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Wang N, Qu S, Kong W, Hua Q, Hong Z, Liu Z, Shi Y. Establishment and validation of novel predictive models to predict bone metastasis in newly diagnosed prostate adenocarcinoma based on single-photon emission computed tomography radiomics. Ann Nucl Med 2024; 38:734-743. [PMID: 38822897 DOI: 10.1007/s12149-024-01942-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 05/12/2024] [Indexed: 06/03/2024]
Abstract
PURPOSE To establish and validate novel predictive models for predicting bone metastasis (BM) in newly diagnosed prostate adenocarcinoma (PCa) via single-photon emission computed tomography radiomics. METHOD In a retrospective review of the clinical single-photon emission computed tomography (SPECT) database, 176 patients (training set: n = 140; validation set: n = 36) who underwent SPECT/CT imaging and were histologically confirmed to have newly diagnosed PCa from June 2016 to June 2022 were enrolled. Radiomic features were extracted from the region of interest (ROI) in a targeted lesion in each patient. Clinical features, including age, total prostate-specific antigen (t-PSA), and Gleason grades, were included. Statistical tests were then employed to eliminate irrelevant and redundant features. Finally, four types of optimized models were constructed for the prediction. Furthermore, fivefold cross-validation was applied to obtain sensitivity, specificity, accuracy, and area under the curve (AUC) for performance evaluation. The clinical usefulness of the multivariate models was estimated through decision curve analysis (DCA). RESULTS A radiomics signature consisting of 27 selected features which were obtained by radiomics' LASSO treatment was significantly correlated with bone status (P < 0.01 for both training and validation sets). Collectively, the models showed good predictive efficiency. The AUC values ranged from 0.87 to 0.98 in four models. The AUC values of the human experts were 0.655 and 0.872 in the training and validation groups, respectively. Most radiomic models showed better diagnostic accuracy than human experts in the training and validation groups. DCA also demonstrated the superiority of the radiomics models compared to human experts. CONCLUSION Radiomics models are superior to humans in differentiating between benign bone and prostate cancer bone metastases; it can be used to facilitate personalized prediction of BM in newly diagnosed PCa patients.
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Affiliation(s)
- Ning Wang
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Shihui Qu
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Weiwei Kong
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Qian Hua
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Zhihui Hong
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Zengli Liu
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Yizhen Shi
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China.
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Garbarino GM, Polici M, Caruso D, Laghi A, Mercantini P, Pilozzi E, van Berge Henegouwen MI, Gisbertz SS, van Grieken NCT, Berardi E, Costa G. Radiomics in Oesogastric Cancer: Staging and Prediction of Preoperative Treatment Response: A Narrative Review and the Results of Personal Experience. Cancers (Basel) 2024; 16:2664. [PMID: 39123392 PMCID: PMC11311587 DOI: 10.3390/cancers16152664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/20/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Oesophageal, gastroesophageal, and gastric malignancies are often diagnosed at locally advanced stage and multimodal therapy is recommended to increase the chances of survival. However, given the significant variation in treatment response, there is a clear imperative to refine patient stratification. The aim of this narrative review was to explore the existing evidence and the potential of radiomics to improve staging and prediction of treatment response of oesogastric cancers. METHODS The references for this review article were identified via MEDLINE (PubMed) and Scopus searches with the terms "radiomics", "texture analysis", "oesophageal cancer", "gastroesophageal junction cancer", "oesophagogastric junction cancer", "gastric cancer", "stomach cancer", "staging", and "treatment response" until May 2024. RESULTS Radiomics proved to be effective in improving disease staging and prediction of treatment response for both oesophageal and gastric cancer with all imaging modalities (TC, MRI, and 18F-FDG PET/CT). The literature data on the application of radiomics to gastroesophageal junction cancer are very scarce. Radiomics models perform better when integrating different imaging modalities compared to a single radiology method and when combining clinical to radiomics features compared to only a radiomics signature. CONCLUSIONS Radiomics shows potential in noninvasive staging and predicting response to preoperative therapy among patients with locally advanced oesogastric cancer. As a future perspective, the incorporation of molecular subgroup analysis to clinical and radiomic features may even increase the effectiveness of these predictive and prognostic models.
