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Xu YH, Lu P, Gao MC, Wang R, Li YY, Guo RQ, Zhang WS, Song JX. Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer. World J Clin Oncol 2024; 15:419-433. [PMID: 38576593 PMCID: PMC10989267 DOI: 10.5306/wjco.v15.i3.419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 01/15/2024] [Accepted: 02/06/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Accurate preoperative prediction of lymph node metastasis (LNM) in esophageal cancer (EC) patients is of crucial clinical significance for treatment planning and prognosis. AIM To develop a clinical radiomics nomogram that can predict the preoperative lymph node (LN) status in EC patients. METHODS A total of 32 EC patients confirmed by clinical pathology (who underwent surgical treatment) were included. Real-time fluorescent quantitative reverse transcription-polymerase chain reaction was used to detect the expression of B7-H3 mRNA in EC tissue obtained during preoperative gastroscopy, and its correlation with LNM was analyzed. Radiomics features were extracted from multi-modal magnetic resonance imaging of EC using Pyradiomics in Python. Feature extraction, data dimensionality reduction, and feature selection were performed using XGBoost model and leave-one-out cross-validation. Multivariable logistic regression analysis was used to establish the prediction model, which included radiomics features, LN status from computed tomography (CT) reports, and B7-H3 mRNA expression, represented by a radiomics nomogram. Receiver operating characteristic area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical application value of the model. RESULTS The relative expression of B7-H3 mRNA in EC patients with LNM was higher than in those without metastasis, and the difference was statistically significant (P < 0.05). The AUC value in the receiver operating characteristic (ROC) curve was 0.718 (95%CI: 0.528-0.907), with a sensitivity of 0.733 and specificity of 0.706, indicating good diagnostic performance. The individualized clinical prediction nomogram included radiomics features, LN status from CT reports, and B7-H3 mRNA expression. The ROC curve demonstrated good diagnostic value, with an AUC value of 0.765 (95%CI: 0.598-0.931), sensitivity of 0.800, and specificity of 0.706. DCA indicated the practical value of the radiomics nomogram in clinical practice. CONCLUSION This study developed a radiomics nomogram that includes radiomics features, LN status from CT reports, and B7-H3 mRNA expression, enabling convenient preoperative individualized prediction of LNM in EC patients.
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Affiliation(s)
- Yan-Han Xu
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Peng Lu
- Department of Imaging, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Ming-Cheng Gao
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Rui Wang
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Yang-Yang Li
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Rong-Qi Guo
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Wei-Song Zhang
- School of Clinical Sciences, Graduate School of Nantong University, Yancheng 226019, Jiangsu Province, China
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
| | - Jian-Xiang Song
- Department of Thoracic Surgery, Yancheng Third People's Hospital, The Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China
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Yang T, Feng J, Yao R, Feng Q, Shen J. CT-based pancreatic radiomics predicts secondary loss of response to infliximab in biologically naïve patients with Crohn's disease. Insights Imaging 2024; 15:69. [PMID: 38472447 DOI: 10.1186/s13244-024-01637-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/27/2024] [Indexed: 03/14/2024] Open
Abstract
OBJECTIVES Predicting secondary loss of response (SLR) to infliximab (IFX) is paramount for tailoring personalized management regimens. Concurrent pancreatic manifestations in patients with Crohn's disease (CD) may correlate with SLR to anti-tumor necrosis factor treatment. This work aimed to evaluate the potential of pancreatic radiomics to predict SLR to IFX in biologic-naive individuals with CD. METHODS Three models were developed by logistic regression analyses to identify high-risk subgroup prone to SLR. The area under the curve (AUC), calibration curve, decision curve analysis (DCA), and integrated discrimination improvement (IDI) were applied for the verification of model performance. A quantitative nomogram was proposed based on the optimal prediction model, and its reliability was substantiated by 10-fold cross-validation. RESULTS In total, 184 CD patients were enrolled in the period January 2016 to February 2022. The clinical model incorporated age of onset, disease duration, disease location, and disease behavior, whereas the radiomics model consisted of five texture features. These clinical parameters and the radiomics score calculated by selected texture features were applied to build the combined model. Compared to other two models, combined model achieved favorable, significantly improved discrimination power (AUCcombined vs clinical 0.851 vs 0.694, p = 0.02; AUCcombined vs radiomics 0.851 vs 0.740, p = 0.04) and superior clinical usefulness, which was further converted into reliable nomogram with an accuracy of 0.860 and AUC of 0.872. CONCLUSIONS The first proposed pancreatic-related nomogram represents a credible, noninvasive predictive instrument to assist clinicians in accurately identifying SLR and non-SLR in CD patients. CRITICAL RELEVANCE STATEMENT This study first built a visual nomogram incorporating pancreatic texture features and clinical factors, which could facilitate clinicians to make personalized treatment decisions and optimize cost-effectiveness ratio for patients with CD. KEY POINTS • The first proposed pancreatic-related model predicts secondary loss of response for infliximab in Crohn's disease. • The model achieved satisfactory predictive accuracy, calibration ability, and clinical value. • The model-based nomogram has the potential to identify long-term failure in advance and tailor personalized management regimens.
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Affiliation(s)
- Tian Yang
- Renji Hospital, School of Medicine, Shanghai Jiao Tong University; Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Shanghai Institute of Digestive Disease, 160# Pu Jian Ave, Shanghai, 200127, China
- NHC Key Laboratory of Digestive Diseases (Renji Hospital, Shanghai Jiaotong University School of Medicine), Shanghai, China
| | - Jing Feng
- Renji Hospital, School of Medicine, Shanghai Jiao Tong University; Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Shanghai Institute of Digestive Disease, 160# Pu Jian Ave, Shanghai, 200127, China
- NHC Key Laboratory of Digestive Diseases (Renji Hospital, Shanghai Jiaotong University School of Medicine), Shanghai, China
| | - Ruchen Yao
- Renji Hospital, School of Medicine, Shanghai Jiao Tong University; Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Shanghai Institute of Digestive Disease, 160# Pu Jian Ave, Shanghai, 200127, China
- NHC Key Laboratory of Digestive Diseases (Renji Hospital, Shanghai Jiaotong University School of Medicine), Shanghai, China
| | - Qi Feng
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pu Jian Road, Shanghai, 200127, China.
| | - Jun Shen
- Renji Hospital, School of Medicine, Shanghai Jiao Tong University; Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Shanghai Institute of Digestive Disease, 160# Pu Jian Ave, Shanghai, 200127, China.
- NHC Key Laboratory of Digestive Diseases (Renji Hospital, Shanghai Jiaotong University School of Medicine), Shanghai, China.
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Okazumi S, Ohira G, Hayano K, Aoyagi T, Imanishi S, Matsubara H. Novel Advances in Qualitative Diagnostic Imaging for Decision Making in Multidisciplinary Treatment for Advanced Esophageal Cancer. J Clin Med 2024; 13:632. [PMID: 38276137 PMCID: PMC10816440 DOI: 10.3390/jcm13020632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/18/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024] Open
Abstract
Background: Recently, neoadjuvant therapy and the succeeding surgery for advanced esophageal cancer have been evaluated. In particular, the response to the therapy has been found to affect surgical outcomes, and thus a precise evaluation of treatment effect is important for this strategy. In this study, articles on qualitative diagnostic modalities to evaluate tumor activities were reviewed, and the diagnostic indices were examined. Methods: For prediction of the effect, perfusion CT and diffusion MRI were estimated. For the histological response evaluation, perfusion CT, diffusion-MRI, and FDG-PET were estimated. For downstaging evaluation of T4, tissue-selective image reconstruction using enhanced CT was estimated and diagnostic indices were reviewed. Results: The prediction of the effect using perfusion CT with 'pre CRT blood flow' and diffusion MRI with 'pre CRT ADC value'; the estimation of the histological response using perfusion CT with 'post CRT blood flow reduction, using diffusion MRI with 'post CRT ADC increasing', and using FDG-PET with 'post CRT SUV reduction'; and the downstaging evaluation of T4 using CT image reconstruction with 'fibrous changed layer' were performed well, respectively. Conclusions: Qualitative imaging modalities for prediction or response evaluation of neoadjuvant therapy for progressive esophageal cancer were useful for the decision making of the treatment strategy of the multidisciplinary treatment.
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Affiliation(s)
- Shinichi Okazumi
- Department of Surgery, Toho University Sakura Medical Center, Chiba 285-8741, Japan;
| | - Gaku Ohira
- Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan; (K.H.); (H.M.)
| | - Koichi Hayano
- Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan; (K.H.); (H.M.)
| | - Tomoyoshi Aoyagi
- Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan; (K.H.); (H.M.)
| | - Shunsuke Imanishi
- Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan; (K.H.); (H.M.)
| | - Hisahiro Matsubara
- Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan; (K.H.); (H.M.)
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Cheng X, Zhang Y, Zhu M, Sun R, Liu L, Li X. Predicting response to CCRT for esophageal squamous carcinoma by a radiomics-clinical SHAP model. BMC Med Imaging 2023; 23:145. [PMID: 37779188 PMCID: PMC10544369 DOI: 10.1186/s12880-023-01089-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND Radical concurrent chemoradiotherapy (CCRT) is frequently used as the first-line treatment for patients with locally advanced esophageal cancer. Unfortunately, some patients respond poorly. To predict response to radical concurrent chemoradiotherapy in pre-treatment patients with esophageal squamous carcinoma (ESCC), and compare the predicting efficacies of radiomics features of primary tumor with or without regional lymph nodes, we developed a radiomics-clinical model based on the positioning CT images. Finally, SHapley Additive exPlanation (SHAP) was used to explain the models. METHODS This retrospective study enrolled 105 patients with medically inoperable and/or unresectable ESCC who underwent radical concurrent chemoradiotherapy (CCRT) between October 2018 and May 2023. Patients were classified into responder and non-responder groups with RECIST standards. The 11 recently admitted patients were chosen as the validation set, previously admitted patients were randomly split into the training set (n = 70) and the testing set (n = 24). Primary tumor site (GTV), the primary tumor and the uninvolved lymph nodes at risk of microscopic disease (CTV) were identified as Regions of Interests (ROIs). 1762 radiomics features from GTV and CTV were respectively extracted and then filtered by statistical differential analysis and Least Absolute Shrinkage and Selection Operator (LASSO). The filtered radiomics features combined with 13 clinical features were further filtered with Mutual Information (MI) algorithm. Based on the filtered features, we developed five models (Clinical Model, GTV Model, GTV-Clinical Model, CTV Model, and CTV-Clinical Model) using the random forest algorithm and evaluated for their accuracy, precision, recall, F1-Score and AUC. Finally, SHAP algorithm was adopted for model interpretation to achieve transparency and utilizability. RESULTS The GTV-Clinical model achieves an AUC of 0.82 with a 95% confidence interval (CI) of 0.76-0.99 on testing set and an AUC of 0.97 with a 95% confidence interval (CI) of 0.84-1.0 on validation set, which are significantly higher than those of other models in predicting ESCC response to CCRT. The SHAP force map provides an integrated view of the impact of each feature on individual patients, while the SHAP summary plots indicate that radiomics features have a greater influence on model prediction than clinical factors in our model. CONCLUSION GTV-Clinical model based on texture features and the maximum diameter of lesion (MDL) may assist clinicians in pre-treatment predicting ESCC response to CCRT.
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Affiliation(s)
- Xu Cheng
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, P.R. China
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China
| | - Yuxin Zhang
- School of Biomedical Engineering, Anhui Medical University, Hefei, 230032, China
| | - Min Zhu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China.
- School of Mathematics and Computer Science, Tongling University, Tongling, China.
| | - Ruixia Sun
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, P.R. China
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China
| | - Lingling Liu
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, P.R. China
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China
| | - Xueling Li
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, P.R. China.
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China.
- School of Biomedical Engineering, Anhui Medical University, Hefei, 230032, China.
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Zhou Y, Song L, Xia J, Liu H, Xing J, Gao J. Radiomics model based on contrast-enhanced CT texture features for pretreatment prediction of overall survival in esophageal neuroendocrine carcinoma. Front Oncol 2023; 13:1225180. [PMID: 37664013 PMCID: PMC10473874 DOI: 10.3389/fonc.2023.1225180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/25/2023] [Indexed: 09/05/2023] Open
Abstract
Background Limited studies have observed the prognostic value of CT images for esophageal neuroendocrine carcinoma (NEC) due to rare incidence and low treatment experience in clinical. In this study, the pretreatment enhanced CT texture features and clinical characteristics were investigated to predict the overall survival of esophageal NEC. Methods This retrospective study included 89 patients with esophageal NEC. The training and testing cohorts comprised 61 (70%) and 28 (30%) patients, respectively. A total of 402 radiomics features were extracted from the tumor region that segmented pretreatment venous phase CT images. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to feature dimension reduction, feature selection, and radiomics signature construction. A radiomics nomogram was constructed based on the radiomics signature and clinical risk factors using a multivariable Cox proportional regression. The performance of the nomogram for the pretreatment prediction of overall survival (OS) was evaluated for discrimination and calibration. Results Only the enhancement degree was an independent factor in clinical variable influenced OS. The radiomics signatures demonstrated good predictability for prognostic status discrimination. The radiomics nomogram integrating texture signatures was slightly superior to the nomogram derived from the combined model with a C-index of 0.844 (95%CI: 0.783-0.905) and 0.847 (95% CI: 0.782-0.912) in the training set, and 0.805 (95%CI: 0.707-0.903) and 0.745 (95% CI: 0.639-0.851) in the testing set, respectively. Conclusion The radiomics nomogram based on pretreatment CT radiomics signature had better prognostic power and predictability of the overall survival in patients with esophageal NEC than the model using combined variables.
