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Zhu ZN, Feng QX, Li Q, Xu WY, Liu XS. Utility of Combined Use of Imaging Features From Abdominopelvic CT and CA 125 to Identify Presence of CT Occult Peritoneal Metastases in Advanced Gastric Cancer. J Comput Assist Tomogr 2024; 48:734-742. [PMID: 38595104 DOI: 10.1097/rct.0000000000001600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
OBJECTIVE The purpose of this study is to identify the presence of occult peritoneal metastasis (OPM) in patients with advanced gastric cancer (AGC) by using clinical characteristics and abdominopelvic computed tomography (CT) features. METHODS This retrospective study included 66 patients with OPM and 111 patients without peritoneal metastasis (non-PM [NPM]) who underwent preoperative contrast-enhanced CT between January 2020 and December 2021. Occult PMs means PMs that are missed by CT but later diagnosed by laparoscopy or laparotomy. Patients with NPM means patients have neither PM nor other distant metastases, indicating there is no evidence of distant metastases in patients with AGC. Patients' clinical characteristics and CT features such as tumor marker, Borrmann IV, enhancement patterns, and pelvic ascites were observed by 2 experienced radiologists. Computed tomography features and clinical characteristics were combined to construct an indicator for identifying the presence of OPM in patients with AGC based on a logistic regression model. Receiver operating characteristic curves and the area under the receiver operating characteristic curve (AUC) were generated to assess the diagnostic performance of the combined indicator. RESULTS Four independent predictors (Borrmann IV, pelvic ascites, carbohydrate antigen 125, and normalized arterial CT value) differed significantly between OPM and NPM and performed outstandingly in distinguishing patients with OPM from those without PM (AUC = 0.643-0.696). The combined indicator showed a higher AUC value than the independent risk factors (0.820 vs 0.643-0.696). CONCLUSIONS The combined indicator based on abdominopelvic CT features and carbohydrate antigen 125 may assist clinicians in identifying the presence of CT OPMs in patients with AGC.
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Affiliation(s)
- Zhen-Ning Zhu
- From the Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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Awais M, Khan N, Khan AK, Rehman A. CT texture analysis for differentiating between peritoneal carcinomatosis and peritoneal tuberculosis: a cross-sectional study. Abdom Radiol (NY) 2024; 49:857-867. [PMID: 37996544 DOI: 10.1007/s00261-023-04103-9] [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: 08/28/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 11/25/2023]
Abstract
PURPOSE Peritoneal carcinomatosis (PC) and peritoneal tuberculosis (PTB) have similar clinical and radiologic imaging features, which make it very difficult to differentiate between the two entities clinically. Our aim was to determine if the CT textural parameters of omental lesions among patients with PC were different from those with PTB. METHODS All patients who had undergone omental biopsy at our institution from January 2010 to December 2018 and had a tissue diagnosis of PC or PTB were eligible for inclusion. Patients who did not have a contrast-enhanced CT abdomen within one month of the omental biopsy were excluded. A region of interest (ROI) was manually drawn over omental lesions and radiomic features were extracted using open-source LIFEx software. Statistical analysis was performed to compare mean differences in CT texture parameters between the PC and PTB groups. RESULTS A total of 66 patients were included in the study of which 38 and 28 had PC and PTB, respectively. Omental lesions in patients with PC had higher mean radiodensity (mean difference: +32.4; p = 0.001), higher mean entropy (mean difference: +0.11; p < 0.001), and lower mean energy (mean difference: -0.024; p = 0.001) compared to those in PTB. Additionally, omental lesions in the PC group had lower gray-level co-occurrence matrix (GLCM) homogeneity (mean difference: -0.073; p < 0.001) and higher GLCM dissimilarity (mean difference: +0.480; p < 0.001) as compared to the PTB group. CONCLUSION CT texture parameters of omental lesions differed significantly between patients with PTB and those with PC, which may help clinicians in differentiating between the two entities.
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Affiliation(s)
- Muhammad Awais
- Department of Radiology, Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Sindh, Pakistan.
| | - Noman Khan
- Department of Radiology, Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Sindh, Pakistan
| | - Ayimen Khalid Khan
- Department of Radiology, Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Sindh, Pakistan
| | - Abdul Rehman
- Department of Medicine, Rutgers-New Jersey Medical School, Newark, NJ, 07103, USA
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Liu P, Ding P, Wu H, Wu J, Yang P, Tian Y, Guo H, Zhao Q. Prediction of occult peritoneal metastases or positive cytology using CT in gastric cancer. Eur Radiol 2023; 33:9275-9285. [PMID: 37414883 DOI: 10.1007/s00330-023-09854-z] [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: 02/03/2023] [Revised: 04/17/2023] [Accepted: 04/19/2023] [Indexed: 07/08/2023]
Abstract
OBJECTIVE Accurate prediction of preoperative occult peritoneal metastasis (OPM) is critical to selecting appropriate therapeutic regimen for gastric cancer (GC). Considering the clinical practicability, we develop and validate a visible nomogram that integrates the CT images and clinicopathological parameters for the individual preoperative prediction of OPM in GC. METHODS This retrospective study included 520 patients who underwent staged laparoscopic exploration or peritoneal lavage cytology (PLC) examination. Univariate and multivariate logistic regression results were used to screen model predictors and construct nomograms of OPM risk. The performance of the model was detected by using ROC, accuracy, and C-index. The bootstrap resampling method was considered internal validation of the model. The Delong test was used to evaluate the difference in AUC between the two models. RESULTS Grade 2 mural stratification, tumor thickness, and the Lauren classification diffuse were significant predictors of OPM (p < 0.05). The nomogram of these three factors (compared with the original model) showed a higher predictive effect (p < 0.001). The area under the curve (AUC) of the model was 0.830 (95% CI 0.788-0.873), and the internally validated AUC of 1000 bootstrap samples was 0.826 (95% CI 0.756-0.870). The sensitivity, specificity, and accuracy were 76.0%, 78.8%, and 78.3%, respectively. CONCLUSIONS CT phenotype-based nomogram demonstrates favorable discrimination and calibration, and it can be conveniently used for preoperative individual risk rating of OPM in GC. CLINICAL RELEVANCE STATEMENT In this study, the preoperative OPM prediction model based on CT images (mural stratification, tumor thickness) combined with pathological parameters (the Lauren classification) showed excellent predictive ability in GC, and it is also suitable for clinicians to use rather than limited to professional radiologists. KEY POINTS • Nomogram based on CT image analysis can effectively predict occult peritoneal metastasis in gastric cancer (training area under the curve (AUC) = 0.830 and bootstrap AUC = 0.826). • Nomogram model combined with CT features performed better than the original model (established using only clinicopathological parameters) in differentiating occult peritoneal metastasis of gastric cancer.
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Affiliation(s)
- Pengpeng Liu
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Ping'an Ding
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Haotian Wu
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Jiaxiang Wu
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Peigang Yang
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Yuan Tian
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Honghai Guo
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Qun Zhao
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China.
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China.
