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Zhang J, Zhang Q, Zhao B, Shi G. Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients. Abdom Radiol (NY) 2024:10.1007/s00261-024-04331-7. [PMID: 38796795 DOI: 10.1007/s00261-024-04331-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 05/29/2024]
Abstract
PURPOSE Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients. METHODS This multi-center study retrospectively included 322 patients diagnosed with gastric cancer from January 2013 to June 2023 at two hospitals. Handcrafted radiomics technique and the EfficientNet V2 neural network were applied to arterial, portal venous, and delayed phase CT images to extract two-dimensional handcrafted and deep learning features. A nomogram model was built by integrating the handcrafted signature, the deep learning signature, with clinical features. Discriminative ability was assessed using the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve. Model fitting was evaluated using calibration curves, and clinical utility was assessed through decision curve analysis (DCA). RESULTS The nomogram exhibited excellent performance. The area under the ROC curve (AUC) was 0.848 [95% confidence interval (CI), 0.793-0.893)], 0.802 (95% CI 0.688-0.889), and 0.751 (95% CI 0.652-0.833) for the training, internal validation, and external validation sets, respectively. The AUCs of the P-R curves were 0.838 (95% CI 0.756-0.895), 0.541 (95% CI 0.329-0.740), and 0.556 (95% CI 0.376-0.722) for the corresponding sets. The nomogram outperformed the clinical model and handcrafted signature across all sets (all P < 0.05). The nomogram model demonstrated good calibration and provided greater net benefit within the relevant threshold range compared to other models. CONCLUSION This study created a deep learning nomogram using CECT images and clinical data to predict NAC response in LAGC patients undergoing surgical resection, offering personalized treatment insights.
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Affiliation(s)
- Jingjing Zhang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China
| | - Qiang Zhang
- Department of Radiation Oncology, The First Hospital of Qinhuangdao, Qinhuangdao, People's Republic of China
| | - Bo Zhao
- Department of Medical Imaging, The First Hospital of Qinhuangdao, Qinhuangdao, People's Republic of China
| | - Gaofeng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.
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Adili D, Mohetaer A, Zhang W. Diagnostic accuracy of radiomics-based machine learning for neoadjuvant chemotherapy response and survival prediction in gastric cancer patients: A systematic review and meta-analysis. Eur J Radiol 2024; 173:111249. [PMID: 38382422 DOI: 10.1016/j.ejrad.2023.111249] [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: 07/24/2023] [Revised: 11/07/2023] [Accepted: 11/30/2023] [Indexed: 02/23/2024]
Abstract
BACKGROUND In recent years, researchers have explored the use of radiomics to predict neoadjuvant chemotherapy outcomes in gastric cancer (GC). Yet, a lingering debate persists regarding the accuracy of these predictions. Against this backdrop, this study was conducted to examine the accuracy of radiomics in predicting the response to neoadjuvant chemotherapy in GC patients. METHODS An exhaustive search of relevant studies was conducted in PubMed, Cochrane, Embase, and Web of Science databases up to February 21, 2023. The radiomics quality scoring (RQS) tool was employed to assess study quality. Tumor response to neoadjuvant chemotherapy and survival outcomes were examined as outcome measures. RESULTS Fourteen studies involving 3,373 GC patients who had received neoadjuvant chemotherapy were incorporated in our meta-analysis. The mean RQS score across all studies was 36.3%, ranging between 0 and 63.9%. On the validation cohort, when the modeling variables were restricted to radiomic features alone, the predictive performance was characterized by a c-index of 0.750 (95% CI: 0.710-0.790), with a sensitivity of 0.67 (95% CI: 0.58-0.75) and a specificity of 0.77 (95% CI: 0.69-0.84) for the prediction of neoadjuvant chemotherapy response. When clinical data was integrated with radiomic features as modeling variables, the predictive performance improved, yielding a c-index of 0.814 (95% CI: 0.780-0.847), a sensitivity of 0.78 [95% CI: 0.70-0.84], and a specificity of 0.73 [95% CI: 0.67-0.79]. CONCLUSIONS Radiomics holds promise to noninvasively predict neoadjuvant chemotherapy response and survival outcomes among patients with locally advanced GC. Additionally, we underscore the need for future multicenter studies and the development of imaging-sourced tools for risk stratification in this patient population.
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Affiliation(s)
- Diliyaer Adili
- Department of Gastrointestinal (Oncology) Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054 China
| | - Aibibai Mohetaer
- Department of Cardiology, The Second Affiliated Hospital of Xinjiang Medical University, Urumqi, 830063 China
| | - Wenbin Zhang
- Department of Gastrointestinal (Oncology) Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054 China
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Tu D, Xu Q, Luan Y, Sun J, Zuo X, Ma C. Integrative analysis of bioinformatics and machine learning to identify cuprotosis-related biomarkers and immunological characteristics in heart failure. Front Cardiovasc Med 2024; 11:1349363. [PMID: 38562184 PMCID: PMC10982316 DOI: 10.3389/fcvm.2024.1349363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/07/2024] [Indexed: 04/04/2024] Open
Abstract
Backgrounds Cuprotosis is a newly discovered programmed cell death by modulating tricarboxylic acid cycle. Emerging evidence showed that cuprotosis-related genes (CRGs) are implicated in the occurrence and progression of multiple diseases. However, the mechanism of cuprotosis in heart failure (HF) has not been investigated yet. Methods The HF microarray datasets GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed CRGs between HF patients and nonfailing donors (NFDs). Four machine learning models were used to identify key CRGs features for HF diagnosis. The expression profiles of key CRGs were further validated in a merged GEO external validation dataset and human samples through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). In addition, Gene Ontology (GO) function enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and immune infiltration analysis were used to investigate potential biological functions of key CRGs. Results We discovered nine differentially expressed CRGs in heart tissues from HF patients and NFDs. With the aid of four machine learning algorithms, we identified three indicators of cuprotosis (DLAT, SLC31A1, and DLST) in HF, which showed good diagnostic properties. In addition, their differential expression between HF patients and NFDs was confirmed through qRT-PCR. Moreover, the results of enrichment analyses and immune infiltration exhibited that these diagnostic markers of CRGs were strongly correlated to energy metabolism and immune activity. Conclusions Our study discovered that cuprotosis was strongly related to the pathogenesis of HF, probably by regulating energy metabolism-associated and immune-associated signaling pathways.
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Affiliation(s)
- Dingyuan Tu
- Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, State Key Laboratory of Frigid Zone Cardiovascular Diseases (SKLFZCD), Shenyang, Liaoning, China
- Department of Cardiology, The 961st Hospital of PLA Joint Logistic Support Force, Qiqihar, Heilongjiang, China
| | - Qiang Xu
- Department of Cardiology, Changhai Hospital, Naval Medical University, Shanghai, China
- Department of Cardiology, Navy 905 Hospital, Naval Medical University, Shanghai, China
| | - Yanmin Luan
- Reproductive Medicine Center, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jie Sun
- Hospital-Acquired Infection Control Department, Yantai Ludong Hospital, Yantai, Shandong, China
| | - Xiaoli Zuo
- Department of Cardiology, The 961st Hospital of PLA Joint Logistic Support Force, Qiqihar, Heilongjiang, China
| | - Chaoqun Ma
- Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, State Key Laboratory of Frigid Zone Cardiovascular Diseases (SKLFZCD), Shenyang, Liaoning, China
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Liu C, Li L, Chen X, Huang C, Wang R, Liu Y, Gao J. Intratumoral and peritumoral radiomics predict pathological response after neoadjuvant chemotherapy against advanced gastric cancer. Insights Imaging 2024; 15:23. [PMID: 38270724 PMCID: PMC10811314 DOI: 10.1186/s13244-023-01584-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 11/25/2023] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND To investigate whether intratumoral and peritumoral radiomics may predict pathological responses after neoadjuvant chemotherapy against advanced gastric cancer. METHODS Clinical, pathological, and CT data from 231 patients with advanced gastric cancer who underwent neoadjuvant chemotherapy at our hospital between July 2014 and February 2022 were retrospectively collected. Patients were randomly divided into a training group (n = 161) and a validation group (n = 70). The support vector machine classifier was used to establish radiomics models. A clinical model was established based on the selected clinical indicators. Finally, the radiomics and clinical models were combined to generate a radiomics-clinical model. ROC analyses were used to evaluate the prediction efficiency for each model. Calibration curves and decision curves were used to evaluate the optimal model. RESULTS A total of 91 cases were recorded with good response and 140 with poor response. The radiomics model demonstrated that the AUC was higher in the combined model than in the intratumoral and peritumoral models (training group: 0.949, 0.943, and 0.846, respectively; validation group: 0.815, 0.778, and 0.701, respectively). Age, Borrmann classification, and Lauren classification were used to construct the clinical model. Among the radiomics-clinical models, the combined-clinical model showed the highest AUC (training group: 0.960; validation group: 0.843), which significantly improved prediction efficiency. CONCLUSION The peritumoral model provided additional value in the evaluation of pathological response after neoadjuvant chemotherapy against advanced gastric cancer, and the combined-clinical model showed the highest predictive efficiency. CRITICAL RELEVANCE STATEMENT Intratumoral and peritumoral radiomics can noninvasively predict the pathological response against advanced gastric cancer after neoadjuvant chemotherapy to guide early treatment decision and provide individual treatment for patients. KEY POINTS 1. Radiomics can predict pathological responses after neoadjuvant chemotherapy against advanced gastric cancer. 2. Peritumoral radiomics has additional predictive value. 3. Radiomics-clinical models can guide early treatment decisions and improve patient prognosis.
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Affiliation(s)
- Chenchen Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Liming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Xingzhi Chen
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Yiyang Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China.
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Zhong H, Wang T, Hou M, Liu X, Tian Y, Cao S, Li Z, Han Z, Liu G, Sun Y, Meng C, Li Y, Jiang Y, Ji Q, Hao D, Liu Z, Zhou Y. Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer. Ann Surg Oncol 2024; 31:421-432. [PMID: 37925653 DOI: 10.1245/s10434-023-14424-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/26/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) images and clinical characteristics to predict the response of metastatic lymph nodes to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC). METHODS We prospectively enrolled 112 patients with LAGC who received NACT from January 2021 to August 2022. After applying the inclusion and exclusion criteria, 98 patients were randomized 7:3 to the training cohort (n = 68) and validation cohort (n = 30). We established and compared three radiomics signatures based on three phases of CT images before and after NACT, namely radiomics-baseline, radiomics-delta, and radiomics-restage. Then, we developed a clinical model, DL model, and a nomogram to predict the response of LAGC after NACT. We evaluated the predictive accuracy and clinical validity of each model using the receiver operating characteristic curve and decision curve analysis, respectively. RESULTS The radiomics-delta signature was the best predictor among the three radiomics signatures. So, we developed and validated a DL delta radiomics nomogram (DLDRN). In the validation cohort, the DLDRN produced an area under the receiver operating curve of 0.94 (95% confidence interval, 0.82-0.96) and demonstrated adequate differentiation of good response to NACT. Furthermore, the DLDRN significantly outperformed the clinical model and DL model (p < 0.001). The clinical utility of the DLDRN was confirmed through decision curve analysis. CONCLUSIONS In patients with LAGC, the DLDRN effectively predicted a therapeutic response in metastatic lymph nodes, which could provide valuable information for individualized treatment.
