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Book . 2025
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ZENODO
Book . 2025
License: CC BY
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Book . 2025
License: CC BY
Data sources: Datacite
ZENODO
Book . 2025
License: CC BY
Data sources: Datacite
ZENODO
Book . 2025
License: CC BY
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Artificial Intelligence for Hard-to-Treat and Unknown-Origin Cancers

Authors: Farrokhi, Mehrdad; Taheri, Niloofar; Fakharzadeh Moghadam, Omid; Armoon, Mahdi; Samimi, Susan; Torkashvand, Narges; Motevalli, Saeed; +27 Authors

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

Abstract

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.

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
Related to Research communities
Cancer Research