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Ho SYA, Tay KV. Systematic review of diagnostic tools for peritoneal metastasis in gastric cancer-staging laparoscopy and its alternatives. World J Gastrointest Surg 2023; 15:2280-2293. [PMID: 37969710 PMCID: PMC10642463 DOI: 10.4240/wjgs.v15.i10.2280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/16/2023] [Accepted: 06/12/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Gastric cancer is one of the leading causes of cancer burden and mortality, often resulting in peritoneal metastasis in advanced stages with negative survival outcomes. Staging laparoscopy has become standard practice for suspected cases before a definitive gastrectomy or palliation. This systematic review aims to compare the efficacy of other diagnostic modalities instead of staging laparoscopy as the alternatives are able to reduce cost and invasive staging procedures. Recently, a radiomic model based on computed tomography and positron emission tomography (PET) has also emerged as another method to predict peritoneal metastasis. AIM To determine if the efficacy of computed tomography, magnetic resonance imaging and PET is comparable with staging laparoscopy. METHODS Articles comparing computed tomography, PET, magnetic resonance imaging, and radiomic models based on computed tomography and PET to staging laparoscopies were filtered out from the Cochrane Library, EMBASE, PubMed, Web of Science, and Reference Citations Analysis (https://www.referencecitationanalysis.com/). In the search for studies comparing computed tomography (CT) to staging laparoscopy, five retrospective studies and three prospective studies were found. Similarly, five retrospective studies and two prospective studies were also included for papers comparing CT to PET scans. Only one retrospective study and one prospective study were found to be suitable for papers comparing CT to magnetic resonance imaging scans. RESULTS Staging laparoscopy outperformed computed tomography in all measured aspects, namely sensitivity, specificity, positive predictive value and negative predictive value. Magnetic resonance imaging and PET produced mixed results, with the former shown to be only marginally better than computed tomography. CT performed slightly better than PET in most measured domains, except in specificity and true negative rates. We speculate that this may be due to the limited F-fluorodeoxyglucose uptake in small peritoneal metastases and in linitis plastica. Radiomic modelling, in its current state, shows promise as an alternative for predicting peritoneal metastases. With further research, deep learning and radiomic modelling can be refined and potentially applied as a preoperative diagnostic tool to reduce the need for invasive staging laparoscopy. CONCLUSION Staging laparoscopy was superior in all measured aspects. However, associated risks and costs must be considered. Refinements in radiomic modelling are necessary to establish it as a reliable screening technique.
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Affiliation(s)
| | - Kon Voi Tay
- Upper GI and Bariatric Division, General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Upper GI and Bariatric Division, General Surgery, Woodlands Health, Singapore 768024, Singapore
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Wu A, Wu C, Zeng Q, Cao Y, Shu X, Luo L, Feng Z, Tu Y, Jie Z, Zhu Y, Zhou F, Huang Y, Li Z. Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer. Sci Rep 2023; 13:8442. [PMID: 37231100 DOI: 10.1038/s41598-023-35155-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/13/2023] [Indexed: 05/27/2023] Open
Abstract
""We employed radiomics and clinical features to develop and validate a preoperative prediction model to estimate the omental metastases status of locally advanced gastric cancer (LAGC). A total of 460 patients (training cohort, n = 250; test cohort, n = 106; validation cohort, n = 104) with LAGC who were confirmed T3/T4 stage by postoperative pathology were continuously collected retrospectively, including clinical data and preoperative arterial phase computed tomography images (APCT). Dedicated radiomics prototype software was used to segment the lesions and extract features from the preoperative APCT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the extracted radiomics features, and a radiomics score model was constructed. Finally, a prediction model of omental metastases status and a nomogram were constructed combining the radiomics scores and selected clinical features. An area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to validate the capability of the prediction model and nomogram in the training cohort. Calibration curves and decision curve analysis (DCA) were used to evaluate the prediction model and nomogram. The prediction model was internally validated by the test cohort. In addition, 104 patients from another hospital's clinical and imaging data were gathered for external validation. In the training cohort, the combined prediction (CP) model (AUC 0.871, 95% CI 0.798-0.945) of the radiomics scores combined with the clinical features, compared with clinical features prediction (CFP) model (AUC 0.795, 95% CI 0.710-0.879) and radiomics scores prediction (RSP) model (AUC 0.805, 95% CI 0.730-0.879), had the better predictive ability. The Hosmer-Lemeshow test of the CP model showed that the prediction model did not deviate from the perfect fitting (p = 0.893). In the DCA, the clinical net benefit of the CP model was higher than that of the CFP model and RSP model. In the test and validation cohorts, the AUC values of the CP model were 0.836 (95% CI 0.726-0.945) and 0.779 (95% CI 0.634-0.923), respectively. The preoperative APCT-based clinical-radiomics nomogram showed good performance in predicting omental metastases status in LAGC, which may contribute to clinical decision-making.
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Affiliation(s)
- Ahao Wu
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Changlei Wu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital, Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Qingwen Zeng
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Yi Cao
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Xufeng Shu
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Lianghua Luo
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Zongfeng Feng
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Yi Tu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Zhigang Jie
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Ya Huang
- Department of Radiology, The Second Affiliated Hospital, Nanchang University, Nanchang, China
| | - Zhengrong Li
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China.
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Li LM, Feng LY, Liu CC, Huang WP, Yu Y, Cheng PY, Gao JB. Can visceral fat parameters based on computed tomography be used to predict occult peritoneal metastasis in gastric cancer? World J Gastroenterol 2023; 29:2310-2321. [PMID: 37124887 PMCID: PMC10134425 DOI: 10.3748/wjg.v29.i15.2310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 01/21/2023] [Accepted: 03/20/2023] [Indexed: 04/14/2023] Open
Abstract
BACKGROUND The preoperative prediction of peritoneal metastasis (PM) in gastric cancer would prevent unnecessary surgery and promptly indicate an appropriate treatment plan.
AIM To explore the predictive value of visceral fat (VF) parameters obtained from preoperative computed tomography (CT) images for occult PM and to develop an individualized model for predicting occult PM in patients with gastric carcinoma (GC).
METHODS A total of 128 confirmed GC cases (84 male and 44 female patients) that underwent CT scans were analyzed and categorized into PM-positive (n = 43) and PM-negative (n = 85) groups. The clinical characteristics and VF parameters of two regions of interest (ROIs) were collected. Univariate and stratified analyses based on VF volume were performed to screen for predictive characteristics for occult PM. Prediction models with and without VF parameters were established by multivariable logistic regression analysis.
RESULTS The mean attenuations of VFROI 1 and VFROI 2 varied significantly between the PM-positive and PM-negative groups (P = 0.044 and 0.001, respectively). The areas under the receiver operating characteristic curves (AUCs) of VFROI 1 and VFROI 2 were 0.599 and 0.657, respectively. The mean attenuation of VFROI 2 was included in the final prediction combined model, but not an independent risk factor of PM (P = 0.068). No significant difference was observed between the models with and without mean attenuation of VF (AUC: 0.749 vs 0.730, P = 0.339).
