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Tumor Progression02:07

Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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Artificial Intelligence in Colon Cancer: Advances, Challenges, and Future Perspectives.

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    Artificial intelligence (AI) is revolutionizing colorectal cancer (CRC) care by improving early detection, diagnosis, and treatment planning. AI integration across various stages promises enhanced accuracy and personalized patient outcomes in oncology.

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    Area of Science:

    • Oncology
    • Medical Informatics
    • Artificial Intelligence

    Background:

    • Colorectal cancer (CRC) presents a significant global health burden with increasing incidence in younger demographics.
    • Current diagnostic and therapeutic approaches face challenges in accuracy and efficiency, necessitating innovative solutions.
    • Artificial intelligence (AI) offers transformative potential across the entire CRC care continuum.

    Purpose of the Study:

    • To review the current applications and impact of AI in colorectal cancer management.
    • To highlight AI's role in enhancing diagnostic accuracy, treatment planning, and patient follow-up.
    • To discuss challenges and future directions for AI adoption in precision oncology for CRC.

    Main Methods:

    • Review of AI applications in endoscopy (computer-aided detection/diagnosis), pathology (whole-slide imaging analysis), radiology (lesion detection, radiomics), and surgical robotics.
    • Analysis of AI's role in treatment planning, prognostic modeling, and survivorship care using multimodal data.
    • Discussion of challenges including data heterogeneity, validation, interpretability, and regulatory hurdles.

    Main Results:

    • AI significantly improves adenoma detection rates in endoscopy (25-50%) and supports real-time lesion characterization.
    • In pathology, AI enables automated triage, in silico prediction of microsatellite instability, and prognostic stratification.
    • Radiology applications include AI-driven lesion detection, staging, and radiomic virtual biopsies for molecular profiling.
    • AI facilitates personalized treatment planning, predicts neoadjuvant response, and identifies therapeutic targets.
    • AI enhances surgical precision through robotic platforms and improves intraoperative safety.
    • Multimodal AI datasets provide superior survival and recurrence predictions, optimizing survivorship care.

    Conclusions:

    • AI is a cornerstone technology poised to enhance precision oncology in colorectal cancer.
    • Widespread adoption requires addressing challenges in data, validation, interpretability, and regulation.
    • Advances in multimodal AI and federated learning are expected to overcome current barriers, optimizing CRC care delivery and patient outcomes.