1
|
Liang Y, Wei Y, Xu F, Wei X. MRI-based radiomic models for the preoperative prediction of extramural venous invasion in rectal cancer: A systematic review and meta-analysis. Clin Imaging 2024; 110:110146. [PMID: 38697000 DOI: 10.1016/j.clinimag.2024.110146] [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: 01/02/2024] [Revised: 03/19/2024] [Accepted: 04/03/2024] [Indexed: 05/04/2024]
Abstract
AIM To estimate the diagnostic value of magnetic resonance imaging (MRI)-based radiomic models in detecting the extramural venous invasion (EMVI) of rectal cancer. MATERIALS AND METHODS Appropriate studies in multiple electronic databases were systematically retrieved. The Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score (RQS) were used to evaluate the eligible studies' methodology quality. Summary accuracy metrics were calculated, and the publication bias was detected using Deek's funnel plot. The sensitivity and meta-regression analysis were performed to investigate the causes of heterogeneity. RESULTS For the seven eligible studies, which included 1175 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.80 (95 % CI, 0.70-0.88), 0.89 (95 % CI, 0.84-0.92), 7.0 (95 % CI, 4.7, 10.4), 0.22 (95 % CI, 0.14, 0.34), and 32 (95 % CI, 16, 65), respectively. The area under the receiver operating characteristic curve (AUC) was 0.91 (95 % CI, 0.88, 0.93). Moderate heterogeneity was found due to I2 values of 38.63 % and 32.29 % in sensitivity and specificity, respectively. Meta-regression analysis suggested that the patient enrollment, number of patients, segmentation method, and RQS score were the source of the heterogeneity. The head-to-head analysis suggested that radiomics model had a higher sensitivity for detection of EMVI than subjective evaluation by radiologist (0.47 vs. 0.73, p ≤ 0.001). CONCLUSION Our study suggests that MRI-based radiomic models have good diagnostic value in detecting EMVI for rectal cancer patients. Nevertheless, more prospective and high-quality studies with larger sample sizes are needed in the future to validate these results.
Collapse
Affiliation(s)
- Yingying Liang
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China; Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, 1 Panfu Road, Guangzhou, Guangdong Province 510180, China
| | - Yaxuan Wei
- Guangzhou Medical University, 195 Dongfengxi road, Guangzhou, Guangdong Province 510180, China
| | - Fan Xu
- Department of Radiology, Guangzhou Red Cross Hospital, Medical College, Jinan University, 396 Tongfu road, Guangzhou, Guangdong Province 510220, China
| | - Xinhua Wei
- The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, China; Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, 1 Panfu Road, Guangzhou, Guangdong Province 510180, China.
| |
Collapse
|
2
|
Yu Y, Gao G, Gao X, Zhang Z, He Y, Shi L, Kang Z. A study on the radiomic correlation between CBCT and pCT scans based on modified 3D-RUnet image segmentation. Front Oncol 2024; 14:1301710. [PMID: 38463234 PMCID: PMC10921553 DOI: 10.3389/fonc.2024.1301710] [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: 09/25/2023] [Accepted: 02/06/2024] [Indexed: 03/12/2024] Open
Abstract
Purpose The present study is based on evidence indicating a potential correlation between cone-beam CT (CBCT) measurements of tumor size, shape, and the stage of locally advanced rectal cancer. To further investigate this relationship, the study quantitatively assesses the correlation between positioning CT (pCT) and CBCT in the radiomics features of these cancers, and examines their potential for substitution. Methods In this study, 103 patients diagnosed with locally advanced rectal cancer and undergoing neoadjuvant chemoradiotherapy were selected as participants. Their CBCT and pCT images were used to divide the participants into two groups: a training set and a validation set, with a 7:3 ratio. An improved conventional 3D-RUNet (CLA-UNet) deep learning model was trained on the training set data and then applied to the validation set. The DSC, HD95 and ASSD were calculated for quantitative evaluation purposes. Then, radiomics features were extracted from 30 patients of the test set. Results The experiments demonstrate that, the modified model achieves an average DSC score 0.792 for pCT and 0.672 for CBCT scans. 1037 features were extracted from each patient's CBCT and pCT images, 73 image features were found to have R values greater than 0.9, including three features related to the staging and prognosis of rectal cancer. Conclusion In this study, we proposed an automatic, fast, and consistent method for rectal cancer GTV segmentation for pCT and CBCT scans. The findings of radiomic results indicate that CBCT images have significant research value in the field of radiomics.
Collapse
Affiliation(s)
- Yanjuan Yu
- College of Electronic Engineering, Zhangzhou Institute of Technology, Zhangzhou, Fujian, China
| | - Guanglu Gao
- Department of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China
| | - Xiang Gao
- Department of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China
| | - Zongkai Zhang
- Department of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China
| | - Yipeng He
- Department of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China
| | - Liwan Shi
- Department of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China
| | - Zheng Kang
- Department of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China
| |
Collapse
|
3
|
Lin X, Jiang H, Zhao S, Hu H, Jiang H, Li J, Jia F. MRI-based radiomics model for preoperative prediction of extramural venous invasion of rectal adenocarcinoma. Acta Radiol 2024; 65:68-75. [PMID: 37097830 DOI: 10.1177/02841851231170364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
BACKGROUND Extramural venous invasion (EMVI) is an important prognostic factor of rectal adenocarcinoma. However, accurate preoperative assessment of EMVI remains difficult. PURPOSE To assess EMVI preoperatively through radiomics technology, and use different algorithms combined with clinical factors to establish a variety of models in order to make the most accurate judgments before surgery. MATERIAL AND METHODS A total of 212 patients with rectal adenocarcinoma between September 2012 and July 2019 were included and distributed to training and validation datasets. Radiomics features were extracted from pretreatment T2-weighted images. Different prediction models (clinical model, logistic regression [LR], random forest [RF], support vector machine [SVM], clinical-LR model, clinical-RF model, and clinical-SVM model) were constructed on the basis of radiomics features and clinical factors, respectively. The area under the curve (AUC) and accuracy were used to assess the predictive efficacy of different models. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. RESULTS The clinical-LR model exhibited the best diagnostic efficiency with an AUC of 0.962 (95% confidence interval [CI] = 0.936-0.988) and 0.865 (95% CI = 0.770-0.959), accuracy of 0.899 and 0.828, sensitivity of 0.867 and 0.818, specificity of 0.913 and 0.833, PPV of 0.813 and 0.720, and NPV of 0.940 and 0.897 for the training and validation datasets, respectively. CONCLUSION The radiomics-based prediction model is a valuable tool in EMVI detection and can assist decision-making in clinical practice.
Collapse
Affiliation(s)
- Xue Lin
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, PR China
- Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China
| | - Hao Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, PR China
| | - Sheng Zhao
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, PR China
| | - Hongbo Hu
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, PR China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, PR China
| | - Jinping Li
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, PR China
| | - Fucang Jia
- Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China
- Pazhou Lab, Guangzhou, PR China *Equal contributors
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Qu X, Zhang L, Ji W, Lin J, Wang G. Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics. Front Oncol 2023; 13:1267838. [PMID: 37941552 PMCID: PMC10628597 DOI: 10.3389/fonc.2023.1267838] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
Objective This study aimed to explore the radiomics model based on magnetic resonance imaging (MRI) T2WI and compare the value of different machine algorithms in preoperatively predicting tumor budding (TB) grading in rectal cancer. Methods A retrospective study was conducted on 266 patients with preoperative rectal MRI examinations, who underwent complete surgical resection and confirmed pathological diagnosis of rectal cancer. Among them, patients from Qingdao West Coast Hospital were assigned as the training group (n=172), while patients from other hospitals were assigned as the external validation group (n=94). Regions of interest (ROIs) were delineated, and image features were extracted and dimensionally reduced using the Least Absolute Shrinkage and Selection Operator (LASSO). Eight machine algorithms were used to construct the models, and the diagnostic performance of the models was evaluated and compared using receiver operating characteristic (ROC) curves and the area under the curve (AUC), as well as clinical utility assessment using decision curve analysis (DCA). Results A total of 1197 features were extracted, and after feature selection and dimension reduction, 11 image features related to TB grading were obtained. Among the eight algorithm models, the support vector machine (SVM) algorithm achieved the best diagnostic performance, with accuracy, sensitivity, and specificity of 0.826, 0.949, and 0.723 in the training group, and 0.713, 0.579, and 0.804 in the validation group, respectively. DCA demonstrated the clinical utility of this radiomics model. Conclusion The radiomics model based on MR T2WI can provide an effective and noninvasive method for preoperative TB grading assessment in patients with rectal cancer.
