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Sun Z, Xia F, Lv W, Li J, Zou Y, Wu J. Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer. Am J Surg 2024; 232:59-67. [PMID: 38272767 DOI: 10.1016/j.amjsurg.2024.01.002] [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: 10/17/2023] [Revised: 12/17/2023] [Accepted: 01/03/2024] [Indexed: 01/27/2024]
Abstract
AIM Preoperative diagnosis of tumor deposits (TDs) in patients with rectal cancer remains a challenge. This study aims to develop and validate a radiomics nomogram based on the combination of T2-weighted (T2WI) and diffusion-weighted MR imaging (DWI) for the preoperative identification of TDs in rectal cancer. MATERIALS AND METHODS A total of 199 patients with rectal cancer who underwent T2WI and DWI were retrospectively enrolled and divided into a training set (n = 159) and a validation set (n = 40). The total incidence of TDs was 37.2 % (74/199). Radiomics features were extracted from T2WI and apparent diffusion coefficient (ADC) images. A radiomics nomogram combining Rad-score (T2WI + ADC) and clinical factors was subsequently constructed. The area under the receiver operating characteristic curve (AUC) was then calculated to evaluate the models. The nomogram is also compared to three machine learning model constructed based on no-Rad scores. RESULTS The Rad-score (T2WI + ADC) achieved an AUC of 0.831 in the training and 0.859 in the validation set. The radiomics nomogram (the combined model), incorporating the Rad-score (T2WI + ADC), MRI-reported lymph node status (mLN-status), and CA19-9, showed good discrimination of TDs with an AUC of 0.854 for the training and 0.923 for the validation set, which was superior to Random Forests, Support Vector Machines, and Deep Learning models. The combined model for predicting TDs outperformed the other three machine learning models showed an accuracy of 82.5 % in the validation set, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 66.7 %, 92.0 %, 83.3 %, and 82.1 %, respectively. CONCLUSION The radiomics nomogram based on Rad-score (T2WI + ADC) and clinical factors provides a promising and effective method for the preoperative prediction of TDs in patients with rectal cancer.
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Affiliation(s)
- Zhen Sun
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Feng Xia
- Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenzhi Lv
- Department of Artificial Intelligence, Julei Technology Company, Wuhan, 430030, China
| | - Jin Li
- Computer Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - You Zou
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jianhong Wu
- Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Tongji Cancer Research Institute, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
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Abbaspour E, Karimzadhagh S, Monsef A, Joukar F, Mansour-Ghanaei F, Hassanipour S. Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis. Int J Surg 2024; 110:3795-3813. [PMID: 38935817 PMCID: PMC11175807 DOI: 10.1097/js9.0000000000001239] [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: 12/27/2023] [Accepted: 02/19/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data. METHODS Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models' diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted. RESULTS Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022-2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78-0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001). CONCLUSION Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.
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Affiliation(s)
- Elahe Abbaspour
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Sahand Karimzadhagh
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Abbas Monsef
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Farahnaz Joukar
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Fariborz Mansour-Ghanaei
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
| | - Soheil Hassanipour
- Gastrointestinal and Liver Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran
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Zhuang Z, Zhang Y, Yang X, Deng X, Wang Z. T2WI-based texture analysis predicts preoperative lymph node metastasis of rectal cancer. Abdom Radiol (NY) 2024; 49:2008-2016. [PMID: 38411692 DOI: 10.1007/s00261-024-04209-8] [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: 11/22/2023] [Revised: 12/31/2023] [Accepted: 01/07/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND To prospectively develop and validate the T2WI texture analysis model based on a node-by-node comparison for improving the diagnostic accuracy of lymph node metastasis (LNM) in rectal cancer. METHODS A total of 381 histopathologically confirmed lymph nodes (LNs) were collected. LNs texture features were extracted from MRI-T2WI. Spearman's rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection to construct the LN rad-score. Then the clinical risk factors and LN texture features were combined to establish combined predictive model. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Decision curve analysis (DCA) and nomogram were used to evaluate the clinical application of the model. RESULTS A total of 107 texture features were extracted from LN-MRI images. After selection and dimensionality reduction, the radiomics prediction model consisting of 8 texture features showed well-predictive performance in the training and validation cohorts (AUC, 0.676; 95% CI 0.582-0.771) (AUC, 0.774; 95% CI 0.648-0.899). A clinical-radiomics prediction model with the best performance was created by combining clinical and radiomics features, 0.818 (95% CI 0.742-0.893) for the training and 0.922 (95% CI 0.863-0.980) for the validation cohort. The LN Rad-score in clinical-radiomics nomogram obtained the highest classification contribution and was well calibrated. DCA demonstrated the superiority of the clinical-radiomics model. CONCLUSION The lymph node T2WI-based texture features can help to improve the preoperative prediction of LNM.
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Affiliation(s)
- Zixuan Zhuang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China.
| | - Yang Zhang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China
| | - Xuyang Yang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China
| | - Xiangbing Deng
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China
| | - Ziqiang Wang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Lane, Chengdu, 610041, Sichuan Province, China
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Yan H, Yang H, Jiang P, Dong L, Zhang Z, Zhou Y, Zeng Q, Li P, Sun Y, Zhu S. A radiomics model based on T2WI and clinical indexes for prediction of lateral lymph node metastasis in rectal cancer. Asian J Surg 2024; 47:450-458. [PMID: 37833219 DOI: 10.1016/j.asjsur.2023.09.156] [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: 06/26/2023] [Revised: 09/19/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
OBJECTIVE The aim of this study was to explore the clinical value of a radiomics prediction model based on T2-weighted imaging (T2WI) and clinical indexes in predicting lateral lymph node (LLN) metastasis in rectal cancer patients. METHODS This was a retrospective analysis of 106 rectal cancer patients who had undergone LLN dissection. The clinical risk factors for LLN metastasis were selected by multivariable logistic regression analysis of the clinical indicators of the patients. The LLN radiomics features were extracted from the pelvic T2WI of the patients. The least absolute shrinkage and selection operator algorithm and backward stepwise regression method were adopted for feature selection. Three LLN metastasis prediction models were established through logistic regression analysis based on the clinical risk factors and radiomics features. Model performance was assessed in terms of discriminability and decision curve analysis in the training, verification and test sets. RESULTS The model based on the combined T2WI radiomics features and clinical risk factors demonstrated the highest accuracy, surpassing the models based solely on either T2WI radiomics features or clinical risk factors. Specifically, the model achieved an AUC value of 0.836 in the test set. Decision curve analysis revealed that this model had the greatest clinical utility for the vast majority of the threshold probability range from 0.4 to 1.0. CONCLUSION Combining T2WI radiomics features with clinical risk factors holds promise for the noninvasive assessment of the biological characteristics of the LLNs in rectal cancer, potentially aiding in therapeutic decision-making and optimizing patient outcomes.
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Affiliation(s)
- Hao Yan
- Department of Oncology, Tianjin Union Medical Center, Nankai University, Tianjin, 300121, China
| | - Hongjie Yang
- Nankai University, Tianjin, 300071, China; The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300121, China; Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, 300121, China
| | | | - Longchun Dong
- Department of Radiology, Tianjin Union Medical Center, Tianjin, 300121, China
| | - Zhichun Zhang
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300121, China; Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, 300121, China
| | - Yuanda Zhou
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300121, China; Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, 300121, China
| | - Qingsheng Zeng
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300121, China; Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, 300121, China
| | - Peng Li
- The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300121, China; Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, 300121, China
| | - Yi Sun
- Nankai University, Tianjin, 300071, China; The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300121, China; Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, 300121, China.
| | - Siwei Zhu
- Department of Oncology, Tianjin Union Medical Center, Nankai University, Tianjin, 300121, China; Nankai University, Tianjin, 300071, China; The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, 300121, China.
