieval (CBIR) approach showed that ViT features can obtain a mean average precision of 0.81 compared to 0.79 of DenseNet201. ViT reduced the issues that occur in convolutional neural networks, including limited receptive field and gradient disappearance, and may be better at interpreting diagnostic information around tumors and surrounding tissues. By using CBIR, the presentation of similar images with the same MSI status would provide more convincing deep learning suggestions for clinical use.
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Affiliation(s)
- Chung-Ming Lo
- Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan
| | - Jeng-Kai Jiang
- Department of Surgery, Division of Colon and Rectal Surgery, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chun-Chi Lin
- Department of Surgery, Division of Colon and Rectal Surgery, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
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Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, Yang H, Wang H, Zhang Y. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes. Biomark Res 2024; 12:12. [PMID: 38273398 PMCID: PMC10809593 DOI: 10.1186/s40364-024-00561-5] [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: 10/20/2023] [Accepted: 01/10/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. METHODS We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the 'Deep-RadScore,' a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. FINDINGS The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. INTERPRETATION This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.
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Affiliation(s)
- Xingping Zhang
- School of Medical Information Engineering, Gannan Medical University, 341000, Ganzhou, China
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Guijuan Zhang
- Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Jiao Yin
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, 110189, Shenyang, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China.
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia.
- Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China.
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Lu T, Ma J, Zou J, Jiang C, Li Y, Han J. CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:597-609. [PMID: 38578874 DOI: 10.3233/xst-230326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
BACKGROUND The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration. OBJECTIVE This study aimed to compare the performance of handcrafted radiomics (HR) features and deep transfer learning (DTL) features in Computed Tomography (CT) of intratumoral and peritumoral regions in predicting the metastatic status of NSCLC lymph nodes in different machine learning classifier models. METHODS We retrospectively collected data of 199 patients with pathologically confirmed NSCLC. All patients were divided into training (n = 159) and validation (n = 40) cohorts, respectively. The best HR and DTL features in the intratumoral and peritumoral regions were extracted and selected, respectively. Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Light Gradient Boosting Machine (Light GBM), Multilayer Perceptron (MLP), and Logistic Regression (LR) models were constructed, and the performance of the models was evaluated. RESULTS Among the five models in the training and validation cohorts, the LR classifier model performed best in terms of HR and DTL features. The AUCs of the training cohort were 0.841 (95% CI: 0.776-0.907) and 0.955 (95% CI: 0.926-0.983), and the AUCs of the validation cohort were 0.812 (95% CI: 0.677-0.948) and 0.893 (95% CI: 0.795-0.991), respectively. The DTL signature was superior to the handcrafted radiomics signature. CONCLUSIONS Compared with the radiomics signature, the DTL signature constructed based on intratumoral and peritumoral areas in CT can better predict NSCLC lymph node metastasis.
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Affiliation(s)
- Tianyu Lu
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Jianbing Ma
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Jiajun Zou
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Chenxu Jiang
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Yangyang Li
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Jun Han
- Department of Radiology, The First Hospital of Jiaxing or The Affiliated Hospital of Jiaxing University, Jiaxing, China
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Chen Y, Wang L, Dong X, Luo R, Ge Y, Liu H, Zhang Y, Wang D. Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer. J Digit Imaging 2023; 36:1323-1331. [PMID: 36973631 PMCID: PMC10042410 DOI: 10.1007/s10278-023-00818-9] [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: 12/09/2022] [Revised: 03/09/2023] [Accepted: 03/13/2023] [Indexed: 03/29/2023] Open
Abstract
The objective of this study is to develop a radiomic signature constructed from deep learning features and a nomogram for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients. Preoperative magnetic resonance imaging data from 479 breast cancer patients with 488 lesions were studied. The included patients were divided into two cohorts by time (training/testing cohort, n = 366/122). Deep learning features were extracted from diffusion-weighted imaging-quantitatively measured apparent diffusion coefficient (DWI-ADC) imaging and dynamic contrast-enhanced MRI (DCE-MRI) by a pretrained neural network of DenseNet121. After the selection of both radiomic and clinicopathological features, deep learning signature and a nomogram were built for independent validation. Twenty-three deep learning features were automatically selected in the training cohort to establish the deep learning signature of ALNM. Three clinicopathological factors, including LN palpability (odds ratio (OR) = 6.04; 95% confidence interval (CI) = 3.06-12.54, P = 0.004), tumor size in MRI (OR = 1.45, 95% CI = 1.18-1.80, P = 0.104), and Ki-67 (OR = 1.01; 95% CI = 1.00-1.02, P = 0.099), were selected and combined with radiomic signature to build a combined nomogram. The nomogram showed excellent predictive ability for ALNM (AUC 0.80 and 0.71 in training and testing cohorts, respectively). The sensitivity, specificity, and accuracy were 65%, 80%, and 75%, respectively, in the testing cohort. MRI-based deep learning radiomics in patients with breast cancer could be used to predict ALNM, providing a noninvasive approach to structuring the treatment strategy.
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Affiliation(s)
- Yanhong Chen
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China
| | - Lijun Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China
| | - Xue Dong
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China
| | - Ran Luo
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China
| | - Yaqiong Ge
- Department of Medicine, GE Healthcare, No. 1, Huatuo Road, 210000, Shanghai, China
| | - Huanhuan Liu
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China
| | - Yuzhen Zhang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China.
| | - Dengbin Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, 200092, Shanghai, China.