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Affiliation(s)
| | - Michela Polici
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Paolo Mercantini
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Emanuela Pilozzi
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Sant’Andrea Hospital, 00189 Rome, Italy
| | - Mark I. van Berge Henegouwen
- Department of Surgery, Amsterdam UMC Location University of Amsterdam, 1081 HV Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, 1081 HV Amsterdam, The Netherlands
| | - Suzanne S. Gisbertz
- Department of Surgery, Amsterdam UMC Location University of Amsterdam, 1081 HV Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, 1081 HV Amsterdam, The Netherlands
| | - Nicole C. T. van Grieken
- Department of Pathology, Amsterdam UMC Location Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Cancer Biology and Immunology, Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Eva Berardi
- Department of Radiology, San Camillo Hospital, ASL RM 1, 00152 Rome, Italy
| | - Gianluca Costa
- Department of Life Science, Health and Health Professions, Link Campus University, 00165 Rome, Italy
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Geng X, Zhang Y, Li Y, Cai Y, Liu J, Geng T, Meng X, Hao F. Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma. Br J Radiol 2024; 97:652-659. [PMID: 38268475 PMCID: PMC11027331 DOI: 10.1093/bjr/tqae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/10/2023] [Accepted: 12/18/2023] [Indexed: 01/26/2024] Open
Abstract
OBJECTIVES This research aimed to develop a radiomics-clinical nomogram based on enhanced thin-section CT radiomics and clinical features for the purpose of predicting the presence or absence of metastasis in lymph nodes among patients with resectable esophageal squamous cell carcinoma (ESCC). METHODS This study examined the data of 256 patients with ESCC, including 140 cases with lymph node metastasis. Clinical information was gathered for each case, and radiomics features were derived from thin-section contrast-enhanced CT with the help of a 3D slicer. To validate risk factors that are independent of the clinical and radiomics models, least absolute shrinkage and selection operator logistic regression analysis was used. A nomogram pattern was constructed based on the radiomics features and clinical characteristics. The receiver operating characteristic curve and Brier Score were used to evaluate the model's discriminatory ability, the calibration plot to evaluate the model's calibration, and the decision curve analysis to evaluate the model's clinical utility. The confusion matrix was used to evaluate the applicability of the model. To evaluate the efficacy of the model, 1000 rounds of 5-fold cross-validation were conducted. RESULTS The clinical model identified esophageal wall thickness and clinical T (cT) stage as independent risk factors, whereas the radiomics pattern was built based on 4 radiomics features chosen at random. Area under the curve (AUC) values of 0.684 and 0.701 are observed for the radiomics approach and clinical model, respectively. The AUC of nomogram combining radiomics and clinical features was 0.711. The calibration plot showed good agreement between the incidence of lymph node metastasis predicted by the nomogram and the actual probability of occurrence. The nomogram model displayed acceptable levels of performance. After 1000 rounds of 5-fold cross-validation, the AUC and Brier score had median values of 0.702 (IQR: 0.65, 7.49) and 0.21 (IQR: 0.20, 0.23), respectively. High-risk patients (risk point >110) were found to have an increased risk of lymph node metastasis [odds ratio (OR) = 5.15, 95% CI, 2.95-8.99] based on the risk categorization. CONCLUSION A successful preoperative prediction performance for metastasis to the lymph nodes among patients with ESCC was demonstrated by the nomogram that incorporated CT radiomics, wall thickness, and cT stage. ADVANCES IN KNOWLEDGE This study demonstrates a novel radiomics-clinical nomogram for lymph node metastasis prediction in ESCC, which helps physicians determine lymph node status preoperatively.