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Miranda J, Horvat N, Araujo-Filho JAB, Albuquerque KS, Charbel C, Trindade BMC, Cardoso DL, de Padua Gomes de Farias L, Chakraborty J, Nomura CH. The Role of Radiomics in Rectal Cancer. J Gastrointest Cancer 2023; 54:1158-1180. [PMID: 37155130 DOI: 10.1007/s12029-022-00909-w] [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] [Accepted: 12/26/2022] [Indexed: 05/10/2023]
Abstract
PURPOSE Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT. METHODS We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically. RESULTS The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice. CONCLUSION Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
| | - Jose A B Araujo-Filho
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | - Kamila S Albuquerque
- Department of Radiology, Hospital Beneficência Portuguesa, 637 Maestro Cardim, Sao Paulo, SP, 01323-001, Brazil
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Bruno M C Trindade
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
| | - Daniel L Cardoso
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | | | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
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Zhang S, Tang B, Yu M, He L, Zheng P, Yan C, Li J, Peng Q. Development and Validation of a Radiomics Model Based on Lymph-Node Regression Grading After Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Int J Radiat Oncol Biol Phys 2023; 117:821-833. [PMID: 37230433 DOI: 10.1016/j.ijrobp.2023.05.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 05/05/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
PURPOSE The response to neoadjuvant chemoradiotherapy (nCRT) varies among patients with locally advanced rectal cancer (LARC), and the treatment response of lymph nodes (LNs) to nCRT is critical in implementing a watch-and-wait strategy. A robust predictive model may help personalize treatment plans to increase the chance that patients achieve a complete response. This study investigated whether radiomics features based on prenCRT magnetic resonance imaging nodes could predict treatment response in preoperative LARC LNs. METHODS AND MATERIALS The study included 78 patients with clinical stage T3-T4, N1-2, and M0 rectal adenocarcinoma who received long-course neoadjuvant radiotherapy before surgery. Pathologists evaluated 243 LNs, of which 173 and 70 were assigned to training and validation cohorts, respectively. For each LN, 3641 radiomics features were extracted from the region of interest in high-resolution T2WI magnetic resonance imaging before nCRT. The least absolute shrinkage and selection operator regression model was used for feature selection and radiomics signature building. A prediction model based on multivariate logistic analysis, combining radiomics signature and selected LN morphologic characteristics, was developed and visualized by drawing a nomogram. The model's performance was assessed by receiver operating characteristic curve analysis and calibration curves. RESULTS The radiomics signature consists of 5 selected features that were effectively discriminated within the training cohort (area under the curve [AUC], 0.908; 95% CI, 0.857%-0.958%) and the validation cohort (AUC, 0.865; 95% CI, 0.757%-0.973%). The nomogram, which consisted of radiomics signature and LN morphologic characteristics (short-axis diameter and border contours), showed better calibration and discrimination in the training and validation cohorts (AUC, 0.925; 95% CI, 0.880%-0.969% and AUC, 0.918; 95% CI, 0.854%-0.983%, respectively). The decision curve analysis confirmed that the nomogram had the highest clinical utility. CONCLUSIONS The nodal-based radiomics model effectively predicts LNs treatment response in patients with LARC after nCRT, which could help personalize treatment plans and guide the implementation of the watch-and-wait approach in these patients.
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Affiliation(s)
- SiYu Zhang
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Bin Tang
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - MingRong Yu
- College of Physical Education, Sichuan Agricultural University, Yaan, China
| | - Lei He
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Ping Zheng
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - ChuanJun Yan
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jie Li
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
| | - Qian Peng
- Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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Dong X, Ren G, Chen Y, Yong H, Zhang T, Yin Q, Zhang Z, Yuan S, Ge Y, Duan S, Liu H, Wang D. Effects of MRI radiomics combined with clinical data in evaluating lymph node metastasis in mrT1-3a staging rectal cancer. Front Oncol 2023; 13:1194120. [PMID: 37909021 PMCID: PMC10614283 DOI: 10.3389/fonc.2023.1194120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 09/22/2023] [Indexed: 11/02/2023] Open
Abstract
Objective To investigate the value of a clinical-MRI radiomics model based on clinical characteristics and T2-weighted imaging (T2WI) for preoperatively evaluating lymph node (LN) metastasis in patients with MRI-predicted low tumor (T) staging rectal cancer (mrT1, mrT2, and mrT3a with extramural spread ≤ 5 mm). Methods This retrospective study enrolled 303 patients with low T-staging rectal cancer (training cohort, n = 213, testing cohort n = 90). A total of 960 radiomics features were extracted from T2WI. Minimum redundancy and maximum relevance (mRMR) and support vector machine were performed to select the best performed radiomics features for predicting LN metastasis. Multivariate logistic regression analysis was then used to construct the clinical and clinical-radiomics combined models. The model performance for predicting LN metastasis was assessed by receiver operator characteristic curve (ROC) and clinical utility implementing a nomogram and decision curve analysis (DCA). The predictive performance for LN metastasis was also compared between the combined model and human readers (2 seniors). Results Fourteen radiomics features and 2 clinical characteristics were selected for predicting LN metastasis. In the testing cohort, a higher positive predictive value of 75.9% for the combined model was achieved than those of the clinical model (44.8%) and two readers (reader 1: 54.9%, reader 2: 56.3%) in identifying LN metastasis. The interobserver agreement between 2 readers was moderate with a kappa value of 0.416. A clinical-radiomics nomogram and decision curve analysis demonstrated that the combined model was clinically useful. Conclusion T2WI-based radiomics combined with clinical data could improve the efficacy in noninvasively evaluating LN metastasis for the low T-staging rectal cancer and aid in tailoring treatment strategies.
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Affiliation(s)
- Xue Dong
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gang Ren
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanhong Chen
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huifang Yong
- Department of Radiology, Integrated Traditional Chinese and Western Medicine Hospital, 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
| | - Shijun Yuan
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yaqiong Ge
- Department of Medicine, GE Healthcare China, Shanghai, China
| | - Shaofeng Duan
- Department of Medicine, GE Healthcare China, Shanghai, China
| | - Huanhuan Liu
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dengbin Wang
- Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Su F, Chao J, Liu P, Zhang B, Zhang N, Luo Z, Han J. Prognostic models for breast cancer: based on logistics regression and Hybrid Bayesian Network. BMC Med Inform Decis Mak 2023; 23:120. [PMID: 37443001 PMCID: PMC10347801 DOI: 10.1186/s12911-023-02224-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND To construct two prognostic models to predict survival in breast cancer patients; to compare the efficacy of the two models in the whole group and the advanced human epidermal growth factor receptor-2-positive (HER2+) subgroup of patients; to conclude whether the Hybrid Bayesian Network (HBN) model outperformed the logistics regression (LR) model. METHODS In this paper, breast cancer patient data were collected from the SEER database. Data processing and analysis were performed using Rstudio 4.2.0, including data preprocessing, model construction and validation. The L_DVBN algorithm in Julia0.4.7 and bnlearn package in R was used to build and evaluate the HBN model. Data with a diagnosis time of 2018(n = 23,384) were distributed randomly as training and testing sets in the ratio of 7:3 using the leave-out method for model construction and internal validation. External validation of the model was done using the dataset of 2019(n = 8128). Finally, the late HER2 + patients(n = 395) was selected for subgroup analysis. Accuracy, calibration and net benefit of clinical decision making were evaluated for both models. RESULTS The HBN model showed that seventeen variables were associated with survival outcome, including age, tumor size, site, histologic type, radiotherapy, surgery, chemotherapy, distant metastasis, subtype, clinical stage, ER receptor, PR receptor, clinical grade, race, marital status, tumor laterality, and lymph node. The AUCs for the internal validation of the LR and HBN models were 0.831 and 0.900; The AUCs for the external validation of the LR and HBN models on the whole population were 0.786 and 0.871; the AUCs for the external validation of the two models on the subgroup population were 0.601 and 0.813. CONCLUSION The accuracy, net clinical benefit, and calibration of the HBN model were better than LR model. The predictive efficacy of both models decreased and the difference was greater in advanced HER2 + patients, which means the HBN model had higher robustness and more stable predictive performance in the subgroup.