CONCLUSION The mean attenuation of VF is a potential auxiliary parameter for predicting occult PM in patients with GC.
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Affiliation(s)
- Li-Ming Li
- Department of Radiology, Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive system Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Lei-Yu Feng
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Chen-Chen Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Wen-Peng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing 100034, China
| | - Yang Yu
- Beijing Branch, Siemens Healthineers Ltd., Shenyang 110011, Liaoning Province, China
| | - Peng-Yun Cheng
- Beijing Branch, Siemens Healthineers Ltd., Shenyang 110011, Liaoning Province, China
| | - Jian-Bo Gao
- Department of Radiology, Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive system Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
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Chen S, Zhang H, Wei H, Tong Y, Chen X. Practical nomogram based on comprehensive CT texture analysis to preoperatively predict peritoneal occult metastasis of gastric cancer patients. Front Oncol 2022; 12:882584. [DOI: 10.3389/fonc.2022.882584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 10/24/2022] [Indexed: 12/03/2022] Open
Abstract
ObjectivesThis study aims to evaluate whether a nomogram based on comprehensive CT texture analysis of primary tumor and peritoneotome combined with conventional CT signs can preoperatively predict peritoneal occult metastasis in gastric cancer patients.MethodsA total of 1,251 patients with gastric cancer (GC) were retrospectively analyzed in Fujian Province Hospital between 2008 and 2020. Patients from the occult peritoneal metastasis (PM) group were initially diagnosed as PM-negative on CT and later confirmed as PM-positive through laparoscopy or surgery. The group without PM was randomly sampled from patients without PM. The preoperative CT signs and texture features and clinical characteristics of patients were retrospectively analyzed. Hazard factors of occult PM were identified by univariate analysis and multivariate logistic regression analysis, which were intended for creating prediction models. A nomogram was established based on the model with the highest predictive efficacy and clinical application value.ResultsA total of 31 patients with occult PM and 165 patients without PM were enrolled in this study. The maximum size, thickness, enhancement, serous involvement of primary GC tumor and ascites on CT, and texture features such as inhomogeneity of the primary tumor, standard deviation, and inhomogeneity of the peritoneum were determined as independent predictors that could be jointly applied to predict occult PM. We separately constructed five forecast models using CT signs, primary tumor texture, peritoneum texture, primary tumor texture + peritoneum texture, and their combination for predicting occult PM. These five prediction models achieved an AUC value of 0.832, 0.70, 0.784, 0.838, and 0.941, respectively. The DeLong test and Decision Curve Analysis (DCA) showed that the joint model, containing three meaningful CT signs (maximum size, thickness, and ascites) and two meaningful texture parameters (inhomogeneity of the primary tumor and inhomogeneity of the peritoneum), possessed the best predictive performance and clinical application (p<0.05). A forecast nomogram was subsequently established from the model above-mentioned. The calibration curves of the nomogram indicated a good consistency (a concordance index of 0.807) between the projection and the actual observation of occult PM.ConclusionsA practical projection nomogram based on the comprehensive CT texture analysis of a primary tumor and peritoneotome combined with conventional CT signs was constructed in our study, which can be conveniently used in preoperative personalized prediction of occult PM for GC patients, and acts as a recommendation for the optimization of clinical management.
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Li HH, Sun B, Tan C, Li R, Fu CX, Grimm R, Zhu H, Peng WJ. The Value of Whole-Tumor Histogram and Texture Analysis Using Intravoxel Incoherent Motion in Differentiating Pathologic Subtypes of Locally Advanced Gastric Cancer. Front Oncol 2022; 12:821586. [PMID: 35223503 PMCID: PMC8864172 DOI: 10.3389/fonc.2022.821586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 01/20/2022] [Indexed: 01/02/2023] Open
Abstract
Purpose To determine if whole-tumor histogram and texture analyses using intravoxel incoherent motion (IVIM) parameters values could differentiate the pathologic characteristics of locally advanced gastric cancer. Methods Eighty patients with histologically confirmed locally advanced gastric cancer who received surgery in our institution were retrospectively enrolled into our study between April 2017 and December 2018. Patients were excluded if they had lesions with the smallest diameter < 5 mm and severe image artifacts. MR scanning included IVIM sequences (9 b values, 0, 20, 40, 60, 100, 150,200, 500, and 800 s/mm2) used in all patients before treatment. Whole tumors were segmented by manually drawing the lesion contours on each slice of the diffusion-weighted imaging (DWI) images (with b=800). Histogram and texture metrics for IVIM parameters values and apparent diffusion coefficient (ADC) values were measured based on whole-tumor volume analyses. Then, all 24 extracted metrics were compared between well, moderately, and poorly differentiated tumors, and between different Lauren classifications, signet-ring cell carcinomas, and other poorly cohesive carcinomas using univariate analyses. Multivariate logistic analyses and multicollinear tests were used to identify independent influencing factors from the significant variables of the univariate analyses to distinguish tumor differentiation and Lauren classifications. ROC curve analyses were performed to evaluate the diagnostic performance of these independent influencing factors for determining tumor differentiation and Lauren classifications and identifying signet-ring cell carcinomas. The interobserver agreement was also conducted between the two observers for image quality evaluations and parameter metric measurements. Results For diagnosing tumor differentiation, the ADCmedian, pure diffusion coefficient median (Dslowmedian), and pure diffusion coefficient entropy (Dslowentropy) showed the greatest AUCs: 0.937, 0.948, and 0.850, respectively, and no differences were found between the three metrics, P>0.05). The 95th percentile perfusion factor (FP P95th) was the best metric to distinguish diffuse-type GCs vs. intestinal/mixed (AUC=0.896). The ROC curve to distinguish signet-ring cell carcinomas from other poorly cohesive carcinomas showed that the Dslowmedian had AUC of 0.738. For interobserver reliability, image quality evaluations showed excellent agreement (interclass correlation coefficient [ICC]=0.85); metrics measurements of all parameters indicated good to excellent agreement (ICC=0.65-0.89), except for the Dfast metric, which showed moderate agreement (ICC=0.41-0.60). Conclusions The whole-tumor histogram and texture analyses of the IVIM parameters based on the biexponential model provided a non-invasive method to discriminate pathologic tumor subtypes preoperatively in patients with locally advanced gastric cancer. The metric FP P95th derived from IVIM performed better in determining Lauren classifications than the mono-exponential model.
