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He Y, Yang M, Hou R, Ai S, Nie T, Chen J, Hu H, Guo X, Liu Y, Yuan Z. Preoperative prediction of perineural invasion and lymphovascular invasion with CT radiomics in gastric cancer. Eur J Radiol Open 2024; 12:100550. [PMID: 38314183 PMCID: PMC10837067 DOI: 10.1016/j.ejro.2024.100550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 01/15/2024] [Accepted: 01/15/2024] [Indexed: 02/06/2024] Open
Abstract
Objectives To determine whether contrast-enhanced CT radiomics features can preoperatively predict lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer (GC). Methods A total of 148 patients were included in the LVI group, and 143 patients were included in the PNI group. Three predictive models were constructed, including clinical, radiomics, and combined models. A nomogram was developed with clinical risk factors to predict LVI and PNI status. The predictive performance of the three models was mainly evaluated using the mean area under the curve (AUC). The performance of three predictive models was assessed concerning calibration and clinical usefulness. Results In the LVI group, the predictive power of the combined model (AUC=0.871, 0.822) outperformed the clinical model (AUC=0.792, 0.728) and the radiomics model (AUC=0.792, 0.728) in both the training and testing cohorts. In the PNI group, the combined model (AUC=0.834, 0.828) also had better predictive power than the clinical model (AUC=0.764, 0.632) and the radiomics model (AUC=0.764, 0.632) in both the training and testing cohorts. The combined models also showed good calibration and clinical usefulness for LVI and PNI prediction. Conclusion CECT-based radiomics analysis might serve as a non-invasive method to predict LVI and PNI status in GC.
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Affiliation(s)
- Yaoyao He
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Miao Yang
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Rong Hou
- Department of Patholoogy, Suizhou Hospital Affiliated to Hubei Medical College, 441300, PR China
| | - Shuangquan Ai
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Tingting Nie
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Jun Chen
- Bayer Healthcare, Wuhan, PR China
| | - Huaifei Hu
- College of Biomedical Engineering, South-Central Minzu University, Wuhan 430074, PR China
| | - Xiaofang Guo
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
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Yin M, Zhang R, Lin J, Zhu S, Liu L, Liu X, Lu J, Xu C, Zhu J. Identification of gastric signet ring cell carcinoma based on endoscopic images using few-shot learning. Dig Liver Dis 2023; 55:1725-1734. [PMID: 37455154 DOI: 10.1016/j.dld.2023.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Deep learning (DL) models perform poorly when there are limited gastric signet ring cell carcinoma (SRCC) samples. Few-shot learning (FSL) can address the small sample problem. METHODS EfficientNetV2-S was first pretrained on ImageNet and then pretrained on esophageal endoscopic images (i.e., base classes: normal vs. early cancer vs. advanced cancer) using transfer learning. Second, images of gastric benign ulcers, adenocarcinoma and SRCC, i.e., novel classes (n = 50 per class), were included. Image features were extracted as vectors using the dual pretrained EfficientNetV2-S. Finally, a k-nearest neighbor classifier was used to identify SRCC. The above proposed 3-way 3-shot FSL framework was conducted in three rounds. RESULTS Dual pretrained FSL performed better than single pretrained FSL, endoscopists and traditional EfficientNetV2-S models. Dual pretrained FSL obtained the highest accuracy (79.4%), sensitivity (68.8%), recall (68.8%), precision (69.3%) and F1-score (0.691), and the senior endoscopist achieved the highest specificity of 93.6% when identifying SRCC. The macro-AUC and F1-score for dual pretraining (0.763 and 0.703, respectively) were higher than those for single pretraining (0.656 and 0.537, respectively), along with endoscopists and traditional EfficientNetV2-S models. The 2-way 3-shot FSL also performed better. CONCLUSION The proposed FSL framework showed practical performance in the differentiation of SRCC on endoscopic images, suggesting the potential of FSL in the computer-aided diagnosis for rare diseases.
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Affiliation(s)
- Minyue Yin
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, Jiangsu, 215006, China
| | - Rufa Zhang
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu No.1 People's Hospital, Suzhou, Jiangsu, 215500, China
| | - Jiaxi Lin
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, Jiangsu, 215006, China
| | - Shiqi Zhu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, Jiangsu, 215006, China
| | - Lu Liu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, Jiangsu, 215006, China
| | - Xiaolin Liu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, Jiangsu, 215006, China
| | - Jianying Lu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, Jiangsu, 215006, China
| | - Chunfang Xu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, Jiangsu, 215006, China
| | - Jinzhou Zhu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, Jiangsu, 215006, China.