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Affiliation(s)
- Hao Zhong
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Tongyu Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Mingyu Hou
- Department of Pathology, Qingdao University Affiliated Qingdao Women and Children's Hospital, Qingdao, Shandong, People's Republic of China
| | - Xiaodong Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yulong Tian
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Shougen Cao
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Zequn Li
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Zhenlong Han
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Gan Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yuqi Sun
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Cheng Meng
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yujun Li
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yanxia Jiang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Qinglian Ji
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Dapeng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Zimin Liu
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
- Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yanbing Zhou
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
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Zhong H, Wang T, Liu X, Tian Y, Zhou Y. ASO Author Reflections: Deep-Learning Radiomics Nomogram Based on Enhanced CT to Predict the Effect of Neoadjuvant Chemotherapy on Metastatic Lymph Nodes in Locally Advanced Gastric Cancer. Ann Surg Oncol 2024; 31:454-455. [PMID: 37930497 DOI: 10.1245/s10434-023-14508-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023]
Affiliation(s)
- Hao Zhong
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Tongyu Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Xiaodong Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yulong Tian
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yanbing Zhou
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
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Deng J, Zhang W, Xu M, Zhou J. Imaging advances in efficacy assessment of gastric cancer neoadjuvant chemotherapy. Abdom Radiol (NY) 2023; 48:3661-3676. [PMID: 37787962 DOI: 10.1007/s00261-023-04046-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/31/2023] [Accepted: 09/03/2023] [Indexed: 10/04/2023]
Abstract
Effective neoadjuvant chemotherapy (NAC) can improve the survival of patients with locally progressive gastric cancer, but chemotherapeutics do not always exhibit good efficacy in all patients. Therefore, accurate preoperative evaluation of the effect of neoadjuvant therapy and the appropriate selection of surgery time to minimize toxicity and complications while prolonging patient survival are key issues that need to be addressed. This paper reviews the role of three imaging methods, morphological, functional, radiomics, and artificial intelligence (AI)-based imaging, in evaluating NAC pathological reactions for gastric cancer. In addition, the advantages and disadvantages of each method and the future application prospects are discussed.
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Affiliation(s)
- Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China
| | - Wenjuan Zhang
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China
| | - Min Xu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China.
- Second Clinical School, Lanzhou University, Lanzhou, 730030, China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China.
- Gansu International Scientifific and Technological Cooperation Base of Medical Imaging Artifificial Intelligence, Lanzhou, 730030, China.
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Ma T, Wang H, Ye Z. Artificial intelligence applications in computed tomography in gastric cancer: a narrative review. Transl Cancer Res 2023; 12:2379-2392. [PMID: 37859746 PMCID: PMC10583011 DOI: 10.21037/tcr-23-201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 08/01/2023] [Indexed: 10/21/2023]
Abstract
Background and Objective Artificial intelligence (AI) is a revolutionary technique which is deeply impacting and reshaping clinical practice in oncology. This review aims to summarize the current status of the clinical application of AI-based computed tomography (CT) for gastric cancer (GC), focusing on diagnosis, genetic status detection and risk prediction of metastasis, prognosis and treatment efficacy. The challenges and prospects for future research will also be discussed. Methods We searched the PubMed/MEDLINE database to identify clinical studies published between 1990 and November 2022 that investigated AI applications in CT in GC. The major findings of the verified studies were summarized. Key Content and Findings AI applications in CT images have attracted considerable attention in various fields such as diagnosis, prediction of metastasis risk, survival, and treatment response. These emerging techniques have shown a high potential to outperform clinicians in diagnostic accuracy and time-saving. Conclusions AI-powered tools showed great potential to increase diagnostic accuracy and reduce radiologists' workload. However, the goal of AI is not to replace human ability but to help oncologists make decisions in their practice. Therefore, radiologists should play a predominant role in AI applications and decide the best ways to integrate these complementary techniques within clinical practice.
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Affiliation(s)
- Tingting Ma
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Hua Wang
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Jiang T, Zhao Z, Liu X, Shen C, Mu M, Cai Z, Zhang B. Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study. Front Oncol 2023; 13:1161237. [PMID: 37731636 PMCID: PMC10507631 DOI: 10.3389/fonc.2023.1161237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 08/16/2023] [Indexed: 09/22/2023] Open
Abstract
Background Machine learning radiomics models are increasingly being used to predict gastric cancer prognoses. However, the methodological quality of these models has not been evaluated. Therefore, this study aimed to evaluate the methodological quality of radiomics studies in predicting the prognosis of gastric cancer, summarize their methodological characteristics and performance. Methods The PubMed and Embase databases were searched for radiomics studies used to predict the prognosis of gastric cancer published in last 5 years. The characteristics of the studies and the performance of the models were extracted from the eligible full texts. The methodological quality, reporting completeness and risk of bias of the included studies were evaluated using the RQS, TRIPOD and PROBAST. The discrimination ability scores of the models were also compared. Results Out of 283 identified records, 22 studies met the inclusion criteria. The study endpoints included survival time, treatment response, and recurrence, with reported discriminations ranging between 0.610 and 0.878 in the validation dataset. The mean overall RQS value was 15.32 ± 3.20 (range: 9 to 21). The mean adhered items of the 35 item of TRIPOD checklist was 20.45 ± 1.83. The PROBAST showed all included studies were at high risk of bias. Conclusion The current methodological quality of gastric cancer radiomics studies is insufficient. Large and reasonable sample, prospective, multicenter and rigorously designed studies are required to improve the quality of radiomics models for gastric cancer prediction. Study registration This protocol was prospectively registered in the Open Science Framework Registry (https://osf.io/ja52b).
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Affiliation(s)
- Tianxiang Jiang
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhou Zhao
- Department of Gastrointestinal Cancer Center, Chongqing University Cancer Hospital, Chongqing, China
| | - Xueting Liu
- Department of Medical Discipline Construction, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyong Shen
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Mingchun Mu
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhaolun Cai
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Bo Zhang
- Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
- Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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Zhang S, Mu W, Dong D, Wei J, Fang M, Shao L, Zhou Y, He B, Zhang S, Liu Z, Liu J, Tian J. The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review. HEALTH DATA SCIENCE 2023; 3:0005. [PMID: 38487199 PMCID: PMC10877701 DOI: 10.34133/hds.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 03/17/2024]
Abstract
Importance Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.
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Affiliation(s)
- Shuaitong Zhang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Wei Mu
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingwei Wei
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Lizhi Shao
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhou
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bingxi He
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
| | - Song Zhang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jianhua Liu
- Department of Oncology, Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Jie Tian
- School of Engineering Medicine, Beihang University, Beijing, China
- Key Laboratory of Big Data-Based Precision Medicine, Beihang University, Ministry of Industry and Information Technology, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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11
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Lyu D, Liang P, Huang C, Chen X, Cheng M, Zhu B, Liu M, Yue S, Gao J. Are radiomic spleen features useful for assessing the differentiation status of advanced gastric cancer? Front Oncol 2023; 13:1167602. [PMID: 37213311 PMCID: PMC10196477 DOI: 10.3389/fonc.2023.1167602] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 04/21/2023] [Indexed: 05/23/2023] Open
Abstract
Background The differentiation status of gastric cancer is related to clinical stage, treatment and prognosis. It is expected to establish a radiomic model based on the combination of gastric cancer and spleen to predict the differentiation degree of gastric cancer. Thus, we aim to determine whether radiomic spleen features can be used to distinguish advanced gastric cancer with varying states of differentiation. Materials and methods January 2019 to January 2021, we retrospectively analyzed 147 patients with advanced gastric cancer confirmed by pathology. The clinical data were reviewed and analyzed. Three radiomics predictive models were built from radiomics features based on gastric cancer (GC), spleen (SP) and combination of two organ position (GC+SP) images. Then, three Radscores (GC, SP and GC+SP) were obtained. A nomogram was developed to predict differentiation statue by incorporating GC+SP Radscore and clinical risk factors. The area under the curve (AUC) of operating characteristics (ROC) and calibration curves were assessed to evaluate the differential performance of radiomic models based on gastric cancer and spleen for advanced gastric cancer with different states of differentiation (poorly differentiated group and non- poorly differentiated group). Results There were 147 patients evaluated (mean age, 60 years ± 11SD, 111 men). Univariate and multivariate logistic analysis identified three clinical features (age, cTNM stage and CT attenuation of spleen arterial phase) were independent risk factors for the degree of differentiation of GC (p =0.004,0.000,0.020, respectively). The clinical radiomics (namely, GC+SP+Clin) model showed powerful prognostic ability in the training and test cohorts with AUCs of 0.97 and 0.91, respectively. The established model has the best clinical benefit in diagnosing GC differentiation. Conclusion By combining radiomic features (GC and spleen) with clinical risk factors, we develop a radiomic nomogram to predict differentiation status in patients with AGC, which can be used to guide treatment decisions.
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Affiliation(s)
- Dongbo Lyu
- The Departments of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Pan Liang
- The Departments of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Pan Liang, ;
| | - Chencui Huang
- The Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Xingzhi Chen
- The Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Ming Cheng
- The Departments of Information Department, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingbing Zhu
- The Departments of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mengru Liu
- The Departments of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Songwei Yue
- The Departments of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianbo Gao
- The Departments of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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12
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Zhang J, Cui Y, Wei K, Li Z, Li D, Song R, Ren J, Gao X, Yang X. Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study. Gastric Cancer 2022; 25:1050-1059. [PMID: 35932353 DOI: 10.1007/s10120-022-01328-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 07/21/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Accurate pre-treatment prediction of neoadjuvant chemotherapy (NACT) resistance in patients with locally advanced gastric cancer (LAGC) is essential for timely surgeries and optimized treatments. We aim to evaluate the effectiveness of deep learning (DL) on computed tomography (CT) images in predicting NACT resistance in LAGC patients. METHODS A total of 633 LAGC patients receiving NACT from three hospitals were included in this retrospective study. The training and internal validation cohorts were randomly selected from center 1, comprising 242 and 104 patients, respectively. The external validation cohort 1 comprised 128 patients from center 2, and the external validation cohort 2 comprised 159 patients from center 3. First, a DL model was developed using ResNet-50 to predict NACT resistance in LAGC patients, and the gradient-weighted class activation mapping (Grad-CAM) was assessed for visualization. Then, an integrated model was constructed by combing the DL signature and clinical characteristics. Finally, the performance was tested in internal and external validation cohorts using area under the receiver operating characteristic (ROC) curves (AUC). RESULTS The DL model achieved AUCs of 0.808 (95% CI 0.724-0.893), 0.755 (95% CI 0.660-0.850), and 0.752 (95% CI 0.678-0.825) in validation cohorts, respectively, which were higher than those of the clinical model. Furthermore, the integrated model performed significantly better than the clinical model (P < 0.05). CONCLUSIONS A CT-based model using DL showed promising performance for predicting NACT resistance in LAGC patients, which could provide valuable information in terms of individualized treatment.