Collapse
Affiliation(s)
- Xueting Qu
- Department of Radiology, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China
| | - Liang Zhang
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Weina Ji
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Jizheng Lin
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guohua Wang
- Department of Radiology, Qingdao Municipal Hospital, Qingdao University, Qingdao, Shandong, China
| |
Collapse
|
6
|
Nougaret S, Rousset P, Lambregts DMJ, Maas M, Gormly K, Lucidarme O, Brunelle S, Milot L, Arrivé L, Salut C, Pilleul F, Hordonneau C, Baudin G, Soyer P, Brun V, Laurent V, Savoye-Collet C, Petkovska I, Gerard JP, Cotte E, Rouanet P, Catalano O, Denost Q, Tan RB, Frulio N, Hoeffel C. MRI restaging of rectal cancer: The RAC (Response-Anal canal-CRM) analysis joint consensus guidelines of the GRERCAR and GRECCAR groups. Diagn Interv Imaging 2023; 104:311-322. [PMID: 36949002 DOI: 10.1016/j.diii.2023.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/09/2023] [Indexed: 03/18/2023]
Abstract
PURPOSE To develop guidelines by international experts to standardize data acquisition, image interpretation, and reporting in rectal cancer restaging with magnetic resonance imaging (MRI). MATERIALS AND METHODS Evidence-based data and experts' opinions were combined using the RAND-UCLA Appropriateness Method to attain consensus guidelines. Experts provided recommendations for reporting template and protocol for data acquisition were collected; responses were analysed and classified as "RECOMMENDED" versus "NOT RECOMMENDED" (if ≥ 80% consensus among experts) or uncertain (if < 80% consensus among experts). RESULTS Consensus regarding patient preparation, MRI sequences, staging and reporting was attained using the RAND-UCLA Appropriateness Method. A consensus was reached for each reporting template item among the experts. Tailored MRI protocol and standardized report were proposed. CONCLUSION These consensus recommendations should be used as a guide for rectal cancer restaging with MRI.
Collapse
Affiliation(s)
- Stephanie Nougaret
- Department of Radiology IRCM, Montpellier Cancer Research Institute, 34000 Montpellier, France; INSERM, U1194, University of Montpellier, 34295, Montpellier, France.
| | - Pascal Rousset
- Department of Radiology, CHU Lyon-Sud, EMR 3738 CICLY, Université Claude-Bernard Lyon 1, 69495 Pierre-Benite, France
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, 1006 BE, Amsterdam, the Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, 1006 BE, Amsterdam, the Netherlands
| | - Kirsten Gormly
- Jones Radiology, Kurralta Park, 5037, Australia; University of Adelaide, North Terrace, Adelaide, South Australia 5000, Australia
| | - Oliver Lucidarme
- Department of Radiology, Pitié-Salpêtrière Hospital, AP-HP, 75013 Paris, France; LIB, INSERM, CNRS, UMR7371-U1146, Sorbonne Université, 75013 Paris, France
| | - Serge Brunelle
- Department of Radiology, Institut Paoli-Calmettes, 13009 Marseille, France
| | - Laurent Milot
- Department of Diagnostic and Interventional Radiology, Hôpital Edouard Herriot, Hospices Civils de Lyon, University of Lyon, 69003 Lyon, France
| | - Lionel Arrivé
- Department of Radiology, Hôpital Saint-Antoine, AP-HP, 75012 Paris, France; Sorbonne Université, 75013 Paris, France
| | - Celine Salut
- CHU de Bordeaux, Department of Radiology, Université de Bordeaux, 33000 Bordeaux, France
| | - Franck Pilleul
- Department of Radiology, Centre Léon Bérard, Lyon, France Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, 69621, Lyon, France
| | | | - Guillaume Baudin
- Department of Radiology, Centre Antoine Lacassagne, 06100 Nice, France
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, AP-HP, 75014 Paris, France; Université Paris Cité, 75006 Paris, France
| | - Vanessa Brun
- Department of Radiology, CHU Hôpital Pontchaillou, 35000 Rennes, France
| | - Valérie Laurent
- Department of Radiology, Nancy University Hospital, Université de Lorraine, 54500 Vandoeuvre-lès-Nancy, France
| | | | - Iva Petkovska
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jean-Pierre Gerard
- Department of Radiotherapy, Centre Antoine Lacassagne, 06000 Nice, France
| | - Eddy Cotte
- Department of Digestive Surgery, Hospices Civils de Lyon, Lyon Sud University Hospital, 69310 Pierre Bénite, France; Lyon 1 Claude Bernard University, 69100 Villeurbanne, France
| | - Philippe Rouanet
- Department of Surgery, Institut Régional du Cancer de Montpellier, Montpellier Cancer Research Institute, INSERM U1194, University of Montpellier, 34295, Montpellier, France
| | - Onofrio Catalano
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
| | - Quentin Denost
- Department of Digestive Surgery, Hôpital Haut-Lévèque, Université de Bordeaux, 33000 Bordeaux, France
| | - Regina Beets Tan
- Department of Radiology, The Netherlands Cancer Institute, 1006 BE, Amsterdam, the Netherlands
| | - Nora Frulio
- CHU de Bordeaux, Department of Radiology, Université de Bordeaux, 33000 Bordeaux, France
| | - Christine Hoeffel
- Department of Radiology, Hôpital Robert Debré & CRESTIC, URCA, 51092 Reims, France
| |
Collapse
|
7
|
Di Costanzo G, Ascione R, Ponsiglione A, Tucci AG, Dell’Aversana S, Iasiello F, Cavaglià E. Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:406-421. [PMID: 37455833 PMCID: PMC10344900 DOI: 10.37349/etat.2023.00142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 03/01/2023] [Indexed: 07/18/2023] Open
Abstract
Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.
Collapse
Affiliation(s)
- Giuseppe Di Costanzo
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Raffaele Ascione
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Anna Giacoma Tucci
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Serena Dell’Aversana
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Francesca Iasiello
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Enrico Cavaglià
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| |
Collapse
|
8
|
Wang KX, Yu J, Xu Q. Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer. BMC Med Imaging 2023; 23:77. [PMID: 37291527 PMCID: PMC10249234 DOI: 10.1186/s12880-023-01027-0] [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: 11/27/2022] [Accepted: 05/23/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND To explore the potential of histogram analysis (HA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the identification of extramural venous invasion (EMVI) in rectal cancer patients. METHODS This retrospective study included preoperative images of 194 rectal cancer patients at our hospital between May 2019 and April 2022. The postoperative histopathological examination served as the reference standard. The mean values of DCE-MRI quantitative perfusion parameters (Ktrans, Kep and Ve) and other HA features calculated from these parameters were compared between the pathological EMVI-positive and EMVI-negative groups. Multivariate logistic regression analysis was performed to establish the prediction model for pathological EMVI-positive status. Diagnostic performance was assessed and compared using the receiver operating characteristic (ROC) curve. The clinical usefulness of the best prediction model was further measured with patients with indeterminate MRI-defined EMVI (mrEMVI) score 2(possibly negative) and score 3 (probably positive). RESULTS The mean values of Ktrans and Ve in the EMVI-positive group were significantly higher than those in the EMVI-negative group (P = 0.013 and 0.025, respectively). Significant differences in Ktrans skewness, Ktrans entropy, Ktrans kurtosis, and Ve maximum were observed between the two groups (P = 0.001,0.002, 0.000, and 0.033, respectively). The Ktrans kurtosis and Ktrans entropy were identified as independent predictors for pathological EMVI. The combined prediction model had the highest area under the curve (AUC) at 0.926 for predicting pathological EMVI status and further reached the AUC of 0.867 in subpopulations with indeterminate mrEMVI scores. CONCLUSIONS Histogram Analysis of DCE-MRI Ktrans maps may be useful in preoperative identification of EMVI in rectal cancer, particularly in patients with indeterminate mrEMVI scores.