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Lo CM, Yang YW, Lin JK, Lin TC, Chen WS, Yang SH, Chang SC, Wang HS, Lan YT, Lin HH, Huang SC, Cheng HH, Jiang JK, Lin CC. Modeling the survival of colorectal cancer patients based on colonoscopic features in a feature ensemble vision transformer. Comput Med Imaging Graph 2023; 107:102242. [PMID: 37172354 DOI: 10.1016/j.compmedimag.2023.102242] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/05/2023] [Accepted: 05/07/2023] [Indexed: 05/14/2023]
Abstract
The prognosis of patients with colorectal cancer (CRC) mostly relies on the classic tumor node metastasis (TNM) staging classification. A more accurate and convenient prediction model would provide a better prognosis and assist in treatment. From May 2014 to December 2017, patients who underwent an operation for CRC were enrolled. The proposed feature ensemble vision transformer (FEViT) used ensemble classifiers to benefit the combinations of relevant colonoscopy features from the pretrained vision transformer and clinical features, including sex, age, family history of CRC, and tumor location, to establish the prognostic model. A total of 1729 colonoscopy images were enrolled in the current retrospective study. For the prediction of patient survival, FEViT achieved an accuracy of 94 % with an area under the receiver operating characteristic curve of 0.93, which was better than the TNM staging classification (90 %, 0.83) in the experiment. FEViT reduced the limited receptive field and gradient disappearance in the conventional convolutional neural network and was a relatively effective and efficient procedure. The promising accuracy of FEViT in modeling survival makes the prognosis of CRC patients more predictable and practical.
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Affiliation(s)
- Chung-Ming Lo
- Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan
| | - Yi-Wen Yang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jen-Kou Lin
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tzu-Chen Lin
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Shone Chen
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shung-Haur Yang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Surgery, National Yang Ming Chiao Tung University Hospital, Yilan, Taiwan
| | - Shih-Ching Chang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Huann-Sheng Wang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yuan-Tzu Lan
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hung-Hsin Lin
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sheng-Chieh Huang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hou-Hsuan Cheng
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jeng-Kai Jiang
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chun-Chi Lin
- Division of Colon and Rectal Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Liu Y, Wei X, Feng X, Liu Y, Feng G, Du Y. Repeatability of radiomics studies in colorectal cancer: a systematic review. BMC Gastroenterol 2023; 23:125. [PMID: 37059990 PMCID: PMC10105401 DOI: 10.1186/s12876-023-02743-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 03/22/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer. METHODS The included studies in this review by searching from the PubMed and Embase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included studies. RESULTS A total of 188 studies was included in this review, of which only two (2/188, 1.06%) studies controlled the influence of individual factors. In addition, the median score of RQS was 11 (out of 36), range-1 to 27. CONCLUSIONS The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. To improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.
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Affiliation(s)
- Ying Liu
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | - Xiaoqin Wei
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | | | - Yan Liu
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Guiling Feng
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Yong Du
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China.
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Pan L, He T, Huang Z, Chen S, Zhang J, Zheng S, Chen X. Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image. Abdom Radiol (NY) 2023; 48:1246-1259. [PMID: 36859730 DOI: 10.1007/s00261-023-03838-9] [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: 11/21/2022] [Revised: 01/27/2023] [Accepted: 01/27/2023] [Indexed: 03/03/2023]
Abstract
OBJECTIVES Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1-T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. This paper introduces three models of radiomics, deep learning, and deep learning-based radiomics to identify T4 OCC. METHODS We established a dataset of computed tomography (CT) images of 164 patients with pathologically confirmed OCC, from which 2537 slides were extracted. First, since T4 tumors penetrate the bowel wall and involve adjacent organs, we explored whether the peritumoral region contributes to the assessment of T4 OCC. Furthermore, we visualized the radiomics and deep learning features using the t-distributed stochastic neighbor embedding technique (t-SNE). Finally, we built a merged model by fusing radiomic features with deep learning features. In this experiment, the performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC). RESULTS In the test cohort, the AUC values predicted by the radiomics model in the dilated region of interest (dROI) was 0.770. And the AUC value of the deep learning model with the patches extended 20-pixel reached 0.936. Combining the characteristics of radiomics and deep learning, our method achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification, and increased the AUC value to 0.950 after the addition of clinical features. CONCLUSION The prediction results of our merged model of deep learning radiomics outperformed the deep learning model and significantly outperformed the radiomics model. The experimental results demonstrate that combining the peritumoral region improves the prediction performance of the radiomics model and the deep learning model.
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Affiliation(s)
- Lin Pan
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Tian He
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Zihan Huang
- School of Future Technology, Harbin Institute of Technology, Harbin, 150000, China
| | - Shuai Chen
- Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Junrong Zhang
- Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Shaohua Zheng
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China.
| | - Xianqiang Chen
- Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
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Wong PK, Chan IN, Yan HM, Gao S, Wong CH, Yan T, Yao L, Hu Y, Wang ZR, Yu HH. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview. World J Gastroenterol 2022; 28:6363-6379. [PMID: 36533112 PMCID: PMC9753055 DOI: 10.3748/wjg.v28.i45.6363] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/25/2022] [Accepted: 11/17/2022] [Indexed: 12/02/2022] Open
Abstract
Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.
