Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram

Chen, Dexin; Lai, Jianbo; Cheng, Jiaxin; Fu, Meiting; Lin, Liyan; Chen, Feng; Lu, Jianping; Chen, Yuning; Huang, Guang-Yao; Yan, Miaojia; Ma, Xiaodan; Li, Guoxin; Chen, Gang; Yan, Jun

doi:10.1016/j.isci.2023.106246

Cited by 8 publications

(5 citation statements)

References 69 publications

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“…The nomogram model excels at translating complex statistical analyses into an intuitive and straightforward graphical representation, which greatly enhances the interpretability and usability of the model [24]. This model integrates various factors, assigns a score to each, and calculates a patient's disease risk or prognosis by aggregating these scores.…”

Section: Discussionmentioning

confidence: 99%

Predictive efficacy of combined tumor markers and gastrin for recurrence after endoscopic submucosal dissection in early gastric cancer patients

Zhao

2024

Am J Transl Res

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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 nonrecurrence 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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Section: Discussionmentioning

confidence: 99%

Predictive efficacy of combined tumor markers and gastrin for recurrence after endoscopic submucosal dissection in early gastric cancer patients

Zhao

2024

Am J Transl Res

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“…Transcriptomics provides insights into gene expression patterns corresponding to various cancer hallmarkers [99] , while metabolomics provides a detailed understanding of the metabolic alterations associated with cancer [100] . Deep pathomics, in which deep learning techniques are applied to histopathological images, can help reveal complex tissue architecture and cellular morphology [101] , [102] , [103] . Combining these more diverse omics data types with radiology genomics, i.e.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Deep Learning of radiology-genomics integration for computational oncology: A mini review

Wang,

Li,

Zeng

2024

Computational and Structural Biotechnology Journal

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“…Leveraging artificial intelligence, pathomics converts pathological images into high-fidelity, high-throughput data amenable to extensive analysis. This approach allows the quantification of pathological diagnoses, molecular expression, and disease prognosis using texture, morphological, and biological characteristics [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer

Wang,

Ruan,

Xie

et al. 2024

Clin Exp Otorhinolaryngol

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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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Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram

Cited by 8 publications

References 69 publications

Predictive efficacy of combined tumor markers and gastrin for recurrence after endoscopic submucosal dissection in early gastric cancer patients

Predictive efficacy of combined tumor markers and gastrin for recurrence after endoscopic submucosal dissection in early gastric cancer patients

Deep Learning of radiology-genomics integration for computational oncology: A mini review

Development and Validation of a Pathomics Model Using Machine Learning to Predict <i>CXCL8</i> Expression and Prognosis in Head and Neck Cancer

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