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Affiliation(s)
- Fan Su
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Jianqian Chao
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
- Department of Medical Insurance, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Pei Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Bowen Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Na Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Zongyu Luo
- Department of Medical Insurance, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Jiaying Han
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
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Peng W, Qiao H, Mo L, Guo Y. Progress in the diagnosis of lymph node metastasis in rectal cancer: a review. Front Oncol 2023; 13:1167289. [PMID: 37519802 PMCID: PMC10374255 DOI: 10.3389/fonc.2023.1167289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Historically, the chief focus of lymph node metastasis research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen a rapid accumulation of massive omics and imaging data catalyzed by the rapid development of advanced technologies. This rapid increase in data has driven improvements in the accuracy of diagnosis of lymph node metastasis, and its analysis further demands new methods and the opportunity to provide novel insights for basic research. In fact, the combination of omics data, imaging data, clinical medicine, and diagnostic methods has led to notable advances in our basic understanding and transformation of lymph node metastases in rectal cancer. Higher levels of integration will require a concerted effort among data scientists and clinicians. Herein, we review the current state and future challenges to advance the diagnosis of lymph node metastases in rectal cancer.
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Affiliation(s)
- Wei Peng
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Huimin Qiao
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Linfeng Mo
- School of Health and Medicine, Guangzhou Huashang Vocational College, Guangzhou, Guangdong, China
| | - You Guo
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
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10
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Shi L, Zhang Y, Hu J, Zhou W, Hu X, Cui T, Yue NJ, Sun X, Nie K. Radiomics for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Prospective Observational Trial. Bioengineering (Basel) 2023; 10:634. [PMID: 37370565 DOI: 10.3390/bioengineering10060634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/26/2023] [Accepted: 05/09/2023] [Indexed: 06/29/2023] Open
Abstract
(1) Background: An increasing amount of research has supported the role of radiomics for predicting pathological complete response (pCR) to neoadjuvant chemoradiation treatment (nCRT) in order to provide better management of locally advanced rectal cancer (LARC) patients. However, the lack of validation from prospective trials has hindered the clinical adoption of such studies. The purpose of this study is to validate a radiomics model for pCR assessment in a prospective trial to provide informative insight into radiomics validation. (2) Methods: This study involved a retrospective cohort of 147 consecutive patients for the development/validation of a radiomics model, and a prospective cohort of 77 patients from two institutions to test its generalization. The model was constructed using T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI to understand the associations with pCR. The consistency of physicians' evaluations and agreement on pathological complete response prediction were also evaluated, with and without the aid of the radiomics model. (3) Results: The radiomics model outperformed both physicians' visual assessments in the prospective test cohort, with an area under the curve (AUC) of 0.84 (95% confidence interval of 0.70-0.94). With the aid of the radiomics model, a junior physician could achieve comparable performance as a senior oncologist. (4) Conclusion: We have built and validated a radiomics model with pretreatment MRI for pCR prediction of LARC patients undergoing nCRT.
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Affiliation(s)
- Liming Shi
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310019, China
| | - Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, 195 Little Albany St., New Brunswick, NJ 08903, USA
| | - Jiamiao Hu
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310019, China
| | - Weiwen Zhou
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310019, China
| | - Xi Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310019, China
| | - Taoran Cui
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, 195 Little Albany St., New Brunswick, NJ 08903, USA
| | - Ning J Yue
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, 195 Little Albany St., New Brunswick, NJ 08903, USA
| | - Xiaonan Sun
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310019, China
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, 195 Little Albany St., New Brunswick, NJ 08903, USA
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11
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Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
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12
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Fang Z, Pu H, Chen XL, Yuan Y, Zhang F, Li H. MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer. Abdom Radiol (NY) 2023; 48:2270-2283. [PMID: 37085730 DOI: 10.1007/s00261-023-03910-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/01/2023] [Accepted: 04/05/2023] [Indexed: 04/23/2023]
Abstract
PURPOSE To investigative the performance of MRI-radiomics analysis derived from T2WI and apparent diffusion coefficients (ADC) images before and after neoadjuvant chemoradiation therapy (nCRT) separately or simultaneously for predicting post-nCRT lymph node status in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: Eighty-three patients (training cohort, n = 57; validation cohort, n = 26) with LARC between June 2017 and December 2022 were retrospectively enrolled. All the radiomics features were extracted from volume of interest on T2WI and ADC images from baseline and post-nCRT MRI. Delta-radiomics features were defined as the difference between radiomics features before and after nCRT. Seven clinical-radiomics models were constructed by combining the most predictive radiomics signatures and clinical parameters selected from support vector machine. Receiver operating characteristic curve (ROC) was used to evaluate the performance of models. The optimum model-based LNM was applied to assess 5-years disease-free survival (DFS) using Kaplan-Meier analysis. The end point was clinical or radiological locoregional recurrence or distant metastasis during postoperative follow-up. RESULTS Clinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training (AUC = 0.895,95%CI:0.838-0.953) and validation cohort (AUC = 0.900,95%CI:0.771-1.000). Clinical-deltaADC radiomics-postT2WI radiomics combined model also showed good performances (AUC = 0.913,95%CI:0.838-0.953) in the training and (AUC = 0.912,95%CI:0.771-1.000) validation cohort. As for subgroup analysis, clinical-deltaADC radiomics combined model showed good performance predicting LNM in ypT0-T2 (AUC = 0.827;95%CI:0.649-1.000) and ypT3-T4 stage (AUC = 0.934;95%CI:0.864-1.000). In ypT0-T2 stage, clinical-deltaADC radiomics combined model-based LNM could assess 5-years DFS (P = 0.030). CONCLUSION Clinical-deltaADC radiomics combined model could predict post-nCRT LNM, and this combined model-based LNM was associated with 5-years DFS in ypT0-T2 stage.
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Affiliation(s)
- Zhu Fang
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Hong Pu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Xiao-Li Chen
- Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, 55#Four Section of South Renmin Road, Wuhou District, Chengdu, 610000, China
| | - Yi Yuan
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Feng Zhang
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Hang Li
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China.