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Affiliation(s)
- Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - In Neng Chan
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - Hao-Ming Yan
- School of Clinical Medicine, China Medical University, Shenyang 110013, Liaoning Province, China
| | - Shan Gao
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, Hubei Province, China
| | - Chi Hong Wong
- Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China
| | - Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Liang Yao
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Ying Hu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Zhong-Ren Wang
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Hon Ho Yu
- Department of Gastroenterology, Kiang Wu Hospital, Macau 999078, China
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Liu H, Yin H, Li J, Dong X, Zheng H, Zhang T, Yin Q, Zhang Z, Lu M, Zhang H, Wang D. A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer. J Magn Reson Imaging 2022; 56:1659-1668. [PMID: 35587946 DOI: 10.1002/jmri.28237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/04/2022] [Accepted: 05/04/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI-based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear. PURPOSE To develop a DL method based on T2-weighted imaging (T2WI) and clinical factors for noninvasively evaluating KRAS mutation in rectal cancer. STUDY TYPE Retrospective. SUBJECTS A total of 376 patients (108 women [28.7%]) with histopathology-confirmed rectal adenocarcinoma and KRAS mutation status. FIELD STRENGTH/SEQUENCE A 3 T, turbo spin echo T2WI and single-shot echo-planar diffusion-weighted imaging (b = 0, 1000 sec/mm2 ). ASSESSMENT A clinical model was constructed with clinical factors (age, gender, carcinoembryonic antigen level, and carbohydrate antigen 199 level) and MRI features (tumor length, tumor location, tumor stage, lymph node stage, and extramural vascular invasion), and two DL models based on modified MobileNetV2 architecture were evaluated for diagnosing KRAS mutation based on T2WI alone (image model) or both T2WI and clinical factors (combined model). The clinical usefulness of these models was evaluated through calibration analysis and decision curve analysis (DCA). STATISTICAL TESTS Mann-Whitney U test, Chi-squared test, Fisher's exact test, logistic regression analysis, receiver operating characteristic curve (ROC), Delong's test, Hosmer-Lemeshow test, interclass correlation coefficients, and Fleiss kappa coefficients (P < 0.05 was considered statistically significant). RESULTS All the nine clinical-MRI characteristics were included for clinical model development. The clinical model, image model, and combined model in the testing cohort demonstrated good calibration and achieved areas under the curve (AUCs) of 0.668, 0.765, and 0.841, respectively. The combined model showed improved performance compared to the clinical model and image model in two cohorts. DCA confirmed the higher net benefit of the combined model than the other two models when the threshold probability is between 0.05 and 0.85. DATA CONCLUSION The proposed combined DL model incorporating T2WI and clinical factors may show good diagnostic performance. Thus, it could potentially serve as a supplementary approach for noninvasively evaluating KRAS mutation in rectal cancer. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Huanhuan Liu
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Jinning Li
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xue Dong
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Zheng
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tingting Zhang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiufeng Yin
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhongyang Zhang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Minda Lu
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huiling Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Dengbin Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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A segmentation-based sequence residual attention model for KRAS gene mutation status prediction in colorectal cancer. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04011-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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12
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Vicini S, Bortolotto C, Rengo M, Ballerini D, Bellini D, Carbone I, Preda L, Laghi A, Coppola F, Faggioni L. A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers. Radiol Med 2022; 127:819-836. [DOI: 10.1007/s11547-022-01512-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 06/01/2022] [Indexed: 12/24/2022]
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Hu J, Xia X, Wang P, Peng Y, Liu J, Xie X, Liao Y, Wan Q, Li X. Predicting Kirsten Rat Sarcoma Virus Gene Mutation Status in Patients With Colorectal Cancer by Radiomics Models Based on Multiphasic CT. Front Oncol 2022; 12:848798. [PMID: 35814386 PMCID: PMC9263192 DOI: 10.3389/fonc.2022.848798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveTo develop and validate radiomics models based on multiphasic CT in predicting Kirsten rat sarcoma virus (KRAS) gene mutation status in patients with colorectal cancer (CRC).Materials and MethodsA total of 231 patients with pathologically confirmed CRC were retrospectively enrolled and randomly divided into training(n=184) and test groups(n=47) in a ratio of 4:1. A total of 1316 quantitative radiomics features were extracted from non-contrast phase (NCP), arterial-phase (AP) and venous-phase (VP) CT for each patient. Four steps were applied for feature selection including Spearman correlation analysis, variance threshold, least absolute contraction and selection operator, and multivariate stepwise regression analysis. Clinical and pathological characteristics were also assessed. Subsequently, three classification methods, logistic regression (LR), support vector machine (SVM) and random tree (RT) algorithm, were applied to develop seven groups of prediction models (NCP, AP, VP, AP+VP, AP+VP+NCP, AP&VP, AP&VP&NCP) for KRAS mutation prediction. The performance of these models was evaluated by receiver operating characteristics curve (ROC) analysis.ResultsAmong the three groups of single-phase models, the AP model, developed by LR algorithm, showed the best prediction performance with an AUC value of 0.811 (95% CI:0.685–0.938) in the test cohort. Compared with the single-phase models, the dual-phase (AP+VP) model with the LR algorithm showed better prediction performance (AUC=0.826, 95% CI:0.700-0.952). The performance of multiphasic (AP+VP+NCP) model with the LR algorithm (AUC=0.811, 95%CI: 0.679-0.944) is comparable to the model with the SVM algorithm (AUC=0.811, 95%CI: 0.695-0.918) in the test cohort, but the sensitivity, specificity, and accuracy of the multiphasic (AP+VP+NCP) model with the LR algorithm were 0.810, 0.808, 0.809 respectively, which were highest among these seven groups of prediction models in the test cohort.ConclusionThe CT radiomics models have the potential to predict KRAS mutation in patients with CRC; different phases may affect the predictive efficacy of radiomics model, of which arterial-phase CT is more informative. The combination of multiphasic CT images can further improve the performance of radiomics model.
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Affiliation(s)
- Jianfeng Hu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaoying Xia
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Peng Wang
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yu Peng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jieqiong Liu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaobin Xie
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuting Liao
- Department of Pharmaceutical Diagnostics, GE Healthcare, Shanghai, China
| | - Qi Wan
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Qi Wan, ; Xinchun Li,
| | - Xinchun Li
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Qi Wan, ; Xinchun Li,
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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Xue T, Peng H, Chen Q, Li M, Duan S, Feng F. Preoperative prediction of KRAS mutation status in colorectal cancer using a CT-based radiomics nomogram. Br J Radiol 2022; 95:20211014. [PMID: 35312376 PMCID: PMC10996413 DOI: 10.1259/bjr.20211014] [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: 09/01/2021] [Revised: 03/08/2022] [Accepted: 03/14/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE This study aimed to develop a model to predict KRAS mutations in colorectal cancer according to radiomic signatures based on CT and clinical risk factors. METHODS This retrospective study included 172 patients with colorectal cancer. All patients were randomized at a 7:3 ratio into a training cohort (n = 121, 38.8% positive for KRAS mutation) and a validation cohort (n = 51, 39.2% positive for KRAS mutation). Radiomics features were extracted from single-slice and full-volume regions of interest on the portal-venous CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was adopted to construct a radiomics signature, and logistic regression was applied to select the significant variables to develop the clinical-radiomics model. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA). RESULTS 1018 radiomics features were extracted from single-slice and full-volume ROIs. Eight features were retained to construct 2D (two-dimensional, 2D) radiomics model. Similarly, eight features were retained to construct 3D (three-dimensional, 3D) radiomics model. The area under the curve (AUC) values of the test cohort were 0.75 and 0.84, respectively. Delong test showed that the integrated nomogram (AUC = 0.92 in the test cohort) had better clinical predictive efficiency than 2D radiomics (p-value < 0.05) model and 3D radiomics model (p-value < 0.05). CONCLUSION The 2D and 3D radiomics models can both predict KRAS mutations. And, the integrated nomogram can be better applied to predict KRAS mutation status in colorectal cancer. ADVANCES IN KNOWLEDGE CT-based radiomics showed satisfactory diagnostic significance for the KRAS status in colorectal cancer, the clinical-combined model may be applied in the individual pre-operative prediction of KRAS mutation.
