Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images

Mirniaharikandehei, Seyedehnafiseh; Heidari, Morteza; Danala, Gopichandh; Lakshmivarahan, S.

doi:10.1016/j.cmpb.2021.105937

Cited by 38 publications

(20 citation statements)

References 41 publications

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“…Studies have proved that the precision, sensitivity and speci city of this model are 65.78%, 43.10% and 87.12%, respectively. However, one of the shortcomings of this study is that the sample size needs to be expanded [21]. Zhou et al used ve machine learning methods to establish a peritoneal metastasis model, and found that machine learning combining clinical indicators and serum markers could predict peritoneal metastasis in gastric cancer [22].…”

Section: Discussionmentioning

confidence: 95%

Development and validation of peritoneal metastasis in gastric cancer based on simplified clinicopathological features and serum tumour markers

Yang

Chen

et al. 2022

Preprint

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Background: Peritoneal metastasis (PM) is not uncommon in patients with gastric cancer(GC), which affects the clinical treatment decisions, but the relevant examination measures are not efficient detection. Our goal is to develop a clinical radiomics nomogram to better predict peritoneal metastases. Methods: 3480 patients from 2 centers were divided into 1 training, 1 internal validation, and 1 external validation cohorts (1949 in the internal training set and 704 in the validation set, and 827 in the external validation cohorts) with clinicopathologically confirmed GC. We recruited 11 clinical factors including age, sex, smoke or not, tumor size, differentiation, Borrmann type, Location, T stage, serum tumor markers (STMs) comprising carbohydrate antigen 19 − 9 (CA19-9), carbohydrate antigen 72 − 4 (CA72-2), and carcinoembryonic antigen(CEA) to develop the radiomics nomogram. For clinical predictive feature selection and establish clinical models, the Statistical methods of analysis of variance (ANOVA), relief and recursive feature elimination (RFE) and logistic regression analysis were used. To develop combined predictive models, tumor diameters, type, tumor location, T stage and STMs were finally selected. The discriminative ability of the nomogram to predict PM is evaluated by the AUC, decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of the nomogram. Results: The AUC of clinical models was 0.762 in the training cohorts, 0.772 in the internal validation cohorts, and 0.758 in the external validation cohort. However, when combined with STMs, the AUC was improved to 0.806,0.839 and 0.801 respectively. DCA analysed that the combined nomogram was of good clinical evaluation value to predict PM in GC. Conclusions The present study proposed a clinical nomogram with a combination of clinical risk factors and radiomics features that can potentially be applied in the individualized preoperative prediction of PM in GC patients.

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Section: Discussionmentioning

confidence: 95%

Development and validation of peritoneal metastasis in gastric cancer based on simplified clinicopathological features and serum tumour markers

Yang

Chen

et al. 2022

Preprint

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“…Ultimately, the proposed model attained appropriate performance with 0.89% of AUC. Mirniaharikandehei et al 50 Hist gradient boosting ("verbose": 2, "random_state": 93, "n_estimators": 8, "max_leaf_nodes": 58, "max_iter": 130, "max_deph": 8, "learning rate": 0.1) 53.95 5 Decision tree (j48 ("random_state": 93, "min_sample_splits": 8, "min_sample_leaf": 1, "max_features": lpg2, "criteria": "Gini")…”

Section: Discussionmentioning

confidence: 99%

“…Ultimately, the proposed model attained appropriate performance with 0.89% of AUC. Mirniaharikandehei et al 50 compared 5 gradients boosting machine (GBM) model performance for predicting gastric cancer metastatic risk and patient survivability. The results showed GBM technique combined with a random projection algorithm yielded significantly higher prediction performance (accuracy = 71.2%).…”

Section: Discussionmentioning

confidence: 99%

Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer

Afrash

Yazdani

Kazemi-Arpanahi³

2022

Clin Med Insights Oncol

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Background: Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients. Methods: A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers, including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB), and adaptive boosting (AdaBoost), were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity, sensitivity, likelihood ratio, and total accuracy. Finally, the system was developed according to the best model. Results: The stage, position, and size of tumor were selected as the 3 top predictors for gastric cancer survival. Among the 6 selected ML algorithms, the HGB classifier with the mean accuracy, mean specificity, mean sensitivity, mean area under the curve, and mean F1-score of 88.37%, 86.24%, 89.72%, 88.11%, and 89.91%, respectively, gained the best performance. Conclusions: The ML models can accurately predict the 5-year survival and potentially act as a customized recommender for decision-making in gastric cancer patients. The developed system in our study can improve the quality of treatment, patient safety, and survival rates; it may guide prescribing more personalized medicine.

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“…Liu et al, followed an integrative approach of combining preoperative biomarkers including tissue biopsies, tumor markers, and CT image objects to predict lymph node metastasis in GEA by applying regression analysis and combined this to a multivariate model [ 161 ]. In parallel, similar image object information have been used to predict the risk of peritoneal metastases using gradient boosting machines [ 162 ]. Others applied DL models to detect metastasis using CT image objects, in addition to adequate staging [ 163 , 164 ].…”

Section: Machine Learning—basic Concepts Specific Applications and Future Directions In Geamentioning

confidence: 99%

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

Klein

Duda

2021

Cancers

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Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.

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Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images

Cited by 38 publications

References 41 publications

Development and validation of peritoneal metastasis in gastric cancer based on simplified clinicopathological features and serum tumour markers

Development and validation of peritoneal metastasis in gastric cancer based on simplified clinicopathological features and serum tumour markers

Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

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