Collapse
Affiliation(s)
- Ke-Xin Wang
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Gulou District, 300 Guangzhou Rd, Nanjing, 210029, Jiangsu, China
| | - Jing Yu
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Gulou District, 300 Guangzhou Rd, Nanjing, 210029, Jiangsu, China
| | - Qing Xu
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Gulou District, 300 Guangzhou Rd, Nanjing, 210029, Jiangsu, China.
| |
Collapse
|
9
|
Chiloiro G, Cusumano D, Romano A, Boldrini L, Nicolì G, Votta C, Tran HE, Barbaro B, Carano D, Valentini V, Gambacorta MA. Delta Radiomic Analysis of Mesorectum to Predict Treatment Response and Prognosis in Locally Advanced Rectal Cancer. Cancers (Basel) 2023; 15:3082. [PMID: 37370692 DOI: 10.3390/cancers15123082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/23/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND The aim of this study is to evaluate the delta radiomics approach based on mesorectal radiomic features to develop a model for predicting pathological complete response (pCR) and 2-year disease-free survival (2yDFS) in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (nCRT). METHODS Pre- and post-nCRT MRIs of LARC patients treated at a single institution from May 2008 to November 2016 were retrospectively collected. Radiomic features were extracted from the GTV and mesorectum. The Wilcoxon-Mann-Whitney test and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the features in predicting pCR and 2yDFS. RESULTS Out of 203 LARC patients, a total of 565 variables were evaluated. The best performing pCR prediction model was based on two GTV features with an AUC of 0.80 in the training set and 0.69 in the validation set. The best performing 2yDFS prediction model was based on one GTV and two mesorectal features with an AUC of 0.79 in the training set and 0.70 in the validation set. CONCLUSIONS The results of this study suggest a possible role for delta radiomics based on mesorectal features in the prediction of 2yDFS in patients with LARC.
Collapse
Affiliation(s)
- Giuditta Chiloiro
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Davide Cusumano
- Mater Olbia Hospital, Strada Statale Orientale Sarda 125, 07026 Olbia, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Giuseppe Nicolì
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Claudio Votta
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Huong Elena Tran
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Brunella Barbaro
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Davide Carano
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168 Rome, Italy
| | | |
Collapse
|
10
|
Jayaprakasam VS, Alvarez J, Omer DM, Gollub MJ, Smith JJ, Petkovska I. Watch-and-Wait Approach to Rectal Cancer: The Role of Imaging. Radiology 2023; 307:e221529. [PMID: 36880951 PMCID: PMC10068893 DOI: 10.1148/radiol.221529] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/09/2022] [Accepted: 11/14/2022] [Indexed: 03/08/2023]
Abstract
The diagnosis and treatment of rectal cancer have evolved dramatically over the past several decades. At the same time, its incidence has increased in younger populations. This review will inform the reader of advances in both diagnosis and treatment. These advances have led to the watch-and-wait approach, otherwise known as nonsurgical management. This review briefly outlines changes in medical and surgical treatment, advances in MRI technology and interpretation, and landmark studies or trials that have led to this exciting juncture. Herein, the authors delve into current state-of-the-art methods to assess response to treatment with MRI and endoscopy. Currently, these methods for avoiding surgery can be used to detect a complete clinical response in as many as 50% of patients with rectal cancer. Finally, the limitations of imaging and endoscopy and future challenges will be discussed.
Collapse
Affiliation(s)
- Vetri Sudar Jayaprakasam
- From the Departments of Radiology (V.S.J., M.J.G., I.P.) and Surgery
(J.A., D.M.O., J.J.S.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave,
Box 29, New York, NY 10065
| | - Janet Alvarez
- From the Departments of Radiology (V.S.J., M.J.G., I.P.) and Surgery
(J.A., D.M.O., J.J.S.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave,
Box 29, New York, NY 10065
| | - Dana M. Omer
- From the Departments of Radiology (V.S.J., M.J.G., I.P.) and Surgery
(J.A., D.M.O., J.J.S.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave,
Box 29, New York, NY 10065
| | - Marc J. Gollub
- From the Departments of Radiology (V.S.J., M.J.G., I.P.) and Surgery
(J.A., D.M.O., J.J.S.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave,
Box 29, New York, NY 10065
| | - J. Joshua Smith
- From the Departments of Radiology (V.S.J., M.J.G., I.P.) and Surgery
(J.A., D.M.O., J.J.S.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave,
Box 29, New York, NY 10065
| | - Iva Petkovska
- From the Departments of Radiology (V.S.J., M.J.G., I.P.) and Surgery
(J.A., D.M.O., J.J.S.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave,
Box 29, New York, NY 10065
| |
Collapse
|
11
|
Tian L, Li N, Xie D, Li Q, Zhou C, Zhang S, Liu L, Huang C, Liu L, Lai S, Wang Z. Extramural vascular invasion nomogram before radical resection of rectal cancer based on magnetic resonance imaging. Front Oncol 2023; 12:1006377. [PMID: 36968215 PMCID: PMC10034136 DOI: 10.3389/fonc.2022.1006377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/28/2022] [Indexed: 03/11/2023] Open
Abstract
PurposeThis study verified the value of magnetic resonance imaging (MRI) to construct a nomogram to preoperatively predict extramural vascular invasion (EMVI) in rectal cancer using MRI characteristics.Materials and methodsThere were 55 rectal cancer patients with EMVI and 49 without EMVI in the internal training group. The external validation group consisted of 54 rectal cancer patients with EMVI and 55 without EMVI. High-resolution rectal T2WI, pelvic diffusion-weighted imaging (DWI) sequences, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were used. We collected the following data: distance between the lower tumor margin and the anal margin, distance between the lower tumor margin and the anorectal ring, tumor proportion of intestinal wall, mrT stage, maximum tumor diameter, circumferential resection margin, superior rectal vein width, apparent diffusion coefficient (ADC), T2WI EMVI score, DWI and DCE-MRI EMVI scores, demographic information, and preoperative serum tumor marker data. Logistic regression analyses were used to identify independent risk factors of EMVI. A nomogram prediction model was constructed. Receiver operating characteristic curve analysis verified the predictive ability of the nomogram. P < 0.05 was considered significant.ResultTumor proportion of intestinal wall, superior rectal vein width, T2WI EMVI score, and carbohydrate antigen 19-9 were significant independent predictors of EMVI in rectal cancer and were used to create the model. The areas under the receiver operating characteristic curve, sensitivities, and specificities of the nomogram were 0.746, 65.45%, and 83.67% for the internal training group, respectively, and 0.780, 77.1%, and 71.3% for the external validation group, respectively.Data conclusionA nomogram including MRI characteristics can predict EMVI in rectal cancer preoperatively and provides a valuable reference to formulate individualized treatment plans and predict prognosis.
