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Xu G, Feng F, Chen W, Xiao Y, Fu Y, Zhou S, Duan S, Li M. Development and External Validation of a CT-Based Radiomics Nomogram to Predict Perineural Invasion and Survival in Gastric Cancer: A Multi-institutional Study. Acad Radiol 2024:S1076-6332(24)00494-X. [PMID: 39127522 DOI: 10.1016/j.acra.2024.07.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 07/20/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a radiomics nomogram utilizing CT data for predicting perineural invasion (PNI) and survival in gastric cancer (GC) patients. MATERIALS AND METHODS A retrospective analysis of 408 GC patients from two institutions: 288 patients from Institution I were divided 7:3 into a training set (n = 203) and a testing set (n = 85); 120 patients from Institution II served as an external validation set. Radiomics features were extracted and screened from CT images. Independent radiomics, clinical, and combined models were constructed to predict PNI. Model discrimination, calibration, clinical utility, and prognostic significance were evaluated using area under the curve (AUC), calibration curves, decision curves analysis, and Kaplan-Meier curves, respectively. RESULTS 15 radiomics features and three clinical factors were included in the final analysis. The AUCs of the radiomics model in the training, testing, and external validation sets were 0.843 (95% CI: 0.788-0.897), 0.831 (95% CI: 0.741-0.920), and 0.802 (95% CI: 0.722-0.882), respectively. A nomogram was developed by integrating significant clinical factors with radiomics features. The AUCs of the nomogram in the training, testing, and external validation sets were 0.872 (95% CI: 0.823-0.921), 0.862 (95% CI: 0.780-0.944), and 0.837 (95% CI: 0.767-0.908), respectively. Survival analysis revealed that the nomogram could effectively stratify patients for recurrence-free survival (Hazard Ratio: 4.329; 95% CI: 3.159-5.934; P < 0.001). CONCLUSION The radiomics-derived nomogram presented a promising tool for predicting PNI in GC and held significant prognostic implications. IMPORTANT FINDINGS The nomogram functioned as a non-invasive biomarker for determining the PNI status. The predictive performance of the nomogram surpassed that of the clinical model (P < 0.05). Furthermore, patients in the high-risk group stratified by the nomogram had a significantly shorter RFS (P < 0.05).
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Affiliation(s)
- Guodong Xu
- Department of Radiology, The Yancheng Clinical College of Xuzhou Medical University, The First people's Hospital of Yancheng, Yancheng 224006, Jiangsu Province, China
| | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | - Wang Chen
- Department of Radiology, The Yancheng Clinical College of Xuzhou Medical University, The First people's Hospital of Yancheng, Yancheng 224006, Jiangsu Province, China
| | - Yong Xiao
- Department of Radiology, The Yancheng Clinical College of Xuzhou Medical University, The First people's Hospital of Yancheng, Yancheng 224006, Jiangsu Province, China
| | - Yigang Fu
- Department of Radiology, The Yancheng Clinical College of Xuzhou Medical University, The First people's Hospital of Yancheng, Yancheng 224006, Jiangsu Province, China
| | - Siyu Zhou
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | | | - Manman Li
- Department of Radiology, The Yancheng Clinical College of Xuzhou Medical University, The First people's Hospital of Yancheng, Yancheng 224006, Jiangsu Province, China.
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Horvat N, Papanikolaou N, Koh DM. Radiomics Beyond the Hype: A Critical Evaluation Toward Oncologic Clinical Use. Radiol Artif Intell 2024; 6:e230437. [PMID: 38717290 PMCID: PMC11294952 DOI: 10.1148/ryai.230437] [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: 10/08/2023] [Revised: 04/14/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024]
Abstract
Radiomics is a promising and fast-developing field within oncology that involves the mining of quantitative high-dimensional data from medical images. Radiomics has the potential to transform cancer management, whereby radiomics data can be used to aid early tumor characterization, prognosis, risk stratification, treatment planning, treatment response assessment, and surveillance. Nevertheless, certain challenges have delayed the clinical adoption and acceptability of radiomics in routine clinical practice. The objectives of this report are to (a) provide a perspective on the translational potential and potential impact of radiomics in oncology; (b) explore frequent challenges and mistakes in its derivation, encompassing study design, technical requirements, standardization, model reproducibility, transparency, data sharing, privacy concerns, quality control, as well as the complexity of multistep processes resulting in less radiologist-friendly interfaces; (c) discuss strategies to overcome these challenges and mistakes; and (d) propose measures to increase the clinical use and acceptability of radiomics, taking into account the different perspectives of patients, health care workers, and health care systems. Keywords: Radiomics, Oncology, Cancer Management, Artificial Intelligence © RSNA, 2024.