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Guo Q, Sun Q, Bian X, Wang M, Dong H, Yin H, Dai X, Fan G, Chen G. Development and validation of a multiphase CT radiomics nomogram for the preoperative prediction of lymphovascular invasion in patients with gastric cancer. Clin Radiol 2023; 78:e552-e559. [PMID: 37117048 DOI: 10.1016/j.crad.2023.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/13/2023] [Accepted: 03/22/2023] [Indexed: 04/30/2023]
Abstract
AIM To develop a nomogram to predict lymphovascular invasion (LVI) in gastric cancer by integrating multiphase computed tomography (CT) radiomics and clinical risk factors. MATERIALS AND METHODS One hundred and seventy-two gastric cancer patients (121 training and 51 validation) with preoperative contrast-enhanced CT images and clinicopathological data were collected retrospectively. The clinical risk factors were selected by univariate and multivariate regression analysis. Radiomic features were extracted and selected from the arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images of each patient. Clinical risk factors, radiomic features, and integration of both were used to develop the clinical model, radiomic models, and nomogram, respectively. RESULTS Radiomic features from AP (n=6), VP (n=6), DP (n=7) CT images and three selected clinical risk factors were used for model development. The nomogram showed better performance than the AP, VP, DP, and clinical models in the training and validation datasets, providing areas under the curves (AUCs) of 0.890 (95% CI: 0.820-0.940) and 0.885 (95% CI:0.765-0.957), respectively. All models indicated good calibration, and decision curve analysis proved that the net benefit of the nomogram was superior to that of the clinical and radiomic models throughout the vast majority of the threshold probabilities. CONCLUSIONS The nomogram integrating multiphase CT radiomics and clinical risk factors showed favourable performance in predicting LVI of gastric cancer, which may benefit clinical practice.
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Affiliation(s)
- Q Guo
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - Q Sun
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - X Bian
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - M Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - H Dong
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - H Yin
- Institute of Advanced Research, Beijing Infervision Technology Co., Ltd, Beijing, China
| | - X Dai
- Department of Pathology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - G Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - G Chen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China.
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Li M, Qin H, Yu X, Sun J, Xu X, You Y, Ma C, Yang L. Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map. Insights Imaging 2023; 14:125. [PMID: 37454355 DOI: 10.1186/s13244-023-01477-8] [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: 02/20/2023] [Accepted: 07/03/2023] [Indexed: 07/18/2023] Open
Abstract
OBJECTIVE To investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer. METHODS A retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance. RESULTS Gender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802). CONCLUSION The R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer. CRITICAL RELEVANCE STATEMENT The radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis.
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Affiliation(s)
- Min Li
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Hongtao Qin
- Department of Radiology and Nuclear Medicine, The First Hospital of Hebei Medical University, No. 89, Donggang Road, Shijiazhuang, 050031, Hebei Province, People's Republic of China
| | - Xianbo Yu
- Siemens Healthineers Ltd., 7, Wangjing Zhonghuan Nanlu, Beijing, 100102, People's Republic of China
| | - Junyi Sun
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Xiaosheng Xu
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Yang You
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Chongfei Ma
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China
| | - Li Yang
- Department of Computed Tomography and Magnetic Resonance, Fourth Hospital of Hebei Medical University, No. 12, JianKang Road, Shijiazhuang, 050010, Hebei Province, People's Republic of China.
