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Zhu Y, Chen H, Wu Y, Jiang T, Wang X, Zheng J, Lin X. Novel nomogram and risk stratification for peritoneal recurrence after curative resection in gastric cancer. Sci Rep 2024; 14:19103. [PMID: 39154083 PMCID: PMC11330521 DOI: 10.1038/s41598-024-70349-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 08/14/2024] [Indexed: 08/19/2024] Open
Abstract
Peritoneal recurrence (PR) in gastric cancer after curative resection has poor prognosis. Therefore, we aimed to construct a nomogram to predict PR, and establish PR score for risk stratification to guide adjuvant chemotherapy. A total of 315 patients with gastric cancer after radical surgery were included, and randomly stratified into training group (n = 221) and validation group (n = 94). Univariate and multivariate analyses were used to determine predictive factors of PR. The nomogram was constructed to predict the risk of PR. We utilized the time-dependent area under the receiver operating characteristic (ROC) curves (AUCs), calibration curves, and decision curve analysis (DCA) to evaluate the performance of the nomogram. Multivariate analysis showed that tumor site, N stage, preoperative CEA, and postoperative CA199 were independent predictors of PR. A nomogram was constructed to predict PR based on these factors. The AUC value was 0.755 in the training group and 0.715 in the validation group. The calibration curves showed good agreement between prediction and observation in the training and validation groups. The decision curve analysis displayed a good net benefit of the nomogram. The novel PR score was developed and patients were stratified into the low-, medium-, and high -risk groups. For the high-risk group, postoperative adjuvant chemotherapy significantly improved patients' overall survival (OS) and disease-free survival (DFS). The establishment of nomogram facilitates the prediction of PR after radical gastrectomy, and a novel PR score may help guide adjuvant chemotherapy for gastric cancer.
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Affiliation(s)
- Yingjiao Zhu
- Department of Medical Oncology, Fujian Medical University Union Hospital, No. 29 Xinquan Street, Fuzhou, 350001, Fujian, China
| | - Hao Chen
- Department of Medical Oncology, Fujian Medical University Union Hospital, No. 29 Xinquan Street, Fuzhou, 350001, Fujian, China
| | - Yahua Wu
- Department of Medical Oncology, Fujian Medical University Union Hospital, No. 29 Xinquan Street, Fuzhou, 350001, Fujian, China
| | - Tao Jiang
- Department of Medical Oncology, Fujian Medical University Union Hospital, No. 29 Xinquan Street, Fuzhou, 350001, Fujian, China
| | - Xinli Wang
- Department of Medical Oncology, Fujian Medical University Union Hospital, No. 29 Xinquan Street, Fuzhou, 350001, Fujian, China
| | - Jianwei Zheng
- Department of Medical Oncology, Fujian Medical University Union Hospital, No. 29 Xinquan Street, Fuzhou, 350001, Fujian, China
| | - Xiaoyan Lin
- Department of Medical Oncology, Fujian Medical University Union Hospital, No. 29 Xinquan Street, Fuzhou, 350001, Fujian, China.
- Fujian Provincial Key Laboratory of Translational Cancer Medicine, Fuzhou, Fujian, China.
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Song H, Xiao X, Han X, Sun Y, Zheng G, Miao Q, Zhang Y, Tan J, Liu G, He Q, Zhou J, Zheng Z, Jiang G. Development and interpretation of a multimodal predictive model for prognosis of gastrointestinal stromal tumor. NPJ Precis Oncol 2024; 8:157. [PMID: 39060449 PMCID: PMC11282065 DOI: 10.1038/s41698-024-00636-4] [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: 10/10/2023] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
Gastrointestinal stromal tumor (GIST) is the most common mesenchymal original tumor in gastrointestinal (GI) tract and is considered to have varying malignant potential. With the advancement of computer science, radiomics technology and deep learning had been applied in medical researches. It's vital to construct a more accurate and reliable multimodal predictive model for recurrence-free survival (RFS) aiding for clinical decision-making. A total of 254 patients underwent surgery and pathologically diagnosed with GIST in The First Hospital of China Medical University from 2019 to 2022 were included in the study. Preoperative contrast enhanced computerized tomography (CE-CT) and hematoxylin/eosin (H&E) stained whole slide images (WSI) were acquired for analysis. In the present study, we constructed a sum of 11 models while the multimodal model (average C-index of 0.917 on validation set in 10-fold cross validation) performed the best on external validation cohort with an average C-index of 0.864. The multimodal model also reached statistical significance when validated in the external validation cohort (n = 42) with a p-value of 0.0088 which pertained to the recurrence-free survival (RFS) comparison between the high and low groups using the optimal threshold on the predictive score. We also explored the biological significance of radiomics and pathomics features by visualization and quantitative analysis. In the present study, we constructed a multimodal model predicting RFS of GIST which was prior over unimodal models. We also proposed hypothesis on the correlation between morphology of tumor cell and prognosis.