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Affiliation(s)
- Yue Zhou
- Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lijie Song
- Department of Oncology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jin Xia
- Department of Oncology, Anyang Tumor Hospital, Anyang, China
| | - Huan Liu
- Advanced Analytics Team, GE Healthcare, Shanghai, China
| | - Jingjing Xing
- Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianbo Gao
- Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Krishna S, Sertic A, Liu Z(A, Liu Z, Darling GE, Yeung J, Wong R, Chen EX, Kalimuthu S, Allen MJ, Suzuki C, Panov E, Ma LX, Bach Y, Jang RW, Swallow CJ, Brar S, Elimova E, Veit-Haibach P. Combination of clinical, radiomic, and "delta" radiomic features in survival prediction of metastatic gastroesophageal adenocarcinoma. Front Oncol 2023; 13:892393. [PMID: 37645426 PMCID: PMC10461093 DOI: 10.3389/fonc.2023.892393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 07/17/2023] [Indexed: 08/31/2023] Open
Abstract
Objectives To identify combined clinical, radiomic, and delta-radiomic features in metastatic gastroesophageal adenocarcinomas (GEAs) that may predict survival outcomes. Methods A total of 166 patients with metastatic GEAs on palliative chemotherapy with baseline and treatment/follow-up (8-12 weeks) contrast-enhanced CT were retrospectively identified. Demographic and clinical data were collected. Three-dimensional whole-lesional radiomic analysis was performed on the treatment/follow-up scans. "Delta" radiomic features were calculated based on the change in radiomic parameters compared to the baseline. The univariable analysis (UVA) Cox proportional hazards model was used to select clinical variables predictive of overall survival (OS) and progression-free survival (PFS) (p-value <0.05). The radiomic and "delta" features were then assessed in a multivariable analysis (MVA) Cox model in combination with clinical features identified on UVA. Features with a p-value <0.01 in the MVA models were selected to assess their pairwise correlation. Only non-highly correlated features (Pearson's correlation coefficient <0.7) were included in the final model. Leave-one-out cross-validation method was used, and the 1-year area under the receiver operating characteristic curve (AUC) was calculated for PFS and OS. Results Of the 166 patients (median age of 59.8 years), 114 (69%) were male, 139 (84%) were non-Asian, and 147 (89%) had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1. The median PFS and OS on treatment were 3.6 months (95% CI 2.86, 4.63) and 9 months (95% CI 7.49, 11.04), respectively. On UVA, the number of chemotherapy cycles and number of lesions at the end of treatment were associated with both PFS and OS (p < 0.001). ECOG status was associated with OS (p = 0.0063), but not PFS (p = 0.054). Of the delta-radiomic features, delta conventional HUmin, delta gray-level zone length matrix (GLZLM) GLNU, and delta GLZLM LGZE were incorporated into the model for PFS, and delta shape compacity was incorporated in the model for OS. Of the treatment/follow-up radiomic features, shape compacity and neighborhood gray-level dependence matrix (NGLDM) contrast were used in both models. The combined 1-year AUC (Kaplan-Meier estimator) was 0.82 and 0.81 for PFS and OS, respectively. Conclusions A combination of clinical, radiomics, and delta-radiomic features may predict PFS and OS in GEAs with reasonable accuracy.
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Affiliation(s)
- Satheesh Krishna
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Andrew Sertic
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Zhihui (Amy) Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Zijin Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Gail E. Darling
- Division of Thoracic Oncology, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Jonathon Yeung
- Division of Thoracic Oncology, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Rebecca Wong
- Division of Radiation Oncology, Princess Margaret Hospital, University Health Network, Toronto, ON, Canada
| | - Eric X. Chen
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Sangeetha Kalimuthu
- Division of Pathology, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Michael J. Allen
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Chihiro Suzuki
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Elan Panov
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Lucy X. Ma
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Yvonne Bach
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Raymond W. Jang
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Carol J. Swallow
- Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Savtaj Brar
- Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Elena Elimova
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
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Oda S, Kuno H, Hiyama T, Sakashita S, Sasaki T, Kobayashi T. Computed tomography-based radiomic analysis for predicting pathological response and prognosis after neoadjuvant chemotherapy in patients with locally advanced esophageal cancer. Abdom Radiol (NY) 2023; 48:2503-2513. [PMID: 37171586 DOI: 10.1007/s00261-023-03938-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/13/2023]
Abstract
PURPOSE Accurate prediction of prognosis and pathological response to neoadjuvant chemotherapy (NAC) is crucial for optimizing treatment strategies for patients with locally advanced esophageal cancer (LA-EC). This study aimed to investigate the use of radiomics for pretreatment CT in predicting the pathological response of patients with LA-EC to NAC. METHODS Overall, 144 patients (145 lesions) with LA-EC who underwent pretreatment contrast-enhanced CT and then received NAC followed by surgery with pathological tumor regression grade (TRG) analysis were enrolled. The obtained dataset was randomly divided into training and validation cohorts using fivefold cross-validation. CT-based radiomic features were extracted followed by the feature selection process using the variance threshold, SelectKBest, and least absolute shrinkage and selection operator methods. The radiomic model was constructed using six machine learning classifiers, and predictive performance was evaluated using ROC curve analysis in the training and validation cohorts. RESULTS All patients were divided into responders (n = 40, 28%) and non-responders (n = 104, 72%) based on the TRG results and a statistically significant split by overall survival analysis (0.899 [0.754-0.961] vs. 0.630 [0.510-0.729], respectively). There were no significant differences between responders and non-responders in terms of age, sex, tumor size, tumor location, or histopathology. The mean AUC of fivefold in the validation cohort was 0.720 (confidence interval [CI]: 0.594-0.982), and the best AUC of the radiomic model using logistic regression to predict the non-responders was 0.815 (CI: 0.626-1.000, sensitivity 0.620, specificity 0.860). CONCLUSION A radiomic model derived from contrast-enhanced CT may help stratify chemotherapy effect prediction and improve clinical decision-making.
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Affiliation(s)
- Shioto Oda
- Department of Diagnostic Radiology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
| | - Hirofumi Kuno
- Department of Diagnostic Radiology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Takashi Hiyama
- Department of Diagnostic Radiology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Shingo Sakashita
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa Japan, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Tomoaki Sasaki
- Department of Diagnostic Radiology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
| | - Tatsushi Kobayashi
- Department of Diagnostic Radiology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan
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Wang Y, Bai G, Huang W, Zhang H, Chen W. A radiomics nomogram based on contrast-enhanced CT for preoperative prediction of Lymphovascular invasion in esophageal squamous cell carcinoma. Front Oncol 2023; 13:1208756. [PMID: 37465108 PMCID: PMC10351375 DOI: 10.3389/fonc.2023.1208756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 06/19/2023] [Indexed: 07/20/2023] Open
Abstract
Background and purpose To develop a radiomics nomogram based on contrast-enhanced computed tomography (CECT) for preoperative prediction of lymphovascular invasion (LVI) status of esophageal squamous cell carcinoma (ESCC). Materials and methods The clinical and imaging data of 258 patients with ESCC who underwent surgical resection and were confirmed by pathology from June 2017 to December 2021 were retrospectively analyzed.The clinical imaging features and radiomic features were extracted from arterial-phase CECT. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature selection and signature construction. Multivariate logistic regression analysis was used to develop a radiomics nomogram prediction model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance and clinical effectiveness of the model in preoperative prediction of LVI status. Results We constructed a radiomics signature based on eight radiomics features after dimensionality reduction. In the training cohort, the area under the curve (AUC) of radiomics signature was 0.805 (95% CI: 0.740-0.860), and in the validation cohort it was 0.836 (95% CI: 0.735-0.911). There were four predictive factors that made up the individualized nomogram prediction model: radiomic signatures, TNRs, tumor lengths, and tumor thicknesses.The accuracy of the nomogram for LVI prediction in the training and validation cohorts was 0.790 and 0.768, respectively, the specificity was 0.800 and 0.618, and the sensitivity was 0.786 and 0.917, respectively. The Delong test results showed that the AUC value of the nomogram model was significantly higher than that of the clinical model and radiomics model in the training and validation cohort(P<0.05). DCA results showed that the radiomics nomogram model had higher overall benefits than the clinical model and the radiomics model. Conclusions This study proposes a radiomics nomogram based on CECT radiomics signature and clinical image features, which is helpful for preoperative individualized prediction of LVI status in ESCC.
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Liu J, Yang X, Mao X, Wang T, Zheng X, Feng G, Dai T, Du X. Predicting the efficacy of radiotherapy for esophageal squamous cell carcinoma based on enhanced computed tomography radiomics and combined models. Front Oncol 2023; 13:1089365. [PMID: 37007134 PMCID: PMC10061127 DOI: 10.3389/fonc.2023.1089365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/24/2023] [Indexed: 03/18/2023] Open
Abstract
PurposeThis study aimed to investigate the ability of enhanced computed tomography (CT)-based radiomics and dosimetric parameters in predicting response to radiotherapy for esophageal cancer.MethodsA retrospective analysis of 147 patients diagnosed with esophageal cancer was performed, and the patients were divided into a training group (104 patients) and a validation group (43 patients). In total, 851 radiomics features were extracted from the primary lesions for analysis. Maximum correlation minimum redundancy and minimum least absolute shrinkage and selection operator were utilized for feature screening of radiomics features, and logistic regression was applied to construct a radiotherapy radiomics model for esophageal cancer. Finally, univariate and multivariate parameters were used to identify significant clinical and dosimetric characteristics for constructing combination models. The area evaluated the predictive performance under the receiver operating characteristics (AUC) curve and the accuracy, sensitivity, and specificity of the training and validation cohorts.ResultsUnivariate logistic regression analysis revealed statistically significant differences in clinical parameters of sex (p=0.031) and esophageal cancer thickness (p=0.028) on treatment response, whereas dosimetric parameters did not differ significantly in response to treatment. The combined model demonstrated improved discrimination between the training and validation groups, with AUCs of 0.78 (95% confidence interval [CI], 0.69–0.87) and 0.79 (95% CI, 0.65–0.93) in the training and validation groups, respectively.ConclusionThe combined model has potential application value in predicting the treatment response of patients with esophageal cancer after radiotherapy.
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Zhang Y, Zhang Y, Peng L, Zhang L. Research Progress on the Predicting Factors and Coping Strategies for Postoperative Recurrence of Esophageal Cancer. Cells 2022; 12:cells12010114. [PMID: 36611908 PMCID: PMC9818463 DOI: 10.3390/cells12010114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/01/2022] [Accepted: 12/20/2022] [Indexed: 12/29/2022] Open
Abstract
Esophageal cancer is one of the malignant tumors with poor prognosis in China. Currently, the treatment of esophageal cancer is still based on surgery, especially in early and mid-stage patients, to achieve the goal of radical cure. However, esophageal cancer is a kind of tumor with a high risk of recurrence and metastasis, and locoregional recurrence and distant metastasis are the leading causes of death after surgery. Although multimodal comprehensive treatment has advanced in recent years, the prediction, prevention and treatment of postoperative recurrence and metastasis of esophageal cancer are still unsatisfactory. How to reduce recurrence and metastasis in patients after surgery remains an urgent problem to be solved. Given the clinical demand for early detection of postoperative recurrence of esophageal cancer, clinical and basic research aiming to meet this demand has been a hot topic, and progress has been observed in recent years. Therefore, this article reviews the research progress on the factors that influence and predict postoperative recurrence of esophageal cancer, hoping to provide new research directions and treatment strategies for clinical practice.
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Affiliation(s)
- Yujie Zhang
- Department of Oncology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan 430030, China
| | - Yuxin Zhang
- Department of Pediatric Surgery, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan 430030, China
| | - Lin Peng
- Department of Oncology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan 430030, China
| | - Li Zhang
- Department of Oncology, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan 430030, China
- Correspondence:
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Zhu C, Mu F, Wang S, Qiu Q, Wang S, Wang L. Prediction of distant metastasis in esophageal cancer using a radiomics-clinical model. Eur J Med Res 2022; 27:272. [PMID: 36463269 PMCID: PMC9719117 DOI: 10.1186/s40001-022-00877-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/16/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Distant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of only 3-10 months. However, few studies have been conducted to predict distant metastasis in EC, owing to a dearth of reliable biomarkers. The purpose of this study was to develop and validate an accurate model for predicting distant metastasis in patients with EC. METHODS A total of 299 EC patients were enrolled and randomly assigned to a training cohort (n = 207) and a validation cohort (n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictors and create a clinical nomogram. Radiomic features were extracted from contrast-enhanced computed tomography (CT) images taken prior to treatment, and least absolute shrinkage and selection operator (Lasso) regression was used to screen the associated features, which were then used to develop a radiomic signature. Based on the screened features, four machine learning algorithms were used to build radiomics models. The joint nomogram with radiomic signature and clinically independent risk factors was developed using the logical regression algorithm. All models were validated and compared by discrimination, calibration, reclassification, and clinical benefit. RESULTS Multivariable analyses revealed that age, N stage, and degree of pathological differentiation were independent predictors of distant metastasis, and a clinical nomogram incorporating these factors was established. A radiomic signature was developed by a set of sixteen features chosen from 851 radiomic features. The joint nomogram incorporating clinical factors and radiomic signature performed better [AUC(95% CI) 0.827(0.742-0.912)] than the clinical nomogram [AUC(95% CI) 0.731(0.626-0.836)] and radiomics predictive models [AUC(95% CI) 0.754(0.652-0.855), LR algorithms]. Calibration and decision curve analyses revealed that the radiomics-clinical nomogram outperformed the other models. In comparison with the clinical nomogram, the joint nomogram's NRI was 0.114 (95% CI 0.075-0.345), and its IDI was 0.071 (95% CI 0.030-0.112), P = 0.001. CONCLUSIONS We developed and validated the first radiomics-clinical nomogram for distant metastasis in EC which may aid clinicians in identifying patients at high risk of distant metastasis.