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Affiliation(s)
- Huan-Huan Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Bo Sun
- Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Cong Tan
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Rong Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Cai-Xia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Robert Grimm
- MR Applications Development, Siemens Healthcare, Erlangen, Germany
| | - Hui Zhu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wei-Jun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
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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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Liu D, Zhang W, Hu F, Yu P, Zhang X, Yin H, Yang L, Fang X, Song B, Wu B, Hu J, Huang Z. A Bounding Box-Based Radiomics Model for Detecting Occult Peritoneal Metastasis in Advanced Gastric Cancer: A Multicenter Study. Front Oncol 2021; 11:777760. [PMID: 34926287 PMCID: PMC8678129 DOI: 10.3389/fonc.2021.777760] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/09/2021] [Indexed: 02/05/2023] Open
Abstract
Purpose To develop a bounding box (BBOX)-based radiomics model for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients. Materials and Methods 599 AGC patients from 3 centers were retrospectively enrolled and were divided into training, validation, and testing cohorts. The minimum circumscribed rectangle of the ROIs for the largest tumor area (R_BBOX), the nonoverlapping area between the tumor and R_BBOX (peritumoral area; PERI) and the smallest rectangle that could completely contain the tumor determined by a radiologist (M_BBOX) were used as inputs to extract radiomic features. Multivariate logistic regression was used to construct a radiomics model to estimate the preoperative probability of OPM in AGC patients. Results The M_BBOX model was not significantly different from R_BBOX in the validation cohort [AUC: M_BBOX model 0.871 (95% CI, 0.814–0.940) vs. R_BBOX model 0.873 (95% CI, 0.820–0.940); p = 0.937]. M_BBOX was selected as the final radiomics model because of its extremely low annotation cost and superior OPM discrimination performance (sensitivity of 85.7% and specificity of 82.8%) over the clinical model, and this radiomics model showed comparable diagnostic efficacy in the testing cohort. Conclusions The BBOX-based radiomics could serve as a simpler reliable and powerful tool for the preoperative diagnosis of OPM in AGC patients. And M_BBOX-based radiomics is simpler and less time consuming.
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Affiliation(s)
- Dan Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Weihan Zhang
- Department of Gastrointestinal Surgery and Laboratory of Gastric Cancer, Collaborative Innovation Center for Biotherapy, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Fubi Hu
- Department of Radiology, First Affiliated Hospital of Chengdu Medical College, Chengdu, China
| | - Pengxin Yu
- Institute of Advanced Research, Infervision, Beijing, China
| | - Xiao Zhang
- Department of Radiology, People's Hospital of Leshan, Leshan, China
| | - Hongkun Yin
- Institute of Advanced Research, Infervision, Beijing, China
| | - Lanqing Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Fang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bing Wu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jiankun Hu
- Department of Gastrointestinal Surgery and Laboratory of Gastric Cancer, Collaborative Innovation Center for Biotherapy, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Zixing Huang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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11
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Saiz Martínez R, Dromain C, Vietti Violi N. Imaging of Gastric Carcinomatosis. J Clin Med 2021; 10:5294. [PMID: 34830575 PMCID: PMC8624519 DOI: 10.3390/jcm10225294] [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: 10/09/2021] [Revised: 11/06/2021] [Accepted: 11/11/2021] [Indexed: 01/17/2023] Open
Abstract
Diagnosing the absence or presence of peritoneal carcinomatosis in patients with gastric cancer, including its extent and distribution, is an essential step in patients' therapeutic management. Such diagnosis still remains a radiological challenge. In this article, we review the strengths and weaknesses of the different imaging techniques for the diagnosis of peritoneal carcinomatosis of gastric origin as well as the techniques' imaging features. We also discuss the assessment of response to treatment and present recommendations for the follow-up of patients with complete surgical resection according to the presence of risk factors of recurrence, as well as discussing future directions for imaging improvement.
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Affiliation(s)
| | - Clarisse Dromain
- Department of Radiology, Lausanne University Hospital and University of Lausanne, 1015 Lausanne, Switzerland;
| | - Naik Vietti Violi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, 1015 Lausanne, Switzerland;
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12
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Kim HY, Chang W, Lee YJ, Park JH, Cho J, Na HY, Ahn H, Hwang SI, Lee HJ, Kim YH, Lee KH. Adrenal Nodules Detected at Staging CT in Patients with Resectable Gastric Cancers Have a Low Incidence of Malignancy. Radiology 2021; 302:129-137. [PMID: 34665031 DOI: 10.1148/radiol.2021211210] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background Guidelines recommending additional imaging for adrenal nodules lack relevant epidemiologic evidence. Purpose To measure the prevalence of adrenal nodules detected at staging CT in patients with potentially resectable gastric cancer and the proportion of patients with malignant nodules among them. Materials and Methods This retrospective study included 10 250 consecutive patients (median age, 63 years; interquartile range, 53-71 years; 6884 men) who underwent staging CT and had potentially resectable gastric cancer in a tertiary center (May 2003 to December 2018). All 10 250 CT studies were retrospectively reviewed, and patients with adrenal nodules (or thickening ≥10 mm) were identified to measure the prevalence of adrenal nodules. Among patients with adrenal nodules, the per-patient proportions of malignant nodules, adrenal metastasis from gastric cancer, and additional adrenal examinations were measured. A secondary analysis was performed by using data from the original CT reports. The same metrics that were used in the retrospective review were assessed. Results The prevalence of adrenal nodules was 4.5% (95% CI: 4.1, 4.9; 462 of 10 250). The proportions of malignant nodules and adrenal metastasis from gastric cancer were 0.4% ( 95% CI: 0.1, 1.6; two of 462) and 0% (95% CI: 0.0, 0.8; 0 of 462), respectively. A total of 27% of the patients (95% CI: 23, 31; 123 of 462) underwent additional adrenal examination. According to original CT reports, the prevalence of adrenal nodules and the proportions of malignant nodules, adrenal metastases from gastric cancer, and additional adrenal examination were 2.7% (95% CI: 2.4, 3.0; 272 of 10 250), 0.7% (95% CI: 0.1, 2.6; two of 272), 0% (95% CI: 0.0, 1.4; 0 of 272), and 42.6% (95% CI: 36.7, 48.8; 116 of 272), respectively. Conclusion Although adrenal nodules were detected frequently on staging CT images of patients with otherwise resectable gastric cancer, these nodules were rarely malignant. ©RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Baumgarten in this issue.
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Affiliation(s)
- Hae Young Kim
- From the Departments of Radiology (H.Y.K., W.C., Y.J.L., J.H.P., J.C., H.A., S.I.H., H.J.L., Y.H.K., K.H.L.) and Pathology (H.Y.N.), Seoul National University Bundang Hospital, 82 Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea (H.Y.N.); Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (K.H.L.); Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (H.J.L., Y.H.K., K.H.L.); and Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea (K.H.L.)
| | - Won Chang
- From the Departments of Radiology (H.Y.K., W.C., Y.J.L., J.H.P., J.C., H.A., S.I.H., H.J.L., Y.H.K., K.H.L.) and Pathology (H.Y.N.), Seoul National University Bundang Hospital, 82 Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea (H.Y.N.); Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (K.H.L.); Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (H.J.L., Y.H.K., K.H.L.); and Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea (K.H.L.)
| | - Yoon Jin Lee
- From the Departments of Radiology (H.Y.K., W.C., Y.J.L., J.H.P., J.C., H.A., S.I.H., H.J.L., Y.H.K., K.H.L.) and Pathology (H.Y.N.), Seoul National University Bundang Hospital, 82 Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea (H.Y.N.); Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (K.H.L.); Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (H.J.L., Y.H.K., K.H.L.); and Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea (K.H.L.)