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Affiliation(s)
- Jiayi Zhang
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou, 215163, Jiangsu, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, Jiangsu, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, Guangdong, China
| | - Kaikai Wei
- Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, Yunnan, China
| | - Dandan Li
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China
| | - Ruirui Song
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China
| | | | - Xin Gao
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou, 215163, Jiangsu, China
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, Jiangsu, China
| | - Xiaotang Yang
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital, Affiliated to Shanxi Medical University, Taiyuan, 030013, Shanxi, China.
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13
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Wang L, Chen Y, Tan J, Ge Y, Xu Z, Wels M, Pan Z. Efficacy and prognostic value of delta radiomics on dual-energy computed tomography for gastric cancer with neoadjuvant chemotherapy: a preliminary study. Acta Radiol 2022; 64:1311-1321. [PMID: 36062762 DOI: 10.1177/02841851221123971] [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: 12/24/2022]
Abstract
BACKGROUND A non-invasive tool for tumor regression grade (TRG) evaluation is urgently needed for gastric cancer (GC) treated with neoadjuvant chemotherapy (NAC). PURPOSE To develop and validate a radiomics signature (RS) to evaluate TRG for locally advanced GC after NAC and assess its prognostic value. MATERIAL AND METHODS A total of 103 patients with GC treated with NAC were retrospectively recruited from April 2018 to December 2019 and were randomly allocated into a training cohort (n = 69) and a validation cohort (n = 34). Delineation was performed on both mixed and iodine-uptake images based on dual-energy computed tomography (DECT). A total of 4094 radiomics features were extracted from the pre-NAC, post-NAC, and delta feature sets. Spearman correlation and the least absolute shrinkage and selection operator were used for dimensionality reduction. Multivariable logistic regression was used for TRG evaluation and generated the optimal RS. Kaplan-Meier survival analysis with the log-rank test was implemented in an independent cohort of 40 patients to validate the prognostic value of the optimal RS. RESULTS Three, five, and six radiomics features were finally selected for the pre-NAC, post-NAC, and delta feature sets. The delta model demonstrated the best performance in assessing TRG in both the training and the validation cohorts (AUCs=0.91 and 0.76, respectively; P>0.1). The optimal RS from the delta model showed a significant capability to predict survival in the independent cohort (P<0.05). CONCLUSION Delta radiomics based on DECT images serves as a potential biomarker for TRG evaluation and shows prognostic value for patients with GC treated with NAC.
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Affiliation(s)
- Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Jingwen Tan
- Department of Radiology, Ruijin Hospital North, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yingqian Ge
- Siemens Healthineers Ltd, Shanghai, PR China
| | - Zhihan Xu
- Siemens Healthineers Ltd, Shanghai, PR China
| | - Michael Wels
- Department of Diagnostic Imaging Computed Tomography Image Analytics, 42406Siemens Healthcare GmbH, Forchheim, Germany
| | - Zilai Pan
- Department of Radiology, Ruijin Hospital North, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
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14
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Chen Q, Zhang L, Liu S, You J, Chen L, Jin Z, Zhang S, Zhang B. Radiomics in precision medicine for gastric cancer: opportunities and challenges. Eur Radiol 2022; 32:5852-5868. [PMID: 35316364 DOI: 10.1007/s00330-022-08704-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 02/20/2022] [Accepted: 02/28/2022] [Indexed: 01/19/2023]
Abstract
OBJECTIVES Radiomic features derived from routine medical images show great potential for personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and quality of radiomic research as well as its potential for identifying biomarkers to predict therapy response and prognosis in patients with GC. METHODS We performed a systematic search of the PubMed and Embase databases for articles published from inception through July 10, 2021. The phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool were applied to evaluate scientific and reporting quality. RESULTS Twenty-five studies consisting of 10,432 patients were included. 96% of studies extracted radiomic features from CT images. Association between radiomic signature and therapy response was evaluated in seven (28%) studies; association with survival was evaluated in 17 (68%) studies; one (4%) study analyzed both. All results of the included studies showed significant associations. Based on the phase classification criteria for image mining studies, 18 (72%) studies were classified as phase II, with two, four, and one studies as discovery science, phase 0 and phase I, respectively. The median RQS score for the radiomic studies was 44.4% (range, 0 to 55.6%). There was extensive heterogeneity in the study population, tumor stage, treatment protocol, and radiomic workflow amongst the studies. CONCLUSIONS Although radiomic research in GC is highly heterogeneous and of relatively low quality, it holds promise for predicting therapy response and prognosis. Efforts towards standardization and collaboration are needed to utilize radiomics for clinical application. KEY POINTS • Radiomics application of gastric cancer is increasingly being reported, particularly in predicting therapy response and survival. • Although radiomics research in gastric cancer is highly heterogeneous and relatively low quality, it holds promise for predicting clinical outcomes. • Standardized imaging protocols and radiomic workflow are needed to facilitate radiomics into clinical use.
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Affiliation(s)
- Qiuying Chen
- Department of Radiology, the First Affiliated Hospital, Jinan University, No.613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China.,Graduate College, Jinan University, Guangzhou, Guangdong, China
| | - Lu Zhang
- Department of Radiology, the First Affiliated Hospital, Jinan University, No.613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China.,Graduate College, Jinan University, Guangzhou, Guangdong, China
| | - Shuyi Liu
- Department of Radiology, the First Affiliated Hospital, Jinan University, No.613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China.,Graduate College, Jinan University, Guangzhou, Guangdong, China
| | - Jingjing You
- Department of Radiology, the First Affiliated Hospital, Jinan University, No.613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China.,Graduate College, Jinan University, Guangzhou, Guangdong, China
| | - Luyan Chen
- Department of Radiology, the First Affiliated Hospital, Jinan University, No.613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China.,Graduate College, Jinan University, Guangzhou, Guangdong, China
| | - Zhe Jin
- Department of Radiology, the First Affiliated Hospital, Jinan University, No.613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China.,Graduate College, Jinan University, Guangzhou, Guangdong, China
| | - Shuixing Zhang
- Department of Radiology, the First Affiliated Hospital, Jinan University, No.613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China. .,Graduate College, Jinan University, Guangzhou, Guangdong, China.
| | - Bin Zhang
- Department of Radiology, the First Affiliated Hospital, Jinan University, No.613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China. .,Graduate College, Jinan University, Guangzhou, Guangdong, China.
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15
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Fang M, Tian J, Dong D. Non-invasively predicting response to neoadjuvant chemotherapy in gastric cancer via deep learning radiomics. EClinicalMedicine 2022; 46:101380. [PMID: 35434584 PMCID: PMC9006631 DOI: 10.1016/j.eclinm.2022.101380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 03/15/2022] [Indexed: 12/02/2022] Open
Affiliation(s)
- Mengjie Fang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East Road, Beijing 100190, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East Road, Beijing 100190, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China
| | - Di Dong
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East Road, Beijing 100190, China
- Corresponding author at: CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East Road, Beijing 100190, China.
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Song R, Cui Y, Ren J, Zhang J, Yang Z, Li D, Li Z, Yang X. CT-based radiomics analysis in the prediction of response to neoadjuvant chemotherapy in locally advanced gastric cancer: A dual-center study. Radiother Oncol 2022; 171:155-163. [DOI: 10.1016/j.radonc.2022.04.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/26/2022] [Accepted: 04/21/2022] [Indexed: 12/24/2022]
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17
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Cui Y, Zhang J, Li Z, Wei K, Lei Y, Ren J, Wu L, Shi Z, Meng X, Yang X, Gao X. A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study. EClinicalMedicine 2022; 46:101348. [PMID: 35340629 PMCID: PMC8943416 DOI: 10.1016/j.eclinm.2022.101348] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/21/2022] [Accepted: 02/23/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Accurate prediction of treatment response to neoadjuvant chemotherapy (NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for personalized medicine. We aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on pretreatment contrast-enhanced computed tomography (CT) images and clinical features to predict the response to NACT in patients with LAGC. METHODS 719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020. The training cohort and internal validation cohort (IVC), comprising 243 and 103 patients, respectively, were randomly selected from center I; the external validation cohort1 (EVC1) comprised 207 patients from center II; and EVC2 comprised 166 patients from another two hospitals. Two imaging signatures, reflecting the phenotypes of the deep learning and handcrafted radiomics features, were constructed from the pretreatment portal venous-phase CT images. A four-step procedure, including reproducibility evaluation, the univariable analysis, the LASSO method, and the multivariable logistic regression analysis, was applied for feature selection and signature building. The integrated DLRN was then developed for the added value of the imaging signatures to independent clinicopathological factors for predicting the response to NACT. The prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the DLRN were used to estimate the disease-free survival (DFS) in the follow-up cohort (n = 300). FINDINGS The DLRN showed satisfactory discrimination of good response to NACT and yielded the areas under the receiver operating curve (AUCs) of 0.829 (95% CI, 0.739-0.920), 0.804 (95% CI, 0.732-0.877), and 0.827 (95% CI, 0.755-0.900) in the internal and two external validation cohorts, respectively, with good calibration in all cohorts (p > 0.05). Furthermore, the DLRN performed significantly better than the clinical model (p < 0.001). Decision curve analysis confirmed that the DLRN was clinically useful. Besides, DLRN was significantly associated with the DFS of patients with LAGC (p < 0.05). INTERPRETATION A deep learning-based radiomics nomogram exhibited a promising performance for predicting therapeutic response and clinical outcomes in patients with LAGC, which could provide valuable information for individualized treatment.
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Key Words
- AIC, Akaike information criterion
- CT, computed tomography
- DCA, decision curve analysis
- DFS, disease free survival
- DLRN, deep learning radiomics nomogram
- Deep learning
- GR, good response
- ICC, interclass correlation coefficient
- IDI, integrated discrimination improvement
- LAGC, locally advanced gastric cancer
- LASSO, least absolute shrinkage and selection operator
- Locally advanced gastric cancer
- NACT, neoadjuvant chemotherapy
- NRI, Net reclassification index
- Neoadjuvant chemotherapy
- PR, poor response
- ROC, Receiver operating characteristic
- ROI, regions of interest
- Radiomics nomogram
- TRG, tumor regression grade
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Affiliation(s)
- Yanfen Cui
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China
- Department of Radiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou 510080, China
| | - Jiayi Zhang
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Zhenhui Li
- Department of Radiology, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming 650118, China
| | - Kaikai Wei
- Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510655, China
| | - Ye Lei
- Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | | | - Lei Wu
- Department of Radiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China
| | - Zhenwei Shi
- Department of Radiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China
| | - Xiaochun Meng
- Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510655, China
- Corresponding authors.
| | - Xiaotang Yang
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China
- Corresponding authors.
| | - Xin Gao
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan 030013, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
- Corresponding author at: Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China.