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Affiliation(s)
- Natally Horvat
- From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (N.H.); Department of Radiology, University of São Paulo, São Paulo, Brazil (N.H.); Computational Clinical Imaging Group, Champalimaud Foundation, Portugal (N.P.); and Department of Radiology, Royal Marsden Hospital, Downs Rd, Sutton SM2 5PT, United Kingdom (N.P., D.M.K.)
| | - Nikolaos Papanikolaou
- From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (N.H.); Department of Radiology, University of São Paulo, São Paulo, Brazil (N.H.); Computational Clinical Imaging Group, Champalimaud Foundation, Portugal (N.P.); and Department of Radiology, Royal Marsden Hospital, Downs Rd, Sutton SM2 5PT, United Kingdom (N.P., D.M.K.)
| | - Dow-Mu Koh
- From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (N.H.); Department of Radiology, University of São Paulo, São Paulo, Brazil (N.H.); Computational Clinical Imaging Group, Champalimaud Foundation, Portugal (N.P.); and Department of Radiology, Royal Marsden Hospital, Downs Rd, Sutton SM2 5PT, United Kingdom (N.P., D.M.K.)
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Cong R, Xu R, Ming J, Zhu Z. Construction of a preoperative nomogram model for predicting perineural invasion in advanced gastric cancer. Front Med (Lausanne) 2024; 11:1344982. [PMID: 38912337 PMCID: PMC11190154 DOI: 10.3389/fmed.2024.1344982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 05/24/2024] [Indexed: 06/25/2024] Open
Abstract
Objective This study aimed to develop and validate a clinical and imaging-based nomogram for preoperatively predicting perineural invasion (PNI) in advanced gastric cancer. Methods A retrospective cohort of 351 patients with advanced gastric cancer who underwent surgical resection was included. Multivariable logistic regression analysis was conducted to identify independent risk factors for PNI and to construct the nomogram. The performance of the nomogram was assessed using calibration curves, the concordance index (C-index), the area under the curve (AUC), and decision curve analysis (DCA). The disparity in disease-free survival (DFS) between the nomogram-predicted PNI-positive group and the nomogram-predicted PNI-negative group was evaluated using the Log-Rank test and Kaplan-Meier analysis. Results Extramural vascular invasion (EMVI), Borrmann classification, tumor thickness, and the systemic inflammation response index (SIRI) emerged as independent risk factors for PNI. The nomogram model demonstrated a commendable AUC value of 0.838. Calibration curves exhibited excellent concordance, with a C-index of 0.814. DCA indicated that the model provided good clinical net benefit. The DFS of the nomogram-predicted PNI-positive group was significantly lower than that of the nomogram-predicted PNI-negative group (p < 0.001). Conclusion This study successfully developed a preoperative nomogram model that not only effectively predicted PNI in gastric cancer but also facilitated postoperative risk stratification.