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Affiliation(s)
- Chao Zhu
- grid.415468.a0000 0004 1761 4893Department of Oncology, Qingdao Central Hospital Affiliated to Qingdao University, Qingdao, 266042 Shandong China ,grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
| | - Fengchun Mu
- grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
| | - Songping Wang
- grid.415468.a0000 0004 1761 4893Department of Oncology, Qingdao Central Hospital Affiliated to Qingdao University, Qingdao, 266042 Shandong China
| | - Qingtao Qiu
- grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
| | - Shuai Wang
- grid.268079.20000 0004 1790 6079Department of Radiation Oncology, Affiliated Hospital of Weifang Medical University, Weifang, 261000 Shandong China
| | - Linlin Wang
- grid.410587.fDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117 Shandong China
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Tankel J, Söderström H, Reizine E, Artho G, Calderone A, Mueller C, Najmeh S, Spicer J, Ferri L, Cools-Lartigue J. Change in Density Not Size of Esophageal Adenocarcinoma During Neoadjuvant Chemotherapy Is Associated with Improved Survival Outcomes. J Gastrointest Surg 2022; 26:2417-25. [PMID: 36214951 DOI: 10.1007/s11605-022-05422-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/16/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND Changes in the size and density of esophageal malignancy during neoadjuvant chemotherapy (NCT) may be useful in predicting overall survival (OS). The aim of this study was to explore this relationship in patients with adenocarcinoma. METHODS A retrospective single-centre cohort study was performed. Consecutive patients with esophageal adenocarcinoma who received NCT followed by en bloc resection with curative intent were identified. Pre- and post-NCT computed tomography scans were reviewed. The percentage difference between the greatest tumor diameter, esophageal wall thickness and tumor density was calculated. Multivariate Cox regression analysis identified variables independently associated with OS. A ROC analysis was performed on radiological markers to identify optimal cut-off points with Kaplan-Meier plots subsequently created. RESULTS Of the 167 identified, 88 (51.5%) had disease of the gastro-esophageal junction and 149 (89.2%) were clinical T3. In total, 122 (73.1%) had node-positive disease. Increased tumor density (HR 1.01 per % change, 95% CI 1.00-1.02, p = 0.007), lymphovascular invasion (HR 3.23, 95% CI 1.34-7.52, p = 0.006) and perineural invasion (HR 2.51, 95% CI 1.03-6.08, p = 0.048) were independently associated with a decrease in OS. Patients who had a decrease in their tumor density during the time they received NCT of ≥ 20% in Hounsfield units had significantly longer OS than those who did not (75.5 months versus 34.4 months, 95% CI 38.83-105.13/18.63-35.07, p = 0.025). CONCLUSIONS Interval changes in the density, not size, of esophageal adenocarcinoma during the time that NCT are independently associated with OS.
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Wang J, Yu X, Zeng J, Li H, Qin P. Radiomics model for preoperative prediction of 3-year survival-based CT image biomarkers in esophageal cancer. Eur Arch Otorhinolaryngol 2022. [PMID: 35857100 DOI: 10.1007/s00405-022-07510-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/04/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE This work aimed to develop a radiomics nomogram to predict 3-year overall survival of esophageal cancer patients after chemoradiotherapy. METHODS A total of 109 esophageal cancer patients, diagnosed from November 2012 to February 2015, were enrolled in this retrospective study. They were randomly divided into training set (77 cases) and verification set (32 cases). Image standardization was performed prior to feature extraction. And then, about 1670 radiomics features were extracted from the pretreatment diagnostic computed tomography image. A radiomics signature was constructed with the lasso algorithm; then, a radiomics score was calculated to reflect survival probability using the radiomics signature for each patient. A radiomics nomogram was developed by incorporating the radiomics score and clinical factors. A clinical model was constructed using clinical factors only. The performance of the nomogram was assessed with respect to its calibration and discrimination. Kaplan-Meier survival analysis was performed. RESULTS Sixteen radiomics features were selected to build the radiomics signature. The radiomics nomogram showed better calibration and classification capacity than the clinical model with AUC 0.96 vs. 0.72 for the training cohort, and 0.87 vs. 0.67 for the validation cohort. The model showed good discrimination with a Harrell's Concordance Index of 0.76 in the training cohort and 0.81 in the validation cohort. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram. A significant difference (p value < 0.05; log-rank test) was observed between the survival curves of the nomogram-predicted survival and non-survival groups. CONCLUSIONS The present study proposed a radiomics-based nomogram involving the radiomics signature and clinical factors. It can be potentially applied in the individual preoperative prediction of 3-year survival in esophageal cancer patients.
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Tao J, Lv R, Liang C, Fang J, Liu D, Lan X, Huang H, Zhang J. Development and Validation of a CT-Based Signature for the Prediction of Distant Metastasis Before Treatment of Non-Small Cell Lung Cancer. Acad Radiol 2022; 29 Suppl 2:S62-72. [PMID: 33402298 DOI: 10.1016/j.acra.2020.12.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/11/2020] [Accepted: 12/11/2020] [Indexed: 01/06/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a radiomics model, a clinical-semantic model and a combined model by using standard methods for the pretreatment prediction of distant metastasis (DM) in patients with non-small-cell lung cancer (NSCLC) and to explore whether the combined model provides added value compared to the individual models. MATERIALS AND METHODS This retrospective study involved 356 patients with NSCLC. According to the image biomarker standardization initiative reference manual, we standardized the image processing and feature extraction using in-house software. Finally, 6692 radiomics features were extracted from each lesion based on contrast-enhanced chest CT images. The least absolute shrinkage selection operator and the recursive feature elimination algorithm were used to select features. The logistic regression classifier was used to build the model. Three models (radiomics model, clinical-semantic model and combined model) were constructed to predict DM in NSCLC. Area under the receiver operating characteristic curves were used to validate the ability of the three models to predict DM. A visual nomogram based on the combined model was developed for DM risk assessment in each patient. RESULTS The receiver operating characteristic curve showed predictive performance for DM of the radiomics model (area under the curve [AUC] values for training and validation were 0.76 [95% CI, 0.704 - 0.820] and 0.76 [95% CI, 0.653 - 0.858], respectively). The combined model had AUCs of 0.78 (95% CI, 0.723 - 0.835) and 0.77 (95% CI, 0.673 - 0.870) in the training and validation cohorts, respectively. Both the radiomics model and combined model performed better than the clinical-semantic model (0.70 [95% CI, 0.634 - 0.760] and 0.67 [95% CI, 0.554 - 0.787] in the training and validation cohorts, respectively). CONCLUSION The radiomics model and combined model may be useful for the prediction of DM in patients with NSCLC.
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Bonnin A, Durot C, Barat M, Djelouah M, Grange F, Mulé S, Soyer P, Hoeffel C. CT texture analysis as a predictor of favorable response to anti-PD1 monoclonal antibodies in metastatic skin melanoma. Diagn Interv Imaging 2021; 103:97-102. [PMID: 34666945 DOI: 10.1016/j.diii.2021.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE The purpose of this study was to determine whether texture analysis features on pretreatment contrast-enhanced computed tomography (CT) images and their evolution can predict treatment response of metastatic skin melanoma (SM) treated with anti-PD1 monoclonal antibodies. MATERIALS AND METHODS Sixty patients (29 men, 31 women; median age, 56 years; age range: 27-91 years) with metastatic SM treated with pembrolizumab (43/60; 72%) or nivolumab (17/60; 28%) were included. Texture analysis of SM metastases was performed on baseline and first post-treatment evaluation CT examinations. Mean gray-level, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales, ranging from fine to coarse. Lasso penalized Cox regression analyses were performed to identify independent variables associated with favorable response to treatment. RESULTS A total of 127 metastases were analyzed, with a median of two metastases per patient. Skewness at fine texture scale (spatial scale filtration [SSF] = 2; Hazard ratio [HR]: 3.51; 95% CI: 2.08-8.57; P = 0.010), skewness at medium texture scale (SSF = 3; HR: 0.56; 95% CI: 0.11-1.59; P = 0.014), variation of entropy at fine texture scale (SSF = 2; HR: 37.76; 95% CI: 3.48-496.22; P = 0.008) and LDH above the threshold of 248 UI/L (HR: 3.56; 95% CI: 1.78-21.35; P = 0.032] were independent predictors of response to treatment. CONCLUSION Pretreatment CT texture analysis-derived tumor skewness and variation of entropy between baseline and first control CT examination may be used as predictors of favorable response to anti-PD1 monoclonal antibodies in patients with metastatic SM.
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Affiliation(s)
- Angèle Bonnin
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France; Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Carole Durot
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France
| | - Maxime Barat
- Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Manel Djelouah
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France
| | - Florent Grange
- Department of Dermatology, Valence Hospital, 26000 Valence, France
| | - Sébastien Mulé
- Department of Radiology, Henri Mondor University Hospital, APH-HP, 94000 Créteil, France
| | - Philippe Soyer
- Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Christine Hoeffel
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France; CRESTIC, Reims Champagne-Ardenne University, 51000 Reims, France.
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Gu L, Liu Y, Guo X, Tian Y, Ye H, Zhou S, Gao F. Computed tomography-based radiomic analysis for prediction of treatment response to salvage chemoradiotherapy for locoregional lymph node recurrence after curative esophagectomy. J Appl Clin Med Phys 2021; 22:71-79. [PMID: 34614265 PMCID: PMC8598151 DOI: 10.1002/acm2.13434] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/15/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
Objective To investigate the capability of computed tomography (CT) radiomic features to predict the therapeutic response and local control of the locoregional recurrence lymph node (LN) after curative esophagectomy by chemoradiotherapy. Methods This retrospective study included 129 LN from 77 patients (training cohort: 102 LN from 59 patients; validation cohort: 27 LN from 18 patients) with postoperative esophageal squamous cell carcinoma (ESCC). The region of the tumor was contoured in pretreatment contrast‐enhanced CT images. The least absolute shrinkage and selection operator with logistic regression was used to identify radiomic predictors in the training cohort. Model performance was evaluated using the area under the receiver operating characteristic curves (AUC). The Kaplan–Meier method was used to determine the local recurrence time of cancer. Results The radiomic model suggested seven features that could be used to predict treatment response. The AUCs in training and validated cohorts were 0.777 (95% CI: 0.667–0.878) and 0.765 (95% CI: 0.556–0.975), respectively. A significant difference in the radiomic scores (Rad‐scores) between response and nonresponse was observed in the two cohorts (p < 0.001, 0.034, respectively). Two features were identified for classifying whether there will be relapse in 2 years. AUC was 0.857 (95% CI: 0.780–0.935) in the training cohort. The local control time of the high Rad‐score group was higher than the low group in both cohorts (p < 0.001 and 0.025, respectively). As inferred from the Cox regression analysis, the low Rad‐score was a high‐risk factor for local recurrence within 2 years. Conclusions The radiomic approach can be used as a potential imaging biomarker to predict treatment response and local control of recurrence LN in ESCC patients.
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Affiliation(s)
- Liang Gu
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China.,Department of Radiation Oncology, The Second Affiliated Hospital of Soochow University, Su Zhou, Jiangsu Province, China
| | - Yangchen Liu
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China
| | - Xinwei Guo
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China
| | - Ye Tian
- Department of Radiation Oncology, The Second Affiliated Hospital of Soochow University, Su Zhou, Jiangsu Province, China
| | - Hongxun Ye
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China
| | - Shaobin Zhou
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China
| | - Fei Gao
- Department of Radiation Oncology, Taixing People's Hospital, Tai Xing, Jiangsu Province, China
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Abstract
Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This surveys emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this surveys final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.
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Lu N, Zhang WJ, Dong L, Chen JY, Zhu YL, Zhang SH, Fu JH, Yin SH, Li ZC, Xie CM. Dual-region radiomics signature: Integrating primary tumor and lymph node computed tomography features improves survival prediction in esophageal squamous cell cancer. Comput Methods Programs Biomed 2021; 208:106287. [PMID: 34311416 DOI: 10.1016/j.cmpb.2021.106287] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 07/10/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Preoperative prognostic biomarkers to guide individualized therapy are still in demand in esophageal squamous cell cancer (ESCC). Some studies reported that radiomic analysis based on CT images has been successfully performed to predict individual survival in EC. The aim of this study was to assess whether combining radiomics features from primary tumor and regional lymph nodes predicts overall survival (OS) better than using single-region features only, and to investigate the incremental value of the dual-region radiomics signature. METHODS In this retrospective study, three radiomics signatures were built from preoperative enhanced CT in a training cohort (n = 200) using LASSO Cox model. Associations between each signature and survival was assessed on a validation cohort (n = 107). Prediction accuracy for the three signatures was compared. By constructing a clinical nomogram and a radiomics-clinical nomogram, incremental prognostic value of the radiomics signature over clinicopathological factors in OS prediction was assessed in terms of discrimination, calibration, reclassification and clinical usefulness. RESULTS The dual-region radiomic signature was an independent factor, significantly associated with OS (HR: 1.869, 95% CI: 1.347, 2.592, P = 1.82e-04), which achieved better OS (C-index: 0.611) prediction either than the single-region signature (C-index:0.594-0.604). The resulted dual-region radiomics-clinical nomogram achieved the best discriminative ability in OS prediction (C-index:0.700). Compared with the clinical nomogram, the radiomics-clinical nomogram improved the calibration and classification accuracy for OS prediction with a total net reclassification improvement (NRI) of 26.9% (P=0.008) and integrated discrimination improvement (IDI) of 6.8% (P<0.001). CONCLUSION The dual-region radiomic signature is an independent prognostic marker and outperforms single-region signature in OS for ESCC patients. Integrating the dual-region radiomics signature and clinicopathological factors improves OS prediction.