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Chen Y, Xu W, Li YL, Liu W, Sah BK, Wang L, Xu Z, Wels M, Zheng Y, Yan M, Zhang H, Ma Q, Zhu Z, Li C. CT-Based Radiomics Showing Generalization to Predict Tumor Regression Grade for Advanced Gastric Cancer Treated With Neoadjuvant Chemotherapy. Front Oncol 2022; 12:758863. [PMID: 35280802 PMCID: PMC8913538 DOI: 10.3389/fonc.2022.758863] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 01/25/2022] [Indexed: 02/03/2023] Open
Abstract
Objective The aim of this study was to develop and validate a radiomics model to predict treatment response in patients with advanced gastric cancer (AGC) sensitive to neoadjuvant therapies and verify its generalization among different regimens, including neoadjuvant chemotherapy (NAC) and molecular targeted therapy. Materials and Methods A total of 373 patients with AGC receiving neoadjuvant therapies were enrolled from five cohorts. Four cohorts of patients received different regimens of NAC, including three retrospective cohorts (training cohort and internal and external validation cohorts) and a prospective Dragon III cohort (NCT03636893). Another prospective SOXA (apatinib in combination with S-1 and oxaliplatin) cohort received neoadjuvant molecular targeted therapy (ChiCTR-OPC-16010061). All patients underwent computed tomography before treatment, and thereafter, tumor regression grade (TRG) was assessed. The primary tumor was delineated, and 2,452 radiomics features were extracted for each patient. Mutual information and random forest were used for dimensionality reduction and modeling. The performance of the radiomics model to predict TRG under different neoadjuvant therapies was evaluated. Results There were 28 radiomics features selected. The radiomics model showed generalization to predict TRG for AGC patients across different NAC regimens, with areas under the curve (AUCs) (95% interval confidence) of 0.82 (0.76~0.90), 0.77 (0.63~0.91), 0.78 (0.66~0.89), and 0.72 (0.66~0.89) in the four cohorts, with no statistical difference observed (all p > 0.05). However, the radiomics model showed poor predictive value on the SOXA cohort [AUC, 0.50 (0.27~0.73)], which was significantly worse than that in the training cohort (p = 0.010). Conclusion Radiomics is generalizable to predict TRG for AGC patients receiving NAC treatments, which is beneficial to transform appropriate treatment, especially for those insensitive to NAC.
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Affiliation(s)
- Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Xu
- Department of General Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan-Ling Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Wentao Liu
- Department of General Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Birendra Kumar Sah
- Department of General Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lan Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhihan Xu
- Siemens Healthineers Ltd., Shanghai, China
| | - Michael Wels
- Department of Diagnostic Imaging Computed Tomography Image Analytics, Siemens Healthcare GmbH, Forchheim, Germany
| | - Yanan Zheng
- Department of General Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Yan
- Department of General Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qianchen Ma
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhenggang Zhu
- Department of General Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chen Li
- Department of General Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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19
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Xie K, Cui Y, Zhang D, He W, He Y, Gao D, Zhang Z, Dong X, Yang G, Dai Y, Li Z. Pretreatment Contrast-Enhanced Computed Tomography Radiomics for Prediction of Pathological Regression Following Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer: A Preliminary Multicenter Study. Front Oncol 2022; 11:770758. [PMID: 35070974 PMCID: PMC8777131 DOI: 10.3389/fonc.2021.770758] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 12/14/2021] [Indexed: 12/11/2022] Open
Abstract
Background Sensitivity to neoadjuvant chemotherapy in locally advanced gastric cancer patients varies; however, an effective predictive marker is currently lacking. We aimed to propose and validate a practical treatment efficacy prediction method based on contrast-enhanced computed tomography (CECT) radiomics. Method Data of l24 locally advanced gastric carcinoma patients who underwent neoadjuvant chemotherapy were acquired retrospectively between December 2012 and August 2020 from three different cancer centers. In total, 1216 radiomics features were initially extracted from each lesion’s pretreatment portal venous phase computed tomography image. Subsequently, a radiomics predictive model was constructed using machine learning software. Clinicopathological data and radiological parameters of the enrolled patients were collected and analyzed retrospectively. Univariate and multivariate logistic regression analyses were performed to screen for independent predictive indices. Finally, we developed an integrated model combining clinicopathological predictive parameters and radiomics features. Result In the training set, 10 (14.9%) patients achieved a good response (GR) after preoperative neoadjuvant chemotherapy (n = 77), whereas in the testing set, seven (17.5%) patients achieved a GR (n = 47). The radiomics predictive model showed competitive prediction efficacy in both the training and independent external validation sets. The areas under the curve (AUC) values were 0.827 (95% confidence interval [CI]: 0.609–1.000) and 0.854 (95% CI: 0.610–1.000), respectively. Similarly, when only the single hospital data were included as an independent external validation set (testing set 2), AUC values of the models were 0.827 (95% CI: 0.650–0.952) and 0.889 (95% CI: 0.663–1.000) in the training set and testing set 2, respectively. Conclusion Our study is the first to discover that CECT radiomics could provide powerful and consistent predictions of therapeutic sensitivity to neoadjuvant chemotherapy among gastric cancer patients across different hospitals.
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Affiliation(s)
- Kun Xie
- Department of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China
| | - Dafu Zhang
- Department of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Weiyang He
- Department of Gastrointestinal Surgery, Sichuan Province Cancer Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yinfu He
- Department of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Depei Gao
- Department of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhiping Zhang
- Department of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xingxiang Dong
- Department of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Guangjun Yang
- Department of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Youguo Dai
- Department of Gastric and Surgery, Yunnan Cancer Hospital, Yunnan Cancer Center, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhenhui Li
- Department of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, the Third Affiliated Hospital of Kunming Medical University, Kunming, China
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20
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Williams TL, Saadat LV, Gonen M, Wei A, Do RKG, Simpson AL. Radiomics in surgical oncology: applications and challenges. Comput Assist Surg (Abingdon) 2021; 26:85-96. [PMID: 34902259 DOI: 10.1080/24699322.2021.1994014] [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: 10/19/2022] Open
Abstract
Surgery is a curative treatment option for many patients with malignant tumors. Increased attention has focused on the combination of surgery with chemotherapy, as multimodality treatment has been associated with promising results in certain cancer types. Despite these data, there remains clinical equipoise on optimal timing and patient selection for neoadjuvant or adjuvant strategies. Radiomics, an emerging field involving the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and contribute to the advance of personalized therapy by helping predict tumor behavior and response to therapy. This review analyzes and summarizes studies that use radiomics with machine learning in patients who have received neoadjuvant and/or adjuvant chemotherapy to predict prognosis, recurrence, survival, and therapeutic response for various cancer types. While studies in both neoadjuvant and adjuvant settings demonstrate above average performance on ability to predict progression-free and overall survival, there remain many challenges and limitations to widespread implementation of this technology. The lack of standardization of common practices to analyze radiomics, limited data sharing, and absence of auto-segmentation have hindered the inclusion and rapid adoption of radiomics in prospective, clinical studies.
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Affiliation(s)
- Travis L Williams
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lily V Saadat
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mithat Gonen
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alice Wei
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber L Simpson
- School of Computing, Queen's University, Kingston, ON, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
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21
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Chidambaram S, Sounderajah V, Maynard N, Markar SR. Diagnostic Performance of Artificial Intelligence-Centred Systems in the Diagnosis and Postoperative Surveillance of Upper Gastrointestinal Malignancies Using Computed Tomography Imaging: A Systematic Review and Meta-Analysis of Diagnostic Accuracy. Ann Surg Oncol 2021; 29:1977-1990. [PMID: 34762214 PMCID: PMC8810479 DOI: 10.1245/s10434-021-10882-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 09/11/2021] [Indexed: 12/24/2022]
Abstract
Background Upper gastrointestinal cancers are aggressive malignancies with poor prognosis, even following multimodality therapy. As such, they require timely and accurate diagnostic and surveillance strategies; however, such radiological workflows necessitate considerable expertise and resource to maintain. In order to lessen the workload upon already stretched health systems, there has been increasing focus on the development and use of artificial intelligence (AI)-centred diagnostic systems. This systematic review summarizes the clinical applicability and diagnostic performance of AI-centred systems in the diagnosis and surveillance of esophagogastric cancers. Methods A systematic review was performed using the MEDLINE, EMBASE, Cochrane Review, and Scopus databases. Articles on the use of AI and radiomics for the diagnosis and surveillance of patients with esophageal cancer were evaluated, and quality assessment of studies was performed using the QUADAS-2 tool. A meta-analysis was performed to assess the diagnostic accuracy of sequencing methodologies. Results Thirty-six studies that described the use of AI were included in the qualitative synthesis and six studies involving 1352 patients were included in the quantitative analysis. Of these six studies, four studies assessed the utility of AI in gastric cancer diagnosis, one study assessed its utility for diagnosing esophageal cancer, and one study assessed its utility for surveillance. The pooled sensitivity and specificity were 73.4% (64.6–80.7) and 89.7% (82.7–94.1), respectively. Conclusions AI systems have shown promise in diagnosing and monitoring esophageal and gastric cancer, particularly when combined with existing diagnostic methods. Further work is needed to further develop systems of greater accuracy and greater consideration of the clinical workflows that they aim to integrate within.
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Affiliation(s)
| | - Viknesh Sounderajah
- Department of Surgery and Cancer, Imperial College London, London, UK.,Institute of Global Health Innovation, Imperial College London, London, UK
| | - Nick Maynard
- Department of Surgery, Churchill Hospital, Oxford University Hospitals NHS Trust, Oxford, UK
| | - Sheraz R Markar
- Department of Surgery and Cancer, Imperial College London, London, UK. .,Department of Surgery, Churchill Hospital, Oxford University Hospitals NHS Trust, Oxford, UK. .,Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
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22
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Xu Q, Sun Z, Li X, Ye C, Zhou C, Zhang L, Lu G. Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy. Eur Radiol 2021; 31:8765-8774. [PMID: 33909133 PMCID: PMC8523390 DOI: 10.1007/s00330-021-07962-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 03/08/2021] [Accepted: 03/29/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To develop and evaluate machine learning models using baseline and restaging computed tomography (CT) for predicting and early detecting pathological downstaging (pDS) with neoadjuvant chemotherapy in advanced gastric cancer (AGC). METHODS We collected 292 AGC patients who received neoadjuvant chemotherapy. They were classified into (a) primary cohort (206 patients with 3-4 cycles chemotherapy) for model development and internal validation, (b) testing cohort I (46 patients with 3-4 cycles chemotherapy) for evaluating models' predictive ability before and after the complete course, and (c) testing cohort II (n = 40) for model evaluation on its performance at early treatment. We extracted 1,231 radiomics features from venous phase CT at baseline and restaging. We selected radiomics models based on 28 cross-combination models and measured the areas under the curve (AUC). Our prediction radiomics (PR) model is designed to predict pDS outcomes using baseline CT. Detection radiomics (DR) model is applied to restaging CT for early pDS detection. RESULTS PR model achieved promising outcomes in two testing cohorts (AUC 0.750, p = .009 and AUC 0.889, p = .000). DR model also showed a good predictive ability (AUC 0.922, p = .000 and AUC 0.850, p = .000), outperforming the commonly used RECIST method (NRI 39.5% and NRI 35.4%). Furthermore, the improved DR model with averaging outcome scores of PR and DR models showed boosted results in two testing cohorts (AUC 0.961, p = .000 and AUC 0.921, p = .000). CONCLUSIONS CT-based radiomics models perform well on prediction and early detection tasks of pDS and can potentially assist surgical decision-making in AGC patients. KEY POINTS • Baseline contrast-enhanced computed tomography (CECT)-based radiomics features were predictive of pathological downstaging, allowing accurate identification of non-responders before therapy. • Restaging CECT-based radiomics features were predictive to achieve pDS after and even at an early stage of neoadjuvant chemotherapy. • Combination of baseline and restaging CECT-based radiomics features was promising for early detection and preoperative evaluation of pathological downstaging of AGC.