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Affiliation(s)
- Xiaotao Geng
- Shandong University Cancer Center, Shandong University, 440 Jiyan Road, Jinan, 250117, China
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yaping Zhang
- Department of Radiology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yang Li
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yuanyuan Cai
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Jie Liu
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Tianxiang Geng
- Department of Biomaterials, Faculty of Dentistry, University of Oslo, Oslo, 0455, Norway
| | - Xiangdi Meng
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Furong Hao
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
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Wu X, Wu H, Miao S, Cao G, Su H, Pan J, Xu Y. Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach. BMC Med Inform Decis Mak 2024; 24:3. [PMID: 38167058 PMCID: PMC10759510 DOI: 10.1186/s12911-023-02386-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/04/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despite this, current techniques, which can be invasive and costly, struggle with achieving the necessary precision, highlighting a pressing need for more effective, non-invasive alternatives. METHOD We developed ResoLSTM-Depth, a deep learning model to distinguish ESCC stages T1-T2 from T3-T4. It integrates ResNet-18 and Long Short-Term Memory (LSTM) networks, leveraging their strengths in spatial and sequential data processing. This method uses arterial phase CT scans from ESCC patients. The dataset was meticulously segmented by an experienced radiologist for effective training and validation. RESULTS Upon performing five-fold cross-validation, the ResoLSTM-Depth model exhibited commendable performance with an accuracy of 0.857, an AUC of 0.901, a sensitivity of 0.884, and a specificity of 0.828. These results were superior to the ResNet-18 model alone, where the average accuracy is 0.824 and the AUC is 0.879. Attention maps further highlighted influential features for depth prediction, enhancing model interpretability. CONCLUSION ResoLSTM-Depth is a promising tool for ESCC invasion depth prediction. It offers potential for improvement in the staging and therapeutic planning of ESCC.
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Affiliation(s)
- Xiaoli Wu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Hao Wu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Shouliang Miao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Guoquan Cao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Huang Su
- Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, Zhejiang, China
| | - Jie Pan
- Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, Zhejiang, China
| | - Yilun Xu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
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Li Y, Yang L, Gu X, Wang Q, Shi G, Zhang A, Yue M, Wang M, Ren J. Computed tomography radiomics identification of T1-2 and T3-4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional? Abdom Radiol (NY) 2024; 49:288-300. [PMID: 37843576 PMCID: PMC10789855 DOI: 10.1007/s00261-023-04070-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND To evaluate two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics analysis for the T stage of esophageal squamous cell carcinoma (ESCC). METHODS 398 patients with pathologically confirmed ESCC were divided into training and testing sets. All patients underwent chest CT scans preoperatively. For each tumor, based on CT images, a 2D region of interest (ROI) was outlined on the largest cross-sectional area, and a 3D ROI was outlined layer by layer on each section of the tumor. The radiomics platform was used for feature extraction. For feature selection, stepwise logistic regression was used. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of the 2D radiomics model versus the 3D radiomics model. The differences were compared using the DeLong test. The value of the clinical utility of the two radiomics models was evaluated. RESULTS 1595 radiomics features were extracted. After screening, two radiomics models were constructed. In the training set, the difference between the area under the curve (AUC) of the 2D radiomics model (AUC = 0.831) and the 3D radiomics model (AUC = 0.830) was not statistically significant (p = 0.973). In the testing set, the difference between the AUC of the 2D radiomics model (AUC = 0.807) and the 3D radiomics model (AUC = 0.797) was also not statistically significant (p = 0.748). A 2D model was equally useful as a 3D model in clinical situations. CONCLUSION The performance of 2D radiomics model is comparable to that of 3D radiomics model in distinguishing between the T1-2 and T3-4 stages of ESCC. In addition, 2D radiomics model may be a more feasible option due to the shorter time required for segmenting the ROI.
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Affiliation(s)
- Yang Li
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Li Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Xiaolong Gu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China.