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Affiliation(s)
- Nian Lu
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China; Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
| | - Wei-Jing Zhang
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China
| | - Lu Dong
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Jun-Ying Chen
- Department of Thoracic Surgery, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China
| | - Yan-Lin Zhu
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China
| | - Sheng-Hai Zhang
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Jian-Hua Fu
- Department of Thoracic Surgery, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China
| | - Shao-Han Yin
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China
| | - Zhi-Cheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People's Republic of China.
| | - Chuan-Miao Xie
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China.
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Nardone V, Boldrini L, Grassi R, Franceschini D, Morelli I, Becherini C, Loi M, Greto D, Desideri I. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers (Basel) 2021; 13:cancers13143590. [PMID: 34298803 PMCID: PMC8303203 DOI: 10.3390/cancers13143590] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary This review based on a literature search aims at showing the impact of Texture Analysis in the prediction of response to neoadjuvant radiotherapy and/or chemoradiotherapy. The manuscript explores radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma and rectal cancer in order to shed a light in the setting of neoadjuvant radiotherapy that can be used to tailor the best subsequent therapeutical strategy. Abstract Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Luca Boldrini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Davide Franceschini
- Radiotherapy and Radiosurgery Department, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Milan, Italy;
| | - Ilaria Morelli
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
- Correspondence: ; Tel.: +39-055-7947719
| | - Carlotta Becherini
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
| | - Mauro Loi
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Daniela Greto
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
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Tang S, Ou J, Liu J, Wu YP, Wu CQ, Chen TW, Zhang XM, Li R, Tang MJ, Yang LQ, Tan BG, Lu FL, Hu J. Application of contrast-enhanced CT radiomics in prediction of early recurrence of locally advanced oesophageal squamous cell carcinoma after trimodal therapy. Cancer Imaging 2021; 21:38. [PMID: 34039403 DOI: 10.1186/s40644-021-00407-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 05/12/2021] [Indexed: 12/13/2022] Open
Abstract
Background Early recurrence of oesophageal squamous cell carcinoma (SCC) is defined as recurrence after surgery within 1 year, and appears as local recurrence, distant recurrence, and lymph node positive and disseminated recurrence. Contrast-enhanced computed tomography (CECT) is recommended for diagnosis of primary tumor and initial staging of oesophageal SCC, but it cannot be used to predict early recurrence. It is reported that radiomics can help predict preoperative stages of oesophageal SCC, lymph node metastasis before operation, and 3-year overall survival of oesophageal SCC patients following chemoradiotherapy by extracting high-throughput quantitative features from CT images. This study aimed to develop models based on CT radiomics and clinical features of oesophageal SCC to predict early recurrence of locally advanced cancer. Methods We collected electronic medical records and image data of 197 patients with confirmed locally advanced oesophageal SCC. These patients were randomly allocated to 137 patients in the training cohort and 60 in the test cohort. 352 radiomics features were extracted by delineating region-of-interest (ROI) around the lesion on CECT images and clinical signature was generated by medical records. The radiomics model, clinical model, the combined model of radiomics and clinical features were developed by radiomics features and/or clinical characteristics. Predicting performance of the three models was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1 score. Results Eleven radiomics features and/or six clinical signatures were selected to build prediction models related to recurrence of locally advanced oesophageal SCC after trimodal therapy. The AUC of integration of radiomics and clinical models was better than that of radiomics or clinical model for the training cohort (0.821 versus 0.754 or 0.679, respectively) and for the validation cohort (0.809 versus 0.646 or 0.658, respectively). Integrated model of radiomics and clinical features showed good performance in predicting early recurrence of locally advanced oesophageal SCC for both the training and validation cohorts (accuracy = 0.730 and 0.733, and F-1score = 0.730 and 0.778, respectively). Conclusions The integrated model of CECT radiomics and clinical features may be a potential imaging biomarker to predict early recurrence of locally advanced oesophageal SCC after trimodal therapy.
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Li Y, Yu M, Wang G, Yang L, Ma C, Wang M, Yue M, Cong M, Ren J, Shi G. Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma. Front Oncol 2021; 11:644165. [PMID: 34055613 PMCID: PMC8162215 DOI: 10.3389/fonc.2021.644165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 03/08/2021] [Indexed: 01/03/2023] Open
Abstract
Objectives To develop a radiomics model based on contrast-enhanced CT (CECT) to predict the lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC) and provide decision-making support for clinicians. Patients and Methods This retrospective study enrolled 334 patients with surgically resected and pathologically confirmed ESCC, including 96 patients with LVI and 238 patients without LVI. All enrolled patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3, with the training cohort containing 234 patients (68 patients with LVI and 166 without LVI) and the testing cohort containing 100 patients (28 patients with LVI and 72 without LVI). All patients underwent preoperative CECT scans within 2 weeks before operation. Quantitative radiomics features were extracted from CECT images, and the least absolute shrinkage and selection operator (LASSO) method was applied to select radiomics features. Logistic regression (Logistic), support vector machine (SVM), and decision tree (Tree) methods were separately used to establish radiomics models to predict the LVI status in ESCC, and the best model was selected to calculate Radscore, which combined with two clinical CT predictors to build a combined model. The clinical model was also developed by using logistic regression. The receiver characteristic curve (ROC) and decision curve (DCA) analysis were used to evaluate the model performance in predicting the LVI status in ESCC. Results In the radiomics model, Sphericity and gray-level non-uniformity (GLNU) were the most significant radiomics features for predicting LVI. In the clinical model, the maximum tumor thickness based on CECT (cThick) in patients with LVI was significantly greater than that in patients without LVI (P<0.001). Patients with LVI had higher clinical N stage based on CECT (cN stage) than patients without LVI (P<0.001). The ROC analysis showed that both the radiomics model (AUC values were 0.847 and 0.826 in the training and testing cohort, respectively) and the combined model (0.876 and 0.867, respectively) performed better than the clinical model (0.775 and 0.798, respectively), with the combined model exhibiting the best performance. Conclusions The combined model incorporating radiomics features and clinical CT predictors may potentially predict the LVI status in ESCC and provide support for clinical treatment decisions.
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Affiliation(s)
- Yang Li
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Meng Yu
- Department of Cardiology, Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Guangda Wang
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Li Yang
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Chongfei Ma
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Mingbo Wang
- Department of Thoracic Surgery, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Meng Yue
- Department of Pathology, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Mengdi Cong
- Department of Computed Tomography and Magnetic Resonance, Children's Hospital of Hebei Province, Shijiazhuang, China
| | | | - Gaofeng Shi
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
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Liu S, Zhang C, Liu R, Li S, Xu F, Liu X, Li Z, Hu Y, Ge Y, Chen J, Zhang Z. CT Texture Analysis for Preoperative Identification of Lymphoma from Other Types of Primary Small Bowel Malignancies. Biomed Res Int 2021; 2021:5519144. [PMID: 33884262 DOI: 10.1155/2021/5519144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/18/2021] [Accepted: 03/22/2021] [Indexed: 01/08/2023]
Abstract
Objectives To explore the application of computed tomography (CT) texture analysis in differentiating lymphomas from other malignancies of the small bowel. Methods Arterial and venous CT images of 87 patients with small bowel malignancies were retrospectively analyzed. The subjective radiological features were evaluated by the two radiologists with a consensus agreement. The region of interest (ROI) was manually delineated along the edge of the lesion on the largest slice, and a total of 402 quantified features were extracted automatically from AK software. The inter- and intrareader reproducibility was evaluated to select highly reproductive features. The univariate analysis and minimum redundancy maximum relevance (mRMR) algorithm were applied to select the feature subsets with high correlation and low redundancy. The multivariate logistic regression analysis based on texture features and radiological features was employed to construct predictive models for identification of small bowel lymphoma. The diagnostic performance of multivariate models was evaluated using receiver operating characteristic (ROC) curve analysis. Results The clinical data (age, melena, and abdominal pain) and radiological features (location, shape, margin, dilated lumen, intussusception, enhancement level, adjacent peritoneum, and locoregional lymph node) differed significantly between the nonlymphoma group and lymphoma group (p < 0.05). The areas under the ROC curve of the clinical model, arterial texture model, and venous texture model were 0.93, 0.92, and 0.87, respectively. Conclusion The arterial texture model showed a great diagnostic value and fitted performance in preoperatively discriminating lymphoma from nonlymphoma of the small bowel.
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Wesdorp NJ, Hellingman T, Jansma EP, van Waesberghe JTM, Boellaard R, Punt CJA, Huiskens J, Kazemier G. Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging 2021; 48:1785-94. [PMID: 33326049 DOI: 10.1007/s00259-020-05142-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/29/2020] [Indexed: 02/08/2023]
Abstract
Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05142-w.
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Rishi A, Zhang GG, Yuan Z, Sim AJ, Song EY, Moros EG, Tomaszewski MR, Latifi K, Pimiento JM, Fontaine JP, Mehta R, Harrison LB, Hoffe SE, Frakes JM. Pretreatment CT and 18 F-FDG PET-based radiomic model predicting pathological complete response and loco-regional control following neoadjuvant chemoradiation in oesophageal cancer. J Med Imaging Radiat Oncol 2020; 65:102-111. [PMID: 33258556 DOI: 10.1111/1754-9485.13128] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 10/21/2020] [Indexed: 01/12/2023]
Abstract
INTRODUCTION To develop a radiomic-based model to predict pathological complete response (pCR) and outcome following neoadjuvant chemoradiotherapy (NACRT) in oesophageal cancer. METHODS We analysed 68 patients with oesophageal cancer treated with NACRT followed by esophagectomy, who had staging 18F-fluorodeoxyglucose (18 F-FDG) positron emission tomography (PET) and computed tomography (CT) scans performed at our institution. An in-house data-chjmirocterization algorithm was used to extract 3D-radiomic features from the segmented primary disease. Prediction models were constructed and internally validated. Composite feature, Fc = α * FPET + (1 - α) * FCT , 0 ≤ α ≤ 1, was constructed for each corresponding CT and PET feature. Loco-regional control (LRC), recurrence-free survival (RFS), metastasis-free survival (MFS) and overall survival (OS) were estimated by Kaplan-Meier analysis, and compared using log-rank test. RESULTS Median follow-up was 59 months. pCR was achieved in 34 (50%) patients. Five-year RFS, LRC, MFS and OS were 67.1%, 88.5%, 75.6% and 57.6%, respectively. Tumour Regression Grade (TRG) 0-1 indicative of complete response or minimal residual disease was significantly associated with improved 5-year LRC [93.7% vs 71.8%; P = 0.020; HR 0.19, 95% CI 0.04-0.85]. Four sepjmirote pCR predictive models were built for CT alone, PET alone, CT+PET and composite. CT, PET and CT+PET models had AUC 0.73 ± 0.08, 0.66 ± 0.08 and 0.77 ± 0.07, respectively. The composite model resulted in an improvement of pCR predicting power with AUC 0.87 ± 0.06. Stratifying patients with a low versus high radiomic score showed clinically relevant improvement in 5-year LRC favouring low-score group (91.1% vs. 80%, 95% CI 0.09-1.77, P = 0.2). CONCLUSION The composite CT/PET radiomics model was highly predictive of pCR following NACRT. Validation in larger data sets is warranted to determine whether the model can predict clinical outcomes.
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Affiliation(s)
- Anupam Rishi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Geoffrey G Zhang
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Zhigang Yuan
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Austin J Sim
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Ethan Y Song
- Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - Eduardo G Moros
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Michal R Tomaszewski
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Kujtim Latifi
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Jose M Pimiento
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Jacques-Pierre Fontaine
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Rutika Mehta
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Louis B Harrison
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Sjmiroh E Hoffe
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Jessica M Frakes
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
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Abstract
The diagnosis of patients with suspected angiomyolipoma relies on the detection of abundant macroscopic intralesional fat, which is always of no use to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma and diagnosis of fp-AML excessively depends on individual experience. Texture analysis was proven to be a potentially useful biomarker for distinguishing between benign and malignant tumors because of its capability of providing objective and quantitative assessment of lesions by analyzing features that are not visible to the human eye. This review aimed to summarize the literature on the use of texture analysis to diagnose patients with fat-poor angiomyolipoma vs those with renal cell carcinoma and to evaluate its current application, limitations, and future challenges in order to avoid unnecessary surgical resection.