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Affiliation(s)
- Ting Xue
- Department of Radiology, Nantong University,
Nantong, Jiangsu, PR China
| | - Hui Peng
- Department of Radiology, Nantong University,
Nantong, Jiangsu, PR China
| | - Qiaoling Chen
- Department of Radiology, Nantong University,
Nantong, Jiangsu, PR China
| | - Manman Li
- Department of Radiology, Nantong University,
Nantong, Jiangsu, PR China
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, Jiangsu,
PR China
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Ying M, Pan J, Lu G, Zhou S, Fu J, Wang Q, Wang L, Hu B, Wei Y, Shen J. Development and validation of a radiomics-based nomogram for the preoperative prediction of microsatellite instability in colorectal cancer. BMC Cancer 2022; 22:524. [PMID: 35534797 PMCID: PMC9087961 DOI: 10.1186/s12885-022-09584-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/21/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Preoperative prediction of microsatellite instability (MSI) status in colorectal cancer (CRC) patients is of great significance for clinicians to perform further treatment strategies and prognostic evaluation. Our aims were to develop and validate a non-invasive, cost-effective reproducible and individualized clinic-radiomics nomogram method for preoperative MSI status prediction based on contrast-enhanced CT (CECT)images. METHODS A total of 76 MSI CRC patients and 200 microsatellite stability (MSS) CRC patients with pathologically confirmed (194 in the training set and 82 in the validation set) were identified and enrolled in our retrospective study. We included six significant clinical risk factors and four qualitative imaging data extracted from CECT images to build the clinics model. We applied the intra-and inter-class correlation coefficient (ICC), minimal-redundancy-maximal-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) for feature reduction and selection. The selected independent prediction clinical risk factors, qualitative imaging data and radiomics features were performed to develop a predictive nomogram model for MSI status on the basis of multivariable logistic regression by tenfold cross-validation. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots and Hosmer-Lemeshow test were performed to assess the nomogram model. Finally, decision curve analysis (DCA) was performed to determine the clinical utility of the nomogram model by quantifying the net benefits of threshold probabilities. RESULTS Twelve top-ranked radiomics features, three clinical risk factors (location, WBC and histological grade) and CT-reported IFS were finally selected to construct the radiomics, clinics and combined clinic-radiomics nomogram model. The clinic-radiomics nomogram model with the highest AUC value of 0.87 (95% CI, 0.81-0.93) and 0.90 (95% CI, 0.83-0.96), as well as good calibration and clinical utility observed using the calibration plots and DCA in the training and validation sets respectively, was regarded as the candidate model for identification of MSI status in CRC patients. CONCLUSION The proposed clinic-radiomics nomogram model with a combination of clinical risk factors, qualitative imaging data and radiomics features can potentially be effective in the individualized preoperative prediction of MSI status in CRC patients and may help performing further treatment strategies.
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Affiliation(s)
- Mingliang Ying
- Department of Radiology, The Second Affiliated Hospital of Soochow University, No.1055 Sanxiang Road, Gusu District, Suzhou, 215004, Jiangsu, China.,Department of Radiology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Jiangfeng Pan
- Department of Radiology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Guanghong Lu
- Department of Radiology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Shaobin Zhou
- Department of Radiology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Jianfei Fu
- Department of Oncology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Qinghua Wang
- Department of Oncology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Lixia Wang
- Department of Pathology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Bin Hu
- Department of Pathology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Yuguo Wei
- Precision Health Institution, GE Healthcare, Xihu District, Hangzhou, China
| | - Junkang Shen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, No.1055 Sanxiang Road, Gusu District, Suzhou, 215004, Jiangsu, China. .,Institute of Radiation Oncology Therapeutics of Soochow University, Suzhou, 215004, China.