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Affiliation(s)
- Qinmei Xu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, Jiangsu, China
| | - Zeyu Sun
- Deepwise AI Lab, Deepwise Inc., No. 8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China
| | - Xiuli Li
- Deepwise AI Lab, Deepwise Inc., No. 8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China
| | - Chen Ye
- Research Institute of General Surgery, Jinling Hospital, Nanjing, 210002, Jiangsu, China
| | - Changsheng Zhou
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, Jiangsu, China
| | - Longjiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, Jiangsu, China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, Jiangsu, China.
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23
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Liu YY, Zhang H, Wang L, Lin SS, Lu H, Liang HJ, Liang P, Li J, Lv PJ, Gao JB. Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study. Front Oncol 2021; 11:740732. [PMID: 34604085 PMCID: PMC8480311 DOI: 10.3389/fonc.2021.740732] [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: 07/13/2021] [Accepted: 08/24/2021] [Indexed: 12/24/2022] Open
Abstract
Objective To build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC). Methods A total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed. Result Among the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model. Conclusion The pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.
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Affiliation(s)
- Yi-Yang Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lan Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shu-Shen Lin
- Department of DI CT Collaboration, Siemens Healthineers Ltd, Shanghai, China
| | - Hao Lu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - He-Jun Liang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
| | - Jun Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Pei-Jie Lv
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Imaging Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China
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24
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Perioperative Chemotherapy with FLOT Scheme in Resectable Gastric Adenocarcinoma: A Preliminary Correlation between TRG and Radiomics. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11199211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Perioperative chemotherapy (p-ChT) with a fluorouracil plus leucovorin, oxaliplatin, and docetaxel (FLOT) scheme is the gold standard of care for locally advanced gastric cancer. We aimed to test CT radiomics performance in early response prediction for p-ChT. Patients with advanced gastric cancer who underwent contrast enhanced CT prior to and post p-ChT were retrospectively enrolled. Histologic evaluation of resected specimens was used as the reference standard, and patients were divided into responders (TRG 1a-1b) and non-responders (TRG 2-3) according to their Becker tumor regression grade (TRG). A volumetric region of interest including the whole tumor tissue was drawn from a CT portal-venous phase before and after p-ChT; 120 radiomic features, both first and second order, were extracted. CT radiomics performances were derived from baseline CT radiomics alone and ΔRadiomics to predict response to p-ChT according to the TRG and tested using a receiver operating characteristic (ROC) curve. The final population comprised 15 patients, 6 (40%) responders and 9 (60%) non-responders. Among pre-treatment CT radiomics parameters, Shape, GLCM, First order, and NGTDM features showed a significant ability to discriminate between responders and non-responders (p < 0.011), with Cluster Shade and Autocorrelation (GLCM features) having AUC = 0.907. ΔRadiomics showed significant differences for Shape, GLRLM, GLSZM, and NGTDM features (p < 0.007). MeshVolume (Shape feature) and LongRunEmphasis (GLRLM feature) had AUC = 0.889. In conclusion, CT radiomics may represent an important supportive approach for the radiologic evaluation of advanced gastric cancer patients.
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Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11101796. [PMID: 34679494 PMCID: PMC8534713 DOI: 10.3390/diagnostics11101796] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/15/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.
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26
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Qin Y, Deng Y, Jiang H, Hu N, Song B. Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction. Front Oncol 2021; 11:631686. [PMID: 34367946 PMCID: PMC8335156 DOI: 10.3389/fonc.2021.631686] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 07/07/2021] [Indexed: 02/05/2023] Open
Abstract
Gastric cancer (GC) is one of the most common cancers and one of the leading causes of cancer-related death worldwide. Precise diagnosis and evaluation of GC, especially using noninvasive methods, are fundamental to optimal therapeutic decision-making. Despite the recent rapid advancements in technology, pretreatment diagnostic accuracy varies between modalities, and correlations between imaging and histological features are far from perfect. Artificial intelligence (AI) techniques, particularly hand-crafted radiomics and deep learning, have offered hope in addressing these issues. AI has been used widely in GC research, because of its ability to convert medical images into minable data and to detect invisible textures. In this article, we systematically reviewed the methodological processes (data acquisition, lesion segmentation, feature extraction, feature selection, and model construction) involved in AI. We also summarized the current clinical applications of AI in GC research, which include characterization, differential diagnosis, treatment response monitoring, and prognosis prediction. Challenges and opportunities in AI-based GC research are highlighted for consideration in future studies.
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Affiliation(s)
- Yun Qin
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yiqi Deng
- Department of Laboratory Medicine, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Na Hu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
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27
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Chen Y, Yuan F, Wang L, Li E, Xu Z, Wels M, Yao W, Zhang H. Evaluation of dual-energy CT derived radiomics signatures in predicting outcomes in patients with advanced gastric cancer after neoadjuvant chemotherapy. Eur J Surg Oncol 2021; 48:339-347. [PMID: 34304951 DOI: 10.1016/j.ejso.2021.07.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/28/2021] [Accepted: 07/15/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND To investigate the prognostic value of dual-energy CT (DECT) based radiomics to predict disease-free survival (DFS) and overall survival (OS) for patients with advanced gastric cancer (AGC) after neoadjuvant chemotherapy (NAC). METHODS From January 2014 to December 2018, a total of 156 AGC patients were enrolled and randomly allocated into a training cohort and a testing cohort at a ratio of 2:1. Volume of interest of primary tumor was delineated on eight image series. Four feature sets derived from pre-NAC and delta radiomics were generated for each survival arm. Random survival forest was used for generating the optimal radiomics signature (RS). Statistical metrics for model evaluation included Harrell's concordance index (C-index) and the average cumulative/dynamic AUC throughout follow-up. A clinical model and a combined Rad-clinical model were built for comparison. RESULTS The pre-IU (derived from iodine uptake images before NAC) RS performed best for DFS and OS in the testing cohort (C-indices, 0.784 and 0.698; the average cumulative/dynamic AUCs, 0.80 and 0.77). When compared with the clinical model, the radiomics model had significantly higher C-index to predict DFS in the testing cohort (0.784 vs. 0.635, p < 0.001), but no statistical difference was found for OS (0.698 vs. 0.680, p = 0.473). The combined Rad-clinical models showed improved performance in the testing cohort, with C-indices of 0.810 and 0.710 for DFS and OS, respectively. CONCLUSION DECT-derived radiomics serves as a promising non-invasive biomarker to predict survival for AGC patients after NAC, providing an opportunity for transforming proper treatment.
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Affiliation(s)
- Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No 197, Rui Jin 2nd Road, Shanghai, 200025, China
| | - Fei Yuan
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No 197, Rui Jin 2nd Road, Shanghai, 200025, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No 197, Rui Jin 2nd Road, Shanghai, 200025, China
| | - Elsie Li
- Shanghai Engineering Research Center for Broadband Technologies & Applications, No 150, Honggu Road, Shanghai, 200336, China
| | - Zhihan Xu
- Siemens Healthineers Ltd, No. 278, Zhouzhu Road, Shanghai, 201318, China
| | - Michael Wels
- Department of Diagnostic Imaging Computed Tomography Image Analytics, Siemens Healthcare GmbH, Siemensstr, 391301, Forchheim, Germany
| | - Weiwu Yao
- Department of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 738, Yuyuan Road, Shanghai, 200050, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No 197, Rui Jin 2nd Road, Shanghai, 200025, China.
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Iacobellis F, Narese D, Berritto D, Brillantino A, Di Serafino M, Guerrini S, Grassi R, Scaglione M, Mazzei MA, Romano L. Large Bowel Ischemia/Infarction: How to Recognize It and Make Differential Diagnosis? A Review. Diagnostics (Basel) 2021; 11:diagnostics11060998. [PMID: 34070924 PMCID: PMC8230100 DOI: 10.3390/diagnostics11060998] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/25/2021] [Accepted: 05/26/2021] [Indexed: 12/19/2022] Open
Abstract
Ischemic colitis represents the most frequent form of intestinal ischemia occurring when there is an acute impairment or chronic reduction in the colonic blood supply, resulting in mucosal ulceration, inflammation, hemorrhage and ischemic necrosis of variable severity. The clinical presentation is variable and nonspecific, so it is often misdiagnosed. The most common etiology is hypoperfusion, almost always associated with generalized atherosclerotic disease. The severity ranges from localized and transient ischemia to transmural necrosis of the bowel wall, becoming a surgical emergency, with significant associated morbidity and mortality. The diagnosis is based on clinical, laboratory suspicion and radiological, endoscopic and histopathological findings. Among the radiological tests, enhanced-CT is the diagnostic investigation of choice. It allows us to make the diagnosis in an appropriate clinical setting, and to define the entity of the ischemia. MR may be adopted in the follow-up in patients with iodine allergy or renal dysfunctions, or younger patients who should avoid radiological exposure. In the majority of cases, supportive therapy is the only required treatment. In this article we review the pathophysiology and the imaging findings of ischemic colitis.
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Affiliation(s)
- Francesca Iacobellis
- Department of General and Emergency Radiology, “Antonio Cardarelli” Hospital, Antonio Cardarelli St. 9, 80131 Naples, Italy; (M.D.S.); (L.R.)
- Correspondence:
| | - Donatella Narese
- Department of Radiology, University of Campania “L. Vanvitelli”, Miraglia 2 Sq., 80138 Naples, Italy; (D.N.); (R.G.)
| | - Daniela Berritto
- Department of Radiology, Hospital “Villa Fiorita”, Appia St., km 199,00, 81043 Capua, Italy;
| | - Antonio Brillantino
- Department of Emergency Surgery, “Antonio Cardarelli” Hospital, Antonio Cardarelli St. 9, 80131 Naples, Italy;
| | - Marco Di Serafino
- Department of General and Emergency Radiology, “Antonio Cardarelli” Hospital, Antonio Cardarelli St. 9, 80131 Naples, Italy; (M.D.S.); (L.R.)
| | - Susanna Guerrini
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, Bracci St. 10, 53100 Siena, Italy;
| | - Roberta Grassi
- Department of Radiology, University of Campania “L. Vanvitelli”, Miraglia 2 Sq., 80138 Naples, Italy; (D.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Mariano Scaglione
- Department of Radiology, James Cook University Hospital, Marton Road, Middlesbrough TS4 3BW, UK;
- Teesside University School of Health and Life Sciences, Middlesbrough TS1 3BX, UK
- Department of Radiology, Pineta Grande Hospital, Domitiana St. km 30/00, 81030 Castel Volturno, Italy
| | - Maria Antonietta Mazzei
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Bracci St. 10, 53100 Siena, Italy;
| | - Luigia Romano
- Department of General and Emergency Radiology, “Antonio Cardarelli” Hospital, Antonio Cardarelli St. 9, 80131 Naples, Italy; (M.D.S.); (L.R.)