| | - Qi Wang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Gaofeng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Andu Zhang
- Department of Radiotherapy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Meng Yue
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Mingbo Wang
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Jialiang Ren
- GE Healthcare China, Beijing, 100176, People's Republic of China
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Zhao B, Yan S, Jia ZY, Zhu HT, Shi YJ, Li XT, Qu JR, Sun YS. CT radiomics in the identification of preoperative understaging in patients with clinical stage T1-2N0 esophageal squamous cell carcinoma. Quant Imaging Med Surg 2023; 13:7996-8008. [PMID: 38106287 PMCID: PMC10722054 DOI: 10.21037/qims-23-275] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 10/11/2023] [Indexed: 12/19/2023]
Abstract
Background Predicting preoperative understaging in patients with clinical stage T1-2N0 (cT1-2N0) esophageal squamous cell carcinoma (ESCC) is critical to customizing patient treatment. Radiomics analysis can provide additional information that reflects potential biological heterogeneity based on computed tomography (CT) images. However, to the best of our knowledge, no studies have focused on identifying CT radiomics features to predict preoperative understaging in patients with cT1-2N0 ESCC. Thus, we sought to develop a CT-based radiomics model to predict preoperative understaging in patients with cT1-2N0 esophageal cancer, and to explore the value of the model in disease-free survival (DFS) prediction. Methods A total of 196 patients who underwent radical surgery for cT1-2N0 ESCC were retrospectively recruited from two hospitals. Among the 196 patients, 134 from Peking University Cancer Hospital were included in the training cohort, and 62 from Henan Cancer Hospital were included in the external validation cohort. Radiomics features were extracted from patients' CT images. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and model construction. A clinical model was also built based on clinical characteristics, and the tumor size [the length, thickness and the thickness-to-length ratio (TLR)] was evaluated on the CT images. A radiomics nomogram was established based on multivariate logistic regression. The diagnostic performance of the models in predicting preoperative understaging was assessed by the area under the receiver operating characteristic curve (AUC). Kaplan-Meier curves with the log-rank test were employed to analyze the correlation between the nomogram and DFS. Results Of the patients, 50.0% (67/134) and 51.6% (32/62) were understaged in the training and validation groups, respectively. The radiomics scores and the TLRs of the tumors were included in the nomogram. The AUCs of the nomogram for predicting preoperative understaging were 0.874 [95% confidence interval (CI): 0.815-0.933] in the training cohort and 0.812 (95% CI: 0.703-0.912) in the external validation cohort. The diagnostic performance of the nomogram was superior to that of the clinical model (P<0.05). The nomogram was an independent predictor of DFS in patients with cT1-2N0 ESCC. Conclusions The proposed CT-based radiomics model could be used to predict preoperative understaging in patients with cT1-2N0 ESCC who have undergone radical surgery.
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Affiliation(s)
- Bo Zhao
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Shuo Yan
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Zheng-Yan Jia
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Hai-Tao Zhu
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yan-Jie Shi
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiao-Ting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Jin-Rong Qu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Ying-Shi Sun
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
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Lei X, Cao Z, Wu Y, Lin J, Zhang Z, Jin J, Ai Y, Zhang J, Du D, Tian Z, Xie C, Yin W, Jin X. Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics. Insights Imaging 2023; 14:174. [PMID: 37840068 PMCID: PMC10577114 DOI: 10.1186/s13244-023-01528-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/19/2023] [Indexed: 10/17/2023] Open
Abstract
BACKGROUND Preoperative stratification is critical for the management of patients with esophageal cancer (EC). To investigate the feasibility and accuracy of PET-CT-based radiomics in preoperative prediction of clinical and pathological stages for patients with EC. METHODS Histologically confirmed 100 EC patients with preoperative PET-CT images were enrolled retrospectively and randomly divided into training and validation cohorts at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) was applied to select optimal radiomics features from PET, CT, and fused PET-CT images, respectively. Logistic regression (LR) was applied to classify the T stage (T1,2 vs. T3,4), lymph node metastasis (LNM) (LNM(-) vs. LNM(+)), and pathological state (pstage) (I-II vs. III-IV) with features from CT (CT_LR_Score), PET (PET_LR_Score), fused PET/CT (Fused_LR_Score), and combined CT and PET features (CT + PET_LR_Score), respectively. RESULTS Seven, 10, and 7 CT features; 7, 8, and 7 PET features; and 3, 6, and 3 fused PET/CT features were selected using mRMR for the prediction of T stage, LNM, and pstage, respectively. The area under curves (AUCs) for T stage, LNM, and pstage prediction in the validation cohorts were 0.846, 0.756, 0.665, and 0.815; 0.769, 0.760, 0.665, and 0.824; and 0.727, 0.785, 0.689, and 0.837 for models of CT_LR_Score, PET_ LR_Score, Fused_ LR_Score, and CT + PET_ LR_Score, respectively. CONCLUSIONS Accurate prediction ability was observed with combined PET and CT radiomics in the prediction of T stage, LNM, and pstage for EC patients. CRITICAL RELEVANCE STATEMENT PET/CT radiomics is feasible and promising to stratify stages for esophageal cancer preoperatively. KEY POINTS • PET-CT radiomics achieved the best performance for Node and pathological stage prediction. • CT radiomics achieved the best AUC for T stage prediction. • PET-CT radiomics is feasible and promising to stratify stages for EC preoperatively.