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13
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Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
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Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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Horvat N, El Homsi M, Miranda J, Mazaheri Y, Gollub MJ, Paroder V. Rectal MRI Interpretation After Neoadjuvant Therapy. J Magn Reson Imaging 2023; 57:353-369. [PMID: 36073323 PMCID: PMC9851947 DOI: 10.1002/jmri.28426] [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: 05/20/2022] [Revised: 08/23/2022] [Accepted: 08/25/2022] [Indexed: 02/01/2023] Open
Abstract
In recent years, several key advances in the management of locally advanced rectal cancer have been made, including the implementation of total mesorectal excision as the standard surgical approach; use of neoadjuvant chemoradiotherapy in selected patients with a high risk of local recurrence, and finally, adoption of organ preservation strategies, through either local excision or nonoperative management in selected patients with clinical complete response following neoadjuvant chemoradiotherapy. This review aims to shed light on the role of rectal MRI in the assessment of treatment response after neoadjuvant therapy, which is especially important given the growing feasibility of nonoperative management. First, an overview of current neoadjuvant therapies and response assessment based on digital rectal examination, endoscopy, and MRI will be provided. Second, the use of a high-quality restaging rectal MRI protocol will be presented. Third, a step-by-step approach to assessing treatment response on restaging rectal MRI following neoadjuvant treatment will be outlined, acknowledging challenges faced by radiologists during MRI interpretation. Finally, research related to response assessment will be discussed. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maria El Homsi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo, Brazil
| | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marc J. Gollub
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Viktoriya Paroder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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15
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MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study. Clin Transl Radiat Oncol 2022; 38:175-182. [DOI: 10.1016/j.ctro.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/12/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
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16
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Xia X, Li D, Du W, Wang Y, Nie S, Tan Q, Gou Q. Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer. Diagnostics (Basel) 2022; 12:diagnostics12102446. [PMID: 36292135 PMCID: PMC9600299 DOI: 10.3390/diagnostics12102446] [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: 09/01/2022] [Revised: 09/23/2022] [Accepted: 10/06/2022] [Indexed: 12/09/2022] Open
Abstract
The accurate prediction of the status of PLNM preoperatively plays a key role in treatment strategy decisions in early-stage cervical cancer. The aim of this study was to develop and validate a radiomics-based nomogram for the preoperative prediction of pelvic lymph node metastatic status in early-stage cervical cancer. One hundred fifty patients were enrolled in this study. Radiomics features were extracted from T2-weighted MRI imaging (T2WI). Based on the selected features, a support vector machine (SVM) algorithm was used to build the radiomics signature. The radiomics-based nomogram was developed incorporating radiomics signature and clinical risk factors. In the training cohort (AUC = 0.925, accuracy = 81.6%, sensitivity = 70.3%, and specificity = 92.0%) and the testing cohort (AUC = 0.839, accuracy = 74.2%, sensitivity = 65.7%, and specificity = 82.8%), clinical models that combine stromal invasion depth, FIGO stage, and MTD perform poorly. The combined model had the highest AUC in the training cohort (AUC = 0.988, accuracy = 95.9%, sensitivity = 92.0%, and specificity = 100.0%) and the testing cohort (AUC = 0.922, accuracy = 87.1%, sensitivity = 85.7%, and specificity = 88.6%) when compared to the radiomics and clinical models. The study may provide valuable guidance for clinical physicians regarding the treatment strategies for early-stage cervical cancer patients.
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Affiliation(s)
- Xueming Xia
- Department of Head and Neck Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Dongdong Li
- Department of Network Engineering, School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Wei Du
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yu Wang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 402103, China
| | - Shihong Nie
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qiaoyue Tan
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Qiheng Gou
- Department of Head and Neck Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Correspondence:
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17
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Zhuang Z, Ma X, Zhang Y, Yang X, Wei M, Deng X, Wang Z. Establishment and validation of nomograms for predicting mesorectal lymph node staging and restaging. Int J Colorectal Dis 2022; 37:2069-2083. [PMID: 36028723 DOI: 10.1007/s00384-022-04244-1] [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] [Accepted: 08/15/2022] [Indexed: 02/04/2023]
Abstract
BACKGROUND Preoperative determination of lymph node (LN) status is crucial in treatment planning for rectal cancer. This study prospectively evaluated the risk factors for lymph node metastasis (LNM) at staging and restaging based on a node-by-node pairing between MRI imaging findings and histopathology and constructed nomograms to evaluate its diagnostic value. METHODS From July 2021 to July 2022, patients with histopathologically verified rectal cancer who underwent MRI before surgery were prospectively enrolled. Histological examination of each LN status in the surgical specimens and anatomical matching with preoperative imaging. Taking histopathological results as the gold standard, federating clinical features from patients and LN imaging features on MRI-T2WI. Risk factors for LN metastasis were identified by multivariate logistic regression analysis and used to create a nomogram. The performance of the nomograms was assessed with calibration plots and bootstrapped-concordance index and validated using validation cohorts. RESULTS A total of 500 target LNs in 120 patients were successfully matched with node-by-node comparisons. A total of 353 LNs did not receive neoadjuvant therapy and 147 LNs received neoadjuvant chemoradiotherapy (neoCRT). Characterization of LNs not receiving neoadjuvant therapy and multivariate regression showed that the short diameter, preoperative CEA level, mrT-stage, border contour, and signal intensity were associated with a high risk of LN metastasis (P < 0.05). The nomogram predicted that the area under the curve was 0.855 (95% CI, 0.794-0.916) and 0.854 (95% CI, 0.727-0.980) in the training and validation cohorts, respectively. In the neoadjuvant therapy group, short diameter, ymrT-stage, internal signal, and MRI-EMVI were associated with LN positivity (P < 0.05), and the area under the curves using the nomogram was 0.912 (95% CI, 0.856-0.968) and 0.915 (95% CI, 0.817-1.000) in two cohorts. The calibration curves demonstrate good agreement between the predicted and actual probabilities for both the training and validation cohorts. CONCLUSION Our nomograms combined with preoperative clinical and imaging biomarkers have the potential to improve the prediction of nodal involvement, which can be used as an essential reference for preoperative N staging and restaging of rectal cancer.
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Affiliation(s)
- Zixuan Zhuang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xueqin Ma
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Zhang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xuyang Yang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Mingtian Wei
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiangbing Deng
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ziqiang Wang
- Department of General Surgery, Colorectal Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
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18
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Zhang YC, Li M, Jin YM, Xu JX, Huang CC, Song B. Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer. World J Gastroenterol 2022; 28:3960-3970. [PMID: 36157536 PMCID: PMC9367222 DOI: 10.3748/wjg.v28.i29.3960] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 03/28/2022] [Accepted: 07/06/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Tumor deposits (TDs) are not equivalent to lymph node (LN) metastasis (LNM) but have become independent adverse prognostic factors in patients with rectal cancer (RC). Although preoperatively differentiating TDs and LNMs is helpful in designing individualized treatment strategies and achieving improved prognoses, it is a challenging task.
AIM To establish a computed tomography (CT)-based radiomics model for preoperatively differentiating TDs from LNM in patients with RC.
METHODS This study retrospectively enrolled 219 patients with RC [TDs+LNM- (n = 89); LNM+ TDs- (n = 115); TDs+LNM+ (n = 15)] from a single center between September 2016 and September 2021. Single-positive patients (i.e., TDs+LNM- and LNM+TDs-) were classified into the training (n = 163) and validation (n = 41) sets. We extracted numerous features from the enhanced CT (region 1: The main tumor; region 2: The largest peritumoral nodule). After deleting redundant features, three feature selection methods and three machine learning methods were used to select the best-performing classifier as the radiomics model (Rad-score). After validating Rad-score, its performance was further evaluated in the field of diagnosing double-positive patients (i.e., TDs+LNM+) by outlining all peritumoral nodules with diameter (short-axis) > 3 mm.
RESULTS Rad-score 1 (radiomics signature of the main tumor) had an area under the curve (AUC) of 0.768 on the training dataset and 0.700 on the validation dataset. Rad-score 2 (radiomics signature of the largest peritumoral nodule) had a higher AUC (training set: 0.940; validation set: 0.918) than Rad-score 1. Clinical factors, including age, gender, location of RC, tumor markers, and radiological features of the largest peritumoral nodule, were excluded by logistic regression. Thus, the combined model was comprised of Rad-scores of 1 and 2. Considering that the combined model had similar AUCs with Rad-score 2 (P = 0.134 in the training set and 0.594 in the validation set), Rad-score 2 was used as the final model. For the diagnosis of double-positive patients in the mixed group [TDs+LNM+ (n = 15); single-positive (n = 15)], Rad-score 2 demonstrated moderate performance (sensitivity, 73.3%; specificity, 66.6%; and accuracy, 70.0%).