Collapse
Affiliation(s)
- Lianfen Tian
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Ningqin Li
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Dong Xie
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Qiang Li
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Chuanji Zhou
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Shilai Zhang
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Lijuan Liu
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Caiyun Huang
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Lu Liu
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
| | - Shaolu Lai
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
- *Correspondence: Zheng Wang, ; Shaolu Lai,
| | - Zheng Wang
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China
- *Correspondence: Zheng Wang, ; Shaolu Lai,
| |
Collapse
|
12
|
Kim HR, Kim SH, Nam KH. Association between Dynamic Contrast-Enhanced MRI Parameters and Prognostic Factors in Patients with Primary Rectal Cancer. Curr Oncol 2023; 30:2543-2554. [PMID: 36826155 PMCID: PMC9955503 DOI: 10.3390/curroncol30020194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/09/2023] [Accepted: 02/18/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND To evaluate the association between perfusion parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with prognostic factors in primary rectal cancer patients. METHODS A sample of 51 patients with pathologically proven rectal adenocarcinoma through surgery were retrospectively enrolled. All the patients underwent preoperative DCE-MRI including 3D-spoiled gradient echo. Two radiologists determined the tumor border after radiologic-pathologic correlation and drew regions of interest. The perfusion parameters, including the volume transfer constant (Ktrans), were calculated under the extended Toft model. The prognostic factors included TN stage, circumferential resection margin, extramural venous invasion, Kirsten-ras mutation, tumor size, carcinoembryonic antigen, and tumor differentiation. The association was assessed via correlation or t-test. For significant prognostic factors, receiver operating characteristic (ROC) curve analyses were performed to estimate the diagnostic predictive values. RESULTS Ktrans only showed a significant difference according to tumor differentiation, between the well-differentiated (n = 6) and moderately differentiated (n = 45) groups (0.127 ± 0.032, 0.084 ± 0.036, p = 0.036). The AUC was 0.838 (95% CI, 0.702-0.929), and the estimated accuracy, sensitivity, and specificity were 87%, 90%, and 60%, respectively. CONCLUSIONS Ktrans showed a significant difference based on tumor differentiation, which may be conducive to prediction of prognosis in primary rectal cancer.
Collapse
Affiliation(s)
- Hye Ri Kim
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan 48108, Republic of Korea
| | - Seung Ho Kim
- Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan 48108, Republic of Korea
- Correspondence: ; Tel.: +82-51-797-0382
| | - Kyung Han Nam
- Department of Pathology, Inje University College of Medicine, Haeundae Paik Hospital, Busan 48108, Republic of Korea
| |
Collapse
|
13
|
Whole-tumor amide proton transfer-weighted imaging histogram analysis to predict pathological extramural venous invasion in rectal adenocarcinoma: a preliminary study. Eur Radiol 2023:10.1007/s00330-023-09418-1. [PMID: 36700956 DOI: 10.1007/s00330-023-09418-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/19/2022] [Accepted: 01/01/2023] [Indexed: 01/27/2023]
Abstract
OBJECTIVES To evaluate amide proton transfer-weighted (APTw)-derived whole-tumor histogram analysis parameters in predicting pathological extramural venous invasion (pEMVI) positive status of rectal adenocarcinoma (RA). METHODS Preoperative MR including APTw imaging of 125 patients with RA (mean 61.4 ± 11.6 years) were retrospectively analyzed. Two radiologists reviewed each case's EMVI status based on the MR-based modified 5-point scale system with conventional MR images. The APTw histogram parameters of primary tumors were obtained automatically using whole-tumor volume histogram analysis. The independent risk factors markedly correlated with pEMVI-positive status were assessed using univariate and multivariate logistic regression analyses. Diagnosis performance was assessed by receiver operating characteristic curve (ROC) analysis. The AUCs were compared using the Delong method. RESULTS Univariate analysis demonstrated that MR-tumor (T) stage, MR-lymph node (N) stage, APTw-10%, APTw-90%, interquartile range, APTw-minimum, APTw-maximum, APTw-mean, APTw-median, entropy, kurtosis, mean absolute deviation (MAD), and robust MAD were significantly related to pEMVI-positive status (all p < 0.05). Multivariate analysis demonstrated that MR-T stage (OR = 4.864, p = 0.018), MR-N stage (OR = 4.967, p = 0.029), interquartile range (OR = 0.892, p = 0.037), APT-minimum (OR = 1.046, p = 0.031), entropy (OR = 11.604, p = 0.006), and kurtosis (OR = 1.505, p = 0.007) were the independent risk factors enabling prediction of pEMVI-positive status. The AUCs for diagnostic ability of conventional MRI assessment, the APTw histogram model, and the combined model (including APTw histogram and clinical variables) were 0.785, 0.853, and 0.918, respectively. The combined model outperformed the APTw histogram model (p = 0.013) and the conventional MRI assessment (p = 0.006). CONCLUSIONS Whole-tumor histogram analysis of APTw images combined with clinical factors showed better diagnosis efficiency in predicting EMVI involvement in RA. KEY POINTS • Rectal adenocarcinomas with pEMVI-positive status are typically associated with higher APTw-SI values. • APTw-minimum, interquartile range, entropy, kurtosis, MR-T stage, and MR-N stage are the independent risk factors for EMVI involvement. • The best prediction for EMVI involvement was obtained with a combined model of APTw histogram and clinical variables (area under the curve, 0.918).
Collapse
|
14
|
Fang J, Sun W, Wu D, Pang P, Guo X, Yu C, Lu W, Tang G. Value of texture analysis based on dynamic contrast-enhanced magnetic resonance imaging in preoperative assessment of extramural venous invasion in rectal cancer. Insights Imaging 2022; 13:179. [DOI: 10.1186/s13244-022-01316-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 10/19/2022] [Indexed: 11/24/2022] Open
Abstract
Abstract
Objective
Accurate preoperative assessment of extramural vascular invasion (EMVI) is critical for the treatment and prognosis of rectal cancer. The aim of our research was to develop an assessment model by texture analysis for preoperative prediction of EMVI.
Materials and methods
This study enrolled 44 rectal patients as train cohort, 7 patients as validation cohort and 18 patients as test cohort. A total of 236 texture features from DCE MR imaging quantitative parameters were extracted for each patient (59 features of Ktrans, Kep, Ve and Vp), and key features were selected by least absolute shrinkage and selection operator regression (LASSO). Finally, clinical independent risk factors, conventional MRI assessment, and T-score were incorporated to construct an assessment model using multivariable logistic regression.
Results
The T-score calculated using the 4 selected key features were significantly correlated with EMVI (p < 0.010). The area under the receiver operating characteristic curve (AUC) was 0.797 for discriminating between EMVI-positive and EMVI-negative patients with a sensitivity of 88.2% and specificity of 70.4%. The conventional MRI assessment of EMVI had a sensitivity of 23.53% and a specificity of 96.30%. The assessment model showed a greatly improved performance with an AUC of 0.954 (sensitivity, 88.2%; specificity, 92.6%) in train cohort, 0.833 (sensitivity, 66.7%; specificity, 100%) in validation cohort and 0.877 in test cohort, respectively.
Conclusions
The assessment model showed an excellent performance in preoperative assessment of EMVI. It demonstrates strong potential for improving the accuracy of EMVI assessment and provide a reliable basis for individualized treatment decisions.