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Affiliation(s)
- Xiyao Lei
- Department of Radiation Oncology, Lishui Municipal Central Hospital, Lishui, 323000, China
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Zhuo Cao
- Department of Respiratory, Lishui People's Hospital, Lishui, 323000, China
| | - Yibo Wu
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Jie Lin
- Department of Nuclear Medicine, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Zhenhua Zhang
- Department of Radiology, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Juebin Jin
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Yao Ai
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Ji Zhang
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Dexi Du
- Department of Radiation Oncology, Lishui Municipal Central Hospital, Lishui, 323000, China
| | - Zhifeng Tian
- Department of Radiation Oncology, Lishui Municipal Central Hospital, Lishui, 323000, China
| | - Congying Xie
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
- Department of Medical and Radiation Oncology, 2nd Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Weiwei Yin
- Department of Nuclear Medicine, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
| | - Xiance Jin
- Department of Radiation Oncology, Lishui Municipal Central Hospital, Lishui, 323000, China.
- Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
- School of Basic Medical Science, Wenzhou Medical University, Wenzhou, 325000, China.
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Yan S, Li FP, Jian L, Zhu HT, Zhao B, Li XT, Shi YJ, Sun YS. CT radiomics features of meso-esophageal fat in predicting overall survival of patients with locally advanced esophageal squamous cell carcinoma treated by definitive chemoradiotherapy. BMC Cancer 2023; 23:477. [PMID: 37231388 DOI: 10.1186/s12885-023-10973-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023] Open
Abstract
OBJECTIVE To investigate the value of CT radiomics features of meso-esophageal fat in the overall survival (OS) prediction of patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS A total of 166 patients with locally advanced ESCC in two medical centers were retrospectively analyzed. The volume of interest (VOI) of meso-esophageal fat and tumor were manually delineated on enhanced chest CT using ITK-SNAP. Radiomics features were extracted from the VOIs by Pyradiomics and then selected using the t-test, the Cox regression analysis, and the least absolute shrinkage and selection operator. The radiomics scores of meso-esophageal fat and tumors for OS were constructed by a linear combination of the selected radiomic features. The performance of both models was evaluated and compared by the C-index. Time-dependent receiver operating characteristic (ROC) analysis was employed to analyze the prognostic value of the meso-esophageal fat-based model. A combined model for risk evaluation was constructed based on multivariate analysis. RESULTS The CT radiomic model of meso-esophageal fat showed valuable performance for survival analysis, with C-indexes of 0.688, 0.708, and 0.660 in the training, internal, and external validation cohorts, respectively. The 1-year, 2-year, and 3-year ROC curves showed AUCs of 0.640-0.793 in the cohorts. The model performed equivalently compared to the tumor-based radiomic model and performed better compared to the CT features-based model. Multivariate analysis showed that meso-rad-score was the only factor associated with OS. CONCLUSIONS A baseline CT radiomic model based on the meso-esophagus provide valuable prognostic information for ESCC patients treated with dCRT.