CONCLUSION Radiomics analysis based on the largest peritumoral nodule can be helpful in preoperatively differentiating between TDs and LNM.
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Affiliation(s)
- Yong-Chang Zhang
- Department of Radiology, Chengdu Seventh People’s Hospital, Chengdu 610213, Sichuan Province, China
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Mou Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yu-Mei Jin
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Jing-Xu Xu
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100080, China
| | - Chen-Cui Huang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing 100080, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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19
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Borgheresi A, De Muzio F, Agostini A, Ottaviani L, Bruno A, Granata V, Fusco R, Danti G, Flammia F, Grassi R, Grassi F, Bruno F, Palumbo P, Barile A, Miele V, Giovagnoni A. Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective. J Clin Med 2022; 11:jcm11092599. [PMID: 35566723 PMCID: PMC9104021 DOI: 10.3390/jcm11092599] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.
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Affiliation(s)
- Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
| | - Letizia Ottaviani
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale IRCCS di Napoli, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
- Correspondence:
| | - Ginevra Danti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Federica Flammia
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Francesca Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Abruzzo Health Unit 1, Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, 67100 L’Aquila, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
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20
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Jayaprakasam VS, Paroder V, Gibbs P, Bajwa R, Gangai N, Sosa RE, Petkovska I, Golia Pernicka JS, Fuqua JL, Bates DDB, Weiser MR, Cercek A, Gollub MJ. MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer. Eur Radiol 2022; 32:971-980. [PMID: 34327580 PMCID: PMC9018044 DOI: 10.1007/s00330-021-08144-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 05/11/2021] [Accepted: 06/02/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To interrogate the mesorectal fat using MRI radiomics feature analysis in order to predict clinical outcomes in patients with locally advanced rectal cancer. METHODS This retrospective study included patients who underwent neoadjuvant chemoradiotherapy for locally advanced rectal cancer from 2009 to 2015. Three radiologists independently segmented mesorectal fat on baseline T2-weighted axial MRI. Radiomics features were extracted from segmented volumes and calculated using CERR software, with adaptive synthetic sampling being employed to combat large class imbalances. Outcome variables included pathologic complete response (pCR), local recurrence, distant recurrence, clinical T-category (cT), post-treatment T category (ypT), and post-treatment N category (ypN). A maximum of eight most important features were selected for model development using support vector machines and fivefold cross-validation to predict each outcome parameter via elastic net regularization. Diagnostic metrics of the final models were calculated, including sensitivity, specificity, PPV, NPV, accuracy, and AUC. RESULTS The study included 236 patients (54 ± 12 years, 135 men). The AUC, sensitivity, specificity, PPV, NPV, and accuracy for each clinical outcome were as follows: for pCR, 0.89, 78.0%, 85.1%, 52.5%, 94.9%, 83.9%; for local recurrence, 0.79, 68.3%, 80.7%, 46.7%, 91.2%, 78.3%; for distant recurrence, 0.87, 80.0%, 88.4%, 58.3%, 95.6%, 87.0%; for cT, 0.80, 85.8%, 56.5%, 89.1%, 49.1%, 80.1%; for ypN, 0.74, 65.0%, 80.1%, 52.7%, 87.0%, 76.3%; and for ypT, 0.86, 81.3%, 84.2%, 96.4%, 46.4%, 81.8%. CONCLUSION Radiomics features of mesorectal fat can predict pathological complete response and local and distant recurrence, as well as post-treatment T and N categories. KEY POINTS • Mesorectal fat contains important prognostic information in patients with locally advanced rectal cancer (LARC). • Radiomics features of mesorectal fat were significantly different between those who achieved complete vs incomplete pathologic response (accuracy 83.9%, 95% CI: 78.6-88.4%). • Radiomics features of mesorectal fat were significantly different between those who did vs did not develop local or distant recurrence (accuracy 78.3%, 95% CI: 72.0-83.7% and 87.0%, 95% CI: 81.6-91.2% respectively).
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Affiliation(s)
- Vetri Sudar Jayaprakasam
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Viktoriya Paroder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
| | - Peter Gibbs
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Raazi Bajwa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Ramon E Sosa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Jennifer S Golia Pernicka
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - James Louis Fuqua
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - David D B Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Martin R Weiser
- Colorectal Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Andrea Cercek
- Department of Medicine, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Marc J Gollub
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
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21
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Antunes JT, Ismail M, Hossain I, Wang Z, Prasanna P, Madabhushi A, Tiwari P, Viswanath SE. RADIomic Spatial TexturAl descripTor (RADISTAT): Quantifying spatial organization of imaging heterogeneity associated with tumor response to treatment. IEEE J Biomed Health Inform 2022; 26:2627-2636. [PMID: 35085099 DOI: 10.1109/jbhi.2022.3146778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Localized disease heterogeneity on imaging extracted via radiomics approaches have recently been associated with disease prognosis and treatment response. Traditionally, radiomics analyses leverage texture operators to derive voxel- or region-wise feature values towards quantifying subtle variations in image appearance within a region-of-interest (ROI). With the goal of mining additional voxel-wise texture patterns from radiomic expression maps, we introduce a new RADIomic Spatial TexturAl descripTor (RADISTAT). This was driven by the hypothesis that quantifying spatial organization of texture patterns within an ROI could allow for better capturing interactions between different tissue classes present in a given region; thus enabling more accurate characterization of disease or response phenotypes. RADISTAT involves: (a) robustly identifying sub-compartments of low, intermediate, and high radiomic expression (i.e. heterogeneity) in a feature map and (b) quantifying spatial organization of sub-compartments via graph interactions. RADISTAT was evaluated in two clinically challenging problems: (1) discriminating nodal/distant metastasis from metastasis-free rectal cancer patients on post-chemoradiation T2w MRI, and (2) distinguishing tumor progression from pseudo-progression in glioblastoma multiforme using post-chemoradiation T1w MRI. Across over 800 experiments, RADISTAT yielded a consistent discriminatory signature for tumor progression (GBM) and disease metastasis (RCa); where its sub-compartments were associated with pathologic tissue types (fibrosis or tumor, determined via fusion of MRI and pathology). In a multi-institutional setting for both clinical problems, RADISTAT resulted in higher classifier performance (11% improvement in AUC, on average) compared to radiomic descriptors. Furthermore, combining RADISTAT with radiomic descriptors resulted in significantly improved performance compared to using radiomic descriptors alone.
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22
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Li Z, Chen F, Zhang S, Ma X, Xia Y, Shen F, Lu Y, Shao C. The feasibility of MRI-based radiomics model in presurgical evaluation of tumor budding in locally advanced rectal cancer. Abdom Radiol (NY) 2022; 47:56-65. [PMID: 34673995 DOI: 10.1007/s00261-021-03311-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/03/2021] [Accepted: 10/04/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE To build and validate a magnetic resonance imaging-based radiomics model to preoperatively evaluate tumor budding (TB) in locally advanced rectal cancer (LARC). METHODS Pathologically confirmed LARC cases submitted to preoperative rectal MRI in two distinct hospitals were enrolled in this retrospective study and assigned to cohort 1 (training set, n = 77; test set, n = 51) and cohort 2 (validation set, n = 96). Radiomics features were obtained from multiple sequences, comprising high-resolution T2, contrast-enhanced T1, and diffusion-weighted imaging (T2WI, CE-T1WI, and DWI, respectively). The least absolute shrinkage and selection operator (LASSO) was utilized to select the optimal features from T2WI, CE-T1WI, DWI, and the combination of multi-sequences, respectively. A support vector machine (SVM) classifier was utilized to construct various radiomics models for discriminating the TB grades. Receiver operating characteristic curve analysis and decision curve analysis (DCA) were carried out to determine the diagnostic value. RESULTS Five optimal features associated with TB grade were determined from combined multi-sequence data. Accordingly, a radiomics model based on combined multi-sequences had an area under the curve of 0.796, with an accuracy of 81.2% in the validation set, showing a better performance in comparison with other models in both cohorts (p < 0.05). DCA exhibited a clinical benefit for this radiomics model. CONCLUSION The novel MRI-based radiomics model combining multiple sequences is an effective and non-invasive approach for evaluating TB grade preoperatively in patients with LARC.