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Zhao H, Gao J, Bai B, Wang R, Yu J, Lu H, Cheng M, Liang P. Development and external validation of a non-invasive imaging biomarker to estimate the microsatellite instability status of gastric cancer and its prognostic value: The combination of clinical and quantitative CT-imaging features. Eur J Radiol 2023; 162:110719. [PMID: 36764010 DOI: 10.1016/j.ejrad.2023.110719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/08/2023] [Accepted: 01/25/2023] [Indexed: 01/28/2023]
Abstract
PURPOSE Molecular testing for microsatellite instability (MSI) status plays a vital role in the clinical management of gastric cancer (GC). Nevertheless, challenges of routinely applied technology for MSI determination exist. This study aimed to develop and validate a non-invasive imaging biomarker for MSI assessment in GC and explore its prognostic value. METHODS We retrospectively recruited 396 GC patients with pretreatment CT images from a single center and a public database and divided them into an original cohort (n = 356) and an external validation cohort (n = 40). The SMOTE algorithm was used to generate a balanced training cohort (n = 192) and the independent radiomics model, clinical model, and radiomics-clinic combined model were constructed for determining MSI status. The models' discrimination, calibration, clinical usefulness, and prognosis significance were evaluated by AUC, calibration, decision curve analyses, and Kaplan-Meier curve analysis, respectively. RESULTS The radiomics-clinic combined model derived from clinical and quantitative CT-based "Radscore" exhibited the best discriminatory abilities of MSI status in all cohorts, with AUCs of 0.836 (95% CI, 0.780-0.893) in the training cohort, 0.834 (95% CI, 0.688-0.981) in the external validation cohort, and 0.750 (95% CI, 0.682-0.819) in the original cohort, respectively. Meanwhile, the combined model demonstrated goodness of fitness, higher clinical net benefits, and significant positive integrated discrimination improvement compared with any independent model. While it showed no significant overall survival- or progression-free survival-based risk stratification ability (p > 0.05). CONCLUSIONS The radiomics-clinic combined model could be a potential non-invasive biomarker for MSI status in GC, which help clinical decision-making, nevertheless, provided limited prognostic ability.
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Affiliation(s)
- Huiping Zhao
- Department of CT, Shaanxi Provincial People's Hospital, No. 256, Youyi West Road, Xi'an 710068, Shaanxi Province, China.
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor & Henan International Joint Laboratory of Medical Imaging & Henan Engineering Laboratory of Tumor Imaging & Henan Key Laboratory of CT Imaging & Zhengzhou Key Laboratory of Medical Imaging Technology and Diagnosis, Zhengzhou 450052, Henan Province, China
| | - Biaosheng Bai
- Department of Radiotherapy, People's Hospital of Bayingolin Mongol Autonomous Prefecture, Korla 841000, Xinjiang, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor & Henan International Joint Laboratory of Medical Imaging & Henan Engineering Laboratory of Tumor Imaging & Henan Key Laboratory of CT Imaging & Zhengzhou Key Laboratory of Medical Imaging Technology and Diagnosis, Zhengzhou 450052, Henan Province, China
| | - Juan Yu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, China
| | - Hao Lu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor & Henan International Joint Laboratory of Medical Imaging & Henan Engineering Laboratory of Tumor Imaging & Henan Key Laboratory of CT Imaging & Zhengzhou Key Laboratory of Medical Imaging Technology and Diagnosis, Zhengzhou 450052, Henan Province, China
| | - Ming Cheng
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor & Henan International Joint Laboratory of Medical Imaging & Henan Engineering Laboratory of Tumor Imaging & Henan Key Laboratory of CT Imaging & Zhengzhou Key Laboratory of Medical Imaging Technology and Diagnosis, Zhengzhou 450052, Henan Province, China; Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor & Henan International Joint Laboratory of Medical Imaging & Henan Engineering Laboratory of Tumor Imaging & Henan Key Laboratory of CT Imaging & Zhengzhou Key Laboratory of Medical Imaging Technology and Diagnosis, Zhengzhou 450052, Henan Province, China
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Nie T, Liu D, Ai S, He Y, Yang M, Chen J, Yuan Z, Liu Y. A radiomics nomogram analysis based on CT images and clinical features for preoperative Lauren classification in gastric cancer. Jpn J Radiol 2022; 41:401-408. [PMID: 36370327 DOI: 10.1007/s11604-022-01360-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 11/01/2022] [Indexed: 11/14/2022]
Abstract
PURPOSE To develop a combined radiomics nomogram based on computed tomography (CT) images and clinical features to preoperatively distinguish Lauren's diffuse-type gastric cancer (GC) from intestinal-type GC. METHODS Ninety-five patients with Lauren's intestinal or diffuse-type GC confirmed by postoperative pathology had their preoperative clinical information and dynamic contrast CT images retrospectively analyzed and were subdivided into training and test groups in a 7:3 ratio. To select the optimal features and construct the radiomic signatures, we extracted, filtered, and minimized the radiomic features from arterial phase (AP) and venous phase (VP) CT images. We constructed four models (clinical model, AP radiomics model, VP radiomics model, and radiomics-clinical model) to assess and compare their predictive performance between the intestinal- and diffuse-type GC. Receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), and the DeLong test were used for assessment and comparison. In this study, radiomic nomograms integrating combined radiomic signatures and clinical characteristics were developed. RESULTS Compared to the AP radiomics model, the VP radiomics model had better performance, with an AUC of 0.832 (95% confidence interval [CI], 0.735, 0.929) in the training cohort and 0.760 (95% CI 0.580, 0.940) in the test cohort. Among the combined models that assessed Lauren's type GC, the model including age and VP radiomics showed the best performance, with an AUC of 0.849 (95% CI 0.758, 0.940) in the training cohort and 0.793 (95% CI 0.629, 0.957) in the test cohort. CONCLUSIONS Nomogram incorporating radiomic signatures and clinical features effectively differentiated Lauren's diffuse-type from intestinal-type GC.