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Affiliation(s)
- Ruochen Cong
- Department of Radiology, Nantong No. 1 People’s Hospital, Nantong, China
| | - Ruonan Xu
- Department of Radiology, Nantong No. 6 People’s Hospital, Nantong, China
| | - Jialei Ming
- Department of Radiology, Nantong No. 1 People’s Hospital, Nantong, China
| | - Zhengqi Zhu
- Department of Radiology, Nantong City Cancer Hospital, Nantong, China
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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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Li J, Yin H, Zhang H, Wang Y, Ma F, Li L, Gao J, Qu J. Preoperative Risk Stratification for Gastric Cancer: The Establishment of Dual-Energy CT-Based Radiomics Using Prospective Datasets at Two Centers. Acad Radiol 2024:S1076-6332(24)00243-5. [PMID: 38734580 DOI: 10.1016/j.acra.2024.04.034] [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: 03/28/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/13/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the performance of dual-energy CT (DECT)-based radiomics models for identifying high-risk histopathologic phenotypes-serosal invasion (pT4a), lymph node metastasis (LNM), lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer. MATERIAL AND METHODS This prospective bi-center study recruited histologically confirmed gastric adenocarcinoma patients who underwent triple-phase enhanced DECT before gastrectomy between January 2021 and July 2023. Radiomics features were extracted from polychromatic/monochromatic (40 keV, 100 keV)/iodine images at arterial/venous/delay phase, respectively. Predictive features were selected in the training dataset using logistic regression classifier, and trained models were applied to the external validation dataset. Performances of clinical models, conventional contrast enhanced CT (CECT) models and DECT models were evaluated using areas under the receiver operating characteristic curve (AUCs). RESULTS In total, 503 patients were recruited: 396 at training dataset (60.1 ± 10.8 years, 110 females, 286 males) and 107 at validation dataset (61.4 ± 9.5 years, 29 females, 78 males). DECT models dichotomizing pT4a, LNM, LVI, and PNI achieved AUCs of 0.891, 0.817, 0.834, and 0.889, respectively, in the validation dataset, similar with the CECT models. In the training dataset, compared to the CECT model, the DECT model provided increased performance for identifying pT4a, LNM, LVI (all P<0.05), and similar performance for stratifying PNI (P = 0.104). The DECT models was associated with patient disease-free survival (all P<0.05). CONCLUSION DECT radiomics can stratify patients preoperatively according to high-risk histopathologic phenotypes for gastric cancer and are associated with patient disease-free survival in the training dataset.
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Affiliation(s)
- Jing Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Hongkun Yin
- Infervision Medical Technology Co., Ltd, Beijing 100025, China
| | - Huiling Zhang
- Infervision Medical Technology Co., Ltd, Beijing 100025, China
| | - Yi Wang
- Department of Pathology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Fei Ma
- Department of Gastrointestinal Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Liming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jinrong Qu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China.
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Zhou H, Zhou J, Qin C, Tian Q, Zhou S, Qin Y, Wu Y, Shi J, Feng F. Preoperative Prediction of Perineural Invasion in Oesophageal Squamous Cell Carcinoma Based on CT Radiomics Nomogram: A Multicenter Study. Acad Radiol 2024; 31:1355-1366. [PMID: 37949700 DOI: 10.1016/j.acra.2023.09.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/13/2023] [Accepted: 09/20/2023] [Indexed: 11/12/2023]
Abstract
RATIONALE AND OBJECTIVES To investigate the value of computed tomography (CT) radiomics nomogram in the preoperative prediction of perineural invasion (PNI) in oesophageal squamous cell carcinoma (ESCC) through a multicenter study. MATERIALS AND METHODS We retrospectively collected postoperative pathological data of 360 ESCC patients with definite PNI status (131 PNI-positive and 229 PNI-negative) from two centres. Radiomic features were extracted from the arterial-phase CT images, and the least absolute shrinkage and selection operator and logistic regression algorithm were used to screen valuable features for identifying the PNI status and calculating the radiomics score (Rad-score). A radiomics nomogram was established by integrating the Rad-score and clinical risk factors. A receiver operating characteristic curve was used to evaluate model performance, and decision curve analysis was used to evaluate the predictive performance of the radiomics nomogram in the training, internal validation, and external validation sets. RESULTS Twenty radiomics features were extracted from a full-volume tumour region of interest to construct the model, and the radiomics nomogram combined with radiomics features and clinical risk factors was superior to the clinical and radiomics models in predicting the PNI status of ESCC patients. The area under the curve values of the radiomics nomogram in the training, internal validation, and external validation sets were 0.856 (0.794-0.918), 0.832 (0.742-0.922), and 0.803 (0.709-0.898), respectively. CONCLUSION The radiomics nomogram based on CT has excellent predictive ability; it can non-invasively predict the preoperative PNI status of ESCC patients and provide a basis for preoperative decision-making.
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Affiliation(s)
- Hui Zhou
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Jianwen Zhou
- Department of Radiology, Dongtai People's Hospital, Yancheng, Jiangsu Province, China (J.Z.)
| | - Cai Qin
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Qi Tian
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Siyu Zhou
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Yihan Qin
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Yutao Wu
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Jian Shi
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.)
| | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu Province, China (H.Z., C.Q., Q.T., S.Z., Y.Q., Y.W., J.S., F.F.).