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Affiliation(s)
- He Song
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China.
| | - XianHao Xiao
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xu Han
- Department of Pathology, The First Hospital and the College of Basic Medical Sciences of China Medical University, Shenyang, Liaoning, China
| | - YeFei Sun
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - GuoLiang Zheng
- Department of Gastric Surgery, Cancer Hospital of China Medical University; Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning, China
| | - Qi Miao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - YuLong Zhang
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - JiaYing Tan
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Gang Liu
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - QianRu He
- The state Key laboratory of Neurology and Oncology Drug Development, Jiangsu Simcere Diagnostics Co.,Ltd, Nanjing, China
| | - JianPing Zhou
- Department of Gastrointestinal Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China.
| | - ZhiChao Zheng
- Department of Gastric Surgery, Cancer Hospital of China Medical University; Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning, China.
| | - GuiYang Jiang
- Department of Pathology, The College of Basic Medical Sciences and The First Hospital of China Medical University, Shenyang, Liaoning, China.
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Zhao B, Liu C, Ni S, Zhang Q. Predictive efficacy of combined tumor markers and gastrin for recurrence after endoscopic submucosal dissection in early gastric cancer patients. Am J Transl Res 2024; 16:2059-2069. [PMID: 38883344 PMCID: PMC11170613 DOI: 10.62347/voto5604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 05/12/2024] [Indexed: 06/18/2024]
Abstract
OBJECTIVE This study aims to evaluate the predictive value of tumor markers combined with gastrin for tumor recurrence after endoscopic submucosal dissection (ESD) in patients with early gastric cancer. METHODS The clinicopathological data of 169 patients with early gastric cancer treated with ESD between March 2019 and January 2021 were retrospectively analyzed. The patients were divided into a relapse group (n=45) and a non-recurrence group (n=124). Clinical data such as carcinoembryonic antigen (CEA), cancer antigen 19-9 (CA19-9), alpha-fetoprotein (AFP), gastrin 17, pepsinogen I and pepsinogen II, as well as tumor size and degree of infiltration were examined to construct a recurrence prediction model using lasso regression. RESULTS The comprehensive model showed superior predictive power (AUC=0.958, C-index=0.966) over biomarker-only models (AUC=0.925), indicating a significant improvement in the prediction of recurrence risk. Decision curve analysis confirmed the clinical utility of the model with a maximum net benefit of 73.37%. Key indicators such as CEA, CA19-9, AFP, gastrin 17 and pepsinogens I and II were statistically significant in predicting recurrence with P values < 0.01. CONCLUSION The comprehensive model combining tumor markers with clinical data provides a more accurate and clinically valuable tool for predicting recurrence in early gastric cancer patients after ESD. This approach facilitates personalized risk assessment and may significantly improve prognostic management, emphasizing the importance of a multifaceted strategy in the management of early gastric cancer.
