Early Cancer Detection: From Oncological Screening to Big Data Analysis and Machine Learning in Patient Evolution

Early cancer detection is a fundamental pillar in improving clinical outcomes and patient survival. In recent years, the integration of artificial intelligence (AI) in oncological screening has revolutionized the way we approach early diagnosis. From the use of machine learning to big data analysis, these technologies are transforming oncology, enabling more precise and rapid identification of tumors.

AI in Oncological Screening

The application of AI in cancer diagnosis has shown promising results in various areas. For instance, in breast cancer, deep learning methods have significantly improved the accuracy of histopathological image classification, addressing challenges such as data imbalance and incorrect labeling. These advancements not only optimize diagnostic time but also reduce the risk of overfitting in classification models.

In the case of colorectal cancer, the use of machine learning algorithms has enabled the identification of potential biomarkers that can enhance early detection and personalized treatment. These biomarkers, such as INHBA and FNBP1, are significantly correlated with patient prognosis, underscoring the importance of AI in patient evolution.

Moreover, the LANTERN study has developed digital human avatars that integrate multi-omic data to create more accurate predictive models in lung cancer. This integration of clinical and genomic data allows for better personalization of treatments and earlier disease detection.

Conclusions

The incorporation of AI in oncological screening and big data analysis is redefining the landscape of early cancer detection. These technologies not only enhance diagnostic accuracy but also facilitate the personalization of treatments, which is crucial for patient evolution. As we continue to advance in this direction, it is essential for healthcare professionals to stay updated on these innovations to maximize their impact in clinical practice.

Referencias

• [1] Accuracy Analysis of Deep Learning Methods in Breast Cancer Classification: A Structured Review.
• [2] Using Machine Learning Methods to Study Colorectal Cancer Tumor Micro-Environment and Its Biomarkers.
• [3] Lung cancer multi-omics digital human avatars for integrating precision medicine into clinical practice: the LANTERN study.

Created 20/1/2025