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Li M, Gong J, Bao Y, Huang D, Peng J, Tong T. Special issue "The advance of solid tumor research in China": Prognosis prediction for stage II colorectal cancer by fusing CT radiomics and deep-learning features of primary lesions and peripheral lymph nodes. Int J Cancer 2022; 152:31-41. [PMID: 35484979 DOI: 10.1002/ijc.34053] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/10/2022] [Accepted: 04/21/2022] [Indexed: 11/11/2022]
Abstract
Currently, the prognosis assessment of stage II colorectal cancer (CRC) remains a difficult clinical problem; therefore, more accurate prognostic predictors must be developed. In this study, we developed a prognostic prediction model for stage II CRC by fusing radiomics and deep-learning (DL) features of primary lesions and peripheral lymph nodes (LNs) in computed tomography (CT) scans. First, two CT radiomics models were built using primary lesion and LN image features. Subsequently, an information fusion method was used to build a fusion radiomics model by combining the tumor and LN image features. Furthermore, a transfer learning method was applied to build a deep convolutional neural network (CNN) model. Finally, the prediction scores generated by the radiomics and CNN models were fused to improve the prognosis prediction performance. The disease-free survival (DFS) and overall survival (OS) prediction areas under the curves (AUCs) generated by the fusion model improved to 0.76±0.08 and 0.91±0.05, respectively. These were significantly higher than the AUCs generated by the models using the individual CT radiomics and deep image features. Applying the survival analysis method, the DFS and OS fusion models yielded concordance index (C-index) values of 0.73 and 0.9, respectively. Hence, the combined model exhibited good predictive efficacy; therefore, it could be used for the accurate assessment of the prognosis of stage II CRC patients. Moreover, it could be used to screen out high-risk patients with poor prognoses, and assist in the formulation of clinical treatment decisions in a timely manner to achieve precision medicine. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Menglei Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Yichao Bao
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Dan Huang
- Department of Pathology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Junjie Peng
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P R China
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Xue T, Peng H, Chen Q, Li M, Duan S, Feng F. Preoperative Prediction of BRAF Mutation Status in Colorectal Cancer Using a Clinical-radiomics Model. Acad Radiol 2022; 29:1298-1307. [PMID: 35033450 DOI: 10.1016/j.acra.2021.12.016] [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: 09/23/2021] [Revised: 12/01/2021] [Accepted: 12/14/2021] [Indexed: 11/30/2022]
Abstract
RATIONALE AND OBJECTIVES This study aimed to develop a clinically practical model to predict V-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutation in colorectal cancer according to radiomic signatures based on computed tomography (CT) and clinical risk factors, and to determine the model's diagnostic accuracy for BRAF mutation status. MATERIALS AND METHODS This retrospective study included 140 patients with colorectal cancer. The significant clinical risk factors were used to build the clinical model; the least absolute shrinkage and selection operator algorithm was adopted to construct a radiomics signature according to imaging features of the tumor lesion, and stepwise logistic regression was applied to select the significant variables to develop the clinical-radiomics model. The predictive performance was evaluated by receiver operating characteristic curve analysis, calibration curve analysis, and decision curve analysis. RESULTS The radscore, generated by 5 selected radiomics features, demonstrated a favorable ability to predict BRAF mutation in both the training (area under the receiver operating characteristic curve [AUC] 0.93) and validation (AUC 0.87) cohorts. Subsequently, integrating two independent predictors (including the radscore and clinical risk factors) into a nomogram exhibited more favorable discriminatory performance, with the AUC improved to 0.95 and 0.88 in both cohorts. Moreover, the accuracy for predicting BRAF mutations was higher than that of the clinical model, ranging from 0.70 to 0.89. CONCLUSION The proposed CT-based radiomics signature is associated with BRAF mutations. The present study also proposes a combined model can potentially be applied in the individual preoperative prediction of BRAF mutation status in colorectal cancer. ADVANCES IN KNOWLEDGE CT-based radiomics showed satisfactory diagnostic significance for the BRAF status in colorectal cancer, the clinical-combined model may be applied in the individual preoperative prediction of BRAF mutation.
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Affiliation(s)
- Ting Xue
- Department of Radiology, Nantong University, Nantong, Jiangsu, PR China
| | - Hui Peng
- Department of Radiology, Nantong University, Nantong, Jiangsu, PR China
| | - Qiaoling Chen
- Department of Radiology, Nantong University, Nantong, Jiangsu, PR China
| | - Manman Li
- Department of Radiology, Nantong University, Nantong, Jiangsu, PR China
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu 226361, PR China.
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Bo L, Zhang Z, Jiang Z, Yang C, Huang P, Chen T, Wang Y, Yu G, Tan X, Cheng Q, Li D, Liu Z. Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features. Front Med (Lausanne) 2021; 8:748144. [PMID: 34869438 PMCID: PMC8636043 DOI: 10.3389/fmed.2021.748144] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 10/06/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives: To develop and validate the model for distinguishing brain abscess from cystic glioma by combining deep transfer learning (DTL) features and hand-crafted radiomics (HCR) features in conventional T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI). Methods: This single-center retrospective analysis involved 188 patients with pathologically proven brain abscess (102) or cystic glioma (86). One thousand DTL and 105 HCR features were extracted from the T1WI and T2WI of the patients. Three feature selection methods and four classifiers, such as k-nearest neighbors (KNN), random forest classifier (RFC), logistic regression (LR), and support vector machine (SVM), for distinguishing brain abscess from cystic glioma were compared. The best feature combination and classifier were chosen according to the quantitative metrics including area under the curve (AUC), Youden Index, and accuracy. Results: In most cases, deep learning-based radiomics (DLR) features, i.e., DTL features combined with HCR features, contributed to a higher accuracy than HCR and DTL features alone for distinguishing brain abscesses from cystic gliomas. The AUC values of the model established, based on the DLR features in T2WI, were 0.86 (95% CI: 0.81, 0.91) in the training cohort and 0.85 (95% CI: 0.75, 0.95) in the test cohort, respectively. Conclusions: The model established with the DLR features can distinguish brain abscess from cystic glioma efficiently, providing a useful, inexpensive, convenient, and non-invasive method for differential diagnosis. This is the first time that conventional MRI radiomics is applied to identify these diseases. Also, the combination of HCR and DTL features can lead to get impressive performance.
