Early detection of lung cancer (LC) significantly increases the likelihood of successful treatment and improves LC survival rates. Currently, screening (mainly low-dose CT scans) is recommended for individuals at high risk. However, the recent increase in the number of LC cases unrelated to the well-known risk factors, and the high false-positive rate of low-dose CT, indicate a need to develop new, non-invasive methods for LC detection. Therefore, we evaluated the use of differential scanning calorimetry (DSC) for LC patients’ diagnosis and predicted survival. Additionally, by applying mass spectrometry, we investigated whether changes in O- and N-glycosylation of plasma proteins could be an underlying mechanism responsible for observed differences in DSC curves of LC and control subjects. Our results indicate selected DSC curve features could be useful for differentiation of LC patients from controls with some capable of distinction between subtypes and stages of LC. DSC curve features also correlate with LC patients’ overall/progression free survival. Moreover, the development of classification models combining patients’ DSC curves with selected plasma protein glycosylation levels that changed in the presence of LC could improve the sensitivity and specificity of the detection of LC. With further optimization and development of the classification method, DSC could provide an accurate, non-invasive, radiation-free strategy for LC screening and diagnosis.

Melanoma is the fifth most common cancer in the United States and the deadliest of all skin cancers. Even with recent advancements in treatment, there is still a 13% two-year recurrence rate, with approximately 30% of recurrences being distant metastases. Identifying patients at high risk for recurrence or advanced disease is critical for optimal clinical decision-making. Currently, there is substantial variability in the selection of screening tests and imaging, with most modalities characterized by relatively low accuracy. In the current study, we built upon a preliminary examination of differential scanning calorimetry (DSC) in the melanoma setting to examine its utility for diagnostic and prognostic assessment. Using regression analysis, we found that selected DSC profile (thermogram) parameters were useful for differentiation between melanoma patients and healthy controls, with more complex models distinguishing melanoma patients with no evidence of disease from patients with active disease. Thermogram features contributing to the third principal component (PC3) were useful for differentiation between controls and melanoma patients, and Cox proportional hazards regression analysis indicated that PC3 was useful for predicting the overall survival of active melanoma patients. With the further development and optimization of the classification method, DSC could complement current diagnostic strategies to improve screening, diagnosis, and prognosis of melanoma patients.

Background: Over the past decade, saliva-based liquid biopsies have emerged as promising tools for the early diagnosis, prognosis, and monitoring of cancer, particularly in high-risk populations. However, challenges persist because of low concentrations and variable modifications of biomarkers linked to tumor development when compared to normal salivary components. Methods: This study explores the application of differential scanning calorimetry (DSC)-based thermal liquid biopsy (TLB) for analyzing saliva and blood plasma samples from head and neck cancer (HNC) patients. Results: Our research identified an effective saliva processing method via high-speed centrifugation and ultrafiltration, resulting in reliable TLB data. Notably, we recorded unique TLB profiles for saliva from 48 HNC patients and 21 controls, revealing distinct differences in thermal transition features that corresponded to salivary protein denaturation. These results indicated the potential of saliva TLB profiles in differentiating healthy individuals from HNC patients and identifying tumor characteristics. In contrast, TLB profiles for blood plasma samples exhibited smaller differences between HNC patients and had less utility for differentiation within HNC. Conclusions: Our findings support the feasibility of saliva-based TLB for HNC diagnostics, with further refinement in sample collection and the incorporation of additional patient variables anticipated to enhance accuracy, ultimately advancing non-invasive diagnostic strategies for HNC detection and monitoring.

Background/Objectives: Differential scanning calorimetry (DSC) analysis of blood plasma, also known as thermal liquid biopsy (TLB), is a promising approach for disease detection and monitoring; however, its wider adoption in clinical settings has been hindered by labor-intensive data processing workflows, particularly baseline correction. Methods: We developed and tested two automated algorithms to address critical bottlenecks in TLB analysis: (1) a baseline-correction algorithm utilizing rolling-variance analysis for endpoint detection, and (2) a signal-detection algorithm that applies auto-regressive integrated moving average (ARIMA)-based stationarity testing to determine whether a profile contains interpretable thermal features. Both algorithms are implemented in ThermogramForge, an open-source R Shiny web application providing an end-to-end workflow for data upload, processing, and report generation. Results: The baseline-correction algorithm demonstrated excellent performance on plasma TLB data (characterized by high heat capacity), matching the quality of rigorous manual processing. However, its performance was less robust for low signal biofluids, such as urine, where weak thermal transitions reduce the reliability of baseline estimation. To address this, a complementary signal-detection algorithm was developed to screen for TLB profiles with discernable thermal transitions prior to baseline correction, enabling users to exclude non-informative data. The signal-detection algorithm achieved near-perfect classification accuracy for TLB profiles with well-defined thermal transitions and maintained a low false-positive rate of 3.1% for true noise profiles, with expected lower performance for borderline cases. The interactive review interface in ThermogramForge further supports quality control and expert refinement. Conclusions: The automated baseline-correction and signal-detection algorithms, together with their web-based implementation, substantially reduce analysis time while maintaining quality, supporting more efficient and reproducible TLB research.