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Affiliation(s)
- Yuhan Zhang
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
| | - Xu Li
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
| | - Yang Lv
- Department of Anesthesia, The Second Hospital of Jilin University, Changchun, China
| | - Xinquan Gu
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
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Wen Q, Yang Z, Zhu J, Qiu Q, Dai H, Feng A, Xing L. Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC. Onco Targets Ther 2020; 13:12003-12013. [PMID: 33244242 PMCID: PMC7685373 DOI: 10.2147/ott.s261068] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 10/13/2020] [Indexed: 11/29/2022] Open
Abstract
Background The present study constructed and validated models to predict PD-L1 and CD8+TILs expression levels in esophageal squamous cell carcinoma (ESCC) patients using radiomics features and clinical factors. Patients and Methods This retrospective study randomly assigned 220 ESCC patients to a discovery dataset (n= 160) and validation dataset (n= 60). A total of 462 radiomics features were extracted from the segmentation of regions of interest (ROIs) based on pretreatment CT images of each patient. The LASSO algorithm was applied to reduce the dimensionality of the data and select features. A multivariable logistic regression analysis was adopted to build radiomics signatures. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive accuracy of these models. Results There was no significant difference between the training and validation datasets for any clinical factors in patients with ESCC. The PD-L1 expression level correlated with the differentiation degree (p= 0.011) and tumor stage (p= 0.032). Smoking status (p= 0.043) and differentiation degree (p= 0.025) were associated with CD8+TILs expression levels. The radiomics signatures achieved good performance in predicting PD-L1 and CD8+TILs with AUCs= 0.784 and 0.764, respectively. The combined model showed a favorable predictive ability compared to radiomics signatures or clinical factors alone and improved the AUCs from 0.669 to 0.871 for PD-L1 and from 0.672 to 0.832 for CD8+TILs. These results were verified in the validation dataset with the AUCs of 0.817 and 0.795, respectively. Conclusion CT-based radiomics features have a potential value for classifying patients according to PD-L1 and CD8+TILs expression levels. The combination of clinical factors and radiomics signatures significantly improved the predictive performance in ESCC.
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Affiliation(s)
- Qiang Wen
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong University, Shandong University, Jinan 250021, People's Republic of China
| | - Zhe Yang
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong University, Shandong University, Jinan 250021, People's Republic of China
| | - Jian Zhu
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University, Jinan 250117, People's Republic of China
| | - Qingtao Qiu
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University, Jinan 250117, People's Republic of China
| | - Honghai Dai
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong University, Shandong University, Jinan 250021, People's Republic of China
| | - Alei Feng
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong University, Shandong University, Jinan 250021, People's Republic of China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Jinan 250117, People's Republic of China
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Duan C, Li N, Niu L, Wang G, Zhao J, Liu F, Liu X, Ren Y, Zhou X. CT texture analysis for the differentiation of papillary renal cell carcinoma subtypes. Abdom Radiol (NY) 2020; 45:3860-3868. [PMID: 32444891 DOI: 10.1007/s00261-020-02588-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE The objective of this study was to investigate whether computed tomography texture analysis can be used to differentiate papillary renal cell carcinoma (PRCC) subtypes. METHOD Sixty-two PRCC tumors were retrospectively evaluated, with 30 type 1 tumors and 32 type 2 tumors. Texture parameters quantified from three-phase contrast-enhanced CT images were compared with least absolute shrinkage and selection operator (LASSO) regression. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for each parameter. The selected texture parameters of each phase were used to generate support vector machine (SVM) classifiers. Decision curve analysis (DCA) of the classification was performed. RESULTS The two texture parameters with the top two AUC values were - 333-7 Correlation (AUC = 0.772) and 45-7 Entropy (AUC = 0.753) in the corticomedullary phase, 333-4 Correlation (AUC = 0.832) and 45-7 Entropy (AUC = 0.841) in the nephrographic phase, and 135-7 Entropy (AUC = 0.858) and - 333-1 InformationMeasureCorr2 (AUC = 0.849) in the excretory phase. Entropy and Correlation have a high correlation with the two types of PRCC and are increased in type 2 PRCC. A model incorporating the texture parameters with the top two AUC values in each phase produced an AUC of 0.922 with an accuracy of 84% (sensitivity = 89% and specificity = 80%). The nephrographic-phase model and the model combining the texture parameters of the three phases can differentiate the two types with the largest net benefit. CONCLUSIONS Computed tomography texture analysis can be used to distinguish type 2 PRCC from type 1 with high accuracy, which may be clinically important.
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Affiliation(s)
- Chongfeng Duan
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 1677, Wu Tai Shan Road, Huangdao District, Qingdao, Shandong, China
| | - Nan Li
- Department of Information Management, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Lei Niu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 1677, Wu Tai Shan Road, Huangdao District, Qingdao, Shandong, China
| | - Gang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 1677, Wu Tai Shan Road, Huangdao District, Qingdao, Shandong, China
| | - Jiping Zhao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 1677, Wu Tai Shan Road, Huangdao District, Qingdao, Shandong, China
| | - Fang Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 1677, Wu Tai Shan Road, Huangdao District, Qingdao, Shandong, China
| | - Xuejun Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 1677, Wu Tai Shan Road, Huangdao District, Qingdao, Shandong, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 1677, Wu Tai Shan Road, Huangdao District, Qingdao, Shandong, China
| | - Xiaoming Zhou
- Department of Radiology, The Affiliated Hospital of Qingdao University, No. 1677, Wu Tai Shan Road, Huangdao District, Qingdao, Shandong, China.
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Ji Z, Cui Y, Peng Z, Gong J, Zhu HT, Zhang X, Li J, Lu M, Lu Z, Shen L, Sun YS. Use of Radiomics to Predict Response to Immunotherapy of Malignant Tumors of the Digestive System. Med Sci Monit 2020; 26:e924671. [PMID: 33077705 PMCID: PMC7586759 DOI: 10.12659/msm.924671] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Despite the promising results of immunotherapy in cancer treatment, new response patterns, including pseudoprogression and hyperprogression, have been observed. Radiomics is the automated extraction of high-fidelity, high-dimensional imaging features from standard medical images, allowing comprehensive visualization and characterization of the tissue of interest and corresponding microenvironment. This study assessed whether radiomics can predict response to immunotherapy in patients with malignant tumors of the digestive system. MATERIAL AND METHODS Computed tomography (CT) images of patients with malignant tumors of the digestive system obtained at baseline and after immunotherapy were subjected to radiomics analyses. Radiomics features were extracted from each image. The formula of the screened features and the final predictive model were obtained using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. RESULTS Imaging analysis was feasible in 87 patients, including 3 with pseudoprogression and 7 with hyperprogression. One hundred ten radiomics features were obtained before and after treatment, including 109 features of the target lesions and 1 of the aorta. Four models were constructed, with the model constructed from baseline and post-treatment CT features having the best classification performance, with a sensitivity, specificity, and AUC of 83.3%, 88.9%, and 0.806, respectively. CONCLUSIONS Radiomics can predict the response of patients with malignant tumors of the digestive system to immunotherapy and can supplement conventional evaluations of response.
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Affiliation(s)
- Zhi Ji
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China (mainland)
| | - Yong Cui
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China (mainland)
| | - Zhi Peng
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China (mainland)
| | - Jifang Gong
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China (mainland)
| | - Hai-Tao Zhu
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China (mainland)
| | - Xiaotian Zhang
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China (mainland)
| | - Jian Li
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China (mainland)
| | - Ming Lu
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China (mainland)
| | - Zhihao Lu
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China (mainland)
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China (mainland)
| | - Ying-Shi Sun
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China (mainland)
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Feng P, Wang ZD, Fan W, Liu H, Pan JJ. Diagnostic advances of artificial intelligence and radiomics in gastroenterology. Artif Intell Gastroenterol 2020; 1:37-50. [DOI: 10.35712/aig.v1.i2.37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/22/2020] [Accepted: 08/27/2020] [Indexed: 02/06/2023] Open
Abstract
Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.
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Affiliation(s)
- Pei Feng
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Zhen-Dong Wang
- Department of Ultrasound, Beijing Sihui Hospital of Traditional Chinese Medicine, Beijing 100022, China
| | - Wei Fan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Heng Liu
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Jing-Jing Pan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
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Conti A, Duggento A, Indovina I, Guerrisi M, Toschi N. Radiomics in breast cancer classification and prediction. Semin Cancer Biol 2020; 72:238-250. [PMID: 32371013 DOI: 10.1016/j.semcancer.2020.04.002] [Citation(s) in RCA: 135] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 03/30/2020] [Accepted: 04/01/2020] [Indexed: 12/15/2022]
Abstract
Breast Cancer (BC) is the common form of cancer in women. Its diagnosis and screening are usually performed through different imaging modalities such as mammography, magnetic resonance imaging and ultrasound. However, mammography and ultrasound-imaging techniques have limited sensitivity and specificity both in identifying lesions and in differentiating malign from benign lesions, especially in presence of dense breast parenchyma. Due to the higher resolution of magnetic resonance images, MRI represents the method with the higher specificity and sensitivity among all the available tools, in both lesions' identification and diagnosis. However, especially for diagnosis, even MRI has limitations that are only partially solved if combined with mammography. Unfortunately, due to the limits of all these imaging tools, in order to have a certain diagnosis, patients often receive painful and costly bioptics procedures. In this context, several computational approaches have been developed to increase sensitivity, while maintaining the same specificity, in BC diagnosis and screening. Amongst these, radiomics has been increasingly gaining ground in oncology to improve cancer diagnosis, prognosis and treatment. Radiomics derives multiple quantitative features from single or multiple medical imaging modalities, highlighting image traits which are not visible to the naked eye and hence significantly augmenting the discriminatory and predictive potential of medical imaging. This review article aims to summarize the state of the art in radiomics-based BC research. The dominating evidence extracted from the literature points towards a high potential of radiomics in disentangling malignant from benign breast lesions, classifying BC types and grades and also in predicting treatment response and recurrence risk. In the era of personalized medicine, radiomics has the potential to improve diagnosis, prognosis, prediction, monitoring, image-based intervention, and assessment of therapeutic response in BC.
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Affiliation(s)
- Allegra Conti
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Via Ardeatina, 306, 00179, Rome, Italy; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Iole Indovina
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Via Ardeatina, 306, 00179, Rome, Italy; Department of Medicine and Surgery, Saint Camillus International University of Health and Medical Sciences, Rome, Italy
| | - Maria Guerrisi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States.
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Lee HN, Kim JI, Shin SY, Kim DH, Kim C, Hong IK. Combined CT texture analysis and nodal axial ratio for detection of nodal metastasis in esophageal cancer. Br J Radiol 2020; 93:20190827. [PMID: 32242741 DOI: 10.1259/bjr.20190827] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE To assess the accuracy of a combination of CT texture analysis (CTTA) and nodal axial ratio to detect metastatic lymph nodes (LNs) in esophageal squamous cell carcinoma (ESCC). METHODS The contrast-enhanced chest CT images of 78 LNs (40 metastasis, 38 benign) from 38 patients with ESCC were retrospectively analyzed. Nodal axial ratios (short-axis/long-axis diameter) were calculated. CCTA parameters (kurtosis, entropy, skewness) were extracted using commercial software (TexRAD) with fine, medium, and coarse spatial filters. Combinations of significant texture features and nodal axial ratios were entered as predictors in logistic regression models to differentiate metastatic from benign LNs, and the performance of the logistic regression models was analyzed using the area under the receiver operating characteristic curve (AUROC). RESULTS The mean axial ratio of metastatic LNs was significantly higher than that of benign LNs (0.81 ± 0.2 vs 0.71 ± 0.1, p = 0.005; sensitivity 82.5%, specificity 47.4%); namely, significantly more round than benign. The mean values of the entropy (all filters) and kurtosis (fine and medium) of metastatic LNs were significantly higher than those of benign LNs (all, p < 0.05). Medium entropy showed the best performance in the AUROC analysis with 0.802 (p < 0.001; sensitivity 85.0%, specificity 63.2%). A binary logistic regression analysis combining the nodal axial ratio, fine entropy, and fine kurtosis identified metastatic LNs with 87.5% sensitivity and 65.8% specificity (AUROC = 0.855, p < 0.001). CONCLUSION The combination of CTTA features and the axial ratio of LNs has the potential to differentiate metastatic from benign LNs and improves the sensitivity for detection of LN metastases in ESCC. ADVANCES IN KNOWLEDGE The combination of CTTA and nodal axial ratio has improved CT sensitivity (up to 87.5%) for the diagnosis of metastatic LNs in esophageal cancer.
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Affiliation(s)
- Han Na Lee
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Jung Im Kim
- Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - So Youn Shin
- Department of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Dae Hyun Kim
- Department of Thoracic Surgery, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Chanwoo Kim
- Department of Nuclear Medicine, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea
| | - Il Ki Hong
- Department of Nuclear Medicine, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Republic of Korea
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Abstract
The aim of this study was to test the hypothesis that computed tomography texture analysis (CTTA) is accurate for response assessment of Hodgkin lymphoma (HL).A total of 100 patients with HL were identified. CTTA in baseline and interim staging was performed generating volume of interests in lymphoma tissue from which CTTA features including 1st, 2nd, and higher order textural features were extracted. Baseline and interim 2-deoxy-fluor-glucose positron emission tomography results were used to determine therapy response and compared to CTTA in terms of patient outcome.At interim, 1st-order features yielded a significant drop (e.g., entropy of heterogeneity, P = .01) or a significant rise (deviation, P < .001), whereas 2nd and higher order features decreased (e.g., entropy of co-occurrence matrix, P < .001). Patients achieving complete remission at end of treatment had a significantly lower entropy of heterogeneity at baseline and interim compared to patients achieving partial remission (P < .05).CT textural features change in parallel to metabolic therapy response, and are therefore a feasible diagnostic tool for a more accurate response assessment of HL.
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Affiliation(s)
| | - Larissa Wanek
- Department of Diagnostic and Interventional Radiology
| | - Hans Bösmüller
- Institute of Pathology, University Hospital of Tübingen, Tübingen, Germany
| | - Birgit Federmann
- Institute of Pathology, University Hospital of Tübingen, Tübingen, Germany
| | - Jan Fritz
- Johns Hopkins University School of Medicine, Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, MD
| | - Martin Sökler
- Department of Internal Medicine II, University Hospital of Tübingen, Tübingen, Germany
| | - Marius Horger
- Department of Diagnostic and Interventional Radiology
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Liu R, Sun M, Zhang G, Lan Y, Yang Z. Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning. Anal Chim Acta 2019; 1092:42-48. [PMID: 31708031 PMCID: PMC6878984 DOI: 10.1016/j.aca.2019.09.065] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 08/30/2019] [Accepted: 09/23/2019] [Indexed: 01/22/2023]
Abstract
Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.