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23
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Zhang S, Yu M, Chen D, Li P, Tang B, Li J. Role of MRI‑based radiomics in locally advanced rectal cancer (Review). Oncol Rep 2021; 47:34. [PMID: 34935061 PMCID: PMC8717123 DOI: 10.3892/or.2021.8245] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Colorectal cancer is the third most common type of cancer, with high morbidity and mortality rates. In particular, locally advanced rectal cancer (LARC) is difficult to treat and has a high recurrence rate. Neoadjuvant chemoradiotherapy (NCRT) is one of the standard treatment programs of LARC. If the response to treatment and prognosis in patients with LARC can be predicted, it will guide clinical decision‑making. Radiomics is characterized by the extraction of high‑dimensional quantitative features from medical imaging data, followed by data analysis and model construction, which can be used for tumor diagnosis, staging, prediction of treatment response and prognosis. In recent years, a number of studies have assessed the role of radiomics in NCRT for LARC. MRI‑based radiomics provides valuable data and is expected to become an imaging biomarker for predicting treatment response and prognosis. The potential of radiomics to guide personalized medicine is widely recognized; however, current limitations and challenges prevent its application to clinical decision‑making. The present review summarizes the applications, limitations and prospects of MRI‑based radiomics in LARC.
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Affiliation(s)
- Siyu Zhang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610041, P.R. China
| | - Mingrong Yu
- College of Physical Education, Sichuan Agricultural University, Ya'an, Sichuan 625000, P.R. China
| | - Dan Chen
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610041, P.R. China
| | - Peidong Li
- Second Department of Gastrointestinal Surgery, The Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, P.R. China
| | - Bin Tang
- Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, Sichuan 610041, P.R. China
| | - Jie Li
- Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, Sichuan 610041, P.R. China
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24
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Bedrikovetski S, Dudi-Venkata NN, Kroon HM, Seow W, Vather R, Carneiro G, Moore JW, Sammour T. Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis. BMC Cancer 2021; 21:1058. [PMID: 34565338 PMCID: PMC8474828 DOI: 10.1186/s12885-021-08773-w] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 09/08/2021] [Indexed: 12/28/2022] Open
Abstract
Background Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer. Methods A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis. Results Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725). Conclusion AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce. Trial registration PROSPERO CRD42020218004. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08773-w.
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Affiliation(s)
- Sergei Bedrikovetski
- Discipline of Surgery, Faculty of Health and Medical Sciences, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia. .,Department of Surgery, Colorectal Unit, Royal Adelaide Hospital, Adelaide, South Australia, Australia.
| | - Nagendra N Dudi-Venkata
- Discipline of Surgery, Faculty of Health and Medical Sciences, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia.,Department of Surgery, Colorectal Unit, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Hidde M Kroon
- Discipline of Surgery, Faculty of Health and Medical Sciences, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia.,Department of Surgery, Colorectal Unit, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Warren Seow
- Discipline of Surgery, Faculty of Health and Medical Sciences, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia
| | - Ryash Vather
- Department of Surgery, Colorectal Unit, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Gustavo Carneiro
- Australian Institute for Machine Learning, School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
| | - James W Moore
- Discipline of Surgery, Faculty of Health and Medical Sciences, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia.,Department of Surgery, Colorectal Unit, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Tarik Sammour
- Discipline of Surgery, Faculty of Health and Medical Sciences, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia.,Department of Surgery, Colorectal Unit, Royal Adelaide Hospital, Adelaide, South Australia, Australia
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25
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Du W, Wang Y, Li D, Xia X, Tan Q, Xiong X, Li Z. Preoperative Prediction of Lymphovascular Space Invasion in Cervical Cancer With Radiomics -Based Nomogram. Front Oncol 2021; 11:637794. [PMID: 34322375 PMCID: PMC8311659 DOI: 10.3389/fonc.2021.637794] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 06/15/2021] [Indexed: 02/05/2023] Open
Abstract
Purpose To build and evaluate a radiomics-based nomogram that improves the predictive performance of the LVSI in cervical cancer non-invasively before the operation. Method This study involved 149 patients who underwent surgery with cervical cancer from February 2017 to October 2019. Radiomics features were extracted from T2 weighted imaging (T2WI). The radiomic features were selected by logistic regression with the least absolute shrinkage and selection operator (LASSO) penalty in the training cohort. Based on the selected features, support vector machine (SVM) algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinical risk factors, the radiomics-based nomogram was developed. The sensitivity, specificity, accuracy, and area under the curve (AUC) and Receiver operating characteristic (ROC) curve were calculated to assess these models. Result The radiomics model performed much better than the clinical model in both training (AUCs 0.925 vs. 0.786, accuracies 87.5% vs. 70.5%, sensitivities 83.6% vs. 41.7% and specificities 90.9% vs. 94.7%) and testing (AUCs 0.911 vs. 0.706, accuracies 84.0% vs. 71.3%, sensitivities 81.1% vs. 43.4% and specificities 86.4% vs. 95.0%). The combined model based on the radiomics signature and tumor stage, tumor infiltration depth and tumor pathology yielded the best performance (training cohort, AUC = 0.943, accuracies 89.5%, sensitivities 85.4% and specificities 92.9%; testing cohort, AUC = 0.923, accuracies 84.6%, sensitivities 84.0% and specificities 85.1%). Conclusion Radiomics-based nomogram was a useful tool for predicting LVSI of cervical cancer. This would aid the selection of the optimal therapeutic strategy and clinical decision-making for individuals.
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Affiliation(s)
- Wei Du
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Collaborative Innovation Center for Biotherapy, Sichuan University, Chengdu, China
| | - Yu Wang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Dongdong Li
- School of Computer Science & Engineering, South China University of Technology, Guangzhou, China
| | - Xueming Xia
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Collaborative Innovation Center for Biotherapy, Sichuan University, Chengdu, China.,Department of Head and Neck Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Qiaoyue Tan
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiaoming Xiong
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Zhiping Li
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Collaborative Innovation Center for Biotherapy, Sichuan University, Chengdu, China
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26
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Li Z, Li S, Zang S, Ma X, Chen F, Xia Y, Chen L, Shen F, Lu Y, Lu J. Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Mucinous Adenocarcinoma Using an MRI-Based Radiomics Nomogram. Front Oncol 2021; 11:671636. [PMID: 34109121 PMCID: PMC8181148 DOI: 10.3389/fonc.2021.671636] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/06/2021] [Indexed: 12/15/2022] Open
Abstract
Objective To build and validate an MRI-based radiomics nomogram to predict the therapeutic response to neoadjuvant chemoradiotherapy (nCRT) in rectal mucinous adenocarcinoma (RMAC). Methods Totally, 92 individuals with pathologically confirmed RMAC administered surgical resection upon nCRT in two different centers were assessed retrospectively (training set, n = 52, validation set, n = 40). Rectal MRI was performed pre-nCRT. Radiomics parameters were obtained from high-resolution T2-weighted images and selected to construct a radiomics signature. Then, radiomics nomogram construction integrated patient variables and the radiomics signature. The resulting radiomics nomogram was utilized to assess the tumor regression grade (TRG). Diagnostic performance was determined by generating receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results Six optimal features related to TRG were obtained to construct a radiomics signature. The nomogram combining the radiomics signature with age and mucin deposit outperformed the radiomics signature alone in the training (AUC, 0.950 vs 0.843, p < 0.05) and validation (AUC, 0.868 vs 0.719, p < 0.05) cohorts. DCA demonstrated a clinical utility for the radiomics nomogram model. Conclusions The established quantitative MRI-based radiomics nomogram is effective in predicting treatment response to neoadjuvant therapy in patients with RMAC.