Collapse
|
15
|
Multiregional Radiomic Signatures Based on Functional Parametric Maps from DCE-MRI for Preoperative Identification of Estrogen Receptor and Progesterone Receptor Status in Breast Cancer. Diagnostics (Basel) 2022; 12:diagnostics12102558. [PMID: 36292247 PMCID: PMC9601361 DOI: 10.3390/diagnostics12102558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/01/2022] [Accepted: 10/07/2022] [Indexed: 11/16/2022] Open
Abstract
Radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been used for breast estrogen receptor (ER) and progesterone receptor (PR) status evaluation. However, the radiomic features of peritumoral regions were not thoroughly analyzed. This study aimed to establish and validate the multiregional radiomic signatures (RSs) for the preoperative identification of the ER and PR status in breast cancer. A total of 443 patients with breast cancer were divided into training (n = 356) and validation (n = 87) sets. Radiomic features were extracted from intra- and peritumoral regions on six functional parametric maps from DCE-MRI. A two-sample t-test, least absolute shrinkage and selection operator regression, and stepwise were used for feature selections. Three RSs for predicting the ER and PR status were constructed using a logistic regression model based on selected intratumoral, peritumoral, and combined intra- and peritumoral radiomic features. The area under the receiver operator characteristic curve (AUC) was used to assess the discriminative performance of three RSs. The AUCs of intra- and peritumoral RSs for identifying the ER status were 0.828/0.791 and 0.755/0.733 in the training and validation sets, respectively. For predicting the PR status, intra- and peritumoral RSs resulted in AUCs of 0.816/0.749 and 0.806/0.708 in the training and validation sets, respectively. Multiregional RSs achieved the best AUCs among three RSs for evaluating the ER (0.851 and 0.833) and PR (0.848 and 0.763) status. In conclusion, multiregional RSs based on functional parametric maps from DCE-MRI showed promising results for preoperatively evaluating the ER and PR status in breast cancer patients. Further studies using a larger cohort from multiple centers are necessary to confirm the reliability of the established models before clinical application.
Collapse
|
16
|
Ma L, Lian S, Liu H, Meng T, Zeng W, Zhong R, Zhong L, Xie C. Diagnostic performance of synthetic magnetic resonance imaging in the prognostic evaluation of rectal cancer. Quant Imaging Med Surg 2022; 12:3580-3591. [PMID: 35782274 PMCID: PMC9246756 DOI: 10.21037/qims-22-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/22/2022] [Indexed: 11/06/2022]
Abstract
Background Numerous factors are related to the prognosis of rectal cancer, including T stage, N stage, metastasis, extramural venous invasion (EMVI), circumferential resection margin (CRM), and tumor differentiation. However, it is still a challenge to precisely evaluate them before therapy; therefore, we investigate whether synthetic magnetic resonance imaging and apparent diffusion coefficient (ADC) values could help predict the prognostic factors of rectal cancer. Methods Eighty-seven patients (55 men and 32 women; mean age, 59±11 years) with pathologically confirmed rectal cancer were enrolled. Preoperative quantitative metrics, including T1, T2, proton density (PD), and ADC values, were measured with diffusion-weighted imaging (DWI) acquired by a single-shot echo-planar sequence and synthetic magnetic resonance imaging acquired by a multi-dynamic multi-echo sequence at 3.0 T, in patients with rectal cancer by two radiologists. We evaluated the diagnostic performance of synthetic magnetic resonance imaging using the independent sample t-test or Mann-Whitney U test and receiver operating characteristic (ROC) curve and multivariate logistic regression analyses and compared the area under the ROC curve of quantitative values using the DeLong test. Results The T2 and PD values showed a significant reduction among patients with poor differentiation and lymph node metastasis in rectal cancer. The area under the ROC curve values of T2 and PD values for predicting magnetic resonance imaging N stage and differentiation were 0.734, 0.682, and 0.673, 0.686, respectively. Moreover, combining T2 and PD values for magnetic resonance imaging N stage slightly improved the area under the ROC curve value of 0.774 (95% CI, 0.673-0.876). In the present study, the ADC and T1 values were not significant in the differentiation or clinical stage of rectal cancer (RC). Conclusions Quantitative T2 and PD values obtained by synthetic magnetic resonance imaging might be used for evaluating prognostic factors of rectal cancer noninvasively. Furthermore, combining T2 and PD values further improved the diagnostic performance of magnetic resonance imaging N staging in rectal cancer. The ADC and T1 values were not significant in the differentiation or clinical stage of RC.
Collapse
Affiliation(s)
- Lidi Ma
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Shanshan Lian
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Huimin Liu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Tiebao Meng
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Weilong Zeng
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Rui Zhong
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Linchang Zhong
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| | - Chuanmiao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
17
|
Zhu K, Chen Z, Cui L, Zhao J, Liu Y, Cao J. The Preoperative Diagnostic Performance of Multi-Parametric Quantitative Assessment in Rectal Carcinoma: A Preliminary Study Using Synthetic Magnetic Resonance Imaging. Front Oncol 2022; 12:682003. [PMID: 35707367 PMCID: PMC9190242 DOI: 10.3389/fonc.2022.682003] [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: 03/17/2021] [Accepted: 04/19/2022] [Indexed: 12/24/2022] Open
Abstract
Objective Synthetic MRI (SyMRI) can reconstruct different contrast-weighted images(T1, T2, PD) and has shorter scan time, easier post-processing and better reproducibility. Some studies have shown splendid correlation with conventional mapping techniques and no degradation in the quality of syMRI images compared with conventional MRI. It is crucial to select an individualized treatment plan based on the preoperative images of rectal carcinoma (RC). We tried to explore the feasibility of syMRI on T, N stage and extramural vascular invasion (EMVI) of rectal cancer. Materials and Methods A total of 100 patients (37 females and 63 males) diagnosed with rectal carcinoma were enrolled. All the patients underwent preoperative pelvic MR examinations including conventional MR sequence and synthetic MRI. Two radiologists evaluated the MRI findings of each rectal carcinoma and EMVI score in consensus. The values for T1, T2 relaxation times and PD value were measured in tumor(ROI-1) and pararectal fat space(ROI-2) and analyzed independently. A receiver operating characteristic (ROC) analysis was performed. Correlations between the T1, T2 and PD values and EMVI score were also evaluated. Results Compared with the normal rectal wall, the values of T1 and T2 relaxation times of the tumor were significantly higher (P <0.001). There was no statistically significant difference in the PD value (P >0.05). As for ROI, the ROI of pararectal fat space(ROI-2) had better significance than rectal cancer lesion (ROI-1). T2 value of ROI-1 and T1 value of ROI-2 were higher in the pEMVI positive group than in the negative group (P=0.002 and 0.001) and T1 value of ROI-2 had better performance with an AUC of 0.787, (95% CI:0.693- 0.882). T1 value, T2 value and PD value from ROI-2 were effective for both T and N stage of rectal cancer. High-grade pathological stage had showed higher T1 value (PT stage=0.013,PN stage=0.035), lower T2 value (PT stage=0.025,PN stage=0.034) and lower PD value (PT stage=0.017). We also enrolled the characteristics with P < 0.05 in the combined model which had better diagnostic efficacy. A significant positive correlation was found between the T1 value of pararectal fat space(ROI-2) and EMVI score (r value = 0.519, P<0.001). The T2 value(r=0.213,P=0.049) and PD value(r=0.354,P=0.001) from ROI-1 was correlated with EMVI score. Correlation analysis did not show any significant associations between T2 value of tumor, T2, PD values of pararectal fat space and EMVI scores. Conclusion Synthetic MRI can provide multi-parameter quantitative image maps with a easier measurement and slightly shorter acquisition time compared with conventional MRI. The measurement of multi-parametric quantitative values contributes to diagnosing the tumor and evaluating T stage, N stage and EMVI. It has the potential to be used as a preoperative diagnostic and grading technique in rectal carcinoma.
Collapse
Affiliation(s)
- Kexin Zhu
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Zhicheng Chen
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Lingling Cui
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jinli Zhao
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Yi Liu
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jibin Cao
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| |
Collapse
|
18
|
Quantitative Evaluation of Extramural Vascular Invasion of Rectal Cancer by Dynamic Contrast-Enhanced Magnetic Resonance Imaging. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:3038308. [PMID: 35694706 PMCID: PMC9173987 DOI: 10.1155/2022/3038308] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 11/17/2022]
Abstract
This study was carried out to explore the preoperative predictive value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in extramural vascular invasion (EMVI) in patients with rectal cancer. 124 patients with rectal cancer were randomly divided into two groups, with 62 groups in each group. One group used conventional magnetic resonance imaging (MRI) and was recorded as the control group. The other group used DCE-MRI and was recorded as the experimental group. The diagnostic value was evaluated by comparing the MRI quantitative parameters of EMVI positive and EMVI negative patients, as well as the area under the curve (AUC) of the receiver operating characteristic curve (ROC), diagnostic sensitivity, and specificity of the two groups. The results showed that the Ktrans and Ve values of EMVI positive patients in the experimental group and the control group were 1.08 ± 0.97 and 1.03 ± 0.93, and 0.68 ± 0.29 and 0.65 ± 0.31, respectively, which were significantly higher than those in EMVI negative patients (P < 0.05). The AUC of EMVI diagnosis in the experimental group and the control group were 0.732 and 0.534 (P < 0.05), the sensitivity was 0.913 and 0.765 (P < 0.05), and the specificity was 0.798 and 0.756 (P > 0.05), respectively. In conclusion, DCE-MRI has a higher diagnostic value than conventional MRI in predicting EMVI in patients with rectal cancer, which was worthy of further clinical promotion.