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Shin J, Lim JS, Huh YM, Kim JH, Hyung WJ, Chung JJ, Han K, Kim S. A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting. Sci Rep 2021; 11:1879. [PMID: 33479398 PMCID: PMC7820605 DOI: 10.1038/s41598-021-81408-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Accepted: 01/06/2021] [Indexed: 01/06/2023] Open
Abstract
This study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external validation cohort (61 patients) who underwent curative resection for LAGC in 2010 without neoadjuvant therapies. Available preoperative clinical factors, including conventional CT staging and endoscopic data, and 438 radiomic features from the preoperative CT were obtained. To predict RFS, a radiomic model was developed using penalized Cox regression with the least absolute shrinkage and selection operator with ten-fold cross-validation. Internal and external validations were performed using a bootstrapping method. With the final 410 patients (58.2 ± 13.0 years-old; 268 female), the radiomic model consisted of seven selected features. In both of the internal and the external validation, the integrated area under the receiver operating characteristic curve values of both the radiomic model (0.714, P < 0.001 [internal validation]; 0.652, P = 0.010 [external validation]) and the merged model (0.719, P < 0.001; 0.651, P = 0.014) were significantly higher than those of the clinical model (0.616; 0.594). The radiomics-based model on preoperative CT images may improve RFS prediction and high-risk stratification in the preoperative setting of LAGC.
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Affiliation(s)
- Jaeseung Shin
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Joon Seok Lim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Yong-Min Huh
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Jie-Hyun Kim
- Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Woo Jin Hyung
- Department of Surgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Jae-Joon Chung
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyunghwa Han
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Sungwon Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
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Prospective evaluation of metabolic intratumoral heterogeneity in patients with advanced gastric cancer receiving palliative chemotherapy. Sci Rep 2021; 11:296. [PMID: 33436659 PMCID: PMC7804009 DOI: 10.1038/s41598-020-78963-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 12/01/2020] [Indexed: 12/23/2022] Open
Abstract
Although metabolic intratumoral heterogeneity (ITH) gives important value on treatment responses and prognoses, its association with treatment outcomes have not been reported in gastric cancer (GC). We aimed to evaluate temporal changes in metabolic ITH and the associations with treatment responses, progression-free survival (PFS), and overall survival (OS) in advanced GC patients. Eighty-five patients with unresectable, locally advanced, or metastatic GC were prospectively enrolled before the first-line palliative chemotherapy and underwent [18F]FDG PET at baseline (TP1) and the first response follow-up evaluation (TP2). Standardized uptake values (SUVs), volumetric parameters, and textural features were evaluated in primary gastric tumor at TP1 and TP2. Of 85 patients, 44 had partial response, 33 had stable disease, and 8 progressed. From TP1 to TP2, metabolic ITH was significantly reduced (P < 0.01), and the degree of the decrease was greater in responders than in non-responders (P < 0.01). Using multiple Cox regression analyses, a low SUVmax at TP2, a high kurtosis at TP2 and larger decreases in the coefficient of variance were associated with better PFS. A low SUVmax at TP2, larger decreases in the metabolic tumor volume and larger decreased in the energy were associated with better OS. Age older than 60 years and responders also showed better OS. An early reduction in metabolic ITH is useful to predict treatment outcomes in advanced GC patients.
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Huang X, Mai J, Huang Y, He L, Chen X, Wu X, Li Y, Yang X, Dong M, Huang J, Zhang F, Liang C, Liu Z. Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT. Clin Breast Cancer 2020; 21:e388-e401. [PMID: 33451965 DOI: 10.1016/j.clbc.2020.12.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 12/08/2020] [Accepted: 12/13/2020] [Indexed: 12/24/2022]
Abstract
INTRODUCTION The purpose of this study was to predict pathologic complete response (pCR) to neoadjuvant therapy in breast cancer using radiomics based on pretreatment staging contrast-enhanced computed tomography (CECT). PATIENTS AND METHODS A total of 215 patients were retrospectively analyzed. Based on the intratumoral and peritumoral regions of CECT images, radiomic features were extracted and selected, respectively, to develop an intratumoral signature and a peritumoral signature with logistic regression in a training dataset (138 patients from November 2015 to October 2017). We also developed a clinical model with the molecular characterization of the tumor. A radiomic nomogram was further constructed by incorporating the intratumoral and peritumoral signatures with molecular characterization. The performance of the nomogram was validated in terms of discrimination, calibration, and clinical utility in an independent validation dataset (77 patients from November 2017 to December 2018). Stratified analysis was performed to develop a subtype-specific radiomic signature for each subgroup. RESULTS Compared with the clinical model (area under the curve [AUC], 0.756), the radiomic nomogram (AUC, 0.818) achieved better performance for pCR prediction in the validation dataset with continuous net reclassification improvement of 0.787 and good calibration. Decision curve analysis suggested the nomogram was clinically useful. Subtype-specific radiomic signatures showed improved AUCs (luminal subgroup, 0.936; human epidermal growth factor receptor 2-positive subgroup, 0.825; and triple negative subgroup, 0.858) for pCR prediction. CONCLUSION This study has revealed a predictive value of pretreatment staging-CECT and successfully developed and validated a radiomic nomogram for individualized prediction of pCR to neoadjuvant therapy in breast cancer, which could assist clinical decision-making and improve patient outcome.
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Affiliation(s)
- Xiaomei Huang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jinhai Mai
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
| | - Yanqi Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Lan He
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xiaomei Wu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yexing Li
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xiaojun Yang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Mengyi Dong
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Jia Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Fang Zhang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Changhong Liang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Zaiyi Liu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
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Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging 2020; 48:1785-1794. [PMID: 33326049 PMCID: PMC8113210 DOI: 10.1007/s00259-020-05142-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/29/2020] [Indexed: 02/08/2023]
Abstract
Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05142-w.
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Tan JW, Wang L, Chen Y, Xi W, Ji J, Wang L, Xu X, Zou LK, Feng JX, Zhang J, Zhang H. Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation. J Cancer 2020; 11:7224-7236. [PMID: 33193886 PMCID: PMC7646171 DOI: 10.7150/jca.46704] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 10/04/2020] [Indexed: 12/23/2022] Open
Abstract
Purpose: To build a dual-energy computed tomography (DECT) delta radiomics model to predict chemotherapeutic response for far-advanced gastric cancer (GC) patients. A semi-automatic segmentation method based on deep learning was designed, and its performance was compared with that of manual segmentation. Methods: This retrospective study included 86 patients with far-advanced GC treated with chemotherapy from September 2016 to December 2017 (66 and 20 in the training and testing cohorts, respectively). Delta radiomics features between the baseline and first follow-up DECT were modeled by random forest to predict the chemotherapeutic response evaluated by the second follow-up DECT. Nine feature subsets from confounding factors and delta radiomics features were used to choose the best model with 10-fold cross-validation in the training cohort. A semi-automatic segmentation method based on deep learning was developed to predict the chemotherapeutic response and compared with manual segmentation in the testing cohort, which was further validated in an independent validation cohort of 30 patients. Results: The best model, constructed by confounding factors and texture features, reached an average AUC of 0.752 in the training cohort. Our proposed semi-automatic segmentation method was more time-effective than manual segmentation, with average saving-time of 11.2333 ± 6.3989 minutes and 9.9889 ±5.5086 minutes in the testing cohort and the independent validation cohort, respectively (both p < 0.05). The predictive ability of the semi-automatic segmentation was also better than that of the manual segmentation both in the testing cohort and the independent validation cohort (AUC: 0.728 vs. 0.687 and 0.828 vs. 0.749, respectively). Conclusion: DECT delta radiomics serves as a promising biomarker for predicting chemotherapeutic response for far-advanced GC. Semi-automatic segmentation based on deep learning shows the potential for clinical use with increased reproducibility and decreased labor costs compared to the manual version.
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Affiliation(s)
- Jing-Wen Tan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lan Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - WenQi Xi
- Department of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Ji
- Department of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xin Xu
- Haohua Technology Co., Ltd, Weihai International Group Building, No. 511 Weihai Road, Shanghai, China
| | - Long-Kuan Zou
- Haohua Technology Co., Ltd, Weihai International Group Building, No. 511 Weihai Road, Shanghai, China
| | - Jian-Xing Feng
- Haohua Technology Co., Ltd, Weihai International Group Building, No. 511 Weihai Road, Shanghai, China
| | - Jun Zhang
- Department of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Huang C, Hu C, Zhu J, Zhang W, Huang J, Zhu Z. Establishment of Decision Rules and Risk Assessment Model for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer. Front Oncol 2020; 10:1638. [PMID: 32984033 PMCID: PMC7492596 DOI: 10.3389/fonc.2020.01638] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/27/2020] [Indexed: 12/26/2022] Open
Abstract
Background: Preoperative accurate prediction of lymph node status is especially important for the formulation of treatment plans for patients with gastric cancer (GC). The purpose of this study was to establish decision rules and a risk assessment model for lymph node metastasis (LNM) in GC using preoperative indicators. Methods: The clinical data of 554 patients who underwent gastrectomy with D2 lymphadenectomy were collected. A 1:1 propensity score matching (PSM) system was used, and the clinical data of the matched 466 patients were further analyzed. The important risk factors for LNM were extracted by the random forest algorithm, and decision rules and nomogram models for LNM were constructed with a classification tree and the "rms" package of R software, respectively. Results: Tumor size (OR: 2.058; P = 0.000), computed tomography (CT) findings (OR: 1.969; P = 0.001), grade (OR: 0.479; P = 0.000), hemoglobin (Hb) (OR: 1.211; P = 0.005), CEA (OR: 1.111; P = 0.017), and CA19-9 (OR: 1.040; P = 0.033) were independent risk factors for LNM in GC. Tumor size did rank first in the ranking of important factors for LNM in GC and was the first-level segmentation of the two initial branches of the classification tree. The accuracy, sensitivity, specificity, and positive predictive value of the decision rules in diagnosing preoperative LNM in GC were 75.6, 85.7, 73.9, 73.5, and 79.3%, respectively. The accuracy, sensitivity, and specificity of the risk assessment model in predicting preoperative LNM in GC were 79.3, 80.3, and 79.4%, respectively. Conclusion: Tumor size was the most important factor for evaluating LNM in GC. This decision rules and nomogram model constructed to take into account tumor size, CT findings, grade, hemoglobin, CEA, and CA19-9 effectively predicted the incidence of LNM in preoperative GC.
