Introduction The translation of biomarkers into binary clinical decisions requires the determination of precise cut-off points. This study validates the TholdStormDX v0.0.1 tool, a mathematical engine that employs Dual Annealing, 2- and 4-parameter logistic fitting, and vectorized Monte Carlo simulations for panel optimization under Boolean OR logic.

Methods The tool was evaluated using datasets from four diagnostic domains (Pulmonary Nodules, Hepatocellular Carcinoma [HCC], Cervical Cancer, and Breast Cancer), along with a prognosis-oriented analytical context (Breast Cancer). Validation followed a strict workflow: characterization and selection of the best individual and combined thresholds in the Training (Train) and Validation (Val) sets, using the Test set in a completely independent manner, solely to assess the model’s performance and generalizability.

Results The tool enabled precise derivation of cut-off points for both individual biomarkers and multivariable combinations. Evaluation on the Test set objectively demonstrated in which scenarios a single biomarker outperforms a complex panel, promoting clinical parsimony. For example, in Breast Cancer diagnosis, an individual predictor outperformed the optimized panel (Sensitivity: 0.953 / Specificity: 0.952 in Test); conversely, in Hepatocellular Carcinoma, the multivariable combination showed superior performance compared to the single marker (Sens: 0.707 / Spe: 0.718 in Test). Additionally, the self-auditing system effectively flagged metric degradation when noisy variables were included, preventing potential issues.

Conclusion TholdStormDX v0.0.1 proves to be a robust and transparent bioinformatics platform for deriving clinical thresholds. Its main contribution lies in mitigating local minima and promoting clinical parsimony, enabling researchers to objectively identify when a single biomarker is sufficient and when a panel provides real added value. Furthermore, it transforms the problem of biological noise into a safety feature: by systematically warning about algorithmic instability, it prevents overfitting and ensures the clinical viability of medical decisions.

Availability The software is free and distributed under the GNU GPLv3 license. TholdStormDX v0.0.1 is written in Python, and its source code is available at the following GitHub address: https://github.com/roberto117343/TholdStormDX.

Contact roberto117343gmail.com

The authors have declared no competing interest.

This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study used (or will use) ONLY openly available human data that were originally located at: 1. https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic 2. https://archive.ics.uci.edu/dataset/16/breast+cancer+wisconsin+prognostic 3. https://archive.ics.uci.edu/dataset/383/cervical+cancer+risk+factors 4. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1196883/full 5. https://datadryad.org/dataset/doi:10.5061/dryad.3n901

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The software is freely available and distributed under the GNU GPLv3 license. TholdStormDX v0.0.1 is written in Python, and its source code is available at the following GitHub repository:https://github.com/roberto117343/TholdStormDX. The datasets used for validation (Breast Cancer, Cervical Cancer, HCC, and Pulmonary Nodules) are from public repositories as described in the manuscript.

https://github.com/roberto117343/TholdStormDX