| | - Ji Hoon Park
- From the Departments of Radiology (H.Y.K., W.C., Y.J.L., J.H.P., J.C., H.A., S.I.H., H.J.L., Y.H.K., K.H.L.) and Pathology (H.Y.N.), Seoul National University Bundang Hospital, 82 Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea (H.Y.N.); Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (K.H.L.); Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (H.J.L., Y.H.K., K.H.L.); and Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea (K.H.L.)
| | - Jungheum Cho
- From the Departments of Radiology (H.Y.K., W.C., Y.J.L., J.H.P., J.C., H.A., S.I.H., H.J.L., Y.H.K., K.H.L.) and Pathology (H.Y.N.), Seoul National University Bundang Hospital, 82 Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea (H.Y.N.); Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (K.H.L.); Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (H.J.L., Y.H.K., K.H.L.); and Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea (K.H.L.)
| | - Hee Young Na
- From the Departments of Radiology (H.Y.K., W.C., Y.J.L., J.H.P., J.C., H.A., S.I.H., H.J.L., Y.H.K., K.H.L.) and Pathology (H.Y.N.), Seoul National University Bundang Hospital, 82 Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea (H.Y.N.); Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (K.H.L.); Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (H.J.L., Y.H.K., K.H.L.); and Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea (K.H.L.)
| | - Hyungwoo Ahn
- From the Departments of Radiology (H.Y.K., W.C., Y.J.L., J.H.P., J.C., H.A., S.I.H., H.J.L., Y.H.K., K.H.L.) and Pathology (H.Y.N.), Seoul National University Bundang Hospital, 82 Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea (H.Y.N.); Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (K.H.L.); Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (H.J.L., Y.H.K., K.H.L.); and Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea (K.H.L.)
| | - Sung Il Hwang
- From the Departments of Radiology (H.Y.K., W.C., Y.J.L., J.H.P., J.C., H.A., S.I.H., H.J.L., Y.H.K., K.H.L.) and Pathology (H.Y.N.), Seoul National University Bundang Hospital, 82 Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea (H.Y.N.); Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (K.H.L.); Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (H.J.L., Y.H.K., K.H.L.); and Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea (K.H.L.)
| | - Hak Jong Lee
- From the Departments of Radiology (H.Y.K., W.C., Y.J.L., J.H.P., J.C., H.A., S.I.H., H.J.L., Y.H.K., K.H.L.) and Pathology (H.Y.N.), Seoul National University Bundang Hospital, 82 Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea (H.Y.N.); Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (K.H.L.); Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (H.J.L., Y.H.K., K.H.L.); and Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea (K.H.L.)
| | - Young Hoon Kim
- From the Departments of Radiology (H.Y.K., W.C., Y.J.L., J.H.P., J.C., H.A., S.I.H., H.J.L., Y.H.K., K.H.L.) and Pathology (H.Y.N.), Seoul National University Bundang Hospital, 82 Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea (H.Y.N.); Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (K.H.L.); Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (H.J.L., Y.H.K., K.H.L.); and Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea (K.H.L.)
| | - Kyoung Ho Lee
- From the Departments of Radiology (H.Y.K., W.C., Y.J.L., J.H.P., J.C., H.A., S.I.H., H.J.L., Y.H.K., K.H.L.) and Pathology (H.Y.N.), Seoul National University Bundang Hospital, 82 Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13620, Korea; Department of Pathology, Seoul National University College of Medicine, Seoul, Korea (H.Y.N.); Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea (K.H.L.); Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (H.J.L., Y.H.K., K.H.L.); and Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea (K.H.L.)
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13
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Zhou C, Wang Y, Ji MH, Tong J, Yang JJ, Xia H. Predicting Peritoneal Metastasis of Gastric Cancer Patients Based on Machine Learning. Cancer Control 2021; 27:1073274820968900. [PMID: 33115287 PMCID: PMC7791448 DOI: 10.1177/1073274820968900] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVE The aim is to explore the prediction effect of 5 machine learning algorithms on peritoneal metastasis of gastric cancer. METHODS 1080 patients with postoperative gastric cancer were divided into a training group and test group according to the ratio of 7:3. The model of peritoneal metastasis was established by using 5 machine learning (gbm(Light Gradient Boosting Machine), GradientBoosting, forest, Logistic and DecisionTree). Python pair was used to analyze the machine learning algorithm. Gbm algorithm is used to show the weight proportion of each variable to the result. RESULT Correlation analysis showed that tumor size and depth of invasion were positively correlated with the recurrence of patients after gastric cancer surgery. The results of the gbm algorithm showed that the top 5 important factors were albumin, platelet count, depth of infiltration, preoperative hemoglobin and weight, respectively. In training group: Among the 5 algorithm models, the accuracy of GradientBoosting and gbm was the highest (0.909); the AUC values of the 5 algorithms are gbm (0.938), GradientBoosting (0.861), forest (0.796), Logistic(0.741) and DecisionTree(0.712) from high to low. In the test group: among the 5 algorithm models, the accuracy of forest, DecisionTree and gbm was the highest (0.907); AUC values ranged from high to low to gbm (0.745), GradientBoosting (0.725), forest (0.696), Logistic (0.680) and DecisionTree (0.657). CONCLUSION Machine learning can predict the peritoneal metastasis in patients with gastric cancer.
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Affiliation(s)
- Chengmao Zhou
- School of Medicine, Southeast University, Nanjing, China
| | - Ying Wang
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mu-Huo Ji
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianhua Tong
- Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Jun Yang
- School of Medicine, Southeast University, Nanjing, China.,Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hongping Xia
- School of Medicine, Southeast University, Nanjing, China.,Department of Pathology, School of Basic Medical Sciences & Sir Run Run Hospital & State Key Laboratory of Reproductive Medicine & Key Laboratory of Antibody Technique of National Health Commission, Nanjing Medical University, Nanjing, China
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14
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Zhang C, Wen HL, Zhang R, Xie SY, Xie CM. Computed tomography radiomics to predict EBER positivity in Epstein-Barr virus-associated gastric adenocarcinomas: a retrospective study. Acta Radiol 2021; 63:1005-1013. [PMID: 34233501 DOI: 10.1177/02841851211029083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND The relevance of Epstein-Barr virus (EBV) in gastric carcinoma has been represented by the existence of EBV-encoded small RNA (EBER) in the tumor cells and has prognostic significance in gastric cancer, while gastric adenocarcinoma represents the most frequently occurring gastric malignancy. PURPOSE To observe the capacity of radiomic features extracted from contrast-enhanced computed tomography (CE-CT) images to differentiate EBER-positive gastric adenocarcinoma from EBER-negative ones. MATERIAL AND METHODS A total of 54 patients with gastric adenocarcinoma (EBER-positive: 27, EBER-negative: 27) were retrospectively examined. Radiomic imaging features were extracted from all regions of interest (ROI) delineated by two experienced radiologists on late arterial phase CT images. We distinguished related radiomic features through the two-tailed t test and applied them to construct a decision tree model to evaluate whether EBER in situ hybridization positive had appeared. RESULTS Nine radiomics features were significantly related to EBER in situ hybridization status (P < 0.05), four of which were used to build the decision tree through backward elimination: Correlation_ AllDirection_offset7, Correlation_ angle135_offset7, RunLengthNonuniformity_ AllDirection_offset1_SD, and HighGreyLevelRunEmphasis_ AllDiretion_offset1_SD. The decision tree model consisted of seven decision nodes and six terminal nodes, three of which demonstrated positive EBER in situ hybridization. The specificity, sensitivity, and accuracy of the model were 84%, 80%, and 81.7%, respectively. The area under the curve of the decision tree model was 0.87. CONCLUSION Radiomics based on CE-CT could be applied to predict EBER in situ hybridization status preoperatively in patients with gastric adenocarcinoma.