Collapse
|
19
|
Ao W, Zhang X, Yao X, Zhu X, Deng S, Feng J. Preoperative prediction of extramural venous invasion in rectal cancer by dynamic contrast-enhanced and diffusion weighted MRI: a preliminary study. BMC Med Imaging 2022; 22:78. [PMID: 35484509 PMCID: PMC9052632 DOI: 10.1186/s12880-022-00810-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/22/2022] [Indexed: 12/29/2022] Open
Abstract
Background To explore the value of the quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters in assessing preoperative extramural venous invasion (EMVI) in rectal cancer. Methods Eighty-two rectal adenocarcinoma patients who had underwent MRI preoperatively were enrolled in this study. The differences in quantitative DCE-MRI and DWI parameters including Krans, Kep and ADC values were analyzed between MR-detected EMVI (mrEMVI)-positive and -negative groups. Multivariate logistic regression analysis was performed to build the combined prediction model for pathologic EMVI (pEMVI) with statistically significant quantitative parameters. The performance of the model for predicting pEMVI was evaluated using receiver operating characteristic (ROC) curve. Results Of the 82 patients, 24 were mrEMVI-positive and 58 were -negative. In the mrEMVI positive group, the Ktrans and Kep values were significantly higher than those in the mrEMVI negative group (P < 0.01), but the ADC values were significantly lower (P < 0.01). A negative correlation was observed between the Ktrans vs ADC values and Kep vs ADC values in patients with rectal cancer. Among the four quantitative parameters, Ktrans and ADC value were independently associated with mrEMVI by multivariate logistic regression analysis. ROC analysis showed that combined prediction model based on quantitative DCE parameters and ADC values had a good prediction efficiency for pEMVI in rectal cancer. Conclusion The quantitative DCE-MRI parameters, Krans, Kep and ADC values play important role in predicting EMVI of rectal cancer, with Ktrans and ADC value being independent predictors of EMVI in rectal cancer.
Collapse
Affiliation(s)
- Weiqun Ao
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Xian Zhang
- Departments of Radiology, Zhuji Affiliated Hospital of Shaoxing University, Zhuji People's Hospital, No. 9 Jianmin Road, Zhuji, 311800, Zhejiang Province, China
| | - Xiuzhen Yao
- Department of Ultrasound, Shanghai Putuo District People's Hospital, Shanghai, China
| | - Xiandi Zhu
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Shuitang Deng
- Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Jianju Feng
- Departments of Radiology, Zhuji Affiliated Hospital of Shaoxing University, Zhuji People's Hospital, No. 9 Jianmin Road, Zhuji, 311800, Zhejiang Province, China.
| |
Collapse
|
20
|
Zhang Y, Peng J, Liu J, Ma Y, Shu Z. Preoperative Prediction of Perineural Invasion Status of Rectal Cancer Based on Radiomics Nomogram of Multiparametric Magnetic Resonance Imaging. Front Oncol 2022; 12:828904. [PMID: 35480114 PMCID: PMC9036372 DOI: 10.3389/fonc.2022.828904] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives To compare the predictive performance of different radiomics signatures from multiparametric magnetic resonance imaging (mpMRI), including four sequences when used individually or combined, and to establish and validate an optimal nomogram for predicting perineural invasion (PNI) in rectal cancer (RC) patients. Methods Our retrospective study included 279 RC patients without preoperative antitumor therapy (194 in the training dataset and 85 in the test dataset) who underwent preoperative mpMRI scan between January 2017 and January 2021. Among them, 72 cases were PNI-positive. Then, clinical and radiological variables were collected, including carcinoembryonic antigen (CEA), radiological tumour stage (T1-4), lymph node stage (N0-2) and so on. Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), apparent diffusion coefficient (ADC), and enhanced T1WI (T1CE) sequences. The clinical model was constructed by integrating the final selected clinical and radiological variables. The radiomics signatures included four single-sequence signatures and one fusion signature were built using the respective remaining optimized features. And the nomogram was constructed based on the independent predictors by using multivariable logistic regression. The area under curve (AUC), DeLong test, calibration curve, and decision curve analysis (DCA) were used to evaluate the performance. Results Ultimately, 20 radiomics features were retained from the four sequences—T1WI (n = 4), T2WI (n = 5), ADC (n = 5), and T1CE (n = 6)—to construct four single-sequence radiomics signatures and one fusion radiomics signature. The fusion radiomics signature performed better than four single-sequence radiomics signatures and clinical model (AUCs of 0.835 and 0.773 vs. 0.680-0.737 and 0.666-0.709 in the training and test datasets, respectively). The nomogram constructed by incorporating CEA, tumour stage and rad-score performed best, with AUCs of 0.869 and 0.864 in the training and test datasets, respectively. Delong test showed that the nomogram was significantly different from the clinical model and four single-sequence radiomics signatures (P < 0.05). Moreover, calibration curves demonstrated good agreement, and DCA highlighted benefits of the nomogram. Conclusions The comprehensive nomogram can preoperatively and noninvasively predict PNI status, provide a convenient and practical tool for treatment strategy, and help optimize individualized clinical decision-making in RC patients.
Collapse
Affiliation(s)
- Yang Zhang
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China
| | - Jiaxuan Peng
- Medical College, Jinzhou Medical University, Jinzhou, China
| | - Jing Liu
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China
| | - Yanqing Ma
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China
| | - Zhenyu Shu
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, China
- *Correspondence: Zhenyu Shu,
| |
Collapse
|
21
|
Gao F, Shi B, Wang P, Wang C, Fang X, Dong J, Lin T. The Value of Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging Combined With Texture Analysis of Evaluating the Extramural Vascular Invasion in Rectal Adenocarcinoma. Front Oncol 2022; 12:813138. [PMID: 35311135 PMCID: PMC8927647 DOI: 10.3389/fonc.2022.813138] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 02/10/2022] [Indexed: 01/28/2023] Open
Abstract
Purpose This study aims to evaluate the value of 3.0T MRI Intravoxel Incoherent motion diffusion-weighted magnetic resonance imaging (IVIM-DWI) combined with texture analysis (TA) for evaluating extramural vascular invasion (EMVI) of rectal adenocarcinoma. Methods Ninety-six patients with pathologically confirmed rectal adenocarcinoma after surgical resections were collected. Patients were divided into the EMVI positive group (n=39) and the EMVI negative group (n=57). We measured the IVIM-DWI parameters and TA parameters of rectal adenocarcinoma. We compare the differences of the above parameters between the two groups and establish a prediction model through multivariate logistic regression analysis. the ROC curve was performed for parameters with each individual and in combination. Results ADC, D, D* value between the two groups were statistically significant (P= 0.015,0.031,0). Six groups of texture parameters were statistically significant between the two groups (P=0.007,0.037,0.011,0.005,0.007,0.002). Logistic regression prediction model shows that GLCM entropy_ALL DIRECTION_offset7_SD and D* are important independent predictors, and the AUC of the regression prediction model was 0.821, the sensitivity was 92.98%, the specificity was 61.54%, and the Yoden index was 0.5452. The AUC was significantly higher than that of other single parameters. Conclusion 3.0T MRI IVIM-DWI parameters combined with texture analysis can provide valuable information for EMVI evaluation of rectal adenocarcinoma before the operation.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Tingting Lin
- *Correspondence: Jiangning Dong, ; Tingting Lin,
| |
Collapse
|
22
|
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.