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Affiliation(s)
- Linlin Bo
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Zijian Zhang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Zekun Jiang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Chao Yang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Pu Huang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Tingyin Chen
- Department of Network Information Center, Xiangya Hospital, Centra South University, Changsha, China
| | - Yifan Wang
- Department of Network Information Center, Xiangya Hospital, Centra South University, Changsha, China
| | - Gang Yu
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Xiao Tan
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, China
| | - Zhixiong Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
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Song K, Zhao Z, Ma Y, Wang J, Wu W, Qiang Y, Zhao J, Chaudhary S. A multitask dual-stream attention network for the identification of KRAS mutation in colorectal cancer. Med Phys 2021; 49:254-270. [PMID: 34806195 DOI: 10.1002/mp.15361] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/11/2021] [Accepted: 11/06/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE It is of great significance to accurately identify the KRAS gene mutation status for patients in tumor prognosis and personalized treatment. Although the computer-aided diagnosis system based on deep learning has gotten all-round development, its performance still cannot meet the current clinical application requirements due to the inherent limitations of small-scale medical image data set and inaccurate lesion feature extraction. Therefore, our aim is to propose a deep learning model based on T2 MRI of colorectal cancer (CRC) patients to identify whether KRAS gene is mutated. METHODS In this research, a multitask attentive model is proposed to identify KRAS gene mutations in patients, which is mainly composed of a segmentation subnetwork and an identification subnetwork. Specifically, at first, the features extracted by the encoder of segmentation model are used as guidance information to guide the two attention modules in the identification network for precise activation of the lesion area. Then the original image of the lesion and the segmentation result are concatenated for feature extraction. Finally, features extracted from the second step are combined with features activated by the attention modules to identify the gene mutation status. In this process, we introduce the interlayer loss function to encourage the similarity of the two subnetwork parameters and ensure that the key features are fully extracted to alleviate the overfitting problem caused by small data set to some extent. RESULTS The proposed identification model is benchmarked primarily using 15-fold cross validation. Three hundred and eighty-two images from 36 clinical cases were used to test the model. For the identification of KRAS mutation status, the average accuracy is 89.95 ± 1.23%, the average sensitivity is 89.29 ± 1.79%, the average specificity is 90.53 ± 2.45%, and the average area under the curve (AUC) is 95.73 ± 0.52%. For segmentation of lesions, the average dice is 88.11 ± 0.86%. CONCLUSIONS We developed a novel deep learning-based model to identify the KRAS status in CRC. We demonstrated the excellent properties of the proposed identification through comparison with ground truth gene mutation status of 36 clinical cases. And all these results show that the novel method has great potential for clinical application.
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Affiliation(s)
- Kai Song
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Zijuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yulan Ma
- College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, China
| | - JiaWen Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Wei Wu
- Department of Clinical Laboratory, Affiliated People's Hospital of Shanxi Medical University, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Suman Chaudhary
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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21
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Segmentation-based multi-scale attention model for KRAS mutation prediction in rectal cancer. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01447-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Ma Y, Wang J, Song K, Qiang Y, Jiao X, Zhao J. Spatial-Frequency dual-branch attention model for determining KRAS mutation status in colorectal cancer with T2-weighted MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106311. [PMID: 34352652 DOI: 10.1016/j.cmpb.2021.106311] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 07/19/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Identifying the KRAS mutation status accurately in medical images is very important for the diagnosis and treatment of colorectal cancer. Despite the substantial progress achieved by existing methods, it remains challenging due to limited annotated dataset, large intra-class variances, and a high degree of inter-class similarities. METHODS To tackle these challenges, we propose a spatial-frequency dual-branch attention model (SF-DBAM) to determine the KRAS mutation status of colorectal cancer patients using a limited T2-weighted MRI dataset. The dataset contains 169 wild-type patients (2151 images) and 137 mutation-type patients (1666 images). The first branch utilizes part of the pre-trained Xception model to capture spatial-domain information and alleviate the small-scale dataset problem. The second branch builds frequency-domain information into cube columns using block-based discrete cosine transform and channel rearrangement. Then the cube columns are fed into convolutional long short-term memory (convLSTM) to explore the effective information between the reconstructed frequency-domain channels. Also, we design a channel enhanced attention module (CEAM) at the end of each branch to make them focus on the lesion areas. Finally, we concatenate the two branches and output the classified results through fully connected layers. RESULTS The proposed method achieves 88.03% overall accuracy with AUC of 94.27% and specificity of 90.75% in 10-fold cross-validation, which is better than the current non-invasive methods for determining KRAS mutation status. CONCLUSIONS We believe that the proposed method can assist physicians to diagnose the KRAS mutation status in patients with colorectal cancer, and other medical problems can benefit from the spatial and frequency domains information.
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Affiliation(s)
- Yulan Ma
- College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Jiawen Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Kai Song
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
| | - Xiong Jiao
- College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, China.
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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23
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Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27:5306-5321. [PMID: 34539134 PMCID: PMC8409167 DOI: 10.3748/wjg.v27.i32.5306] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/13/2021] [Accepted: 07/22/2021] [Indexed: 02/06/2023] Open
Abstract
The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Francesca Boccadifuoco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging, National Council of Research, Napoli 80131, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
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Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study. Eur Radiol 2021; 32:405-414. [PMID: 34170367 DOI: 10.1007/s00330-021-08104-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/11/2021] [Accepted: 05/27/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To assess the value of contrast-enhanced (CE) diagnostic CT scans characterized through radiomics as predictors of recurrence for patients with stage II and III colorectal cancer in a two-center context. MATERIALS AND METHODS This study included 193 patients diagnosed with stage II and III colorectal adenocarcinoma from 1 July 2008 to 15 March 2017 in two different French University Hospitals. To compensate for the variability in two-center data, a statistical harmonization method Bootstrapped ComBat (B-ComBat) was used. Models predicting disease-free survival (DFS) were built using 3 different machine learning (ML): (1) multivariate regression (MR) with 10-fold cross-validation after feature selection based on least absolute shrinkage and selection operator (LASSO), (2) random forest (RF), and (3) support vector machine (SVM), both with embedded feature selection. RESULTS The performance for both balanced and 95% sensitivity models was systematically higher after our proposed B-ComBat harmonization compared to the use of the original untransformed data. The most clinically relevant performance was achieved by the multivariate regression model combining a clinical variable (postoperative chemotherapy) with two radiomics shape descriptors (compactness and least axis length) with a BAcc of 0.78 and an MCC of 0.6 associated with a required sensitivity of 95%. The resulting stratification in terms of DFS was significant (p = 0.00021), especially compared to the use of unharmonized original data (p = 0.17). CONCLUSIONS Radiomics models derived from contrast-enhanced CT could be trained and validated in a two-center cohort with a good predictive performance of recurrence in stage II et III colorectal cancer patients. KEY POINTS • Adjuvant therapy decision in colorectal cancer can be a challenge in medical oncology. • Radiomics models, derived from diagnostic CT, trained and validated in a two-center cohort, could predict recurrence in stage II and III colorectal cancer patients. • Identifying patients with a low risk of recurrence, these models could facilitate treatment optimization and avoid unnecessary treatment.