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Affiliation(s)
- Cheng Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China, Guangzhou, PR China
| | - Hai-lin Wen
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China, Guangzhou, PR China
| | - Rong Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China, Guangzhou, PR China
| | - Shu-yi Xie
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China, Guangzhou, PR China
| | - Chuan-miao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China, Guangzhou, PR China
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15
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Jiang Y, Liang X, Wang W, Chen C, Yuan Q, Zhang X, Li N, Chen H, Yu J, Xie Y, Xu Y, Zhou Z, Li G, Li R. Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning. JAMA Netw Open 2021; 4:e2032269. [PMID: 33399858 PMCID: PMC7786251 DOI: 10.1001/jamanetworkopen.2020.32269] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
IMPORTANCE Occult peritoneal metastasis frequently occurs in patients with advanced gastric cancer and is poorly diagnosed with currently available tools. Because the presence of peritoneal metastasis precludes the possibility of curative surgery, there is an unmet need for a noninvasive approach to reliably identify patients with occult peritoneal metastasis. OBJECTIVE To assess the use of a deep learning model for predicting occult peritoneal metastasis based on preoperative computed tomography images. DESIGN, SETTING, AND PARTICIPANTS In this multicenter, retrospective cohort study, a deep convolutional neural network, the Peritoneal Metastasis Network (PMetNet), was trained to predict occult peritoneal metastasis based on preoperative computed tomography images. Data from a cohort of 1225 patients with gastric cancer who underwent surgery at Sun Yat-sen University Cancer Center (Guangzhou, China) were used for training purposes. To externally validate the model, data were collected from 2 independent cohorts comprising a total of 753 patients with gastric cancer who underwent surgery at Nanfang Hospital (Guangzhou, China) or the Third Affiliated Hospital of Southern Medical University (Guangzhou, China). The status of peritoneal metastasis for all patients was confirmed by pathological examination of pleural specimens obtained during surgery. Detailed clinicopathological data were collected for each patient. Data analysis was performed between September 1, 2019, and January 31, 2020. MAIN OUTCOMES AND MEASURES The area under the receiver operating characteristic curve (AUC) and decision curve were analyzed to evaluate performance in predicting occult peritoneal metastasis. RESULTS A total of 1978 patients (mean [SD] age, 56.0 [12.2] years; 1350 [68.3%] male) were included in the study. The PMetNet model achieved an AUC of 0.946 (95% CI, 0.927-0.965), with a sensitivity of 75.4% and a specificity of 92.9% in external validation cohort 1. In external validation cohort 2, the AUC was 0.920 (95% CI, 0.848-0.992), with a sensitivity of 87.5% and a specificity of 98.2%. The discrimination performance of PMetNet was substantially higher than conventional clinicopathological factors (AUC range, 0.51-0.63). In multivariable logistic regression analysis, PMetNet was an independent predictor of occult peritoneal metastasis. CONCLUSIONS AND RELEVANCE The findings of this cohort study suggest that the PMetNet model can serve as a reliable noninvasive tool for early identification of patients with clinically occult peritoneal metastasis, which will inform individualized preoperative treatment decision-making and may avoid unnecessary surgery and complications. These results warrant further validation in prospective studies.
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Affiliation(s)
- Yuming Jiang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Xiaokun Liang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Wei Wang
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chuanli Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qingyu Yuan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiaodong Zhang
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Na Li
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Hao Chen
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jiang Yu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yaoqin Xie
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zhiwei Zhou
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Guoxin Li
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
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16
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Chen QY, Liu ZY, Zhong Q, Jiang W, Zhao YJ, Li P, Wang JB, Lin JX, Lu J, Cao LL, Lin M, Tu RH, Huang ZN, Lin JL, Zheng HL, Que SJ, Zheng CH, Huang CM, Xie JW. An Intraoperative Model for Predicting Survival and Deciding Therapeutic Schedules: A Comprehensive Analysis of Peritoneal Metastasis in Patients With Advanced Gastric Cancer. Front Oncol 2020; 10:550526. [PMID: 33102217 PMCID: PMC7546781 DOI: 10.3389/fonc.2020.550526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 08/14/2020] [Indexed: 12/24/2022] Open
Abstract
Background and Objective: No specialized prognostic model for patients with gastric cancer with peritoneal metastasis (GCPM) exists for intraoperative clinical decision making. This study aims to establish a new prognostic model to provide individual treatment decisions for patients with GCPM. Method: This retrospective analysis included 324 patients with GCPM diagnosed pathologically by laparoscopy from January 2007 to January 2018 who were randomly assigned to different sets (227 in the training set and 97 in the internal validation set). A nomogram was established from preoperative and intraoperative variables determined by a Cox model. The predictive ability and clinical applicability of the PM nomogram (PMN) were compared with the 15th Japanese Classification of Gastric Carcinoma (JCGC) Staging Guidelines for PM (P1abc). Additional external validation was performed using a dataset (n = 39) from the First Affiliated Hospital of University of Science and Technology of China. Results: The median survival time was 8 (range, 1–90) months. In the training set, each PMN substage had significantly different survival curves (P < 0.001), and the PMN was superior to the P1abc based on the results of time-dependent receiver operating characteristic curve, C-index, Akaike information criterion and likelihood ratio chi-square analyses. In the internal and external validation sets, the PMN was also better than the P1abc in terms of its predictive ability. Of the PMN1 patients, those undergoing palliative resection had better overall survival (OS) than those undergoing exploratory surgery (P < 0.05). Among the patients undergoing exploratory surgery, those who received chemotherapy exhibited better OS than those who did not (P < 0.05). Among the patients who received palliative resection, only PMN1 patients exhibited better OS following chemotherapy (P < 0.05). Conclusion: We developed and validated a simple, specific PM model for patients with GCPM that can predict prognosis well and guide treatment decisions.