Collapse
|
23
|
Zhao L, Liang M, Yang Y, Xie L, Zhang H, Zhao X. The added value of full and reduced field-of-view apparent diffusion coefficient maps for the evaluation of extramural venous invasion in rectal cancer. Abdom Radiol (NY) 2022; 47:48-55. [PMID: 34665287 DOI: 10.1007/s00261-021-03319-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/07/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To investigate the added value of the quantitative analysis of full and reduced field-of-view apparent diffusion coefficient (fADC and rADC) maps for evaluating extramural venous invasion (EMVI) in rectal cancer. MATERIALS AND METHODS A total of 94 rectal cancer patients who underwent direct surgical resection were enrolled in this prospective study. The EMVI status of each patient was evaluated on T2-weighted imaging. The mean values of fADC and rADC within the whole tumor were obtained, and histogram parameters were also extracted. Multivariate binary logistic regression analysis was used to analyze independent predictors of EMVI and construct combined models. Receiver operating characteristic (ROC) curves were applied to assess the diagnostic performance. RESULTS The energy, skewness, total energy, and kurtosis of fADC map, and the energy and total energy of rADC map were significantly different between the EMVI-positive and EMVI-negative groups (all P < 0.05). Multivariate logistic regression analysis revealed that kurtosis of fADC and circumferential percentage of tumor were independent predictors of EMVI (odds ratio 1.684 and 2.647, P = 0.020 and 0.009). These two parameters combined with subjective evaluation demonstrated the superior diagnostic performance with the area under the ROC curve, sensitivity, specificity, and accuracy of 0.841 (95% CI 0.752-0.909), 0.739, 0.803, and 0.809, respectively. CONCLUSION Whole-tumor histogram analysis of ADC map could potentially provide additional information to improve the diagnostic efficiency for assessing EMVI in rectal cancer, which may be beneficial for treatment decision-making.
Collapse
Affiliation(s)
- Li Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yang Yang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | | | - Hongmei Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| |
Collapse
|
24
|
Lin X, Zhao S, Jiang H, Jia F, Wang G, He B, Jiang H, Ma X, Li J, Shi Z. A radiomics-based nomogram for preoperative T staging prediction of rectal cancer. Abdom Radiol (NY) 2021; 46:4525-4535. [PMID: 34081158 PMCID: PMC8435521 DOI: 10.1007/s00261-021-03137-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/17/2021] [Accepted: 05/21/2021] [Indexed: 12/15/2022]
Abstract
Purpose To investigate the value of a radiomics-based nomogram in predicting preoperative T staging of rectal cancer. Methods A total of 268 eligible rectal cancer patients from August 2012 to December 2018 were enrolled and allocated into two datasets: training (n = 188) and validation datasets (n = 80). Another set of 32 patients from January 2019 to July 2019 was included in a prospective analysis. Pretreatment T2-weighted images were used to radiomics features extraction. Feature selection and radiomics score (Rad-score) construction were performed through a least absolute shrinkage and selection operator regression analysis. The nomogram, which included Rad-scores and clinical factors, was built using multivariate logistic regression. Discrimination, calibration, and clinical utility were used to evaluate the performance of the nomogram. Results The Rad-score containing nine selected features was significantly related to T staging. Patients who had locally advanced rectal cancer (LARC) generally had higher Rad-scores than patients with early-stage rectal cancer. The nomogram incorporated Rad-scores and carcinoembryonic antigen levels and showed good discrimination, with an area under the curve (AUC) of 0.882 (95% confidence interval [CI] 0.835–0.930) in the training dataset and 0.846 (95% CI 0.757–0.936) in the validation dataset. The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. A prospective analysis demonstrated that the AUC of the nomogram to predict LARC was 0.859 (95% CI 0.730–0.987). Conclusion A radiomics-based nomogram is a novel method for predicting LARC and can provide support in clinical decision making. Supplementary Information The online version contains supplementary material available at 10.1007/s00261-021-03137-1.
Collapse
Affiliation(s)
- Xue Lin
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Sheng Zhao
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
| | - Fucang Jia
- Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Guisheng Wang
- Department of Radiology, the Third medical centre, Chinese PLA General Hospital, Beijing, China.
| | - Baochun He
- Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hao Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiao Ma
- Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jinping Li
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zhongxing Shi
- Department of Interventional Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| |
Collapse
|
25
|
Shu Z, Mao D, Song Q, Xu Y, Pang P, Zhang Y. Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer. Eur Radiol 2021; 32:1002-1013. [PMID: 34482429 DOI: 10.1007/s00330-021-08242-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/21/2021] [Accepted: 08/02/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To compare multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion (EMVI) in rectal cancer using different machine learning algorithms and to develop and validate the best diagnostic model. METHODS We retrospectively analyzed 317 patients with rectal cancer. Of these, 114 were EMVI positive and 203 were EMVI negative. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, diffusion-weighted imaging, and enhanced T1-weighted imaging of rectal cancer, followed by the dimension reduction of the features. Logistic regression, support vector machine, Bayes, K-nearest neighbor, and random forests algorithms were trained to obtain the radiomics signatures. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each radiomics signature. The best radiomics signature was selected and combined with clinical and radiological characteristics to construct a joint model for predicting EMVI. Finally, the predictive performance of the joint model was assessed. RESULTS The Bayes-based radiomics signature performed well in both the training set and the test set, with the AUCs of 0.744 and 0.738, sensitivities of 0.754 and 0.728, and specificities of 0.887 and 0.918, respectively. The joint model performed best in both the training set and the test set, with the AUCs of 0.839 and 0.835, sensitivities of 0.633 and 0.714, and specificities of 0.901 and 0.885, respectively. CONCLUSIONS The joint model demonstrated the best diagnostic performance for the preoperative prediction of EMVI in patients with rectal cancer. Hence, it can be used as a key tool for clinical individualized EMVI prediction. KEY POINTS • Radiomics features from magnetic resonance imaging can be used to predict extramural venous invasion (EMVI) in rectal cancer. • Machine learning can improve the accuracy of predicting EMVI in rectal cancer. • Radiomics can serve as a noninvasive biomarker to monitor the status of EMVI.
Collapse
Affiliation(s)
- Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Dewang Mao
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qiaowei Song
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yuyun Xu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Yang Zhang
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
| |
Collapse
|
26
|
Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27:5306-5321. [PMID: 34539134 PMCID: PMC8409167 DOI: 10.3748/wjg.v27.i32.5306] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/13/2021] [Accepted: 07/22/2021] [Indexed: 02/06/2023] Open
Abstract
The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.
Collapse
Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Francesco Verde
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Francesca Boccadifuoco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging, National Council of Research, Napoli 80131, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples 80131, Italy
| |
Collapse
|
27
|
Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer. Diagnostics (Basel) 2021; 11:diagnostics11081491. [PMID: 34441425 PMCID: PMC8395031 DOI: 10.3390/diagnostics11081491] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/06/2021] [Accepted: 08/11/2021] [Indexed: 12/22/2022] Open
Abstract
This study aimed to establish and validate a radiomics nomogram using the radiomics score (rad-score) based on multiregional diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) features combined with clinical factors for evaluating HER-2 2+ status of breast cancer. A total of 223 patients were retrospectively included. Radiomic features were extracted from multiregional DWI and ADC images. Based on the intratumoral, peritumoral, and combined regions, three rad-scores were calculated using the logistic regression model. Independent parameters were selected among clinical factors and combined rad-score (com-rad-score) using multivariate logistic analysis and used to construct a radiomics nomogram. The performance of the nomogram was evaluated using calibration, discrimination, and clinical usefulness. The areas under the receiver operator characteristic curve (AUCs) of intratumoral and peritumoral rad-scores were 0.824/0.763 and 0.794/0.731 in the training and validation cohorts, respectively. Com-rad-score achieved the highest AUC (0.860/0.790) among three rad-scores. ER status and com-rad-score were selected to establish the nomogram, which yielded good discrimination (AUC: 0.883/0.848) and calibration. Decision curve analysis demonstrated the clinical value of the nomogram in the validation cohort. In conclusion, radiomics nomogram, including clinical factors and com-rad-score, showed favorable performance for evaluating HER-2 2+ status in breast cancer.