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Affiliation(s)
- Renmeng Liu
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA
| | - Mei Sun
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA
| | - Genwei Zhang
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA
| | - Yunpeng Lan
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA
| | - Zhibo Yang
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.
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Kim HS, Kim YJ, Kim KG, Park JS. Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer. Sci Rep. 2019;9:17389. [PMID: 31757989 PMCID: PMC6874598 DOI: 10.1038/s41598-019-53831-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 11/06/2019] [Indexed: 12/11/2022] Open
Abstract
Pancreatic cancer is a lethal disease, and resistance to chemotherapy is a critical factor influencing the postoperative prognosis. Tumour heterogeneity is an important indicator of chemoresistance. Therefore, we analysed tumour heterogeneity in preoperative computed tomography scans by performing texture analysis using the grey-level run-length matrix and analysed the correlation of survival with the value obtained in these analyses. We analysed 116 consecutive patients who underwent curative resection and had preoperative contrast-enhanced computed tomography data available for analysis. A region of interest was drawn on all slices with a visible tumour and normal pancreas on the arterial phase computed tomography scans; the correlation of pathological characteristics with grey-level run-length matrix features was analysed. We then performed Kaplan–Meier survival curve analysis among pancreatic cancer patients. The grey-level non-uniformity values in grey-level run-length matrix features for tumours were higher than those for normal pancreas. High grey-level non-uniformity values represent a non-uniform texture, i.e., heterogeneity. Grey-level run-length matrix features showed that recurrence-free survival was shorter in the group with high grey-level non-uniformity 135 values (p = 0.025). Our analyses of the correlation between pathological outcomes and grey-level run-length matrix features in pancreatic cancer patients showed that grey-level non-uniformity values were powerful prognostic indicators.
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Piazzese C, Foley K, Whybra P, Hurt C, Crosby T, Spezi E. Discovery of stable and prognostic CT-based radiomic features independent of contrast administration and dimensionality in oesophageal cancer. PLoS One 2019; 14:e0225550. [PMID: 31756181 PMCID: PMC6874382 DOI: 10.1371/journal.pone.0225550] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 11/06/2019] [Indexed: 02/07/2023] Open
Abstract
The aim of this work was to investigate radiomic analysis of contrast and non-contrast enhanced planning CT images of oesophageal cancer (OC) patients in terms of stability, dimensionality and contrast agent dependency. The prognostic significance of CT-based radiomic features was also evaluated. Different 2D and 3D radiomic features were extracted from contrast and non-contrast enhanced CT images of 213 patients from the multi-centre SCOPE1 randomised controlled trial (RCT) in OC. Feature stability was evaluated by randomly dividing patients into three groups and identifying textures with similar distributions among groups with a Kruskal-Wallis analysis. A paired two-sided Wilcoxon signed rank test was used to assess for significant differences in the remaining corresponding 2D and 3D stable features. A prognostic model was constructed using clinical characteristics and remaining filtered features. The discriminative ability of significant variables was tested using Kaplan-Meier analysis. A total of 238 2D and 3D radiomic features were computed from oesophageal CT images. More than 75 features were stable if extracted from homogeneous cohort (contrast or non-contrast enhanced CT images) and inhomogeneous cohort (contrast and non-contrast enhanced CT images). Among the remaining corresponding stable features computed from both cohorts, only 4 features did not show a statistically significant difference if obtained in 2D or in 3D (p-value < 0.05). A Cox regression model constructed using 5 clinical variables (age, sex, tumour, node and metastasis (TNM) stage, WHO performance status and contrast administration) and 4 radiomic variables (inverse varianceGLCM, large distance emphasisGLDZM, zone distance non uniformity normGLDZM, zone distance varianceGLDZM), identified one radiomic feature (zone distance varianceGLDZM) that was significantly associated with overall survival (p-value = 0.032, HR = 1.25, 95% CI = 1.02-1.52). A significant difference in overall survival between groups was found when considering a threshold of zone distance varianceGLDZM equals to 1.70 (X2 = 7.692, df = 1, p-value = 0.006). Zone distance varianceGLDZM was identified as the only stable CT radiomic feature statistically correlated with overall survival, independent of dimensionality and contrast administration. This feature was able to identify high-risk patients and if validated, could be the subject of a future clinical trial aiming to improve clinical decision making and personalise OC treatment.
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Affiliation(s)
- Concetta Piazzese
- School of Engineering, Cardiff University, Cardiff, United Kingdom
- Velindre Cancer Centre, Cardiff, United Kingdom
| | | | - Philip Whybra
- School of Engineering, Cardiff University, Cardiff, United Kingdom
| | - Chris Hurt
- Centre for Trials Research, Cardiff, United Kingdom
| | - Tom Crosby
- Velindre Cancer Centre, Cardiff, United Kingdom
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, United Kingdom
- Velindre Cancer Centre, Cardiff, United Kingdom
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Deng Y, Soule E, Cui E, Samuel A, Shah S, Lall C, Sundaram C, Sandrasegaran K. Usefulness of CT texture analysis in differentiating benign and malignant renal tumours. Clin Radiol 2019; 75:108-115. [PMID: 31668402 DOI: 10.1016/j.crad.2019.09.131] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 09/12/2019] [Indexed: 12/22/2022]
Abstract
AIM To elucidate visually imperceptible differences between benign and malignant renal tumours using computed tomography texture analysis (CTTA) using filtration histogram based parameters. MATERIALS AND METHODS A retrospective study was performed by texture analysis of pretreatment contrast-enhanced CT examinations in 354 histopathologically confirmed renal cell carcinomas (RCCs) and 147 benign renal tumours. A region-of-interest was drawn encompassing the largest cross-section of the tumour on venous phase axial CT. CTTA features of entropy, kurtosis, mean positive pixel density, and skewness at different spatial filters were calculated and compared in an attempt to differentiate benign lesions from malignancy. RESULTS Entropy with fine spatial filter was significantly higher in RCC than benign renal tumours (p=0.022). Entropy with fine and medium filters was higher in RCC than lipid-poor angiomyolipoma (p=0.050 and 0.052, respectively). Entropy >5.62 had high specificity of 85.7%, but low sensitivity of 31.3%, respectively, for predicting RCC. CONCLUSIONS Differences in entropy were helpful in differentiating RCC from lipid-poor angiomyolipoma, and chromophobe RCC from oncocytoma. This technique may be useful to differentiate lesions that appear equivocal on visual assessment or alter management in poor surgical candidates.
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Affiliation(s)
- Y Deng
- Department of Radiology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - E Soule
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - E Cui
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun YAT-SEN University, Jiangmen, China
| | - A Samuel
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - S Shah
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - C Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - C Sundaram
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - K Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology, Mayo Clinic, Phoenix, AZ, USA.
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Ou J, Li R, Zeng R, Wu CQ, Chen Y, Chen TW, Zhang XM, Wu L, Jiang Y, Yang JQ, Cao JM, Tang S, Tang MJ, Hu J. CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study. Cancer Imaging 2019; 19:66. [PMID: 31619297 PMCID: PMC6796480 DOI: 10.1186/s40644-019-0254-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 09/13/2019] [Indexed: 11/25/2022] Open
Abstract
Background Computed tomography (CT) is commonly used in all stages of oesophageal squamous cell carcinoma (SCC) management. Compared to basic CT features, CT radiomic features can objectively obtain more information about intratumour heterogeneity. Although CT radiomics has been proved useful for predicting treatment response to chemoradiotherapy in oesophageal cancer, the best way to use CT radiomic biomarkers as predictive markers for determining resectability of oesophageal SCC remains to be developed. This study aimed to develop CT radiomic features related to resectability of oesophageal SCC with five predictive models and to determine the most predictive model. Methods Five hundred ninety-one patients with oesophageal SCC undergoing contrast-enhanced CT were enrolled in this study, and were composed by 270 resectable cases and 321 unresectable cases. Of the 270 resectable oesophageal SCCs, 91 cases were primary resectable tumours; and the remained 179 cases received neoadjuvant therapy after CT, shrank on therapy, and changed to resectable tumours. Four hundred thirteen oesophageal SCCs including 189 resectable cancers and 224 unresectable cancers were randomly allocated to the training cohort; and 178 oesophageal SCCs including 81 resectable tumours and 97 unresectable tumours were allocated to the validation group. Four hundred ninety-five radiomic features were extracted from CT data for identifying resectability of oesophageal SCC. Useful radiomic features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomic features were chosen using multivariable logistic regression, random forest, support vector machine, X-Gradient boost and decision tree classifiers. Discriminating performance was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1score. Results Eight radiomic features were selected to create radiomic models related to resectability of oesophageal SCC (P-values < 0.01 for both cohorts). Multivariable logistic regression model showed the best performance (AUC = 0.92 ± 0.04 and 0.87 ± 0.02, accuracy = 0.87 and 0.86, and F-1score = 0.93 and 0.86 in training and validation cohorts, respectively) in comparison with any other model (P-value < 0.001). Good calibration was observed for multivariable logistic regression model. Conclusion CT radiomic models could help predict resectability of oesophageal SCC, and multivariable logistic regression model is the most predictive model.
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Affiliation(s)
- Jing Ou
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 63# Wenhua Road, Nanchong, 637000, Sichuan, China
| | - Rui Li
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 63# Wenhua Road, Nanchong, 637000, Sichuan, China
| | - Rui Zeng
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 63# Wenhua Road, Nanchong, 637000, Sichuan, China
| | - Chang-Qiang Wu
- Sichuan Key Laboratory of Medical Imaging, North Sichuan Medical College, Nanchong, 637000, Sichuan, China
| | - Yong Chen
- Sichuan Key Laboratory of Medical Imaging, North Sichuan Medical College, Nanchong, 637000, Sichuan, China
| | - Tian-Wu Chen
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 63# Wenhua Road, Nanchong, 637000, Sichuan, China.
| | - Xiao-Ming Zhang
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 63# Wenhua Road, Nanchong, 637000, Sichuan, China
| | - Lan Wu
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 63# Wenhua Road, Nanchong, 637000, Sichuan, China
| | - Yu Jiang
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 63# Wenhua Road, Nanchong, 637000, Sichuan, China
| | - Jian-Qiong Yang
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 63# Wenhua Road, Nanchong, 637000, Sichuan, China
| | - Jin-Ming Cao
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 63# Wenhua Road, Nanchong, 637000, Sichuan, China
| | - Sun Tang
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 63# Wenhua Road, Nanchong, 637000, Sichuan, China
| | - Meng-Jie Tang
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 63# Wenhua Road, Nanchong, 637000, Sichuan, China
| | - Jiani Hu
- Department of Radiology, Wayne State University, Detroit, MI, USA
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Yang Z, He B, Zhuang X, Gao X, Wang D, Li M, Lin Z, Luo R. CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy. J Radiat Res 2019; 60:538-545. [PMID: 31111948 PMCID: PMC6640907 DOI: 10.1093/jrr/rrz027] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 02/18/2019] [Indexed: 02/05/2023]
Abstract
The objective of this study was to build models to predict complete pathologic response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC) patients using radiomic features. A total of 55 consecutive patients pathologically diagnosed as having ESCC were included in this study. Patients were divided into a training cohort (44 patients) and a testing cohort (11 patients). The logistic regression analysis using likelihood ratio forward selection was performed to select the predictive clinical parameters for pCR, and the least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomic predictors in the training cohort. Model performance in the training and testing groups was evaluated using the area under the receiver operating characteristic curves (AUC). The multivariate logistic regression analysis identified no clinical predictors for pCR. Thus, only radiomic features selected by LASSO were used to build prediction models. Three logistic regression models for pCR prediction were developed in the training cohort, and they were able to predict pCR well in both the training (AUC, 0.84-0.86) and the testing cohorts (AUC, 0.71-0.79). There were no differences between these AUCs. We developed three predictive models for pCR after nCRT using radiomic parameters and they demonstrated good model performance.