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Affiliation(s)
- Qi-Yue Chen
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zhi-Yu Liu
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Qing Zhong
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Wen Jiang
- Division of Life Sciences and Medicine, Department of Gastrointestinal Surgery, The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China, Hefei, China.,Anhui Provincial Hospital Affiliated With Anhui Medical University, Hefei, China
| | - Ya-Jun Zhao
- Division of Life Sciences and Medicine, Department of Gastrointestinal Surgery, The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China, Hefei, China.,Anhui Provincial Hospital Affiliated With Anhui Medical University, Hefei, China
| | - Ping Li
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Jia-Bin Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Jian-Xian Lin
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Jun Lu
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Long-Long Cao
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Mi Lin
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Ru-Hong Tu
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Ze-Ning Huang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Ju-Li Lin
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Hua-Long Zheng
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Si-Jin Que
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Chao-Hui Zheng
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Chang-Ming Huang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Jian-Wei Xie
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
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17
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Feng P, Wang ZD, Fan W, Liu H, Pan JJ. Diagnostic advances of artificial intelligence and radiomics in gastroenterology. Artif Intell Gastroenterol 2020; 1:37-50. [DOI: 10.35712/aig.v1.i2.37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/22/2020] [Accepted: 08/27/2020] [Indexed: 02/06/2023] Open
Abstract
Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.
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Affiliation(s)
- Pei Feng
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Zhen-Dong Wang
- Department of Ultrasound, Beijing Sihui Hospital of Traditional Chinese Medicine, Beijing 100022, China
| | - Wei Fan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Heng Liu
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Jing-Jing Pan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
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18
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Dong D, Tang L, Li ZY, Fang MJ, Gao JB, Shan XH, Ying XJ, Sun YS, Fu J, Wang XX, Li LM, Li ZH, Zhang DF, Zhang Y, Li ZM, Shan F, Bu ZD, Tian J, Ji JF. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer. Ann Oncol 2020; 30:431-438. [PMID: 30689702 PMCID: PMC6442651 DOI: 10.1093/annonc/mdz001] [Citation(s) in RCA: 263] [Impact Index Per Article: 65.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Background Occult peritoneal metastasis (PM) in advanced gastric cancer (AGC) patients is highly possible to be missed on computed tomography (CT) images. Patients with occult PMs are subject to late detection or even improper surgical treatment. We therefore aimed to develop a radiomic nomogram to preoperatively identify occult PMs in AGC patients. Patients and methods A total of 554 AGC patients from 4 centers were divided into 1 training, 1 internal validation, and 2 external validation cohorts. All patients’ PM status was firstly diagnosed as negative by CT, but later confirmed by laparoscopy (PM-positive n = 122, PM-negative n = 432). Radiomic signatures reflecting phenotypes of the primary tumor (RS1) and peritoneum region (RS2) were built as predictors of PM from 266 quantitative image features. Individualized nomograms of PM status incorporating RS1, RS2, or clinical factors were developed and evaluated regarding prediction ability. Results RS1, RS2, and Lauren type were significant predictors of occult PM (all P < 0.05). A nomogram of these three factors demonstrated better diagnostic accuracy than the model with RS1, RS2, or clinical factors alone (all net reclassification improvement P < 0.05). The area under curve yielded was 0.958 [95% confidence interval (CI) 0.923–0.993], 0.941 (95% CI 0.904–0.977), 0.928 (95% CI 0.886–0.971), and 0.920 (95% CI 0.862–0.978) for the training, internal, and two external validation cohorts, respectively. Stratification analysis showed that this nomogram had potential generalization ability. Conclusion CT phenotypes of both primary tumor and nearby peritoneum are significantly associated with occult PM status. A nomogram of these CT phenotypes and Lauren type has an excellent prediction ability of occult PM, and may have significant clinical implications on early detection of occult PM for AGC.
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Affiliation(s)
- D Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Radiology Department, , Peking University Cancer Hospital & Institute, Beijing; University of Chinese Academy of Sciences, Beijing
| | - L Tang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Radiology Department, , Peking University Cancer Hospital & Institute, Beijing
| | - Z-Y Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing
| | - M-J Fang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing; University of Chinese Academy of Sciences, Beijing
| | - J-B Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou
| | - X-H Shan
- Department of Radiology, Affiliated People's Hospital of Jiangsu University, Zhenjiang
| | - X-J Ying
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing
| | - Y-S Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Radiology Department, , Peking University Cancer Hospital & Institute, Beijing
| | - J Fu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Radiology Department, , Peking University Cancer Hospital & Institute, Beijing
| | - X-X Wang
- Department of Radiology, Affiliated People's Hospital of Jiangsu University, Zhenjiang
| | - L-M Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou
| | - Z-H Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming
| | - D-F Zhang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming
| | - Y Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing
| | - Z-M Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing
| | - F Shan
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing
| | - Z-D Bu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing
| | - J Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing; University of Chinese Academy of Sciences, Beijing; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China.
| | - J-F Ji
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing.
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19
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Yardimci AH, Sel I, Bektas CT, Yarikkaya E, Dursun N, Bektas H, Afsar CU, Gursu RU, Yardimci VH, Ertas E, Kilickesmez O. Computed tomography texture analysis in patients with gastric cancer: a quantitative imaging biomarker for preoperative evaluation before neoadjuvant chemotherapy treatment. Jpn J Radiol 2020; 38:553-560. [PMID: 32140880 DOI: 10.1007/s11604-020-00936-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 02/18/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE The aim of the study is to explore the role of computed tomography texture analysis (CT-TA) for predicting clinical T and N stages and tumor grade before neoadjuvant chemotherapy treatment in gastric cancer (GC) patients during the preoperative period. MATERIALS AND METHODS CT images of 114 patients with GC were included in this retrospective study. Following pre-processing steps, textural features were extracted using MaZda software in the portal venous phase. We evaluated and analyzed texture features of six principal categories for differentiating between T stages (T1,2 vs T3,4), N stages (N+ vs N-) and grades (low-intermediate vs. high). Classification was performed based on texture parameters with high model coefficients in linear discriminant analysis (LDA). RESULTS Dimension-reduction steps yielded five textural features for T stage, three for N stage and two for tumor grade. The discriminatory capacities of T stage, N stage and tumor grade were 90.4%, 81.6% and 64.5%, respectively, when LDA algorithm was employed. CONCLUSION CT-TA yields potentially useful imaging biomarkers for predicting the T and N stages of patients with GC and can be used for preoperative evaluation before neoadjuvant treatment planning.