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Affiliation(s)
- Tingting Nie
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China
| | - Dan Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China
| | - Shuangquan Ai
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China
| | - Yaoyao He
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China
| | - Miao Yang
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China
| | - Jun Chen
- GE Healthiness, Shanghai, 200126, People's Republic of China
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China.
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430079, Hubei, People's Republic of China.
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Wang Q, Chen Y, Qin S, Liu X, Liu K, Xin P, Zhao W, Yuan H, Lang N. Prognostic Value and Quantitative CT Analysis in RANKL Expression of Spinal GCTB in the Denosumab Era: A Machine Learning Approach. Cancers (Basel) 2022; 14:5201. [PMID: 36358621 PMCID: PMC9658803 DOI: 10.3390/cancers14215201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/15/2023] Open
Abstract
The receptor activator of the nuclear factor kappa B ligand (RANKL) is the therapeutic target of denosumab. In this study, we evaluated whether radiomics signature and machine learning analysis can predict RANKL status in spinal giant cell tumors of bone (GCTB). This retrospective study consisted of 107 patients, including a training set (n = 82) and a validation set (n = 25). Kaplan-Meier survival analysis was used to validate the prognostic value of RANKL status. Radiomic feature extraction of three heterogeneous regions (VOIentire, VOIedge, and VOIcore) from pretreatment CT were performed. Followed by feature selection using Selected K Best and least absolute shrinkage and selection operator (LASSO) analysis, three classifiers (random forest (RF), support vector machine, and logistic regression) were used to build models. The area under the curve (AUC), accuracy, F1 score, recall, precision, sensitivity, and specificity were used to evaluate the models' performance. Classification of 75 patients with eligible follow-up based on RANKL status resulted in a significant difference in progression-free survival (p = 0.035). VOIcore-based RF classifier performs best. Using this model, the AUCs for the training and validation cohorts were 0.880 and 0.766, respectively. In conclusion, a machine learning approach based on CT radiomic features could discriminate prognostically significant RANKL status in spinal GCTB, which may ultimately aid clinical decision-making.
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Affiliation(s)
- Qizheng Wang
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Yongye Chen
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Siyuan Qin
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Xiaoming Liu
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Yongteng North Road, Haidian District, Beijing 100089, China
- Beijing United Imaging Research Institute of Intelligent Imaging, Yongteng North Road, Haidian District, Beijing 100089, China
| | - Ke Liu
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Peijin Xin
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Weili Zhao
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
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A radiomics model predicts the response of patients with advanced gastric cancer to PD-1 inhibitor treatment. Aging (Albany NY) 2022; 14:907-922. [PMID: 35073519 PMCID: PMC8833127 DOI: 10.18632/aging.203850] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 01/11/2022] [Indexed: 12/24/2022]
Abstract
Programmed cell death 1 (PD1) inhibitors have shown promising treatment effects in advanced gastric cancer, the beneficiary population not definite. This study aimed to construct an individualized radiomics model to predict the treatment benefits of PD-1 inhibitors in gastric cancer. Patients with advanced gastric cancer treated with PD-1 inhibitors were randomly divided into a training set (n = 58) and a validation set (n = 29). CT imaging data were extracted from medical records, and an individual radiomics nomogram was generated based on the imaging features and clinicopathological risk factors. Discrimination performance was evaluated by Harrell’s c-index and receiver operator characteristic (ROC) curve analyses. The areas under the ROC curves (AUCs) were analyzed to predict anti-PD-1 efficacy and survival. We found that the radiomics nomogram could predict the response of gastric cancer to anti-PD-1 treatment. The AUC was 0.865 with a 95% CI of 0.812-0.828 in the training set, while the AUC was 0.778 with a 95% CI of 0.732–0.776 in the validation set. The diagnostic performance of the radiomics was significantly higher than that of the clinical factors (p < 0.01). Patients with a low risk of disease progression discriminated by the radiomics nomogram had longer progression-free survival than those with a high risk (6.5 vs. 3.2 months, HR 1.99, 95% CI: 1.19-3.31, p = 0.009). The radiomics nomogram based on CT imaging features and clinical risk factors could predict the treatment benefits of PD-1 inhibitors in advanced gastric cancer, enabling it to guide decision-making regarding clinical treatment.
