Published December 5, 2025 | Version v2
Book Open

Artificial Intelligence for Hard-to-Treat and Unknown-Origin Cancers

Authors/Creators

Description

Artificial intelligence is rapidly transforming the landscape of oncology, bringing new capabilities to the diagnosis and treatment of hard to treat and unknown origin cancers. These malignancies often present late, resist conventional therapy, and lack clear biological markers that guide personalized care. In cancers of unknown primary origin, clinicians struggle to determine where the disease began, making treatment decisions uncertain and less effective. Likewise, aggressive tumor types such as advanced sarcomas, rare gastrointestinal cancers, or refractory head and neck tumors often challenge standard medical approaches, leaving patients with limited options and poor prognoses. AI provides powerful tools to address these gaps by analyzing complex datasets with remarkable speed and precision. Machine learning models can evaluate radiologic scans, pathology slides, genomic sequencing, and clinical histories to detect patterns that escape human observation. Through these analytic strengths, AI can identify the most likely tissue of origin, predict therapeutic sensitivities, and assist in selecting targeted treatments that improve outcomes. Real time monitoring systems enhance patient management by detecting early signs of recurrence or treatment toxicity before they lead to irreversible harm. Furthermore, AI accelerates drug discovery and expands opportunities for innovative combination therapies. By modeling tumor evolution and resistance pathways, AI may help clinicians stay ahead of aggressive disease behavior. The overarching goal is to replace diagnostic uncertainty and treatment guesswork with data driven, personalized precision. As AI continues to mature, it holds significant promise to improve survival and quality of life for patients facing some of the most challenging cancer diagnoses in modern medicine.

Files

Book63.pdf

Files (869.1 kB)

Name Size Download all
md5:5508578fff80932493850eafe3390db7
869.1 kB Preview Download