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Affiliation(s)
- Aytul Hande Yardimci
- Department of Radiology, Istanbul Training and Research Hospital, Kasap İlyas Mah., Org. Abdurrahman Nafiz Gürman Cd., Fatih, 34098, Istanbul, Turkey.
| | - Ipek Sel
- Department of Radiology, Istanbul Training and Research Hospital, Kasap İlyas Mah., Org. Abdurrahman Nafiz Gürman Cd., Fatih, 34098, Istanbul, Turkey
| | - Ceyda Turan Bektas
- Department of Radiology, Istanbul Training and Research Hospital, Kasap İlyas Mah., Org. Abdurrahman Nafiz Gürman Cd., Fatih, 34098, Istanbul, Turkey
| | - Enver Yarikkaya
- Department of Pathology, Istanbul Training and Research Hospital, Istanbul, Turkey
| | - Nevra Dursun
- Department of Pathology, Istanbul Training and Research Hospital, Istanbul, Turkey
| | - Hasan Bektas
- Department of General Surgery, Istanbul Training and Research Hospital, Istanbul, Turkey
| | - Cigdem Usul Afsar
- Department of Medical Oncology, Acıbadem Mehmet Ali Aydınlar University Medical Faculty, Istanbul, Turkey
| | - Rıza Umar Gursu
- Department of Medical Oncology, Istanbul Training and Research Hospital, Istanbul, Turkey
| | | | - Elif Ertas
- Department of Biostatistics, Mersin University, Mersin, Turkey
| | - Ozgur Kilickesmez
- Department of Radiology, Istanbul Training and Research Hospital, Kasap İlyas Mah., Org. Abdurrahman Nafiz Gürman Cd., Fatih, 34098, Istanbul, Turkey
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20
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Wang Z, Chen JQ, Liu JL, Tian L. Issues on peritoneal metastasis of gastric cancer: an update. World J Surg Oncol 2019; 17:215. [PMID: 31829265 PMCID: PMC6907197 DOI: 10.1186/s12957-019-1761-y] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 11/26/2019] [Indexed: 12/19/2022] Open
Abstract
Background Peritoneal metastasis (PM) is one of the most common forms of metastasis with a very poor prognosis in patients with gastric cancer (GC). The mechanisms, diagnosis, and management of PM remain controversial. Main body Stephen Paget’s “seed-and-soil” hypothesis gives us an illustration of the mechanisms of PM. Recently, hematogenous metastasis and exosomes from GC are identified as novel mechanisms for PM. Diagnostic accuracy of conventional imaging modalities for PM is not satisfactory, but texture analysis may be a useful adjunct for the prediction of PM. Biological markers in peritoneal washings are helpful in identifying patients at high risk of PM, but many limitations remain to be overcome. Response of PM from systemic chemotherapy alone is very limited. However, conversion therapy is confirmed to be safe and able to prolong the survival of GC patients with PM. As an important part of conversion therapy, intraperitoneal chemotherapy with taxanes has become an ideal approach with several advantages. Additionally, gastrectomy should be considered in patients who would tolerate surgery if a remarkable response to chemotherapy was observed. Conclusion Texture analysis is a reliable adjunct for the prediction of PM, and conversion therapy provides a new choice for GC patients with PM. The underlying mechanisms and new biological markers for GC patients with PM should be the direction of future studies. Furthermore, significant aspects of conversion therapy, such as timing and method of the operation, and the indications remain to be clarified.
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Affiliation(s)
- Zhen Wang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, China.
| | - Jun-Qiang Chen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, China.
| | - Jin-Lu Liu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, China
| | - Lei Tian
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi Zhuang Autonomous Region, China
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21
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Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer. Eur Radiol 2019; 30:239-246. [DOI: 10.1007/s00330-019-06368-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 06/23/2019] [Accepted: 07/11/2019] [Indexed: 01/08/2023]
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22
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Gabelloni M, Faggioni L, Neri E. Imaging biomarkers in upper gastrointestinal cancers. BJR Open 2019; 1:20190001. [PMID: 33178936 PMCID: PMC7592483 DOI: 10.1259/bjro.20190001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 02/23/2019] [Accepted: 03/29/2019] [Indexed: 12/02/2022] Open
Abstract
In parallel with the increasingly widespread availability of high performance imaging platforms and recent progresses in pathobiological characterisation and treatment of gastrointestinal malignancies, imaging biomarkers have become a major research topic due to their potential to provide additional quantitative information to conventional imaging modalities that can improve accuracy at staging and follow-up, predict outcome, and guide treatment planning in an individualised manner. The aim of this review is to briefly examine the status of current knowledge about imaging biomarkers in the field of upper gastrointestinal cancers, highlighting their potential applications and future perspectives in patient management from diagnosis onwards.
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Affiliation(s)
- Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, Pisa, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, Pisa, Italy
| | - Emanuele Neri
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, Pisa, Italy
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23
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Biondi M, Vanzi E, De Otto G, Carbone SF, Nardone V, Banci Buonamici F. Effects of CT FOV displacement and acquisition parameters variation on texture analysis features. ACTA ACUST UNITED AC 2018; 63:235021. [DOI: 10.1088/1361-6560/aaefac] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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24
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Klaassen R, Larue RTHM, Mearadji B, van der Woude SO, Stoker J, Lambin P, van Laarhoven HWM. Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients. PLoS One 2018; 13:e0207362. [PMID: 30440002 PMCID: PMC6237370 DOI: 10.1371/journal.pone.0207362] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 10/30/2018] [Indexed: 02/06/2023] Open
Abstract
In this study we investigate a CT radiomics approach to predict response to chemotherapy of individual liver metastases in patients with esophagogastric cancer (EGC). In eighteen patients with metastatic EGC treated with chemotherapy, all liver metastases were manually delineated in 3D on the pre-treatment and evaluation CT. From the pre-treatment CT scans 370 radiomics features were extracted per lesion. Random forest (RF) models were generated to discriminate partial responding (PR, >65% volume decrease, including 100% volume decrease), and complete remission (CR, only 100% volume decrease) lesions from other lesions. RF-models were build using a leave one out strategy where all lesions of a single patient were removed from the dataset and used as validation set for a model trained on the lesions of the remaining patients. This process was repeated for all patients, resulting in 18 trained models and one validation set for both the PR and CR datasets. Model performance was evaluated by receiver operating characteristics with corresponding area under the curve (AUC). In total 196 liver metastases were delineated on the pre-treatment CT, of which 99 (51%) lesions showed a decrease in size of more than 65% (PR). From the PR set a total of 47 (47% of RL, 24% of initial) lesions were no longer detected in CT scan 2 (CR). The RF-model for PR lesions showed an average training AUC of 0.79 (range: 0.74-0.83) and 0.65 (95% ci: 0.57-0.73) for the combined validation set. The RF-model for CR lesions had an average training AUC of 0.87 (range: 0.83-0.90) and 0.79 (95% ci 0.72-0.87) for the validation set. Our findings show that individual response of liver metastases varies greatly within and between patients. A CT radiomics approach shows potential in discriminating responding from non-responding liver metastases based on the pre-treatment CT scan, although further validation in an independent patient cohort is needed to validate these findings.
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Affiliation(s)
- Remy Klaassen
- Amsterdam UMC, University of Amsterdam, Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam, Netherlands
- Amsterdam UMC, University of Amsterdam, LEXOR, Laboratory for Experimental Oncology and Radiobiology, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Ruben T. H. M. Larue
- The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht Comprehensive Cancer Centre, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Banafsche Mearadji
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Stephanie O. van der Woude
- Amsterdam UMC, University of Amsterdam, Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Jaap Stoker
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht Comprehensive Cancer Centre, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Hanneke W. M. van Laarhoven
- Amsterdam UMC, University of Amsterdam, Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam, Netherlands
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