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Huang J, Chen Y, Zhang Y, Xie J, Liang Y, Yuan W, Zhou T, Gao R, Wen R, Xia Y, Long L. Comparison of clinical-computed tomography model with 2D and 3D radiomics models to predict occult peritoneal metastases in advanced gastric cancer. Abdom Radiol (NY) 2022; 47:66-75. [PMID: 34636930 DOI: 10.1007/s00261-021-03287-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/10/2021] [Accepted: 09/11/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE To compare the ability of a clinical-computed tomography (CT) model vs. 2D and 3D radiomics models for predicting occult peritoneal metastasis (PM) in patients with advanced gastric cancer (AGC). METHODS In this retrospective study, we included 49 patients with occult PM and 49 control patients (without PM) who underwent preoperative CT and subsequent surgery between January 2016 and December 2018. Clinical information and CT semantic features were collected, and CT radiomics features were extracted. A predictive clinical-CT model was created using multivariate logistic regression. The least absolute shrinkage and selection operator algorithm and logistic regression were used for constructing 2D and 3D radiomics models. These models were validated with an external cohort (n = 30). Receiver operating characteristics curve with area under the curve (AUC), sensitivity, and specificity were used to evaluate predictive performance. RESULTS Tumor size, mild ascites, and serum CA125 were independent factors predictive of occult PM. The clinical-CT model of these independent factors showed better diagnostic performance than 2D and 3D radiomics models. In the external validation cohort, the AUCs of different models were as follows-clinical-CT model: 0.853 (sensitivity, 66.7%; specificity, 93.3%); 2D radiomics model: 0.622 (sensitivity, 80.0%; specificity, 46.7%); and 3D radiomics model: 0.676 (sensitivity, 60.0%; specificity, 86.0%). The clinical-CT model nomogram showed good clinical predictive efficiency to assess occult PM. CONCLUSION The clinical-CT model was better than the radiomics models in predicting occult PM in AGC.
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Affiliation(s)
- Jiang Huang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, Guangxi, China
| | - Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuying Zhang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, Guangxi, China
| | - Jinhuan Xie
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, Guangxi, China
| | - Yiqiong Liang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, Guangxi, China
| | - Wenzhao Yuan
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, Guangxi, China
| | - Ting Zhou
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, Guangxi, China
| | - Ruizhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Yuwei Xia
- Huiying Medical Technology Co. Ltd, Beijing, 100192, China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, Guangxi, China.