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25
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Zhao N, Cao Y, Yang J, Li H, Wu K, Wang J, Peng T, Cai K. Serum Tumor Markers Combined With Clinicopathological Characteristics for Predicting MMR and KRAS Status in 2279 Chinese Colorectal Cancer Patients: A Retrospective Analysis. Front Oncol 2021; 11:582244. [PMID: 34221952 PMCID: PMC8247475 DOI: 10.3389/fonc.2021.582244] [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: 07/11/2020] [Accepted: 05/03/2021] [Indexed: 12/09/2022] Open
Abstract
Although serum tumor markers (STMs), clinicopathological characteristics and the status of KRAS and MMR play an important role in optimizing the treatment and prognosis of colorectal cancer, their interrelationships remain largely unknown. A retrospective analysis of 2279 patients who tested for KRAS and MMR status, and STM measurements prior to treatment over the past four years was conducted. Of the 784 patients tested for KRAS and 2279 patients tested for MMR status, KRAS mutations and dMMR were identified in 276 patients (35.20%) and 177 patients (7.77%), respectively. Logistic regression analysis demonstrated that right colon, well and moderate differentiation and negative CA19-9 were independent predictors for KRAS mutations. The ROC curve yielded an AUC of 0.609 through the combination of these three factors. Age < 65 was an independent predictive factor for dMMR, along with tumor size > 4.6 cm, right colon, poor differentiation, harvested lymph nodes ≥ 22, no lymph node metastasis, no perineural invasion, negative CEA and positive CA72-4. When the nine criteria were used together, the AUC was 0.849. In summary, both STMs and clinicopathological characteristics were found to be significantly associated with the status of KRAS and MMR. The combination of these two factors possessed a strong predictive power for KRAS mutations and dMMR among CRC patients.
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Affiliation(s)
- Ning Zhao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yinghao Cao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jia Yang
- Department of Gastrointestinal Surgery, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hang Li
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ke Wu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiliang Wang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Peng
- Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kailin Cai
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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26
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La Greca Saint-Esteven A, Vuong D, Tschanz F, van Timmeren JE, Dal Bello R, Waller V, Pruschy M, Guckenberger M, Tanadini-Lang S. Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers (Basel) 2021; 13:cancers13123015. [PMID: 34208595 PMCID: PMC8234501 DOI: 10.3390/cancers13123015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/10/2021] [Accepted: 06/11/2021] [Indexed: 12/23/2022] Open
Abstract
Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.
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Affiliation(s)
- Agustina La Greca Saint-Esteven
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
- Correspondence:
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Fabienne Tschanz
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Janita E. van Timmeren
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Verena Waller
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Martin Pruschy
- Laboratory of Applied Radiobiology, Department of Radiation Oncology, University of Zurich, 8091 Zurich, Switzerland; (F.T.); (V.W.); (M.P.)
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, 8091 Zurich, Switzerland; (D.V.); (J.E.v.T.); (R.D.B.); (M.G.); (S.T.-L.)
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Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists. World J Gastroenterol 2021; 27:2681-2709. [PMID: 34135549 PMCID: PMC8173384 DOI: 10.3748/wjg.v27.i21.2681] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/29/2021] [Accepted: 04/22/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.
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Affiliation(s)
- Bo Cao
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ke-Cheng Zhang
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Bo Wei
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Lin Chen
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
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28
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Zhang W, Yin H, Huang Z, Zhao J, Zheng H, He D, Li M, Tan W, Tian S, Song B. Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer. Cancer Med 2021; 10:4164-4173. [PMID: 33963688 PMCID: PMC8209621 DOI: 10.1002/cam4.3957] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Microsatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and immunotherapy in rectal cancer and serves as a prognostic biomarker for clinical outcomes. Our objective was to develop and validate a deep learning model that could preoperatively predict the MSI status of rectal cancer based on magnetic resonance images. METHODS This single-center retrospective study included 491 rectal cancer patients with pathologically proven microsatellite status. Patients were randomly divided into the training/validation cohort (n = 395) and the testing cohort (n = 96). A clinical model using logistic regression was constructed to discriminate MSI status using only clinical factors. Based on a modified MobileNetV2 architecture, deep learning models were tested for the predictive ability of MSI status from magnetic resonance images, with or without integrating clinical factors. RESULTS The clinical model correctly classified 37.5% of MSI status in the testing cohort, with an AUC value of 0.573 (95% confidence interval [CI], 0.468 ~ 0.674). The pure imaging-based model and the combined model correctly classified 75.0% and 85.4% of MSI status in the testing cohort, with AUC values of 0.820 (95% CI, 0.718 ~ 0.884) and 0.868 (95% CI, 0.784 ~ 0.929), respectively. Both deep learning models performed better than the clinical model (p < 0.05). There was no statistically significant difference between the deep learning models with or without integrating clinical factors. CONCLUSIONS Deep learning based on high-resolution T2-weighted magnetic resonance images showed a good predictive performance for MSI status in rectal cancer patients. The proposed model may help to identify patients who would benefit from chemotherapy or immunotherapy and determine individualized therapeutic strategies for these patients.
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Affiliation(s)
- Wei Zhang
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
- Department of RadiologySichuan Provincial Corps HospitalChinese People's Armed Police ForcesLeshanChina
| | - Hongkun Yin
- Institute of Advanced ResearchInferVisionBeijingChina
| | - Zixing Huang
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
| | - Jian Zhao
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
- Department of RadiologySichuan Provincial Corps HospitalChinese People's Armed Police ForcesLeshanChina
| | - Haoyu Zheng
- Department of RadiologySichuan Provincial Corps HospitalChinese People's Armed Police ForcesLeshanChina
| | - Du He
- Department of PathologyWest China HospitalSichuan UniversityChengduChina
| | - Mou Li
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
| | - Weixiong Tan
- Institute of Advanced ResearchInferVisionBeijingChina
| | - Song Tian
- Institute of Advanced ResearchInferVisionBeijingChina
| | - Bin Song
- Department of RadiologyWest China HospitalSichuan UniversityChengduChina
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29
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Wang Y, Wang Y, Guo C, Xie X, Liang S, Zhang R, Pang W, Huang L. Cancer genotypes prediction and associations analysis from imaging phenotypes: a survey on radiogenomics. Biomark Med 2020; 14:1151-1164. [PMID: 32969248 DOI: 10.2217/bmm-2020-0248] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
In this paper, we present a survey on the progress of radiogenomics research, which predicts cancer genotypes from imaging phenotypes and investigates the associations between them. First, we present an overview of the popular technology modalities for obtaining diagnostic medical images. Second, we summarize recently used methodologies for radiogenomics analysis, including statistical analysis, radiomics and deep learning. And then, we give a survey on the recent research based on several types of cancers. Finally, we discuss these studies and propose possible future research directions. In conclusion, we have identified strong correlations between cancer genotypes and imaging phenotypes. In addition, with the rapid growth of medical data, deep learning models show great application potential for radiogenomics.
