An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker

Güleken, Zozan; Jakubczyk, Paweł; Paja, Wiesław; Pancerz, Krzysztof; Wosiak, Agnieszka; Yaylım, İlhan; Tarhan, Nevzat; Hakan, Mehmet Tolgahan; Sönmez, Dilara; Sarıbal, Devrim; Arıkan, Soykan; Depciuch, Joanna

doi:10.1016/j.cmpb.2023.107523

Cited by 30 publications

(4 citation statements)

References 38 publications

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“…While the Raman peaks at 1161 cm −1 , 1259 cm −1 , and 1516 cm −1 were corresponding to the vibration of the protein. 49 The Raman peaks at 1387 cm −1 and 1447 cm −1 were the vibration peaks of the CH 2 functional groups in proteins and lipids. 48 In addition, Fig.…”

Section: Resultsmentioning

confidence: 99%

A dense SERS substrate of the AgNPs@GO compound film for detecting homocysteine molecules

He,

Yang,

et al. 2023

Anal. Methods

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Section: Resultsmentioning

confidence: 99%

A dense SERS substrate of the AgNPs@GO compound film for detecting homocysteine molecules

He,

Yang,

et al. 2023

Anal. Methods

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“…In general, such FTIR and Raman spectrometry experiments make it possible to check the absorption of wavelengths of different wavelengths penetrating the biological–chemical sample and tissue under study. The absorption of specific wavelengths can distinguish between different samples, for example, and identify diseases [ 34 ]. Application areas can be found in abundance, especially where the data are continuous in nature.…”

Section: Discussionmentioning

confidence: 99%

Application of the Fuzzy Approach for Evaluating and Selecting Relevant Objects, Features, and Their Ranges

Paja

2023

Entropy

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Relevant attribute selection in machine learning is a key aspect aimed at simplifying the problem, reducing its dimensionality, and consequently accelerating computation. This paper proposes new algorithms for selecting relevant features and evaluating and selecting a subset of relevant objects in a dataset. Both algorithms are mainly based on the use of a fuzzy approach. The research presented here yielded preliminary results of a new approach to the problem of selecting relevant attributes and objects and selecting appropriate ranges of their values. Detailed results obtained on the Sonar dataset show the positive effects of this approach. Moreover, the observed results may suggest the effectiveness of the proposed method in terms of identifying a subset of truly relevant attributes from among those identified by traditional feature selection methods.

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“…The candidate models were the Oblique Random Survival Forest (ORSF) model [40,41], the Cox Proportional Hazard model (Cox), a Cox model with the least absolute shrinkage and selection operator penalization (LASSO Cox) [42], CoxBoost [43], Survival Gradient Boosting Machine (GBM) [44], Extreme Gradient Boosting survival regression (XGBoost) [45], Deephit [46], Deepsurv [47], DNNsurv [48], logistic-hazard model [49], and PC-hazard model [50], because they are famous and novel models currently used in survival analysis. We used the default hyperparameters of those models given by the "mlr3" package in R [51].…”

Section: Candidate Modelsmentioning

confidence: 99%

Development and Validation of a Novel Machine Learning Model to Predict the Survival of Patients with Gastrointestinal Neuroendocrine Neoplasms

Liu,

Chen,

Dai

et al. 2024

Neuroendocrinology

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Introduction: Well-calibrated models for personalized prognostication of patients with gastrointestinal neuroendocrine neoplasms (GINENs) are limited. This study aimed to develop and validate a machine-learning model to predict the survival of patients with GINENs. Methods: oblique random survival forest (ORSF) model, Cox proportional hazard risk model, Cox model with least absolute shrinkage and selection operator penalization, CoxBoost, Survival Gradient Boosting Machine, Extreme Gradient Boosting survival regression, DeepHit, DeepSurv, DNNSurv, Logistic-Hazard model, and PC-Hazard model were compared. We further tuned hyperparameters and selected variables for the best-performing ORSF. Then, the final ORSF model was validated. Results: 43444 patients with GINENs were included. The median (interquartile range) survival time was 53 (19-102) months. The ORSF model performed best, in which age, histology, M stage, tumor size, primary tumor site, sex, tumor number, surgery, lymph nodes removed, N stage, race, and grade were ranked as important variables. However, chemotherapy and radiotherapy were not necessary for the ORSF model. The ORSF model had an overall C-index of 0.86 (95% confidence interval, 0.85-0.87). The area under the receiver operation curves at 1-, 3-, 5-, and 10-year were 0.91, 0.89, 0.87, and 0.80, respectively. The decision curve analysis showed superior clinical usefulness of the ORSF model than the American Joint Committee on Cancer Stage. A nomogram and an online tool were given. Conclusion: The machine-learning ORSF model could precisely predict the survival of patients with GINENs, with the ability to identify patients at high risk for death and probably guide clinical practice.

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An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker

Cited by 30 publications

References 38 publications

A dense SERS substrate of the AgNPs@GO compound film for detecting homocysteine molecules

A dense SERS substrate of the AgNPs@GO compound film for detecting homocysteine molecules

Application of the Fuzzy Approach for Evaluating and Selecting Relevant Objects, Features, and Their Ranges

Development and Validation of a Novel Machine Learning Model to Predict the Survival of Patients with Gastrointestinal Neuroendocrine Neoplasms

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