Computer methods and programs in biomedicine · 2026
Machine learning classification of normal and malignant cells on the basis of their viscoelastic properties.
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Thomas-Chemin Ophélie, Séverac Childérick, Abidine Yara, Trevisiol Emmanuelle, Dague Etienne
Аннотация
BACKGROUND AND OBJECTIVE: Cell mechanics, elasticity and viscoelasticity, are key markers of biological states like cancer. Atomic force microscopy (AFM) is ideal for such studies, but its low throughput limits large-scale use. Two solutions exist: automation for higher throughput, or high-density measurements for richer data. The latter enables machine learning (ML)-based classification, with viscoelastic parameters offering unique insights beyond static measures like Young's modulus.
METHODS: This study used dynamic mechanical analysis (DMA) to classify cells, focusing on viscoelastic descriptors (storage/loss moduli) across frequencies. Normal (RWPE-1) and grade IV cancerous (PC3-GFP) prostate cells were probed at 1-200Hz, generating 304 features per cell. The fuzzy logic-based LAMDA algorithm, trained on 19 selected features, classified cells using 40 samples per line.
RESULTS: PC3-GFP cells showed higher deformability and heterogeneity, behaving more like viscous fluids at low frequencies. The model achieved 79% classification accuracy. Adding features improved performance, suggesting fewer training samples may suffice with rich datasets. A sensitivity-optimized threshold reduced false negatives in cancer detection.
CONCLUSIONS: Combining viscoelastic analysis with ML effectively discriminates normal and malignant cells. Future work could refine training and integrate new features, though acquisition time remains a challenge. This approach offers a promising framework for mechanome-based diagnostics, with applications in cancer and stem cell research.
METHODS: This study used dynamic mechanical analysis (DMA) to classify cells, focusing on viscoelastic descriptors (storage/loss moduli) across frequencies. Normal (RWPE-1) and grade IV cancerous (PC3-GFP) prostate cells were probed at 1-200Hz, generating 304 features per cell. The fuzzy logic-based LAMDA algorithm, trained on 19 selected features, classified cells using 40 samples per line.
RESULTS: PC3-GFP cells showed higher deformability and heterogeneity, behaving more like viscous fluids at low frequencies. The model achieved 79% classification accuracy. Adding features improved performance, suggesting fewer training samples may suffice with rich datasets. A sensitivity-optimized threshold reduced false negatives in cancer detection.
CONCLUSIONS: Combining viscoelastic analysis with ML effectively discriminates normal and malignant cells. Future work could refine training and integrate new features, though acquisition time remains a challenge. This approach offers a promising framework for mechanome-based diagnostics, with applications in cancer and stem cell research.