Early detection of malignancy is imperative, yet existing diagnostic approaches struggle to identify small peripheral lesions. This study evaluated a novel imaging modality, heat diffusion analysis, to assess its ability to differentiate between malignant and normal lung tissue. Considering that lung cancer is the leading cause of cancer-related mortality worldwide, lung tumors were induced in mice in a preclinical ex vivo model to evaluate the proposed technology. The HTOScan System was used to analyze the thermal characteristics of 60 sites from excised lungs, including normal and abnormal regions. The algorithm classified pixels as high- or low-risk for malignancy. The HTOScan System demonstrated a high accuracy of 97%, with 94% sensitivity and 98% specificity compared to the gold standard of histopathology. The technology successfully differentiated abnormal from normal tissue ex vivo based on differences in thermal diffusivity. This proof-of-concept study suggests that combining heat diffusion imaging techniques with machine learning algorithms could enable the HTOScan System to identify malignant lesions accurately with high confidence. The technique shows promise as a real-time decision support tool for cancer detection, pending further in vivo validation. This novel functional-imaging approach could improve the identification of peripheral lesions and the guidance of biopsies during bronchoscopy.
Gat et al. (Mon,) studied this question.