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Affiliation(s)
- Zhining Yang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, Guangdong, China
| | - Binghui He
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, Guangdong, China
- Department of Radiation Oncology, Donghua Hospital Affiliated to Zhongshan University,1 Dongcheng East Road, Dongguan, Guangdong, China
| | - Xinyu Zhuang
- Eye Center, Medical Center—University of Freiburg, Killianstraße, Freiburg Germany
| | - Xiaoying Gao
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, Guangdong, China
| | - Dandan Wang
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, Guangdong, China
| | - Mei Li
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, Guangdong, China
| | - Zhixiong Lin
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, 7 Raoping Road, Shantou, Guangdong, China
| | - Ren Luo
- Department of Radiation Oncology, Medical Center—University of Freiburg, Robert-Koch-Str. 3, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
- Corresponding author. Department of Radiation Oncology, Medical Center—University of Freiburg, Robert-Koch-Str. 3, D-79106 Freiburg, Germany; Faculty of Biology, University of Freiburg, D-79104 Freiburg, Germany. Tel: +49-17645735432; Fax:+49-761 270-95130; ;
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Reinert CP, Federmann B, Hofmann J, Bösmüller H, Wirths S, Fritz J, Horger M. Computed tomography textural analysis for the differentiation of chronic lymphocytic leukemia and diffuse large B cell lymphoma of Richter syndrome. Eur Radiol 2019; 29:6911-6921. [PMID: 31236702 DOI: 10.1007/s00330-019-06291-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 05/11/2019] [Accepted: 05/28/2019] [Indexed: 10/26/2022]
Abstract
OBJECTIVE To test the hypothesis that both indolent and aggressive chronic lymphocytic leukemia (CLL) can be differentiated from diffuse large B cell lymphoma (DLBCL) of Richter syndrome (RS) by CT texture analysis (CTTA) of involved lymph nodes. MATERIAL AND METHODS We retrospectively included 52 patients with indolent CLL (26/52), aggressive CLL (8/52), and DLBCL of RS (18/52), who underwent standardized contrast-enhanced CT. In main lymphoma tissue, VOIs were generated from which CTTA features including first-, second-, and higher-order textural features were extracted. CTTA features were compared between the entire CLL group, the indolent CLL subtype, the aggressive CLL subtype, and DLBCL using a Kruskal-Wallis test. All p values were adjusted after the Bonferroni correction. ROC analyses for significant CTTA features were performed to determine cut-off values for differentiation between the groups. RESULTS Compared with DLBCL of RS, CTTA of the entire CLL group showed significant differences of entropy heterogeneity (p < 0.001), mean intensity (p < 0.001), mean average (p = 0.02), and number non-uniformity gray-level dependence matrix (NGLDM) (p = 0.03). Indolent CLL significantly differed for entropy (p < 0.001), uniformity of heterogeneity (p = 0.02), mean intensity (p < 0.001), and mean average (p = 0.01). Aggressive CLL showed significant differences in mean intensity (p = 0.04). For differentiation between CLL and DLBCL of RS, cut-off values for mean intensity and entropy of heterogeneity were defined (e.g., 6.63 for entropy heterogeneity [aggressive CLL vs. DLBCL]; sensitivity 0.78; specificity 0.63). CONCLUSIONS CTTA features of ultrastructure and vascularization significantly differ in CLL compared with that in DLBCL of Richter syndrome, allowing complementary to visual features for noninvasive differentiation by contrast-enhanced CT. KEY POINTS • Richter transformation of CLL into DLBCL results in structural changes in lymph node architecture and vascularization that can be detected by CTTA. • First-order CT textural features including intensity and heterogeneity significantly differ between both indolent CLL and aggressive CLL and DLBCL of Richter syndrome. • CT texture analysis allows for noninvasive detection of Richter syndrome which is of prognostic value.
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Affiliation(s)
- C P Reinert
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany.
| | - B Federmann
- Department of Pathology and Neuropathology, University Hospital Tübingen, Liebermeisterstraße 8, 72076, Tübingen, Germany
| | - J Hofmann
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany
| | - H Bösmüller
- Department of Pathology and Neuropathology, University Hospital Tübingen, Liebermeisterstraße 8, 72076, Tübingen, Germany
| | - S Wirths
- Department of Hematology and Oncology, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076, Tübingen, Germany
| | - J Fritz
- Russell H. Morgan Department of Radiology and Radiological, Johns Hopkins University School of Medicine, Science, 601 N. Caroline Street, JHOC 3142, Baltimore, MD, 21287, USA
| | - M Horger
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany
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Ravanelli M, Agazzi GM, Tononcelli E, Roca E, Cabassa P, Baiocchi G, Berruti A, Maroldi R, Farina D. Texture features of colorectal liver metastases on pretreatment contrast-enhanced CT may predict response and prognosis in patients treated with bevacizumab-containing chemotherapy: a pilot study including comparison with standard chemotherapy. Radiol Med 2019; 124:877-86. [DOI: 10.1007/s11547-019-01046-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 05/13/2019] [Indexed: 02/06/2023]
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Durot C, Mulé S, Soyer P, Marchal A, Grange F, Hoeffel C. Metastatic melanoma: pretreatment contrast-enhanced CT texture parameters as predictive biomarkers of survival in patients treated with pembrolizumab. Eur Radiol 2019; 29:3183-3191. [PMID: 30645669 DOI: 10.1007/s00330-018-5933-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 11/11/2018] [Accepted: 11/29/2018] [Indexed: 12/15/2022]
Abstract
PURPOSE To determine whether texture analysis features on pretreatment contrast-enhanced computed tomography (CT) images can predict overall survival (OS) and progression-free survival (PFS) in patients with metastatic malignant melanoma (MM) treated with an anti-PD-1 monoclonal antibody, pembrolizumab. MATERIALS AND METHODS This institutional-approved retrospective study included 31 patients with metastatic MM treated with pembrolizumab. Texture analysis of 74 metastatic lesions was performed on CT scanners obtained within 1 month before treatment. Mean gray-level, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales, ranging from fine to coarse. Lasso penalized Cox regression analyses were performed to identify independent predictors of OS and PFS. RESULTS Median OS and PFS were 357 days (range 42-1355) and 99 days (range 35-1185), respectively. Skewness at coarse texture scale (SSF = 6; HR (CI 95%) = 6.017 (1.39, 26.056), p = 0.016), Response evaluation criteria in solid tumors (RECIST) conclusion (HR (CI 95%) = 3.41 (1.17, 9.89), p = 0.024), and body weight (HR (CI 95%) = 0.96 (0.92, 0.995), p = 0.026) were independent predictors of OS. Skewness at coarse texture scale (SSF = 6; HR (CI 95%) = 4.55 (1.46, 14.13), p = 0.0089) and RECIST conclusion (HR (CI 95%) = 10.63 (3.11, 36.29), p = 0.00016) were independent predictors of PFS. Skewness values above - 0.55 at coarse texture scale were significantly associated with both lower OS and lower PFS after administration of pembrolizumab. CONCLUSION Pretreatment CT texture analysis-derived tumor skewness may act as predictive biomarker of OS and PFS in patients with metastatic MM treated with pembrolizumab. KEY POINTS • Pretreatment skewness at coarse texture scale in metastases from malignant melanoma was an independent predictor of overall survival and progression-free survival. • Skewness values above -0.55 at coarse texture scale were significantly associated with both lower OS and lower PFS after administration of pembrolizumab. • In patients with metastatic MM, texture analysis performed on pretreatment CT may act as a useful tool to select the best candidates for pembrolizumab therapy.
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Affiliation(s)
- Carole Durot
- Department of Radiology, Reims University Hospital, 45 rue Cognacq-jay, 51092, Reims, France.
| | - Sébastien Mulé
- Department of Radiology, Henri Mondor University Hospital, Créteil, France
| | - Philippe Soyer
- Department of Radiology, Cochin University Hospital, Paris, France
| | - Aude Marchal
- Department of Biopathology, Reims University Hospital, Reims, France
| | - Florent Grange
- Department of Dermatology, Reims University Hospital, Reims, France
| | - Christine Hoeffel
- Department of Radiology, Reims University Hospital, 45 rue Cognacq-jay, 51092, Reims, France
- CRESTIC, University of Reims Champagne-Ardenne, Reims, France
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Nie K, Al-Hallaq H, Li XA, Benedict SH, Sohn JW, Moran JM, Fan Y, Huang M, Knopp MV, Michalski JM, Monroe J, Obcemea C, Tsien CI, Solberg T, Wu J, Xia P, Xiao Y, El Naqa I. NCTN Assessment on Current Applications of Radiomics in Oncology. Int J Radiat Oncol Biol Phys 2019; 104:302-315. [PMID: 30711529 PMCID: PMC6499656 DOI: 10.1016/j.ijrobp.2019.01.087] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 01/17/2019] [Accepted: 01/23/2019] [Indexed: 02/06/2023]
Abstract
Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.
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Affiliation(s)
- Ke Nie
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, New Jersey.
| | - Hania Al-Hallaq
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - Jason W Sohn
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Jean M Moran
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mi Huang
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Michael V Knopp
- Division of Imaging Science, Department of Radiology, Ohio State University, Columbus, Ohio
| | - Jeff M Michalski
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - James Monroe
- Department of Radiation Oncology, St. Anthony's Cancer Center, St. Louis, Missouri
| | - Ceferino Obcemea
- Radiation Research Program, National Cancer Institute, Bethesda, Maryland
| | - Christina I Tsien
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Timothy Solberg
- Department of Radiation Oncology, University of California-San Francisco, San Francisco, California
| | - Jackie Wu
- Department of Radiation Oncology, Duke University, Durham, North Carolina
| | - Ping Xia
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio
| | - Ying Xiao
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Issam El Naqa
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
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Bashir U, Weeks A, Goda JS, Siddique M, Goh V, Cook GJ. Measurement of 18F-FDG PET tumor heterogeneity improves early assessment of response to bevacizumab compared with the standard size and uptake metrics in a colorectal cancer model. Nucl Med Commun 2019; 40:611-617. [PMID: 30893213 PMCID: PMC6553522 DOI: 10.1097/mnm.0000000000000992] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 01/24/2019] [Indexed: 12/24/2022]
Abstract
PURPOSE Treatment of metastatic colorectal cancer frequently includes antiangiogenic agents such as bevacizumab. Size measurements are inadequate to assess treatment response to these agents, and newer response assessment criteria are needed. We aimed to evaluate F-FDG PET-derived texture parameters in a preclinical colorectal cancer model as alternative metrics of response to treatment with bevacizumab. MATERIALS AND METHODS Fourteen CD1 athymic mice injected in the flank with 5×106 LS174T cells (human colorectal carcinoma) were either untreated controls (n=7) or bevacizumab treated (n=7). After 2 weeks, mice underwent F-FDG PET/CT. Calliper-measured tumor growth (Δvol) and final tumor volume (Volcal), F-FDG PET metabolically active volume (Volmet), mean metabolism (Metmean), and maximum metabolism (Metmax) were measured. Twenty-four texture features were compared between treated and untreated mice. Immunohistochemical mean tumor vascular density was estimated by anti-CD-34 staining after tumor resection. RESULTS Treated mice had significantly lower tumor vascular density (P=0.032), confirming the antiangiogenic therapeutic effect of bevacizumab. None of the conventional measures were different between the two groups: Δvol (P=0.9), Volcal (P=0.7), Volmet (P=0.28), Metmax (P=0.7), or Metmean (P=0.32). One texture parameter, GLSZM-SZV (visually indicating that the F-FDG PET images of treated mice comprise uniformly sized clusters of different activity) had significantly different means between the two groups of mice (P=0.001). CONCLUSION F-FDG PET derived texture parameters, particularly GLSZM-SZV, may be valid biomarkers of tumor response to treatment with bevacizumab, before change in volume.
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Affiliation(s)
- Usman Bashir
- Department of Radiology, Barts and London NHS Trust
| | - Amanda Weeks
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences
| | - Jayant S. Goda
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences
| | - Muhammad Siddique
- Department of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences
| | - Vicky Goh
- Department of Radiology, Barts and London NHS Trust
- Department of Radiology, Guy’s Hospital, London, UK
| | - Gary J. Cook
- Department of Radiology, Barts and London NHS Trust
- PET Imaging Centre and the Division of Imaging Sciences and Biomedical Engineering, King’s College London
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Deng Y, Soule E, Samuel A, Shah S, Cui E, Asare-Sawiri M, Sundaram C, Lall C, Sandrasegaran K. CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade. Eur Radiol 2019; 29:6922-6929. [PMID: 31127316 DOI: 10.1007/s00330-019-06260-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 04/01/2019] [Accepted: 04/30/2019] [Indexed: 12/20/2022]
Abstract
OBJECTIVE CT texture analysis (CTTA) using filtration-histogram-based parameters has been associated with tumor biologic correlates such as glucose metabolism, hypoxia, and tumor angiogenesis. We investigated the utility of these parameters for differentiation of clear cell from papillary renal cancers and prediction of Fuhrman grade. METHODS A retrospective study was performed by applying CTTA to pretreatment contrast-enhanced CT scans in 290 patients with 298 histopathologically confirmed renal cell cancers of clear cell and papillary types. The largest cross section of the tumor on portal venous phase axial CT was chosen to draw a region of interest. CTTA comprised of an initial filtration step to extract features of different sizes (fine, medium, coarse spatial scales) followed by texture quantification using histogram analysis. RESULTS A significant increase in entropy with fine and medium spatial filters was demonstrated in clear cell RCC (p = 0.047 and 0.033, respectively). Area under the ROC curve of entropy at fine and medium spatial filters was 0.804 and 0.841, respectively. An increased entropy value at coarse filter correlated with high Fuhrman grade tumors (p = 0.01). The other texture parameters were not found to be useful. CONCLUSION Entropy, which is a quantitative measure of heterogeneity, is increased in clear cell renal cancers. High entropy is also associated with high-grade renal cancers. This parameter may be considered as a supplementary marker when determining aggressiveness of therapy. KEY POINTS • CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis may help to separate different types of renal cancers. • CT texture analysis may enhance individualized treatment of renal cancers.
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Affiliation(s)
- Yu Deng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Erik Soule
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Aster Samuel
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sakhi Shah
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Enming Cui
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun YAT-SEN University, Jiangmen, China
| | - Michael Asare-Sawiri
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Oncology, Hope Regional Cancer Center, Panama, FL, USA
| | - Chandru Sundaram
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Chandana Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL, USA
| | - Kumaresan Sandrasegaran
- Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, USA.
- Department of Radiology, Mayo Clinic, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.