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Affiliation(s)
- Chao Huang
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Cegui Hu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jinfeng Zhu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wenjun Zhang
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jun Huang
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhengming Zhu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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Chen YH, Xiao J, Chen XJ, Wang HS, Liu D, Xiang J, Peng JS. Nomogram for predicting pathological complete response to neoadjuvant chemotherapy in patients with advanced gastric cancer. World J Gastroenterol 2020; 26:2427-2439. [PMID: 32476803 PMCID: PMC7243641 DOI: 10.3748/wjg.v26.i19.2427] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 04/23/2020] [Accepted: 04/27/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Survival benefit of neoadjuvant chemotherapy (NAC) for advanced gastric cancer (AGC) is a debatable issue. Studies have shown that the survival benefit of NAC is dependent on the pathological response to chemotherapy drugs. For those who achieve pathological complete response (pCR), NAC significantly prolonged prolapsed-free survival and overall survival. For those with poor response, NAC yielded no survival benefit, only toxicity and increased risk for tumor progression during chemotherapy, which may hinder surgical resection. Thus, predicting pCR to NAC is of great clinical significance and can help achieve individualized treatment in AGC patients.
AIM To establish a nomogram for predicting pCR to NAC for AGC patients.
METHODS Two-hundred and eight patients diagnosed with AGC who received NAC followed by resection surgery from March 2012 to July 2019 were enrolled in this study. Their clinical data were retrospectively analyzed by logistic regression analysis to determine the possible predictors for pCR. Based on these predictors, a nomogram model was developed and internally validated using the bootstrap method.
RESULTS pCR was confirmed in 27 patients (27/208, 13.0%). Multivariate logistic regression analysis showed that higher carcinoembryonic antigen level, lymphocyte ratio, lower monocyte count and tumor differentiation grade were associated with higher pCR. Concordance statistic of the established nomogram was 0.767.
CONCLUSION A nomogram predicting pCR to NAC was established. Since this nomogram exhibited satisfactory predictive power despite utilizing easily available pretreatment parameters, it can be inferred that this nomogram is practical for the development of personalized treatment strategy for AGC patients.
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Affiliation(s)
- Yong-He Chen
- Department of Gastrointestinal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, Guangdong Province, China
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou 510655, Guangdong Province, China
| | - Jian Xiao
- Department of Medical Oncology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, Guangdong Province, China
| | - Xi-Jie Chen
- Department of Gastrointestinal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, Guangdong Province, China
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou 510655, Guangdong Province, China
| | - Hua-She Wang
- Department of Gastrointestinal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, Guangdong Province, China
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou 510655, Guangdong Province, China
| | - Dan Liu
- Department of Laboratory Science, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510655, Guangdong Province, China
| | - Jun Xiang
- Department of Gastrointestinal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, Guangdong Province, China
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou 510655, Guangdong Province, China
| | - Jun-Sheng Peng
- Department of Gastrointestinal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, Guangdong Province, China
- Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou 510655, Guangdong Province, China
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Global updates in the treatment of gastric cancer: a systematic review. Part 2: perioperative management, multimodal therapies, new technologies, standardization of the surgical treatment and educational aspects. Updates Surg 2020; 72:355-378. [PMID: 32306277 DOI: 10.1007/s13304-020-00771-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 04/11/2020] [Indexed: 12/24/2022]
Abstract
Gastric cancer is the fifth malignancy and the third cause of cancer death worldwide, according to the global cancer statistics presented in 2018. Its definition and staging have been revised in the eight edition of the AJCC/TNM classification, which took effect in 2018. Novel molecular classifications for GC have been recently established and the process of translating these classifications into clinical practice is ongoing. The cornerstone of GC treatment is surgical, in a context of multimodal therapy. Surgical treatment is being standardized, and is evolving according to new anatomical concepts and to the recent technological developments. This is leading to a massive improvement in the use of mini-invasive techniques. Mini-invasive techniques aim to be equivalent to open surgery from an oncologic point of view, with better short-term outcomes. The persecution of better short-term outcomes also includes the optimization of the perioperative management, which is being implemented on large scale according to the enhanced recovery after surgery principles. In the era of precision medicine, multimodal treatment is also evolving. The long-time-awaited results of many trials investigating the role for preoperative and postoperative management have been published, changing the clinical practice. Novel investigations focused both on traditional chemotherapeutic regimens and targeted therapies are currently ongoing. Modern platforms increase the possibility for further standardization of the different treatments, promote the use of big data and open new possibilities for surgical learning. This systematic review in two parts assesses all the current updates in GC treatment.
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Chen X, Yang Z, Yang J, Liao Y, Pang P, Fan W, Chen X. Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study. Cancer Imaging 2020; 20:24. [PMID: 32248822 PMCID: PMC7132895 DOI: 10.1186/s40644-020-00302-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 03/06/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients. METHODS In total, 160 surgically resected patients were retrospectively analyzed, and seven predictive models were constructed. Three radiomics predictive models were built from radiomics features based on arterial (A), venous (V) and combination of two phase (A + V) images. Then, three Radscores (A-Radscore, V-Radscore and A + V-Radscore) were obtained. Another four predictive models were constructed by the three Radscores and clinical risk factors through multivariate logistic regression. A nomogram was developed to predict LVI by incorporating A + V-Radscore and clinical risk factors. Kaplan-Meier curve and log-rank test were utilized to analyze the outcome of LVI. RESULTS Radiomics related to tumor size and intratumoral inhomogeneity were the top-ranked LVI predicting features. The related Radscores showed significant differences according to LVI status (P < 0.01). Univariate logistic analysis identified three clinical features (T stage, N stage and AJCC stage) and three Radscores as LVI predictive factors. The Clinical-Radscore (namely, A + V + C) model that used all these factors showed a higher performance (AUC = 0.856) than the clinical (namely, C, including T stage, N stage and AJCC stage) model (AUC = 0.810) and the A + V-Radscore model (AUC = 0.795) in the train cohort. For patients without LVI and with LVI, the median progression-free survival (PFS) was 11.5 and 8.0 months (P < 0.001),and the median OS was 20.2 and 17.0 months (P = 0.3), respectively. In the Clinical-Radscore-predicted LVI absent and LVI present groups, the median PFS was 11.0 and 8.0 months (P = 0.03), and the median OS was 20.0 and 18.0 months (P = 0.05), respectively. N stage, LVI status and Clinical-Radscore-predicted LVI status were associated with disease-specific recurrence or mortality. CONCLUSIONS Radiomics features based on CECT may serve as potential markers to successfully predict LVI and PFS, but no evidence was found that these features were related to OS. Considering that it is a single central study, multi-center validation studies will be required in the future to verify its clinical feasibility.
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Affiliation(s)
- Xiaofeng Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, Guangdong, 514031, People's Republic of China
| | - Zhiqi Yang
- Department of Radiology, Meizhou People's Hospital, Meizhou, Guangdong, 514031, People's Republic of China
| | - Jiada Yang
- Department of Radiology, Meizhou People's Hospital, Meizhou, Guangdong, 514031, People's Republic of China
| | - Yuting Liao
- GE Healthcare, Guangzhou, Guangdong, People's Republic of China, 510623
| | - Peipei Pang
- GE Healthcare, Hangzhou, Zhejiang, People's Republic of China, 311100
| | - Weixiong Fan
- Department of Radiology, Meizhou People's Hospital, Meizhou, Guangdong, 514031, People's Republic of China
| | - Xiangguang Chen
- Department of Radiology, Meizhou People's Hospital, Meizhou, Guangdong, 514031, People's Republic of China.
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Tian X, Sun C, Liu Z, Li W, Duan H, Wang L, Fan H, Li M, Li P, Wang L, Liu P, Tian J, Chen C. Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis. Front Oncol 2020; 10:77. [PMID: 32117732 PMCID: PMC7010718 DOI: 10.3389/fonc.2020.00077] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 01/15/2020] [Indexed: 12/11/2022] Open
Abstract
Objective: To investigate whether pre-treatment CT-derived radiomic features could be applied for prediction of clinical response to neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC). Patients and Methods: Two hundred and seventy-seven LACC patients treated with NACT followed by surgery/radiotherapy were included in this multi-institution retrospective study. One thousand and ninety-four radiomic features were extracted from venous contrast enhanced and non-enhanced CT imaging for each patient. Five combined methods of feature selection were used to reduce dimension of features. Radiomics signature was constructed by Random Forest (RF) method in a primary cohort of 221 patients. A combined model incorporating radiomics signature with clinical factors was developed using multivariable logistic regression. Prediction performance was then tested in a validation cohort of 56 patients. Results: Radiomics signature containing pre- and post-contrast imaging features can adequately distinguish chemotherapeutic responders from non-responders in both primary and validation cohorts [AUCs: 0.773 (95% CI, 0.701-0.845) and 0.816 (95% CI, 0.690-0.942), respectively] and remain relatively stable across centers. The combined model has a better predictive performance with an AUC of 0.803 (95% CI, 0.734-0.872) in the primary set and an AUC of 0.821 (95% CI, 0.697-0.946) in the validation set, compared to radiomics signature alone. Both models showed good discrimination, calibration. Conclusion: Newly developed radiomic model provided an easy-to-use predictor of chemotherapeutic response with improved predictive ability, which might facilitate optimal treatment strategies tailored for individual LACC patients.
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Affiliation(s)
- Xin Tian
- Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Caixia Sun
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Weili Li
- Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hui Duan
- Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lu Wang
- Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Huijian Fan
- Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Mingwei Li
- Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Pengfei Li
- Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lihui Wang
- Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Ping Liu
- Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
| | - Chunlin Chen
- Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Li Y, Cheng Z, Gevaert O, He L, Huang Y, Chen X, Huang X, Wu X, Zhang W, Dong M, Huang J, Huang Y, Xia T, Liang C, Liu Z. A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer. Chin J Cancer Res 2020; 32:62-71. [PMID: 32194306 PMCID: PMC7072015 DOI: 10.21147/j.issn.1000-9604.2020.01.08] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Accepted: 12/03/2019] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2 (HER2) status in patients with gastric cancer. METHODS This retrospective study included 134 patients with gastric cancer (HER2-negative: n=87; HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training (n=94) and validation (n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts. RESULTS The radiomics signature was significantly associated with HER2 status in both training (P<0.001) and validation (P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen (CEA) level demonstrated good discriminative performance for HER2 status prediction, with an area under the curve (AUC) of 0.799 [95% confidence interval (95% CI): 0.704-0.894] in the training cohort and 0.771 (95% CI: 0.607-0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful. CONCLUSIONS We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment.