Collapse
|
28
|
Preoperative evaluation of extramural venous invasion in rectal cancer using radiomics analysis of relaxation maps from synthetic MRI. Abdom Radiol (NY) 2021; 46:3815-3825. [PMID: 33743017 DOI: 10.1007/s00261-021-03021-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 02/19/2021] [Accepted: 02/25/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To establish a radiomics nomogram based on relaxation maps for predicting the extramural venous invasion (EMVI) of rectal cancer (RC) and compare the diagnostic efficacy of the nomogram and subjective assessment by radiologists. MATERIAL AND METHODS Among 94 RC patients receiving direct surgical resection, 65 were randomly allocated to the training cohort and 29 to the validation cohort. Radiomics features were extracted from synthetic magnetic resonance imaging including T1, T2, and proton density (PD) maps. The least absolute shrinkage and selection operator methods were used for dimension reduction, feature selection, and radiomics model building. Multivariable logistic regression analysis was used for nomogram development. The performance of the nomogram was assessed with respect to its calibration, receiver operating characteristics (ROC) curve, and decision curve analysis. RESULTS The radiomics model demonstrated good predictive efficacy for EMVI, with an area under the ROC curve (AUC), sensitivity, and specificity of 0.912 (95% confidence interval (CI), 0.837-0.986), 0.824, and 0.875 in the training cohort and 0.877 (95% CI 0.751-1.000), 0.833, and 0.826 in the validation cohort. The nomogram had good diagnostic performance, with AUCs of 0.925 (95% CI 0.862-0.988) and 0.899 (95% CI 0.782-1.000) in the training and validation cohort. Furthermore, the radiomics signature showed better diagnostic efficiency than the subjective assessment by both readers (AUC =0.912 vs. 0.732 and 0.763, P = 0.023 and 0.028, respectively). CONCLUSION A radiomics nomogram was developed to preoperatively predict EMVI in RC patients. The application of the radiomics model based on relaxation maps could improve the diagnostic efficacy of EMVI.
Collapse
|
29
|
Hou M, Sun JH. Emerging applications of radiomics in rectal cancer: State of the art and future perspectives. World J Gastroenterol 2021; 27:3802-3814. [PMID: 34321845 PMCID: PMC8291019 DOI: 10.3748/wjg.v27.i25.3802] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 05/06/2021] [Accepted: 05/21/2021] [Indexed: 02/06/2023] Open
Abstract
Rectal cancer (RC) is the third most commonly diagnosed cancer and has a high risk of mortality, although overall survival rates have improved. Preoperative assessments and predictions, including risk stratification, responses to therapy, long-term clinical outcomes, and gene mutation status, are crucial to guide the optimization of personalized treatment strategies. Radiomics is a novel approach that enables the evaluation of the heterogeneity and biological behavior of tumors by quantitative extraction of features from medical imaging. As these extracted features cannot be captured by visual inspection, the field holds significant promise. Recent studies have proved the rapid development of radiomics and validated its diagnostic and predictive efficacy. Nonetheless, existing radiomics research on RC is highly heterogeneous due to challenges in workflow standardization and limitations of objective cohort conditions. Here, we present a summary of existing research based on computed tomography and magnetic resonance imaging. We highlight the most salient issues in the field of radiomics and analyze the most urgent problems that require resolution. Our review provides a cutting-edge view of the use of radiomics to detect and evaluate RC, and will benefit researchers dedicated to using this state-of-the-art technology in the era of precision medicine.
Collapse
Affiliation(s)
- Min Hou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang Province, China
| | - Ji-Hong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang Province, China
| |
Collapse
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
Liu S, Yu X, Yang S, Hu P, Hu Y, Chen X, Li Y, Zhang Z, Li C, Lu Q. Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer. Front Oncol 2021; 11:610338. [PMID: 33842316 PMCID: PMC8033032 DOI: 10.3389/fonc.2021.610338] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 03/11/2021] [Indexed: 12/24/2022] Open
Abstract
Objective To establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning. Methods The clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves. Results The radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801. Conclusion The radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.
Collapse
Affiliation(s)
- Siye Liu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiaoping Yu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Songhua Yang
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Pingsheng Hu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Yingbin Hu
- Department of Intestinal Oncology Surgery, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiaoyan Chen
- Department of Pathology, Hunan Cancer Hospital, Changsha, China
| | - Yilin Li
- Department of Pathology, Hunan Cancer Hospital, Changsha, China
| | - Zhe Zhang
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Cheng Li
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Qiang Lu
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| |
Collapse
|
32
|
Zhao L, Liang M, Xie L, Yang Y, Zhang H, Zhao X. Prediction of pathological prognostic factors of rectal cancer by relaxation maps from synthetic magnetic resonance imaging. Eur J Radiol 2021; 138:109658. [PMID: 33744506 DOI: 10.1016/j.ejrad.2021.109658] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/09/2021] [Accepted: 03/14/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE To explore the feasibility of relaxation maps from synthetic MRI for predicting pathological prognostic factors of rectal cancer (RC) and to compare the predictive performance of quantitative values and conventional subjective evaluation. MATERIAL AND METHODS A total of 94 patients with pathologically proven RC who underwent direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI including synthetic MRI was performed. The mean T1, T2, and PD value of the whole tumor was obtained to preoperatively assess the pathological T stage, N stage, extramural venous invasion (EMVI), differentiation, and perineural invasion. Receiver operating characteristic curves were used to explore the predictive performance for assessing the prognostic factors. The T stage, N stage and EMVI status on conventional T2WI were evaluated and compared with the quantitative values. RESULTS The T2 value decreased significantly in patients with positive perineural invasion, lymph node metastasis (LNM), EMVI, and higher T stage RC (p = 0.007 and < 0.001). The T1 value of LNM and EMVI positive groups was significantly lower than those of the negative groups (p = 0.034 and 0.011). For predicting N stage and EMVI, the T2 value demonstrated good performance with an AUC of 0.883 (95 % confidence interval, CI, 0.801-0.940) and 0.821 (95 % CI, 0.729-0.893); the T2 value was superior to the T1 value and subjective evaluation of radiologists (all p < 0.05). CONCLUSION Synthetic MRI is a promising tool for noninvasive evaluation of prognostic factors of RC by generating relaxation maps.
Collapse
Affiliation(s)
- Li Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Lizhi Xie
- GE Healthcare, No.1 Tongji South Road, Beijing, 100176, China.
| | - Yang Yang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Hongmei Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| |
Collapse
|
33
|
Alwalid O, Long X, Xie M, Yang J, Cen C, Liu H, Han P. CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture. Front Neurol 2021; 12:619864. [PMID: 33692741 PMCID: PMC7937935 DOI: 10.3389/fneur.2021.619864] [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: 10/21/2020] [Accepted: 01/18/2021] [Indexed: 12/24/2022] Open
Abstract
Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture. Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms. Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89-0.95] and 0.86 [95% CI: 0.80-0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. -1.60 and 2.35 vs. -1.01 on training and test cohorts, respectively, p < 0.001). Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.
Collapse
Affiliation(s)
- Osamah Alwalid
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xi Long
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Mingfei Xie
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Jiehua Yang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Chunyuan Cen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | | | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| |
Collapse
|
34
|
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.
Collapse
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
| |
Collapse
|