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Huang P, Sun LY, Zhang YQ. A Hopeful Natural Product, Pristimerin, Induces Apoptosis, Cell Cycle Arrest, and Autophagy in Esophageal Cancer Cells. Anal Cell Pathol (Amst) 2019; 2019:6127169. [PMID: 31218209 DOI: 10.1155/2019/6127169] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Accepted: 04/18/2019] [Indexed: 02/07/2023] Open
Abstract
Esophageal cancer is one of the most common malignant digestive diseases worldwide. Although many approaches have been established for the treatment of esophageal cancer, the survival outcome has not improved. Pristimerin is a quinone methide triterpenoid with anticancer, antiangiogenic, anti-inflammatory, and antiprotozoal activities. However, the role of pristimerin in cancers such as esophageal cancer is unclear. In this study, we investigated the role and mechanisms of action of pristimerin in esophageal cancer. First, we found that pristimerin can induce apoptosis in esophageal cancer in vivo and in vitro. CCK-8 and clonogenic assays showed that pristimerin decreased the growth of Eca109 cells. In addition, we found that pristimerin decreased the protein expression of CDK2, CDK4, cyclin E, and BCL-2 and increased the expression of CDKN1B. Meanwhile, pristimerin elevated the ratio of LC3-II/LC3-I. Otherwise, downregulation of CDKN1B can reduce the esophageal cancer tumor growth induced by pristimerin. In conclusion, our findings revealed an important role of pristimerin in esophageal cancer and suggest that pristimerin might be a potential therapeutic agent for this cancer.
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Jin X, Zheng X, Chen D, Jin J, Zhu G, Deng X, Han C, Gong C, Zhou Y, Liu C, Xie C. Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics. Eur Radiol. 2019;29:6080-6088. [PMID: 31028447 DOI: 10.1007/s00330-019-06193-w] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/29/2018] [Accepted: 03/20/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE To investigate the treatment response prediction feasibility and accuracy of an integrated model combining computed tomography (CT) radiomic features and dosimetric parameters for patients with esophageal cancer (EC) who underwent concurrent chemoradiation (CRT) using machine learning. METHODS The radiomic features and dosimetric parameters of 94 EC patients were extracted and modeled using Support Vector Classification (SVM) and Extreme Gradient Boosting algorithm (XGBoost). The 94-sample dataset was randomly divided into a 70-sample training subset and a 24-sample independent test set while keeping the class proportions intact via stratification. A receiver operating characteristic (ROC) curve was used to assess the performance of models using radiomic features alone and using combined radiomic features and dosimetric parameters. RESULTS A total of 42 radiomic features and 18 dosimetric parameters plus the patients' characteristic parameters were extracted for these 94 cases (58 responders and 36 non-responders). XGBoost plus principal component analysis (PCA) achieved an accuracy and area under the curve of 0.708 and 0.541, respectively, for models with radiomic features combined with dosimetric parameters, and 0.689 and 0.479, respectively, for radiomic features alone. Image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the model. The dosimetric parameters of gross tumor volume (GTV) homogeneity index (HI), Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model. CONCLUSIONS The model with radiomic features combined with dosimetric parameters is promising and outperforms that with radiomic features alone in predicting the treatment response of patients with EC who underwent CRT. KEY POINTS • The model with radiomic features combined with dosimetric parameters is promising in predicting the treatment response of patients with EC who underwent CRT. • The model with radiomic features combined with dosimetric parameters (prediction accuracy of 0.708 and AUC of 0.689) outperforms that with radiomic features alone (best prediction accuracy of 0.625 and AUC of 0.412). • The image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the treatment response prediction model. The dosimetric parameters of GTV HI, Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model.
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Zhang YH, Herlin G, Rouvelas I, Nilsson M, Lundell L, Brismar TB. Texture analysis of computed tomography data using morphologic and metabolic delineation of esophageal cancer-relation to tumor type and neoadjuvant therapy response. Dis Esophagus 2019; 32:5123416. [PMID: 30295752 DOI: 10.1093/dote/doy096] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 08/15/2018] [Accepted: 09/05/2018] [Indexed: 12/11/2022]
Abstract
The prognostic values of image-based tumor texture analysis based on computed tomography (CT) and of limiting the segmented tumor volume to metabolically active regions using fludeoxyglucose-positron emission tomography (FDG-PET) were studied in 25 patients with esophageal adenocarcinoma and 11 patients with squamous cell carcinoma. The aims of this study are to describe their CT-image-based texture characteristics before and after neoadjuvant therapy and to evaluate whether limiting the examined tumor volume to metabolically active regions detected with FDG-PET image data would further improve their value. Textural parameters (homogeneity, energy, entropy, contrast, and correlation) based on gray-level co-occurrence matrices (GLCM) were calculated for 3D volumes of segmented esophageal tumors before and after neoadjuvant chemotherapy or radiochemotherapy. Histopathological data after surgical resection and textural parameters before and after neoadjuvant treatment were compared using the Mann-Whitney U test. Significant differences in the textural parameters were observed between adenocarcinoma and squamous cell carcinoma for homogeneity, energy, inertia, and correlation. The use of contrast media during scanning resulted in significant differences in homogeneity, energy, entropy, and inertia for adenocarcinoma but not squamous cell carcinoma. There was also a significant difference in all textural parameters between pathological T status for ypT0-ypT2 and ypT3-ypT4 adenocarcinomas, but not in squamous cell carcinoma patients. No additional value was found from using PET image data to aid segmentation of CT images.
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Affiliation(s)
- Y-H Zhang
- Department of Diagnostic Radiology, Centre for Digestive Diseases, Karolinska Institutet, CLINTEC, Karolinska University Hospital, Stockholm, Sweden
| | - G Herlin
- Department of Diagnostic Radiology, Centre for Digestive Diseases, Karolinska Institutet, CLINTEC, Karolinska University Hospital, Stockholm, Sweden
| | - I Rouvelas
- Department of Surgery, Centre for Digestive Diseases, Karolinska Institutet, CLINTEC, Karolinska University Hospital, Stockholm, Sweden
| | - M Nilsson
- Department of Surgery, Centre for Digestive Diseases, Karolinska Institutet, CLINTEC, Karolinska University Hospital, Stockholm, Sweden
| | - L Lundell
- Department of Surgery, Centre for Digestive Diseases, Karolinska Institutet, CLINTEC, Karolinska University Hospital, Stockholm, Sweden
| | - T B Brismar
- Department of Diagnostic Radiology, Centre for Digestive Diseases, Karolinska Institutet, CLINTEC, Karolinska University Hospital, Stockholm, Sweden
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Zhang YH, Fischer MA, Lehmann H, Johnsson Å, Rouvelas I, Herlin G, Lundell L, Brismar TB. Computed tomography volumetry of esophageal cancer - the role of semiautomatic assessment. BMC Med Imaging 2019; 19:17. [PMID: 30767773 PMCID: PMC6377716 DOI: 10.1186/s12880-019-0317-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 01/28/2019] [Indexed: 01/16/2023] Open
Abstract
Background The clinical and research value of Computed Tomography (CT) volumetry of esophageal cancer tumor size remains controversial. Development in CT technique and image analysis has made CT volumetry less cumbersome and it has gained renewed attention. The aim of this study was to assess esophageal tumor volume by semi-automatic measurements as compared to manual. Methods A total of 23 esophageal cancer patients (median age 65, range 51–71), undergoing CT in the portal-venous phase for tumor staging, were retrospectively included between 2007 and 2012. One radiology resident and one consultant radiologist measured the tumor volume by semiautomatic segmentation and manual segmentation. Reproducibility of the respective measurements was assessed by intraclass correlation coefficients (ICC) and by average deviation from mean. Results Mean tumor volume was 46 ml (range 5-137 ml) using manual segmentation and 42 ml (range 3-111 ml) using semiautomatic segmentation. Semiautomatic measurement provided better inter-observer agreement than traditional manual segmentation. The ICC was significantly higher for semiautomatic segmentation in comparison to manual segmentation (0.86, 0.56, p < 0.01). The average absolute percentage difference from mean was reduced from 24 to 14% (p < 0.001) when using semiautomatic segmentation. Conclusions Semiautomatic analysis outperforms manual analysis for assessment of esophageal tumor volume, improving reproducibility.
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Affiliation(s)
- Yi-Hua Zhang
- Department of Diagnostic Radiology and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden. .,Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology, Karolinska University Hospital, Huddinge, 141 86, Stockholm, Sweden.
| | - Michael A Fischer
- Department of Diagnostic Radiology and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden
| | - Henrik Lehmann
- Department of Diagnostic Radiology and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden
| | - Åse Johnsson
- Department of Radiology, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.,Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Ioannis Rouvelas
- Department of Surgery, Centre for Digestive Diseases and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden
| | - Gunnar Herlin
- Department of Diagnostic Radiology and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden
| | - Lars Lundell
- Department of Surgery, Centre for Digestive Diseases and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden
| | - Torkel B Brismar
- Department of Diagnostic Radiology and Karolinska Institutet, Karolinska University Hospital, CLINTEC, Stockholm, Sweden
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Klaassen R, Larue RTHM, Mearadji B, van der Woude SO, Stoker J, Lambin P, van Laarhoven HWM. Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients. PLoS One 2018; 13:e0207362. [PMID: 30440002 PMCID: PMC6237370 DOI: 10.1371/journal.pone.0207362] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 10/30/2018] [Indexed: 02/06/2023] Open
Abstract
In this study we investigate a CT radiomics approach to predict response to chemotherapy of individual liver metastases in patients with esophagogastric cancer (EGC). In eighteen patients with metastatic EGC treated with chemotherapy, all liver metastases were manually delineated in 3D on the pre-treatment and evaluation CT. From the pre-treatment CT scans 370 radiomics features were extracted per lesion. Random forest (RF) models were generated to discriminate partial responding (PR, >65% volume decrease, including 100% volume decrease), and complete remission (CR, only 100% volume decrease) lesions from other lesions. RF-models were build using a leave one out strategy where all lesions of a single patient were removed from the dataset and used as validation set for a model trained on the lesions of the remaining patients. This process was repeated for all patients, resulting in 18 trained models and one validation set for both the PR and CR datasets. Model performance was evaluated by receiver operating characteristics with corresponding area under the curve (AUC). In total 196 liver metastases were delineated on the pre-treatment CT, of which 99 (51%) lesions showed a decrease in size of more than 65% (PR). From the PR set a total of 47 (47% of RL, 24% of initial) lesions were no longer detected in CT scan 2 (CR). The RF-model for PR lesions showed an average training AUC of 0.79 (range: 0.74-0.83) and 0.65 (95% ci: 0.57-0.73) for the combined validation set. The RF-model for CR lesions had an average training AUC of 0.87 (range: 0.83-0.90) and 0.79 (95% ci 0.72-0.87) for the validation set. Our findings show that individual response of liver metastases varies greatly within and between patients. A CT radiomics approach shows potential in discriminating responding from non-responding liver metastases based on the pre-treatment CT scan, although further validation in an independent patient cohort is needed to validate these findings.
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Affiliation(s)
- Remy Klaassen
- Amsterdam UMC, University of Amsterdam, Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam, Netherlands
- Amsterdam UMC, University of Amsterdam, LEXOR, Laboratory for Experimental Oncology and Radiobiology, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Ruben T. H. M. Larue
- The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht Comprehensive Cancer Centre, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Banafsche Mearadji
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Stephanie O. van der Woude
- Amsterdam UMC, University of Amsterdam, Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Jaap Stoker
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht Comprehensive Cancer Centre, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Hanneke W. M. van Laarhoven
- Amsterdam UMC, University of Amsterdam, Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam, Netherlands
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Larue RTHM, Klaassen R, Jochems A, Leijenaar RTH, Hulshof MCCM, van Berge Henegouwen MI, Schreurs WMJ, Sosef MN, van Elmpt W, van Laarhoven HWM, Lambin P. Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer. Acta Oncol 2018; 57:1475-1481. [PMID: 30067421 DOI: 10.1080/0284186x.2018.1486039] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Radiomic features retrieved from standard CT-images have shown prognostic power in several tumor sites. In this study, we investigated the prognostic value of pretreatment CT radiomic features to predict overall survival of esophageal cancer patients after chemoradiotherapy. MATERIAL AND METHODS Two datasets of independent centers were analyzed, consisting of esophageal cancer patients treated with concurrent chemotherapy (Carboplatin/Paclitaxel) and 41.4Gy radiotherapy, followed by surgery if feasible. In total, 1049 radiomic features were calculated from the primary tumor volume. Recursive feature elimination was performed to select the 40 most relevant predictors. Using these 40 features and six clinical variables as input, two random forest (RF) models predicting 3-year overall survival were developed. RESULTS In total 165 patients from center 1 and 74 patients from center 2 were used. The radiomics-based RF model yielded an area under the curve (AUC) of 0.69 (95%CI 0.61-0.77), with the top-5 most important features for 3-year survival describing tumor heterogeneity after wavelet filtering. In the validation dataset, the RF model yielded an AUC of 0.61 (95%CI 0.47-0.75). Kaplan Meier plots were significantly different between risk groups in the training dataset (p = .027) and borderline significant in the validation dataset (p = .053). The clinical RF model yielded AUCs of 0.63 (95%CI 0.54-0.71) and 0.62 (95%CI 0.49-0.76) in the training and validation dataset, respectively. Risk groups did not reach a significant correlation with pathological response in the primary tumor. CONCLUSIONS A RF model predicting 3-year overall survival based on pretreatment CT radiomic features was developed and validated in two independent datasets of esophageal cancer patients. The radiomics model had better prognostic power compared to the model using standard clinical variables.
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Affiliation(s)
- Ruben T. H. M. Larue
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Remy Klaassen
- Department of Medical Oncology, Academic Medical Centre, Amsterdam, The Netherlands
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Ralph T. H. Leijenaar
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | | | - Wendy M. J. Schreurs
- Department of Nuclear Medicine, Zuyderland Medical Centre, Heerlen, The Netherlands
| | - Meindert N. Sosef
- Department of Surgery, Zuyderland Medical Centre, Heerlen, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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