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Affiliation(s)
- Zhihui Li
- Department of Radiology, RuiJin Hospital LuWan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Shuai Li
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Shuqin Zang
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Xiaolu Ma
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Fangying Chen
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Yuwei Xia
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Liuping Chen
- Department of Radiology, RuiJin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Fu Shen
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Yong Lu
- Department of Radiology, RuiJin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Shanghai, China
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27
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Li C, Yin J. Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients. Front Oncol 2021; 11:671354. [PMID: 34041033 PMCID: PMC8141802 DOI: 10.3389/fonc.2021.671354] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/12/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose To develop and validate a radiomics nomogram based on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) features for the preoperative prediction of lymph node (LN) metastasis in rectal cancer patients. Materials and Methods One hundred and sixty-two patients with rectal cancer confirmed by pathology were retrospectively analyzed, who underwent T2WI and DWI sequences. The data sets were divided into training (n = 97) and validation (n = 65) cohorts. For each case, a total of 2,752 radiomic features were extracted from T2WI, and ADC images derived from diffusion-weighted imaging. A two-sample t-test was used for prefiltering. The least absolute shrinkage selection operator method was used for feature selection. Three radiomics scores (rad-scores) (rad-score 1 for T2WI, rad-score 2 for ADC, and rad-score 3 for the combination of both) were calculated using the support vector machine classifier. Multivariable logistic regression analysis was then used to construct a radiomics nomogram combining rad-score 3 and independent risk factors. The performances of three rad-scores and the nomogram were evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to assess the clinical usefulness of the radiomics nomogram. Results The AUCs of the rad-score 1 and rad-score 2 were 0.805, 0.749 and 0.828, 0.770 in the training and validation cohorts, respectively. The rad-score 3 achieved an AUC of 0.879 in the training cohort and an AUC of 0.822 in the validation cohort. The radiomics nomogram, incorporating the rad-score 3, age, and LN size, showed good discrimination with the AUC of 0.937 for the training cohort and 0.884 for the validation cohort. DCA confirmed that the radiomics nomogram had clinical utility. Conclusions The radiomics nomogram, incorporating rad-score based on features from the T2WI and ADC images, and clinical factors, has favorable predictive performance for preoperative prediction of LN metastasis in patients with rectal cancer.
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Affiliation(s)
- Chunli Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Sugai Y, Kadoya N, Tanaka S, Tanabe S, Umeda M, Yamamoto T, Takeda K, Dobashi S, Ohashi H, Takeda K, Jingu K. Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients. Radiat Oncol 2021; 16:80. [PMID: 33931085 PMCID: PMC8086112 DOI: 10.1186/s13014-021-01810-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/21/2021] [Indexed: 02/08/2023] Open
Abstract
Background Radiomics is a new technology to noninvasively predict survival prognosis with quantitative features extracted from medical images. Most radiomics-based prognostic studies of non-small-cell lung cancer (NSCLC) patients have used mixed datasets of different subgroups. Therefore, we investigated the radiomics-based survival prediction of NSCLC patients by focusing on subgroups with identical characteristics. Methods A total of 304 NSCLC (Stages I–IV) patients treated with radiotherapy in our hospital were used. We extracted 107 radiomic features (i.e., 14 shape features, 18 first-order statistical features, and 75 texture features) from the gross tumor volume drawn on the free breathing planning computed tomography image. Three feature selection methods [i.e., test–retest and multiple segmentation (FS1), Pearson's correlation analysis (FS2), and a method that combined FS1 and FS2 (FS3)] were used to clarify how they affect survival prediction performance. Subgroup analysis for each histological subtype and each T stage applied the best selection method for the analysis of All data. We used a least absolute shrinkage and selection operator Cox regression model for all analyses and evaluated prognostic performance using the concordance-index (C-index) and the Kaplan–Meier method. For subgroup analysis, fivefold cross-validation was applied to ensure model reliability. Results In the analysis of All data, the C-index for the test dataset is 0.62 (FS1), 0.63 (FS2), and 0.62 (FS3). The subgroup analysis indicated that the prediction model based on specific histological subtypes and T stages had a higher C-index for the test dataset than that based on All data (All data, 0.64 vs. SCCall, 060; ADCall, 0.69; T1, 0.68; T2, 0.65; T3, 0.66; T4, 0.70). In addition, the prediction models unified for each T stage in histological subtype showed a different trend in the C-index for the test dataset between ADC-related and SCC-related models (ADCT1–ADCT4, 0.72–0.83; SCCT1–SCCT4, 0.58–0.71). Conclusions Our results showed that feature selection methods moderately affected the survival prediction performance. In addition, prediction models based on specific subgroups may improve the prediction performance. These results may prove useful for determining the optimal radiomics-based predication model. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-021-01810-9.
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Affiliation(s)
- Yuto Sugai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Shunpei Tanabe
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Mariko Umeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Suguru Dobashi
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Haruna Ohashi
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Ken Takeda
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
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Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice. Diagnostics (Basel) 2021; 11:diagnostics11050756. [PMID: 33922483 PMCID: PMC8146913 DOI: 10.3390/diagnostics11050756] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/08/2021] [Accepted: 04/21/2021] [Indexed: 12/24/2022] Open
Abstract
While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.
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Li J, Zhou Y, Wang X, Yu Y, Zhou X, Luan K. Histogram Analysis of Diffusion-Weighted Magnetic Resonance Imaging as a Biomarker to Predict Lymph Node Metastasis in T3 Stage Rectal Carcinoma. Cancer Manag Res 2021; 13:2983-2993. [PMID: 33833581 PMCID: PMC8021267 DOI: 10.2147/cmar.s298907] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 03/03/2021] [Indexed: 01/08/2023] Open
Abstract
Purpose This study investigated the predictive value of apparent diffusion coefficient (ADC) histogram parameters of the primary tumor for regional lymph node metastasis (LNM) in pathological T3 stage rectal cancer. Patients and Methods We retrospectively studied 175 patients with T3 stage rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging, between January 2015 and October 2017. Based on pathological analysis of surgical specimens, 113 patients were classified into the LN− group and 62 in the LN+ group. We analyzed clinical data, radiological characteristics and histogram parameters derived from ADC maps. Then, receiver operating characteristic curve (ROC) analyses were generated to determine the best diagnostic performance. Results The mean (p=0.002, cutoff=1.08×10–3 s/mm2), coefficient of variation (CV) (p=0.040, cutoff=0.249) of the ADC map, carbohydrate antigen 199, and N stage with magnetic resonance (mrN stage) were independent factors for LNM. Combining these factors yielded the best diagnostic performance, with the area under the ROC curve of 0.838, 72.9% sensitivity, 79.1% specificity, 65.2% positive predictive value, and 84.5% negative predictive value. Conclusion With the mean >1.08×10–3 s/mm2 and CV <0.249, the ADC improved the diagnostic performance of LNM in T3 stage rectal cancer, which could assist surgeons with neoadjuvant chemoradiotherapy.