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Affiliation(s)
- Shuo Yan
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Fei-Ping Li
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Lian Jian
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Hai-Tao Zhu
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Bo Zhao
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Xiao-Ting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Yan-Jie Shi
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
| | - Ying-Shi Sun
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
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9
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Zheng SJ, Zheng CP, Zhai TT, Xu XE, Zheng YQ, Li ZM, Li EM, Liu W, Xu LY. Development and Validation of a New Staging System for Esophageal Squamous Cell Carcinoma Patients Based on Combined Pathological TNM, Radiomics, and Proteomics. Ann Surg Oncol 2023; 30:2227-2241. [PMID: 36587172 DOI: 10.1245/s10434-022-13026-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/06/2022] [Indexed: 01/02/2023]
Abstract
OBJECTIVE This study aimed to construct a new staging system for patients with esophageal squamous cell carcinoma (ESCC) based on combined pathological TNM (pTNM) stage, radiomics, and proteomics. METHODS This study collected patients with radiomics and pTNM stage (Cohort 1, n = 786), among whom 103 patients also had proteomic data (Cohort 2, n = 103). The Cox regression model with the least absolute shrinkage and selection operator, and the Cox proportional hazards model were used to construct a nomogram and predictive models. Concordance index (C-index) and the integrated area under the time-dependent receiver operating characteristic (ROC) curve (IAUC) were used to evaluate the predictive models. The corresponding staging systems were further assessed using Kaplan-Meier survival curves. RESULTS For Cohort 1, the RadpTNM4c staging systems, constructed based on combined pTNM stage and radiomic features, outperformed the pTNM4c stage in both the training dataset 1 (Train1; IAUC 0.711 vs. 0.706, p < 0.001) and the validation dataset 1 (Valid1; IAUC 0.695 vs. 0.659, p < 0.001; C-index 0.703 vs. 0.674, p = 0.029). For Cohort 2, the ProtRadpTNM2c staging system, constructed based on combined pTNM stage, radiomics, and proteomics, outperformed the pTNM2c stage in both the Train2 (IAUC 0.777 vs. 0.610, p < 0.001; C-index 0.898 vs. 0.608, p < 0.001) and Valid2 (IAUC 0.746 vs. 0.608, p < 0.001; C-index 0.889 vs. 0.641, p = 0.009) datasets. CONCLUSIONS The ProtRadpTNM2c staging system, based on combined pTNM stage, radiomic, and proteomic features, improves the predictive performance of the classical pTNM staging system.
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Affiliation(s)
- Shao-Jun Zheng
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, 515041, Guangdong, China
- Department of Surgical Oncology, Shantou Central Hospital, Shantou, 515041, Guangdong, China
| | - Chun-Peng Zheng
- Department of Surgical Oncology, Shantou Central Hospital, Shantou, 515041, Guangdong, China.
| | - Tian-Tian Zhai
- Department of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, 515041, Guangdong, China
| | - Xiu-E Xu
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, 515041, Guangdong, China
| | - Ya-Qi Zheng
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, 515041, Guangdong, China
| | - Zhi-Mao Li
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, 515041, Guangdong, China
| | - En-Min Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, 515041, Guangdong, China
| | - Wei Liu
- College of Science, Heilongjiang Institute of Technology, Harbin, Heilongjiang, China
| | - Li-Yan Xu
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou, 515041, Guangdong, China
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10
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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] [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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11
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Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28:6002-6016. [PMID: 36405385 PMCID: PMC9669820 DOI: 10.3748/wjg.v28.i42.6002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.
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Affiliation(s)
- Qi Mao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Mao-Ting Zhou
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Zhang-Ping Zhao
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Ning Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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12
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Tu JX, Lin XT, Ye HQ, Yang SL, Deng LF, Zhu RL, Wu L, Zhang XQ. Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer. Front Oncol 2022; 12:972357. [PMID: 36091151 PMCID: PMC9453500 DOI: 10.3389/fonc.2022.972357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 07/29/2022] [Indexed: 12/09/2022] Open
Abstract
ObjectiveUsing visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated.MethodsOn April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used.ResultsA total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal “New England Journal of Medicine” of Medicine, General & Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future.ConclusionsAn increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.
