AFM in clinical oncology: Towards a theoretical framework for highly reproducible mechanical measurements for cancer diagnosis and treatment monitoring

Atomic Force Microscopy (AFM) is a well-established and powerful technique used across fields ranging from materials science to biology and mechanobiology. It is also emerging as a valuable tool in clinical oncology, enabling the identification of nanomechanical biomarkers for cancer diagnosis. However, its effective integration into routine clinical practice remains unrealized. Cancer classification is typically based on the distribution of nanoscale mechanical properties, which may appear as single- or multi-peak profiles, yet the absence of a standardized theoretical framework for reproducible measurements remains a major obstacle.This paper proposes a robust analytical approach to address two primary sources of error in AFM-based cancer diagnosis: uncertainties in cantilever spring constant calibration and in the determination of the tip–sample contact point. Through a physics-based analysis, it is demonstrated that uncertainties in the spring constant (k) lead to a linear scaling of the calculated Young’s modulus (E), without altering the fundamental shape of the mechanical property distribution. Using weighted skew-normal distribution modeling, it is shown that diagnostic nanomechanical fingerprints remain identifiable even under significant calibration variability. Furthermore, a general rule for contact-point error propagation is established, showing that Young’s modulus estimation from contact stiffness reduces error sensitivity by at least a factor of three for spherical indenters and at least a factor of two for conical indenters, compared with force–indentation fitting using the classical Hertzian equations. Additionally, a threshold condition is defined using an elementary forced-oscillation model to identify the regime in which viscoelastic effects are minimized and the elastic approximation becomes valid. These results enhance the reliability of AFM as a diagnostic tool and provide a standardized methodology for monitoring treatment outcomes through quantitative mechanical signatures, supporting its future integration into clinical workflows.