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Affiliation(s)
- Jin Li
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, Heilongjiang Province, People's Republic of China
| | - Yang Zhou
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, Heilongjiang Province, People's Republic of China.,Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, 150001, Heilongjiang Province, People's Republic of China
| | - Xinxin Wang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, 150001, Heilongjiang Province, People's Republic of China
| | - Yanyan Yu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, 150001, Heilongjiang Province, People's Republic of China
| | - Xueyan Zhou
- School of Technology, Harbin University, Harbin, 150001, Heilongjiang Province, People's Republic of China
| | - Kuan Luan
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, Heilongjiang Province, People's Republic of China
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Li Z, Ma X, Shen F, Lu H, Xia Y, Lu J. Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models. BMC Med Imaging 2021; 21:30. [PMID: 33593304 PMCID: PMC7885409 DOI: 10.1186/s12880-021-00560-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 01/31/2021] [Indexed: 12/14/2022] Open
Abstract
Background To validate and compare various MRI-based radiomics models to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) of rectal cancer. Methods A total of 80 patients with locally advanced rectal cancer (LARC) who underwent surgical resection after nCRT were enrolled retrospectively. Rectal MR images were scanned pre- and post-nCRT. The radiomics features were extracted from T2-weighted images, then reduced separately by least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Four classifiers of Logistic Regression, Random Forest (RF), Decision Tree and K-nearest neighbor (KNN) models were constructed to assess the tumor regression grade (TRG) and pathologic complete response (pCR), respectively. The diagnostic performances of models were determined with leave-one-out cross-validation by generating receiver operating characteristic curves and decision curve analysis. Results Three features related to the TRG and 11 features related to the pCR were obtained by LASSO. Top five principal components representing a cumulative contribution of 80% to overall features were selected by PCA. For TRG, the area under the curve (AUC) of RF model was 0.943 for LASSO and 0.930 for PCA, higher than other models (P < 0.05 for both). As for pCR, the AUCs of KNN for LASSO and PCA were 0.945 and 0.712, higher than other models (P < 0.05 for both). The DCA showed that LASSO algorithm was clinically superior to PCA. Conclusion MRI-based radiomics models demonstrated good performance for evaluating the treatment response of LARC after nCRT and LASSO algorithm yielded more clinical benefit.
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Affiliation(s)
- Zhihui Li
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
| | - Xiaolu Ma
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
| | - Fu Shen
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China.
| | - Haidi Lu
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
| | - Yuwei Xia
- Huiying Medical Technology Co., Ltd, Beijing, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
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Zheng Y, Liu X, Zhong Y, Lv F, Yang H. A Preliminary Study for Distinguish Hormone-Secreting Functional Adrenocortical Adenoma Subtypes Using Multiparametric CT Radiomics-Based Machine Learning Model and Nomogram. Front Oncol 2020; 10:570502. [PMID: 33117700 PMCID: PMC7552922 DOI: 10.3389/fonc.2020.570502] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 08/20/2020] [Indexed: 12/12/2022] Open
Abstract
Purpose: To explore the application value of multiparametric computed tomography (CT) radiomics in non-invasive differentiation between aldosterone-producing and cortisol-producing functional adrenocortical adenomas. Methods: This retrospective review analyzed 83 patients including 41 patients with aldosterone-producing adenoma and 42 patients with cortisol-producing adenoma. The quantitative radiomics features were extracted from the complete unenhanced, arterial, and venous phase CT images. A comparative study of several frequently used machine learning models (linear discriminant analysis, logistic regression, random forest, and support vector machine) combined with different feature selection methods was implemented in order to determine which was most advantageous for differential diagnosis using radiomics features. Then, the integrated model using the combination of radiomic signature and clinic-radiological features was built, and the associated calibration curve was also presented. The diagnostic performance of these models was estimated and compared using the area under the receiver operating characteristic (ROC) curve (AUC). Result: In the radiomics-based machine learning model, logistic regression model with LASSO (least absolute shrinkage and selection operator) outperformed the other models, which yielded a sensitivity of 0.935, a specificity of 0.823, and an accuracy of 0.887 [AUC = 0.882, 95% confidence interval (CI) = 0.819-0.945]. Moreover, the nomogram representing the integrated model achieved good discrimination performances, which yielded a sensitivity of 0.915, a specificity of 0.928, and an accuracy of 0.922 (AUC = 0.902, 95% CI = 0.822-0.982), and it was better than that of the radiomics model alone. Conclusion: This study found that the combination of multiparametric radiomics signature and clinic-radiological features can non-invasively differentiate the subtypes of hormone-secreting functional adrenocortical adenomas, which may have good potential for facilitating the diagnosis and treatment in clinical practice.
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Affiliation(s)
- Yineng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yi Zhong
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Haitao Yang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Zhu HT, Zhang XY, Shi YJ, Li XT, Sun YS. A Deep Learning Model to Predict the Response to Neoadjuvant Chemoradiotherapy by the Pretreatment Apparent Diffusion Coefficient Images of Locally Advanced Rectal Cancer. Front Oncol 2020; 10:574337. [PMID: 33194680 PMCID: PMC7658629 DOI: 10.3389/fonc.2020.574337] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/12/2020] [Indexed: 12/11/2022] Open
Abstract
Background and Purpose Pretreatment prediction of the response to neoadjuvant chemoradiotherapy (NCRT) helps to determine the subsequent plans for the patients with locally advanced rectal cancer (LARC). If the good responders (GR) and non-good responders (non-GR) can be accurately predicted, they can choose to intensify the neoadjuvant chemoradiotherapy to decrease the risk of tumor progression during NCRT and increase the chance of organ preservation. Compared with radiomics methods, deep learning (DL) may adaptively extract features from the images without the need of feature definition. However, DL suffers from limited training samples and signal discrepancy among different scanners. This study aims to construct a DL model to predict GRs by training apparent diffusion coefficient (ADC) images from different scanners. Methods The study retrospectively recruited 700 participants, chronologically divided into a training group (n = 500) and a test group (n = 200). Deep convolutional neural networks were constructed to classify GRs and non-GRs. The networks were designed with a max-pooling layer parallelized by a center-cropping layer to extract features from both the macro and micro scale. ADC images and T2-weighted images were collected at 1.5 Tesla and 3.0 Tesla. The networks were trained by the image patches delineated by radiologists in ADC images and T2-weighted images, respectively. Pathological results were used as the ground truth. The deep learning models were evaluated on the test group and compared with the prediction by mean ADC value. Results Area under curve (AUC) of receiver operating characteristic (ROC) is 0.851 (95% CI: 0.789–0.914) for DL model with ADC images (DL_ADC), significantly larger (P = 0.018, Z = 2.367) than that of mean ADC with AUC = 0.723 (95% CI: 0.637–0.809). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of DL_ADC model are 94.3%, 68.3%, 87.4% and 83.7%, respectively. The DL model with T2-weighted images (DL_T2) produces an AUC of 0.721 (95% CI: 0.640–0.802), significantly (P = 0.000, Z = 3.554) lower than that of DL_ADC model. Conclusion Deep learning model reveals the potential of pretreatment apparent diffusion coefficient images for the prediction of good responders to neoadjuvant chemoradiotherapy.
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Affiliation(s)
- Hai-Tao Zhu
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
| | - Xiao-Yan Zhang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
| | - Yan-Jie Shi
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
| | - Xiao-Ting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
| | - Ying-Shi Sun
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
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Sammour T, Bedrikovetski S. Radiomics for Diagnosing Lateral Pelvic Lymph Nodes in Rectal Cancer: Artificial Intelligence Enabling Precision Medicine? Ann Surg Oncol 2020; 27:4082-4083. [PMID: 32761428 DOI: 10.1245/s10434-020-08978-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 07/22/2020] [Indexed: 12/27/2022]
Affiliation(s)
- Tarik Sammour
- Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, SA, Australia.
| | - Sergei Bedrikovetski
- Discipline of Surgery, Faculty of Health and Medical Science, School of Medicine, University of Adelaide, Adelaide, SA, Australia
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