Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

Huss, Ralf; Raffler, Johannes; Märkl, Bruno

doi:10.1002/cnr2.1796

Cited by 8 publications

(3 citation statements)

References 106 publications

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“…AI has become a powerful tool in the analysis and interpretation of various types of biological data, including pathology, genomics, transcriptomics, proteomics, and metabolomics (Figure 5). By leveraging machine learning (ML) and deep learning (DL) algorithms, AI has revolutionized the analysis of the TME by enabling the discovery of complex patterns and relationships within large and diverse datasets [105][106][107].…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Advancements in technology for characterizing the tumor immune microenvironment

Yan,

Ju,

Huang

et al. 2024

Int. J. Biol. Sci.

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Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number of patients. The biological basis for the success of immunotherapy is the complex interaction between tumor cells and tumor immune microenvironment (TIME). Historically, research on tumor immune constitution was limited to the analysis of one or two markers, more novel technologies are needed to interpret the complex interactions between tumor cells and TIME. In recent years, major advances have already been made in depicting TIME at a considerably elevated degree of throughput, dimensionality and resolution, allowing dozens of markers to be labeled simultaneously, and analyzing the heterogeneity of tumour-immune infiltrates in detail at the single cell level, depicting the spatial landscape of the entire microenvironment, as well as applying artificial intelligence (AI) to interpret a large amount of complex data from TIME. In this review, we summarized emerging technologies that have made contributions to the field of TIME, and provided prospects for future research.

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Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Advancements in technology for characterizing the tumor immune microenvironment

Yan,

Ju,

Huang

et al. 2024

Int. J. Biol. Sci.

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“…The Random Forest Algorithm technique, on the other hand, employs some analytical functions or formulas. This technique, as well as Gini (Coefficient, Index, or Ratio), Entropy, and Mean Squared Error (MSE) [19], can be used by the Random Forest Algorithm.…”

Section: Random Forest Algorithmmentioning

confidence: 99%

Enhanced Diabetic Prediction Using Fuzzy C-Means Preprocessing and Random Forest Ensemble Learning

Bhatti,

Mahboob,

Naeem

et al. 2023

VFAST trans. softw. eng.

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Diabetes claims the lives of thousands each year, and many individuals remain oblivious to their condition until it reaches a critical stage. This study presents a data mining-based approach aimed at enhancing the early detection and prediction of diabetes, utilizing data from the Pima Indian Diabetes dataset. Despite the adaptability of fuzzy C-Means for various data types, the ultimate outcome of the clustering process hinges on the initial placement of cluster centers. Additionally, precision in data clustering is crucial; it can furnish either extensive, well-grouped data for the random forest or limited data, constraining its efficacy. Our principal objective was to enhance the accuracy of fuzzy C-means clustering and the random forest. To boost the model's performance, we incorporated PCA, fuzzy c-means, and the Random Forest approach. Various algorithmic combinations were employed, and the results unequivocally demonstrate that our model surpasses the original outcomes of the Pima Indian Diabetes Dataset in terms of accuracy. The diabetic prediction model achieved a remarkable accuracy of 97.40\% through the utilization of PCA, logistic regression, and K-Means. However, when employing PCA in conjunction with fuzzy C-means and random forests, an even higher accuracy of 98.96\% was attained. Empirical evidence confirms that the implementation of PCA significantly enhanced the accuracy of both the fuzzy C-means clustering approach and the random forest classifier, deviating from previous findings. To improve the model's performance, we used PCA, fuzzy c-means, and the Random Forest approach. Different algorithm combinations were used, and the results clearly show that our model outperforms the original Pima Indian Diabetes Dataset outcomes in terms of accuracy. The diabetic prediction model was improved to 97.40% accuracy using PCA, logistic regression, and K -Means. Using PCA with fuzzy C-means and random forests, however, we achieved an accuracy of 98.96%. Based on empirical evidence, it has been demonstrated that the implementation of PCA improved the accuracy of the fuzzy C-means clustering approach and the random forest classifier. These findings differ from previous findings.

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“…According to the review article by Huss et al, 2 digital biomarkers and artificial intelligence are potential tools for guiding immune therapy selection and advancing precision oncology. This underscores the importance of incorporating digital and computational approaches, alongside standardized strategies in histopathology, and the use of mathematical tools to aid clinical and diagnostic decision‐making as fundamental principles in the realm of “precision oncology”.…”

mentioning

confidence: 99%