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.
**背景/目的：** 血浆差示扫描量热分析，亦称热液体活检，是一种用于疾病检测与监测的颇具前景的方法；然而，其在临床环境中的更广泛应用一直受到数据处理流程（尤其是基线校正）劳动密集型的阻碍。**方法：** 我们开发并测试了两种自动化算法，以解决热液体活检分析中的关键瓶颈：(1) 一种利用滚动方差分析进行端点检测的基线校正算法；(2) 一种应用基于自回归积分滑动平均模型的平稳性检验来确定图谱是否包含可解释热特征的信号检测算法。这两种算法均在开源R Shiny网络应用程序ThermogramForge中实现，该程序提供了数据上传、处理和报告生成的一站式工作流程。**结果：** 基线校正算法在血浆热液体活检数据上表现出色，其处理质量可与严格的**手动处理**相媲美。然而，该算法对于尿液等低信号生物流体的处理鲁棒性较差，因为微弱的热转变降低了基线估计的可靠性。为此，我们开发了一种互补的信号检测算法，用于在基线校正前筛选出具有可辨别热转变的热液体活检图谱，使用户能够排除无信息数据。该信号检测算法对于具有明确热转变的图谱实现了近乎完美的分类准确度，并对真实噪声图谱保持了3.1%的低假阳性率，对于临界情况其性能预期会有所降低。ThermogramForge中的交互式审阅界面进一步支持质量控制和专家精修。**结论：** 自动化的基线校正和信号检测算法及其基于网络的实现，在保持质量的同时显著缩短了分析时间，有助于开展更高效、可重复性更强的热液体活检研究。
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