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Affiliation(s)
- Yao Wang
- Key Laboratory of Symbol Computation & Knowledge Engineering, Ministry of Education, College of Computer Science & Technology, Jilin University, Changchun, 130012, PR China
| | - Yan Wang
- Key Laboratory of Symbol Computation & Knowledge Engineering, Ministry of Education, College of Computer Science & Technology, Jilin University, Changchun, 130012, PR China.,School of Artificial Intelligence, Jilin University, Changchun 130012, PR China
| | - Chunjie Guo
- Department of Radiology, The First Hospital of Jilin University, Changchun 130012, PR China
| | - Xuping Xie
- Key Laboratory of Symbol Computation & Knowledge Engineering, Ministry of Education, College of Computer Science & Technology, Jilin University, Changchun, 130012, PR China
| | - Sen Liang
- State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, PR China
| | - Ruochi Zhang
- School of Artificial Intelligence, Jilin University, Changchun 130012, PR China
| | - Wei Pang
- School of Mathematical & Computer Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK
| | - Lan Huang
- Key Laboratory of Symbol Computation & Knowledge Engineering, Ministry of Education, College of Computer Science & Technology, Jilin University, Changchun, 130012, PR China.,Zhuhai Laboratory of Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Department of Computer Science & Technology, Zhuhai College of Jilin University, Zhuhai 519041, China
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30
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Chen BB. Artificial intelligence in pancreatic disease. Artif Intell Med Imaging 2020; 1:19-30. [DOI: 10.35711/aimi.v1.i1.19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 06/18/2020] [Accepted: 06/19/2020] [Indexed: 02/06/2023] Open
Abstract
In recent years, the application of artificial intelligence (AI) in radiology has been growing rapidly, fueled by the availability of large datasets, advances in computing power, and newly developed algorithms. Progress in AI applied to medical imaging analyses has transformed these images into quantitative data, termed radiomics. When combined with patients’ clinical data, these models, when developed by machine learning, have the potential to improve diagnostic, prognostic, and predictive accuracy. Currently, limited literature is available on the use of radiomics for pancreatic disease. Here, we will review recent studies in the application of AI in a variety of pancreatic diseases, mainly involving lesion detection, tumor characterization, tumor grading, response, and prognosis evaluation. Finally, we will also discuss the challenges and prospects in the field of radiomics for pancreatic disease.
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Affiliation(s)
- Bang-Bin Chen
- Department of Medical Imaging, National Taiwan University Hospital, Taipei 10016, Taiwan
- Department of Radiology, College of Medicine, National Taiwan University, Taipei 10016, Taiwan
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31
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Kagiyama N, Shrestha S, Cho JS, Khalil M, Singh Y, Challa A, Casaclang-Verzosa G, Sengupta PP. A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound. EBioMedicine 2020; 54:102726. [PMID: 32268274 PMCID: PMC7139137 DOI: 10.1016/j.ebiom.2020.102726] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 03/04/2020] [Accepted: 03/04/2020] [Indexed: 02/06/2023] Open
Abstract
Background Maturation of ultrasound myocardial tissue characterization may have far-reaching implications as a widely available alternative to cardiac magnetic resonance (CMR) for risk stratification in left ventricular (LV) remodeling. Methods We extracted 328 texture-based features of myocardium from still ultrasound images. After we explored the phenotypes of myocardial textures using unsupervised similarity networks, global LV remodeling parameters were predicted using supervised machine learning models. Separately, we also developed supervised models for predicting the presence of myocardial fibrosis using another cohort who underwent cardiac magnetic resonance (CMR). For the prediction, patients were divided into a training and test set (80:20). Findings Texture-based tissue feature extraction was feasible in 97% of total 534 patients. Interpatient similarity analysis delineated two patient groups based on the texture features: one group had more advanced LV remodeling parameters compared to the other group. Furthermore, this group was associated with a higher incidence of cardiac deaths (p = 0.001) and major adverse cardiac events (p < 0.001). The supervised models predicted reduced LV ejection fraction (<50%) and global longitudinal strain (<16%) with area under the receiver-operator-characteristics curves (ROC AUC) of 0.83 and 0.87 in the hold-out test set, respectively. Furthermore, the presence of myocardial fibrosis was predicted from only ultrasound myocardial texture with an ROC AUC of 0.84 (sensitivity 86.4% and specificity 83.3%) in the test set. Interpretation Ultrasound texture-based myocardial tissue characterization identified phenotypic features of LV remodeling from still ultrasound images. Further clinical validation may address critical barriers in the adoption of ultrasound techniques for myocardial tissue characterization. Funding None.
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Affiliation(s)
- Nobuyuki Kagiyama
- West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Sirish Shrestha
- West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Jung Sun Cho
- West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Muhammad Khalil
- West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Yashbir Singh
- West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Abhiram Challa
- West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Grace Casaclang-Verzosa
- West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Partho P Sengupta
- West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA.
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Wang J, Cui Y, Shi G, Zhao J, Yang X, Qiang Y, Du Q, Ma Y, Kazihise NGF. Multi-branch cross attention model for prediction of KRAS mutation in rectal cancer with t2-weighted MRI. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01658-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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