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Affiliation(s)
- Jia-xin Tu
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Xue-ting Lin
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Hui-qing Ye
- School of Public Health, Nanchang University, Nanchang, China
| | - Shan-lan Yang
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Li-fang Deng
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Ruo-ling Zhu
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Lei Wu
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
- *Correspondence: Lei Wu, ; Xiao-qiang Zhang,
| | - Xiao-qiang Zhang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
- *Correspondence: Lei Wu, ; Xiao-qiang Zhang,
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Jayaprakasam VS, Gibbs P, Gangai N, Bajwa R, Sosa RE, Yeh R, Greally M, Ku GY, Gollub MJ, Paroder V. Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma? Cancers (Basel) 2022; 14:cancers14123035. [PMID: 35740700 PMCID: PMC9221147 DOI: 10.3390/cancers14123035] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 06/15/2022] [Accepted: 06/16/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary PET/CT is an important staging modality in the baseline assessment of locally advanced esophageal squamous cell carcinoma. Accurate staging and response prediction in these patients is essential for management. The aim of this retrospective study was to assess the usefulness of 18F-FDG PET/CT radiomics features in predicting outcomes such as tumor and nodal categories, PET-based response to induction chemotherapy, progression-free survival, and overall survival. In a final cohort of 74 patients, we found that the developed radiomics models can predict these clinical and prognostic outcomes with reasonable accuracy, similar or better than those derived from conventional imaging. Future studies with a larger cohort would be helpful in establishing the significance of these models. Abstract This study aimed to assess the usefulness of radiomics features of 18F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category—70.4%, 70.4%, and 81.5%, respectively; cN category—69.0%, 86.2%, and 86.2%, respectively; PET response—60.0%, 66.7%, and 70.0%, respectively; PFS—60.7%, 75.0%, and 75.0%, respectively; and OS—51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.
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Affiliation(s)
- Vetri Sudar Jayaprakasam
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (V.S.J.); (R.Y.)
| | - Peter Gibbs
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
| | - Raazi Bajwa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
| | - Ramon E. Sosa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (V.S.J.); (R.Y.)
| | | | - Geoffrey Y. Ku
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Marc J. Gollub
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
| | - Viktoriya Paroder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
- Correspondence:
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14
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Jayaprakasam VS, Gibbs P, Gangai N, Bajwa R, Sosa RE, Yeh R, Greally M, Ku GY, Gollub MJ, Paroder V. Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma? Cancers (Basel) 2022. [PMID: 35740700 DOI: 10.3390/cancers14123035%' and 2*3*8=6*8 and 'xj6b'!='xj6b%] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This study aimed to assess the usefulness of radiomics features of 18F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.
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Affiliation(s)
- Vetri Sudar Jayaprakasam
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Peter Gibbs
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Raazi Bajwa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ramon E Sosa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | | | - Geoffrey Y Ku
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Marc J Gollub
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Viktoriya Paroder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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15
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Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma? Cancers (Basel) 2022. [PMID: 35740700 DOI: 10.3390/cancers14123035'||'] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This study aimed to assess the usefulness of radiomics features of 18F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.
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16
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Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma? Cancers (Basel) 2022. [PMID: 35740700 DOI: 10.3390/cancers14123035'||dbms_pipe.receive_message(chr(98)||chr(98)||chr(98),15)||'] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This study aimed to assess the usefulness of radiomics features of 18F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.
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Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma? Cancers (Basel) 2022. [PMID: 35740700 DOI: 10.3390/cancers14123035' and 2*3*8=6*8 and 'vlhr'='vlhr] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This study aimed to assess the usefulness of radiomics features of 18F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.
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18
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Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma? Cancers (Basel) 2022. [PMID: 35740700 DOI: 10.3390/cancers14123035" and 2*3*8=6*8 and "bx4s"="bx4s] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This study aimed to assess the usefulness of radiomics features of 18F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.
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Jayaprakasam VS, Gibbs P, Gangai N, Bajwa R, Sosa RE, Yeh R, Greally M, Ku GY, Gollub MJ, Paroder V. Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma? Cancers (Basel) 2022; 14:3035. [PMID: 35740700 PMCID: PMC9221147 DOI: 10.3390/cancers14123035&n999822=v982537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This study aimed to assess the usefulness of radiomics features of 18F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.
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Affiliation(s)
- Vetri Sudar Jayaprakasam
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (V.S.J.); (R.Y.)
| | - Peter Gibbs
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
| | - Raazi Bajwa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
| | - Ramon E. Sosa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (V.S.J.); (R.Y.)
| | | | - Geoffrey Y. Ku
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Marc J. Gollub
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
| | - Viktoriya Paroder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (P.G.); (N.G.); (R.B.); (R.E.S.); (M.J.G.)
- Correspondence:
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