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Jia H, Li R, Liu Y, Zhan T, Li Y, Zhang J. Preoperative Prediction of Perineural Invasion and Prognosis in Gastric Cancer Based on Machine Learning through a Radiomics-Clinicopathological Nomogram. Cancers (Basel) 2024; 16:614. [PMID: 38339364 PMCID: PMC10854857 DOI: 10.3390/cancers16030614] [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: 12/28/2023] [Revised: 01/26/2024] [Accepted: 01/27/2024] [Indexed: 02/12/2024] Open
Abstract
PURPOSE The aim of this study was to construct and validate a nomogram for preoperatively predicting perineural invasion (PNI) in gastric cancer based on machine learning, and to investigate the impact of PNI on the overall survival (OS) of gastric cancer patients. METHODS Data were collected from 162 gastric patients and analyzed retrospectively, and radiomics features were extracted from contrast-enhanced computed tomography (CECT) scans. A group of 42 patients from the Cancer Imaging Archive (TCIA) were selected as the validation set. Univariable and multivariable analyses were used to analyze the risk factors for PNI. The t-test, Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select radiomics features. Radscores were calculated and logistic regression was applied to construct predictive models. A nomogram was developed by combining clinicopathological risk factors and the radscore. The area under the curve (AUC) values of receiver operating characteristic (ROC) curves, calibration curves and clinical decision curves were employed to evaluate the performance of the models. Kaplan-Meier analysis was used to study the impact of PNI on OS. RESULTS The univariable and multivariable analyses showed that the T stage, N stage and radscore were independent risk factors for PNI (p < 0.05). A nomogram based on the T stage, N stage and radscore was developed. The AUC of the combined model yielded 0.851 in the training set, 0.842 in the testing set and 0.813 in the validation set. The Kaplan-Meier analysis showed a statistically significant difference in OS between the PNI group and the non-PNI group (p < 0.05). CONCLUSIONS A machine learning-based radiomics-clinicopathological model could effectively predict PNI in gastric cancer preoperatively through a non-invasive approach, and gastric cancer patients with PNI had relatively poor prognoses.
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Affiliation(s)
- Heng Jia
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China; (H.J.); (T.Z.)
| | - Ruzhi Li
- Department of Endoscopic Center, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing 210031, China;
| | - Yawei Liu
- Department of General Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, China;
| | - Tian Zhan
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China; (H.J.); (T.Z.)
| | - Yuan Li
- Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Jianping Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China; (H.J.); (T.Z.)
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Ren XC, Liang P. Analysis of influencing factors of nerve invasion in locally advanced gastric cancer. Abdom Radiol (NY) 2023; 48:3005-3011. [PMID: 37289214 DOI: 10.1007/s00261-023-03970-6] [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: 03/20/2023] [Revised: 05/22/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVES Accurate preoperative diagnosis of locally advanced gastric cancer (GC) with nerve invasion is very important for guiding the clinical formulation of a reasonable treatment plan, improving treatment efficacy, and improving prognosis. The present study sought to analyze and evaluate the clinicopathological features of locally advanced GC, and to explore the risk factors associated with the state of nerve invasion. METHODS The clinicopathological data of 296 patients with locally advanced GC were retrospectively analyzed in our hospital from July 2011 to December 2020 who underwent radical gastrectomy. PNI is defined as a tumor close to the nerve and involving at least 33% of its circumference or tumor cells within any of the 3 layers of the nerve sheath. The patient's age, gender, tumor location, T stage, N stage, TNM stage, degree of differentiation, Lauren classification, microvascular invasion, as well as TAP, AFP, CEA, CA125, CA199, CA724, CA153, tumor thickness, longest diameter, and plain CT value, arterial phase CT value, venous phase CT value, arterial phase enhancement rate, venous phase enhancement rate were assessed. RESULTS A total of 296 patients with locally advanced GC were included, and 226 (76.35%) were positive for nerve invasion. Univariate analysis showed that tumor T stage, N stage, TNM stage, Lauren classification, tumor thickness, and longest diameter were related to the state of nerve invasion (P < 0.05). Multivariate analysis showed that tumor TNM stage was an independent risk factor for nerve invasion (OR 0.393, 95%CI 0.165-0.939, P = 0.036). CONCLUSIONS Tumor TNM stage is an independent risk factor for nerve invasion (+) in patients with locally advanced GC. Patients at high risk of nerve invasion should be followed closely and, if necessary, performed pathological examinations.
