Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system

Zhang, Zhiqiao; He, Tingshan; Huang, Liwen; Li, Jing; Wang, Peng

doi:10.1016/j.csbj.2021.04.025

Cited by 7 publications

(4 citation statements)

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“…Technical advances in statistics and artificial intelligence (AI) enable computer engineers and health scientists to work closely to improve the prognosis using multifactorial analysis, conventional logistic regression, and Cox analysis. 21 , 24 , 25 The accuracy of such predictions is significantly higher than the experimental predictions. In addition, research shows that traditional statistical methods do not provide as accurate analyzes as AI.…”

Section: Introductionmentioning

confidence: 87%

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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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Section: Introductionmentioning

confidence: 87%

“…These methods currently play an important role in increasing the accuracy of predicting cancer vulnerability, recurrence, and survival. 19 , 24 , 26 …”

Section: Introductionmentioning

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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“…43 (4) 668-681 • Octubre -Diciembre 2021 que ha permitido el descubrimiento de nuevos mecanismos oncogénicos y la estratificación de pacientes y enfermedades (35). La información utilizada para dilucidar los mecanismos primarios en la evolución del cáncer ha permitido elucidar el metabolismo oxidativo de las células tumorales (36), validar la utilidad de biomarcadores predictivos como la metilación del promotor de la MGMT en tumores de estirpe glial (37), proyectar el efecto terapéutico de la inmunoterapia en cáncer gástrico (38), confirmar la utilidad in silico de mutaciones particulares en cáncer de pulmón (38), y considerar a la transición epitelio mesenquimal como parte de la resistencia en cáncer de seno (39), entre muchos otros.…”

Section: Desafíos En La Interpretación De La Genómica Tumoralunclassified

Caminando a hombros de gigantes: intersección entre la genómica y la IA

Cardona¹,

Patino

Jaller

et al. 2022

Med.

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El presente manuscrito revisa las aplicaciones actuales de la inteligencia artificial (IA) en genómica funcional. La reciente explosión de la IA sigue a los notables logros que ha hecho posible el “aprendizaje profundo”, junto con una explosión de “grandes conjuntos de datos” que pueden satisfacer su necesidad. Esto ha sido posible gracias a los enormes avances en el campo de las tecnologías de alto rendimiento, aplicadas para determinar cómo los componentes individuales de un sistema biológico trabajan juntos para lograr diferentes procesos. Las disciplinas que contribuyen a este volumen de datos se conocen colectivamente como genómica funcional. Consisten en estudios de: i) la información contenida en el ADN (genómica); ii) las modificaciones que el ADN puede sufrir de forma reversible (epigenómica); iii) las transcripciones de ARN originadas por un genoma (transcriptómica); iv) el conjunto de modificaciones químicas que decoran diferentes tipos de transcripciones del ARN (epitranscriptómica); v) los productos de las transcripciones que codifican proteínas (proteómica); y vi) las pequeñas moléculas producidas a partir del metabolismo celular (metabolómica) presentes en un organismo o sistema en un momento dado, en condiciones fisiológicas o patológicas.

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“…With the development of “big data” analytics and data mining algorithms, precision medicine has witnessed significant advances in several research fields. Several precision medicine studies have been able to predict individual mortality curves for specific patients based on clinical information and have provided convenient predictive web tools for patients ( Zhang et al, 2021 ; He et al, 2021 ; Lin et al, 2021 ). This convenient, predictive web tool could help patients better evaluate the risk for death and reasonably facilitate individual treatment decisions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of individual mortality risk among patients with chronic obstructive pulmonary disease: a convenient, online, individualized, predictive mortality risk tool based on a retrospective cohort study

Zhou

Huang

et al. 2022

PeerJ

Self Cite

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Background Chronic obstructive pulmonary disease (COPD) is a serious condition with a poor prognosis. No clinical study has reported an individual-level mortality risk curve for patients with COPD. As such, the present study aimed to construct a prognostic model for predicting individual mortality risk among patients with COPD, and to provide an online predictive tool to more easily predict individual mortality risk in this patient population. Patients and methods The current study retrospectively included data from 1,255 patients with COPD. Random survival forest plots and Cox proportional hazards regression were used to screen for independent risk factors in patients with COPD. A prognostic model for predicting mortality risk was constructed using eight risk factors. Results Cox proportional hazards regression analysis identified eight independent risk factors among COPD patients: B-type natriuretic peptide (hazard ratio [HR] 1.248 [95% confidence interval (CI) 1.155–1.348]); albumin (HR 0.952 [95% CI 0.931–0.974); age (HR 1.033 [95% CI 1.022–1.044]); globulin (HR 1.057 [95% CI 1.038–1.077]); smoking years (HR 1.011 [95% CI 1.006–1.015]); partial pressure of arterial carbon dioxide (HR 1.012 [95% CI 1.007–1.017]); granulocyte ratio (HR 1.018 [95% CI 1.010–1.026]); and blood urea nitrogen (HR 1.041 [95% CI 1.017–1.066]). A prognostic model for predicting risk for death was constructed using these eight risk factors. The areas under the time-dependent receiver operating characteristic curves for 1, 3, and 5 years were 0.784, 0.801, and 0.806 in the model cohort, respectively. Furthermore, an online predictive tool, the “Survival Curve Prediction System for COPD patients”, was developed, providing an individual mortality risk predictive curve, and predicted mortality rate and 95% CI at a specific time. Conclusion The current study constructed a prognostic model for predicting an individual mortality risk curve for COPD patients after discharge and provides a convenient online predictive tool for this patient population. This predictive tool may provide valuable prognostic information for clinical treatment decision making during hospitalization and health management after discharge (https://zhangzhiqiao15.shinyapps.io/Smart_survival_predictive_system_for_COPD/).

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Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system

Cited by 7 publications

References 50 publications

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

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

Caminando a hombros de gigantes: intersección entre la genómica y la IA

Prediction of individual mortality risk among patients with chronic obstructive pulmonary disease: a convenient, online, individualized, predictive mortality risk tool based on a retrospective cohort study

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