Machine learning analysis reveals tumor stiffness and hypoperfusion as biomarkers predictive of cancer treatment efficacy

Englezos, Demetris; Voutouri, Chrysovalantis; Stylianopoulos, Triantafyllos

doi:10.1016/j.tranon.2024.101944

Cited by 3 publications

(1 citation statement)

References 39 publications

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“… 133 , 138 , 159 , 160 SWE-derived stiffness measures by accounting for average values of the elastic modulus over the entire tumor region or by applying machine learning methods to identify complex patterns and subvisual features that have been used not only for cancer detection 161 but also for the prediction of tumor response to therapy. 159 , 162 …”

Section: Introductionmentioning

confidence: 99%

Modulating cancer mechanopathology to restore vascular function and enhance immunotherapy

Mpekris,

Panagi,

Charalambous

et al. 2024

Cell Reports Medicine

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Section: Introductionmentioning

confidence: 99%

Modulating cancer mechanopathology to restore vascular function and enhance immunotherapy

Mpekris,

Panagi,

Charalambous

et al. 2024

Cell Reports Medicine

Self Cite

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Invited Review for 20th Anniversary Special Issue of PLRev “AI for Mechanomedicine”

Xie,

Tian,

et al. 2024

Physics of Life Reviews

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Heterogeneity of tumor biophysical properties and their potential role as prognostic markers

Markl,

Nieder,

Sandoval-Bojorquez

et al. 2024

CANCER HETEROG PLAST

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Progress in our knowledge of tumor regulatory mechanisms and complexity led to the understanding of the physical parameters of cancer cells and their microenvironment, including the mechanical, thermal, and electrical properties, solid stress, and liquid pressure, as critical regulators of tumor progression and potential prognostic markers associated with clinical outcomes. The biological hallmarks of cancer and physical abnormalities of tumors are mutually reinforced, promoting a vicious cycle of tumor progression. A comprehensive analysis of the biological and physical tumor parameters is critical for developing more robust prognostic and diagnostic markers and for improving treatment efficiency. Like the biological tumor traits, physical tumor features are characterized by inter- and intratumoral heterogeneity. The dynamic changes of physical tumor properties during progression and as a result of tumor treatment highlight the necessity of their spatial and temporal analysis in clinical settings. This review focuses on the biological basis of the tumor-specific physical traits, the state-of-the-art methods of their analyses, and the perspective of clinical translation. The importance of tumor physical parameters for disease progression and therapy resistance, as well as current treatment strategies to monitor and target tumor physical traits in clinics, is highlighted.

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Machine learning analysis reveals tumor stiffness and hypoperfusion as biomarkers predictive of cancer treatment efficacy

Cited by 3 publications

References 39 publications

Modulating cancer mechanopathology to restore vascular function and enhance immunotherapy

Modulating cancer mechanopathology to restore vascular function and enhance immunotherapy

Invited Review for 20th Anniversary Special Issue of PLRev “AI for Mechanomedicine”

Heterogeneity of tumor biophysical properties and their potential role as prognostic markers

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