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Affiliation(s)
- Xiu-Chun Ren
- Department of Ultrasonography, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital, Zhengzhou University, No. 1 Eastern Jianshe Road, Zhengzhou, 450052, China.
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Gao X, Cui J, Wang L, Wang Q, Ma T, Yang J, Ye Z. The value of machine learning based radiomics model in preoperative detection of perineural invasion in gastric cancer: a two-center study. Front Oncol 2023; 13:1205163. [PMID: 37388227 PMCID: PMC10303108 DOI: 10.3389/fonc.2023.1205163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/30/2023] [Indexed: 07/01/2023] Open
Abstract
Purpose To establish and validate a machine learning based radiomics model for detection of perineural invasion (PNI) in gastric cancer (GC). Methods This retrospective study included a total of 955 patients with GC selected from two centers; they were separated into training (n=603), internal testing (n=259), and external testing (n=93) sets. Radiomic features were derived from three phases of contrast-enhanced computed tomography (CECT) scan images. Seven machine learning (ML) algorithms including least absolute shrinkage and selection operator (LASSO), naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) and support vector machine (SVM) were trained for development of optimal radiomics signature. A combined model was constructed by aggregating the radiomic signatures and important clinicopathological characteristics. The predictive ability of the radiomic model was then assessed with receiver operating characteristic (ROC) and calibration curve analyses in all three sets. Results The PNI rates for the training, internal testing, and external testing sets were 22.1, 22.8, and 36.6%, respectively. LASSO algorithm was selected for signature establishment. The radiomics signature, consisting of 8 robust features, revealed good discrimination accuracy for the PNI in all three sets (training set: AUC = 0.86; internal testing set: AUC = 0.82; external testing set: AUC = 0.78). The risk of PNI was significantly associated with higher radiomics scores. A combined model that integrated radiomics and T stage demonstrated enhanced accuracy and excellent calibration in all three sets (training set: AUC = 0.89; internal testing set: AUC = 0.84; external testing set: AUC = 0.82). Conclusion The suggested radiomics model exhibited satisfactory prediction performance for the PNI in GC.
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Affiliation(s)
- Xujie Gao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jingli Cui
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of General Surgery, Weifang People’s Hospital, Weifang, Shandong, China
| | - Lingwei Wang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Qiuyan Wang
- Department of Radiology, Weifang People’s Hospital, Weifang, Shandong, China
| | - Tingting Ma
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Jilong Yang
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin, China
- Department of Radiology, Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Zheng H, Zheng Q, Jiang M, Chen D, Han C, Yi J, Ai Y, Yan J, Jin X. Evaluation the benefits of additional radiotherapy for gastric cancer patients after D2 resection using CT based radiomics. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01646-1. [PMID: 37188857 DOI: 10.1007/s11547-023-01646-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 05/02/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVES The value of adding radiotherapy (RT) is still unclear for patients with gastric cancer (GC) after D2 lymphadenectomy. The purpose of this study is to predict and compare the overall survival (OS) and disease-free survival (DFS) of GC patients treated by chemotherapy and chemoradiation based on contrast-enhanced CT (CECT) radiomics. METHODS A total of 154 patients treated by chemotherapy and chemoradiation in authors' hospital were retrospectively reviewed and randomly divided into the training and testing cohorts (7:3). Radiomics features were extracted from contoured tumor volumes in CECT using the pyradiomics software. Radiomics score and nomogram with integrated clinical factors were developed to predict the OS and DFS and evaluated with Harrell's Consistency Index (C-index). RESULTS Radiomics score achieved a C index of 0.721(95%CI: 0.681-0.761) and 0.774 (95%CI: 0.738-0.810) in the prediction of DFS and OS for GC patients treated by chemotherapy and chemoradiation, respectively. The benefits of additional RT only demonstrated in subgroup of GC patients with Lauren intestinal type and perineural invasion (PNI). Integrating clinical factors further improved the prediction ability of radiomics models with a C-index of 0.773 (95%CI: 0.736-0.810) and 0.802 (95%CI: 0.765-0.839) for DFS and OS, respectively. CONCLUSIONS CECT based radiomics is feasible to predict the OS and DFS for GC patients underwent chemotherapy and chemoradiation after D2 resection. The benefits of additional RT only observed in GC patients with intestinal cancer and PNI.