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Affiliation(s)
- Yexing Li
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Graduate College, Shantou University Medical College, Shantou 515041, China
| | - Zixuan Cheng
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine, and Department of Biomedical Data Science, Stanford University, California 94305, USA
| | - Lan He
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Yanqi Huang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Xin Chen
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Graduate College, Southern Medical University, Guangzhou 510515, China
- Department of Radiology, Guangzhou First People’s Hospital, Guangzhou 510180, China
| | - Xiaomei Huang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Graduate College, Southern Medical University, Guangzhou 510515, China
| | - Xiaomei Wu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - Wen Zhang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Graduate College, Southern Medical University, Guangzhou 510515, China
- Department of Radiology, Zhuhai Hospital of Traditional Chinese and Western Medicine, Zhuhai 519000, China
| | - Mengyi Dong
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Graduate College, Southern Medical University, Guangzhou 510515, China
| | - Jia Huang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Graduate College, Shantou University Medical College, Shantou 515041, China
| | - Yucun Huang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Graduate College, Southern Medical University, Guangzhou 510515, China
- Department of Radiology, The Fifth People’s Hospital of Zhuhai, Zhuhai 519055, China
| | - Ting Xia
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Graduate College, Shantou University Medical College, Shantou 515041, China
- Changhong Liang, PhD. Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Er Road, Guangzhou 510080, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Graduate College, Shantou University Medical College, Shantou 515041, China
- Zaiyi Liu, PhD. Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Er Road, Guangzhou 510080, China.
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Abstract
Objective: To review the application of radiomics in gastric cancer and its challenges as well as future prospects. Data sources: A research for relevant studies were performed in PubMed with the terms of “radiomics,” “texture analysis,” and “gastric cancer.” The search was updated until February 28th, 2019. Study selection: All original articles regarding the investigation of texture analysis or radiomics in gastric cancer were retrieved. Only papers written in English were included. Results: A total of 17 original articles were selected in final. It is shown that radiomics has yielded moderate to excellent performance in a spectrum of respects including differential diagnosis, assessment of histological differential degree, evaluation of tumor stage, prediction of response to therapy, and prognosis in gastric cancer. Yet, a number of challenges are facing both radiomics itself and its application in gastric cancer. Conclusions: Radiomics holds great potential in facilitating decision-making in gastric cancer. With the standardization of work-flow and advancement of machine learning methods, radiomics is expected to make great breakthroughs in precision medicine of gastric cancer.
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Application of machine learning techniques to analyze anastomosis integrity after Total gastrectomy for prediction of clinical leakage. HEALTH AND TECHNOLOGY 2019. [DOI: 10.1007/s12553-019-00334-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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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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Liu JY, Deng JY, Zhang NN, Liu HF, Sun WL, He WT, Wang Y, Zhang L, Liang H. Clinical significance of skip lymph-node metastasis in pN1 gastric-cancer patients after curative surgery. Gastroenterol Rep (Oxf) 2019; 7:193-198. [PMID: 31217983 PMCID: PMC6573797 DOI: 10.1093/gastro/goz008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Revised: 11/30/2018] [Accepted: 01/10/2019] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND In addition to the stepwise manner of lymph-node metastasis from the primary tumour, the skip lymph-node metastasis (SLNM) was identified as a low-incidence metastasis of gastric cancer (GC). So far, both the mechanism and outcome of SLNM have not been elucidated completely. The purpose of this study was to analyse the clinical significance and the potential mechanism of SLNM in GC patients who had lymph-node metastasis. METHODS Clinicopathological data and follow-up information of 505 GC patients who had lymph-node metastasis were analysed to demonstrate the significance of SLNM in evaluating the prognostic outcome. According to the pathological results, all GC patients who had lymph-node metastasis were categorized into three groups: patients with the perigastric lymph-node metastasis, patients with the perigastric and extragastric lymph-node metastasis and patients with SLNM.Results: Among the 505 GC patients who had lymph-node metastasis, 24 (4.8%) had pathologically identified SLNM. The location of lymph-node metastasis was not significantly associated with 5-year survival rate and overall survival (OS) (P = 0.194). The stratified survival analysis results showed that the status of SLNM was significantly associated with the OS in patients with pN1 GC (P = 0.001). The median OS was significantly shorter in 19 pN1 GC patients with SLNM than in 100 patients with perigastric lymph-node metastasis (P < 0.001). The case-control matched logistic regression analysis results showed that tumour size (P = 0.002) was the only clinicopathological factor that may predict SLNM in pN1 GC patients undergoing curative surgery. Among the 19 pN1 GC patients with SLNM, 17 (89.5%) had metastatic lymph nodes along the common hepatic artery, around the celiac artery or in the hepatoduodenal ligament. CONCLUSIONS SLNM may be considered a potentially practicable indicator for prognosis among various subgroups of pN1 GC patients.
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Affiliation(s)
- Jin-Yuan Liu
- Department of Gastroenterology, Tianjin Medical University Cancer Hospital, City Key Laboratory of Tianjin Cancer Center and National Clinical Research Center for Cancer, Tianjin, P. R. China
- Department of General Surgery, The Affiliated Hospital of Nankai University, Tianjin, P. R. China
| | - Jing-Yu Deng
- Department of Gastroenterology, Tianjin Medical University Cancer Hospital, City Key Laboratory of Tianjin Cancer Center and National Clinical Research Center for Cancer, Tianjin, P. R. China
| | - Nan-Nan Zhang
- Department of Gastroenterology, Tianjin Medical University Cancer Hospital, City Key Laboratory of Tianjin Cancer Center and National Clinical Research Center for Cancer, Tianjin, P. R. China
| | - Hui-Fang Liu
- Department of Gastroenterology, Tianjin Medical University Cancer Hospital, City Key Laboratory of Tianjin Cancer Center and National Clinical Research Center for Cancer, Tianjin, P. R. China
| | - Wei-Lin Sun
- Department of Gastroenterology, Tianjin Medical University Cancer Hospital, City Key Laboratory of Tianjin Cancer Center and National Clinical Research Center for Cancer, Tianjin, P. R. China
| | - Wen-Ting He
- Department of Gastroenterology, Tianjin Medical University Cancer Hospital, City Key Laboratory of Tianjin Cancer Center and National Clinical Research Center for Cancer, Tianjin, P. R. China
| | - Yan Wang
- Department of General Surgery, The Affiliated Hospital of Nankai University, Tianjin, P. R. China
| | - Li Zhang
- Department of Gastroenterology, Tianjin Medical University Cancer Hospital, City Key Laboratory of Tianjin Cancer Center and National Clinical Research Center for Cancer, Tianjin, P. R. China
| | - Han Liang
- Department of Gastroenterology, Tianjin Medical University Cancer Hospital, City Key Laboratory of Tianjin Cancer Center and National Clinical Research Center for Cancer, Tianjin, P. R. China
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Predicting pathological subtypes and stages of thymic epithelial tumors using DWI: value of combining ADC and texture parameters. Eur Radiol 2019; 29:5330-5340. [DOI: 10.1007/s00330-019-06080-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 01/16/2019] [Accepted: 02/07/2019] [Indexed: 12/20/2022]
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Bai H, Deng J, Zhang N, Liu H, He W, Liu J, Liang H. Predictive values of multidetector-row computed tomography combined with serum tumor biomarkers in preoperative lymph node metastasis of gastric cancer. Chin J Cancer Res 2019; 31:453-462. [PMID: 31354214 PMCID: PMC6613502 DOI: 10.21147/j.issn.1000-9604.2019.03.07] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Objective Multidetector-row computed tomography (MDCT) and serum tumor biomarkers are commonly used to evaluate the preoperative lymph node metastasis and the clinical staging of gastric cancer (GC). This study intends to evaluate the clinical predictive value of MDCT and serum tumor biomarkers in lymph node metastasis of GC. Methods The clinicopathologic data of 445 GC patients who underwent radical gastrectomy were retrospectively analyzed to evaluate the diagnostic value of MDCT and serum tumor biomarkers in lymph node metastatic staging of GC before surgery. Results With the multinomial logistic regression analysis, the independent relative factors of lymph node metastasis of GC were identified as tumor size, depth of tumor invasion, vessel invasion, vascular embolus, and soft tissue invasion. The optimal critical value of the short diameter of lymph nodes detected by MDCT scanning for evaluation of preoperative lymph node metastasis was 6.0 mm, with 75.7% as predictive accuracy of lymph node metastasis compared to the postoperative pathological results of GC patients. In addition, the critical value of the short diameter of lymph nodes combined with serum tumor biomarkers [including carbohydrate antigen (CA)-724 and CA-199] could show an enhancement of predictive sensitivity of lymph node metastasis (up to 89.3%) before surgery. Conclusions MDCT combined with serum tumor biomarkers should be adopted to improve preoperative sensitivity and accuracy of lymph node metastasis for GC patients.
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Affiliation(s)
- Huihui Bai
- Department of Gastric Cancer, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Jingyu Deng
- Department of Gastric Cancer, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Nannan Zhang
- Department of Gastric Cancer, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Huifang Liu
- Department of Gastric Cancer, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Wenting He
- Department of Gastric Cancer, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Jinyuan Liu
- Department of Gastric Cancer, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
| | - Han Liang
- Department of Gastric Cancer, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China
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Lu S, Yan M, Li C, Yan C, Zhu Z, Lu W. Machine-learning-assisted prediction of surgical outcomes in patients undergoing gastrectomy. Chin J Cancer Res 2019; 31:797-805. [PMID: 31814683 PMCID: PMC6856706 DOI: 10.21147/j.issn.1000-9604.2019.05.09] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Objective Postoperative complications adversely affected the prognosis in patients with gastric cancer. This study intends to investigate the feasibility of using machine-learning model to predict surgical outcomes in patients undergoing gastrectomy. Methods In this study, cancer patients who underwent gastrectomy at Shanghai Rui Jin Hospital in 2017 were randomly assigned to a development or validation cohort in a 9:1 ratio. A support vector classification (SVC) model to predict surgical outcomes in patients undergoing gastrectomy was developed and further validated. Results A total of 321 patients with 32 features were collected. The positive and negative outcomes of postoperative complication after gastrectomy appeared in 100 (31.2%) and 221 (68.8%) patients, respectively. The SVC model was constructed to predict surgical outcomes in patients undergoing gastrectomy. The accuracy of 10-fold cross validation and external verification was 78.17% and 78.12%, respectively. Further, an online web server has been developed to share the SVC model for machine-learning-assisted prediction of surgical outcomes in patients undergoing gastrectomy in the future procedures, which is accessible at the web address: http://47.100.47.97:5005/r_model_prediction. Conclusions The SVC model was a useful predictor for measuring the risk of postoperative complications after gastrectomy, which may help stratify patients with different overall status for choice of surgical procedure or other treatments. It can be expected that machine-learning models in cancer informatics research are possibly shareable and accessible via web address all over the world.
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Affiliation(s)
- Sheng Lu
- Department of General Surgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Digestive Surgery, Shanghai 200025, China
| | - Min Yan
- Department of General Surgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Digestive Surgery, Shanghai 200025, China
| | - Chen Li
- Department of General Surgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Digestive Surgery, Shanghai 200025, China
| | - Chao Yan
- Department of General Surgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Digestive Surgery, Shanghai 200025, China
| | - Zhenggang Zhu
- Department of General Surgery, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Institute of Digestive Surgery, Shanghai 200025, China
| | - Wencong Lu
- Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China
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