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10
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Wang J, Zhong L, Zhou X, Chen D, Li R. Value of multiphase contrast-enhanced CT with three-dimensional reconstruction in detecting depth of infiltration, lymph node metastasis, and extramural vascular invasion of gastric cancer. J Gastrointest Oncol 2021; 12:1351-1362. [PMID: 34532093 DOI: 10.21037/jgo-21-276] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/29/2021] [Indexed: 12/13/2022] Open
Abstract
Background Multiphase contrast-enhanced computed tomography (CECT) can reveal the location, morphology, size, and enhancement pattern of gastric cancer (GC), whereas the three-dimensional reconstruction (3DR) technique can better display the relationships of the lesions with surrounding structures, the feeding vessels, and lymph node metastasis. Here, we investigated the value of multi-phase CECT with 3DR in detecting depth of infiltration, lymph node metastasis, and extramural vascular invasion (EMVI) of GC. Methods The clinical and imaging data of 132 GC patients admitted to the Chongqing Hospital of Traditional Chinese Medicine and the Third Affiliated Hospital of Chongqing Medical University during the period from January 2012 to October 2019 were collected. All patients received plain and multiphase contrast-enhanced CT scans. The agreement between the results of preoperative CT evaluation and the surgical/pathological findings was compared. Results (I) CT findings of GC of 3 differentiation levels: on the multiphase CECT, the peak enhancement percentage was highest in the portal venous phase. The CT values significantly differed among the arterial, portal venous, and equilibrium phases (P<0.05); the differences in the arterial, portal venous, and equilibrium phases were statistically significant among the well-, moderately, and poorly differentiated groups (all P<0.05); finally, the difference in the equilibrium phase was statistically significant between the well- and moderately differentiated groups (P<0.05). (II) Preoperative CT and postoperative pathology had good consistency in T staging (Kappa =0.667). (III) The Kappa values between the preoperative CT-diagnosed lymph node metastasis and postoperative pathologically showing an increasing consistency with the increase of CT enhancement differences. (IV) Preoperative CT and postoperative pathology had good consistency in N staging (Kappa =0.779). (V) Preoperative CT in displaying arterial supply to the stomach. The rate of positive EMVI was 32.6% (43/132) on preoperative CT. The positive EMVI diagnosed by preoperative CT was correlated with tumor size, growth pattern, tissue differentiation degree, T stage, and N stage (all P<0.05). Conclusions Multiphase CECT combined with 3DR has high diagnostic performance in detecting the depth of infiltration, lymph node metastasis, and EMVI of GC.
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Affiliation(s)
- Junda Wang
- Department of Ultrasound, The Third Affiliated Hospital, Chongqing Medical University, Chongqing, China.,Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Lijuan Zhong
- Department of Radiology, Leshan People's Hospital, Leshan, China
| | - Xinjie Zhou
- Department of Ultrasound, The Third Affiliated Hospital, Chongqing Medical University, Chongqing, China.,Department of Radiology, The Third Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Demei Chen
- Department of Ultrasound, The Third Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Rui Li
- Department of Ultrasound, The Third Affiliated Hospital, Chongqing Medical University, Chongqing, China
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11
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Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features. Tomography 2021; 7:477-487. [PMID: 34564303 PMCID: PMC8482265 DOI: 10.3390/tomography7030041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/05/2021] [Accepted: 09/14/2021] [Indexed: 12/16/2022] Open
Abstract
Aim was to develop a user-friendly method for creating parametric maps that would provide a comprehensible visualization and allow immediate quantification of radiomics features. For this, a self-explanatory graphical user interface was designed, and for the proof of concept, maps were created for CT and MR images and features were compared to those from conventional extractions. Especially first-order features were concordant between maps and conventional extractions, some even across all examples. Potential clinical applications were tested on CT and MR images for the differentiation of pulmonary lesions. In these sample applications, maps of Skewness enhanced the differentiation of non-malignant lesions and non-small lung carcinoma manifestations on CT images and maps of Variance enhanced the differentiation of pulmonary lymphoma manifestations and fungal infiltrates on MR images. This new and simple method for creating parametric maps makes radiomics features visually perceivable, allows direct feature quantification by placing a region of interest, can improve the assessment of radiological images and, furthermore, can increase the use of radiomics in clinical routine.
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12
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Qin Y, Deng Y, Jiang H, Hu N, Song B. Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction. Front Oncol 2021; 11:631686. [PMID: 34367946 PMCID: PMC8335156 DOI: 10.3389/fonc.2021.631686] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 07/07/2021] [Indexed: 02/05/2023] Open
Abstract
Gastric cancer (GC) is one of the most common cancers and one of the leading causes of cancer-related death worldwide. Precise diagnosis and evaluation of GC, especially using noninvasive methods, are fundamental to optimal therapeutic decision-making. Despite the recent rapid advancements in technology, pretreatment diagnostic accuracy varies between modalities, and correlations between imaging and histological features are far from perfect. Artificial intelligence (AI) techniques, particularly hand-crafted radiomics and deep learning, have offered hope in addressing these issues. AI has been used widely in GC research, because of its ability to convert medical images into minable data and to detect invisible textures. In this article, we systematically reviewed the methodological processes (data acquisition, lesion segmentation, feature extraction, feature selection, and model construction) involved in AI. We also summarized the current clinical applications of AI in GC research, which include characterization, differential diagnosis, treatment response monitoring, and prognosis prediction. Challenges and opportunities in AI-based GC research are highlighted for consideration in future studies.