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Affiliation(s)
- Haoze Zheng
- Department of Radiotherapy Center, 1St Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiao Zheng
- Department of Radiotherapy Center, 1St Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mengmeng Jiang
- Department of Radiology, 1St Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Didi Chen
- Department of Radiotherapy Center, 1St Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ce Han
- Department of Radiotherapy Center, 1St Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jinling Yi
- Department of Radiotherapy Center, 1St Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yao Ai
- Department of Radiotherapy Center, 1St Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jingyi Yan
- Department of Radiotherapy Center, 1St Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Department of Gastrointestinal Surgery, 1St Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Xiance Jin
- Department of Radiotherapy Center, 1St Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- School of Basic Medical Science, Wenzhou Medical University, Wenzhou, China.
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Li J, Xu S, Wang Y, Fang M, Ma F, Xu C, Hailiang L. Spectral CT-based nomogram for preoperative prediction of perineural invasion in locally advanced gastric cancer: a prospective study. Eur Radiol 2023:10.1007/s00330-023-09464-9. [PMID: 36826503 DOI: 10.1007/s00330-023-09464-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/15/2022] [Accepted: 01/22/2023] [Indexed: 02/25/2023]
Abstract
OBJECTIVES This work focused on developing and validating the spectral CT-based nomogram to preoperatively predict perineural invasion (PNI) for locally advanced gastric cancer (LAGC). METHODS This work prospectively included 196 surgically resected LAGC patients (139 males, 57 females, 59.55 ± 11.97 years) undergoing triple enhanced spectral CT scans. Patients were labeled as perineural invasion (PNI) positive and negative according to pathologic reports, then further split into primary (n = 130) and validation cohort (n = 66). We extracted clinicopathological information, follow-up data, iodine concentration (IC), and normalized IC values against to aorta (nICs) at arterial/venous/delayed phases (AP/VP/DP). Clinicopathological features and IC values between PNI positive and negative groups were compared. Multivariable logistic regression was performed to screen independent risk factors of PNI. Then, a nomogram was established, and its capability was determined by ROC curves. Its clinical use was evaluated by decision curve analysis. The correlations of PNI and the nomogram with patients' survival were explored by log-rank survival analysis. RESULTS Borrmann classification, tumor thickness, and nICDP were independent predictors of PNI and used to build the nomogram. The nomogram yielded higher AUCs of 0.853 (0.744-0.928) and 0.782 (0.701-0.850) in primary and validation cohorts than any other parameters (p < 0.05). Both PNI and the nomogram were related to post-surgical treatment planning. Only PNI was associated with disease-free survival in the primary cohort (p < 0.05). CONCLUSION This work prospectively established a spectral CT-based nomogram, which can effectively predict PNI preoperatively and potentially guide post-surgical treatment strategy in LAGC. KEY POINTS • The present prospective study established a spectral CT-based nomogram for preoperative prediction of perineural invasion in LAGC. • The proposed nomogram, including morphological features and the quantitative iodine concentration values from spectral CT, had the potential to predict PNI for LAGC before surgery, along with guide post-surgical treatment planning. • Normalized iodine concentration at the delayed phase was the most valuable quantitative parameter, suggesting the importance of delayed enhancement in gastric CT.
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Affiliation(s)
- Jing Li
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Shuning Xu
- Department of Gastrointestinal Oncology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Yi Wang
- Department of Pathology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Mengjie Fang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Fei Ma
- Department of Gastrointestinal Surgery, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, Henan, China
| | - Chunmiao Xu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127 Dongming Road, Zhengzhou, 450008, Henan, China
| | - Li Hailiang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), No. 127 Dongming Road, Zhengzhou, 450008, Henan, China.
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Ren T, Zhang W, Li S, Deng L, Xue C, Li Z, Liu S, Sun J, Zhou J. Combination of clinical and spectral-CT parameters for predicting lymphovascular and perineural invasion in gastric cancer. Diagn Interv Imaging 2022; 103:584-593. [DOI: 10.1016/j.diii.2022.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/19/2022] [Accepted: 07/19/2022] [Indexed: 11/03/2022]
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