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Affiliation(s)
- Bo Zhao
- Department of Surgery, The First People's Hospital of Xianyang Xianyang 712000, Shaanxi, China
| | - Chonglin Liu
- Department of Surgery, Chunhua County Hospital in Xianyang City Xianyang 712000, Shaanxi, China
| | - Shan Ni
- Department of Gastroenterology, The First People's Hospital of Lanzhou City Lanzhou 730050, Gansu, China
| | - Qiyong Zhang
- Department of Gastroenterology, The First People's Hospital of Lanzhou City Lanzhou 730050, Gansu, China
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Zhang YF, Zhou C, Guo S, Wang C, Yang J, Yang ZJ, Wang R, Zhang X, Zhou FH. Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer. J Cancer Res Clin Oncol 2024; 150:78. [PMID: 38316655 PMCID: PMC10844393 DOI: 10.1007/s00432-023-05574-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 12/04/2023] [Indexed: 02/07/2024]
Abstract
PURPOSE Bone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathological images has been highlighted. However, there is a limited understanding of the potential of early prediction of bone metastasis through the feature combination method from various sources. This study presents a method of integrating multimodal data to enhance the feasibility of early diagnosis of bone metastasis in prostate cancer. METHODS AND MATERIALS Overall, 211 patients diagnosed with prostate cancer (PCa) at Gansu Provincial Hospital between January 2017 and February 2023 were included in this study. The patients were randomized (8:2) into a training group (n = 169) and a validation group (n = 42). The region of interest (ROI) were segmented from the three magnetic resonance imaging (MRI) sequences (T2WI, DWI, and ADC), and pathological features were extracted from tissue sections (hematoxylin and eosin [H&E] staining, 10 × 20). A deep learning (DL) model using ResNet 50 was employed to extract deep transfer learning (DTL) features. The least absolute shrinkage and selection operator (LASSO) regression method was utilized for feature selection, feature construction, and reducing feature dimensions. Different machine learning classifiers were used to build predictive models. The performance of the models was evaluated using receiver operating characteristic curves. The net clinical benefit was assessed using decision curve analysis (DCA). The goodness of fit was evaluated using calibration curves. A joint model nomogram was eventually developed by combining clinically independent risk factors. RESULTS The best prediction models based on DTL and pathomics features showed area under the curve (AUC) values of 0.89 (95% confidence interval [CI], 0.799-0.989) and 0.85 (95% CI, 0.714-0.989), respectively. The AUC for the best prediction model based on radiomics features and combining radiomics features, DTL features, and pathomics features were 0.86 (95% CI, 0.735-0.979) and 0.93 (95% CI, 0.854-1.000), respectively. Based on DCA and calibration curves, the model demonstrated good net clinical benefit and fit. CONCLUSION Multimodal radiomics and pathomics serve as valuable predictors of the risk of bone metastases in patients with primary PCa.
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Affiliation(s)
- Yun-Feng Zhang
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Chuan Zhou
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Sheng Guo
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Chao Wang
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Jin Yang
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Zhi-Jun Yang
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Rong Wang
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
- Department of Nuclear Medicine, Gansu Provincial Hospital, Lanzhou, 730000, China
| | - Xu Zhang
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Feng-Hai Zhou
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China.
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China.
- Department of Urology, Gansu Provincial Hospital, Lanzhou, 730000, China.
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Wang W, Ruan S, Xie Y, Fang S, Yang J, Li X, Zhang Y. Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer. Clin Exp Otorhinolaryngol 2024; 17:85-97. [PMID: 38246983 PMCID: PMC10933807 DOI: 10.21053/ceo.2023.00026] [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: 10/16/2023] [Revised: 01/06/2024] [Accepted: 01/19/2024] [Indexed: 01/23/2024] Open
Abstract
OBJECTIVES The necessity to develop a method for prognostication and to identify novel biomarkers for personalized medicine in patients with head and neck squamous cell carcinoma (HNSCC) cannot be overstated. Recently, pathomics, which relies on quantitative analysis of medical imaging, has come to the forefront. CXCL8, an essential inflammatory cytokine, has been shown to correlate with overall survival (OS). This study examined the relationship between CXCL8 mRNA expression and pathomics features and aimed to explore the biological underpinnings of CXCL8. METHODS Clinical information and transcripts per million mRNA sequencing data were obtained from The Cancer Genome Atlas (TCGA)-HNSCC dataset. We identified correlations between CXCL8 mRNA expression and patient survival rates using a Kaplan-Meier survival curve. A retrospective analysis of 313 samples diagnosed with HNSCC in the TCGA database was conducted. Pathomics features were extracted from hematoxylin and eosin-stained images, and then the minimum redundancy maximum relevance, with recursive feature elimination (mRMR-RFE) method was applied, followed by screening with the logistic regression algorithm. RESULTS Kaplan-Meier curves indicated that high expression of CXCL8 was significantly associated with decreased OS. The logistic regression pathomics model incorporated 16 radiomics features identified by the mRMR-RFE method in the training set and demonstrated strong performance in the testing set. Calibration plots showed that the probability of high gene expression predicted by the pathomics model was in good agreement with actual observations, suggesting the model's high clinical applicability. CONCLUSION The pathomics model of CXCL8 mRNA expression serves as an effective tool for predicting prognosis in patients with HNSCC and can aid in clinical decision-making. Elevated levels of CXCL8 expression may lead to reduced DNA damage and are associated with a pro-inflammatory tumor microenvironment, offering a potential therapeutic target.
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Affiliation(s)
- Weihua Wang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Suyu Ruan
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yuhang Xie
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shengjian Fang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Junxian Yang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xueyan Li
- Department of Nursing, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yu Zhang
- Department of Otolaryngology-Head and Neck Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
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