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Affiliation(s)
- Yun Qin
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yiqi Deng
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Na Hu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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13
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Berbís MA, Aneiros-Fernández J, Mendoza Olivares FJ, Nava E, Luna A. Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases. World J Gastroenterol 2021; 27:4395-4412. [PMID: 34366612 PMCID: PMC8316909 DOI: 10.3748/wjg.v27.i27.4395] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/14/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023] Open
Abstract
The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization (i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.
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Affiliation(s)
| | - José Aneiros-Fernández
- Department of Pathology, Hospital Universitario Clínico San Cecilio, Granada 18012, Spain
| | | | - Enrique Nava
- Department of Communications Engineering, University of Málaga, Malaga 29016, Spain
| | - Antonio Luna
- MRI Unit, Department of Radiology, HT Médica, Jaén 23007, Spain
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14
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Kudou M, Kosuga T, Otsuji E. Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives. Artif Intell Gastroenterol 2020; 1:71-85. [DOI: 10.35712/aig.v1.i4.71] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 10/28/2020] [Accepted: 11/12/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) using machine or deep learning algorithms is attracting increasing attention because of its more accurate image recognition ability and prediction performance than human-aid analyses. The application of AI models to gastrointestinal (GI) clinical oncology has been investigated for the past decade. AI has the capacity to automatically detect and diagnose GI tumors with similar diagnostic accuracy to expert clinicians. AI may also predict malignant potential, such as tumor histology, metastasis, patient survival, resistance to cancer treatments and the molecular biology of tumors, through image analyses of radiological or pathological imaging data using complex deep learning models beyond human cognition. The introduction of AI-assisted diagnostic systems into clinical settings is expected in the near future. However, limitations associated with the evaluation of GI tumors by AI models have yet to be resolved. Recent studies on AI-assisted diagnostic models of gastric and colorectal cancers in the endoscopic, pathological, and radiological fields were herein reviewed. The limitations and future perspectives for the application of AI systems in clinical settings have also been discussed. With the establishment of a multidisciplinary team containing AI experts in each medical institution and prospective studies, AI-assisted medical systems will become a promising tool for GI cancer.
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Affiliation(s)
- Michihiro Kudou
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
- Department of Surgery, Kyoto Okamoto Memorial Hospital, Kyoto 613-0034, Japan
| | - Toshiyuki Kosuga
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
- Department of Surgery, Saiseikai Shiga Hospital, Ritto 520-3046, Japan
| | - Eigo Otsuji
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
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15
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Radiotherapy benefited the survival of patients with intestinal-type gastric adenocarcinoma: a SEER population-based study. Clin Transl Oncol 2020; 23:164-171. [PMID: 32488805 DOI: 10.1007/s12094-020-02408-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 05/23/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND Currently, the role of adjuvant radiotherapy (RT) in the treatment of patients with intestinal-type gastric adenocarcinoma (IGA) has not been well established. This study aimed to elucidate the survival impact of RT on such patients. METHODS The Surveillance, Epidemiology, and End Results (SEER) database was utilized to select eligible patients. The recruited patients were dichotomized into those not received RT versus those received RT. The 1:1 propensity score matching (PSM) analysis was conducted to balance the confounding factors between the two comparison groups. The categorical variables were assessed by Chi-square test. Cancer-specific survival (CSS) and overall survival (OS) of the patients were compared by Kaplan-Meier (KM) methods. Cox proportional hazard models were used to identify prognostic factors associated with CSS. RESULTS A total of 3572 eligible patients were enrolled for our analysis, of which, 2896(81.1%) patients did not receive RT and 676(18.9%) patients received RT. Before PSM, except race and tumor size, significant differences in patients' baseline characteristics were observed in no RT versus RT group. The KM plots before PSM indicated that RT exerted significant survival benefits for the recruited patients (p < 0.001). After PSM, most confounders were well balanced between the two comparison groups. The KM plots showed significantly superior CSS and OS in the RT group (p < 0.05). Grade IV, stage II-IV, and N3 were identified as independent risk factors, while LN examined > 15 and RT were independent protective factors for favorable prognosis. Subgroup survival analysis revealed that RT brought a significant CSS advantage for the stage IV patients. CONCLUSION Based on PSM analysis of the cohort from SEER database, RT showed significant survival benefits for patients with IGA. Our study supports adjuvant RT for this specific cohort.
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