Open-source Python library + no-code web dashboard for evaluating oncology AI models at clinical decision thresholds. [P]
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一个名为 oncothresh 的开源 Python 库已被开发出来，用于在临床决策阈值下评估肿瘤学 AI 模型。与衡量全局一致性的传统指标不同，oncothresh 专注于模型在精确截止点（例如决定患者是否被标记、活检或治疗）的可靠性。它提供了敏感性、特异性、PPV、NPV、引导置信区间和决策曲线净收益等指标，支持肿瘤细胞学和 Ki-67 评分等任务。…
Most classification metrics for oncology AI models (AUC, ICC, MAE) measure global agreement. They don't answer the question that actually matters at the point of care: how reliable is this model at the exact cutoff that decides whether a patient gets flagged, biopsied, or treated?
I built oncothresh to evaluate models at a specific clinical threshold rather than in aggregate: sensitivity/specificity/PPV/NPV at the cutoff, bootstrap confidence intervals, threshold-sensitivity curves, boundary-weighted calibration, decision-curve net benefit, and number-needed-to-test. It's a small, dependency-light Python library (numpy/scipy/scikit-learn/pydantic) built for tasks like tumor cellularity, Ki-67, TMB, and PD-L1 scoring, where a continuous model output gets collapsed into a yes/no clinical decision at a fixed cutoff.
Pathology-specific benchmarks like PathBench and PathBench-MIL evaluate foundation models globally but don't evaluate at predefined clinical thresholds with uncertainty quantification, which is the gap this fills.
There's also a companion web dashboard (oncothresh-web) for people who want the same analysis without writing code: upload a CSV of predictions and labels, pick a threshold, get the full set of charts plus a downloadable PDF report. docker compose up and it's running locally, no cloud dependency.
- Library: github.com/omkaradhali/oncothresh
- Dashboard: github.com/omkaradhali/oncothresh-web
Still v0.1, so I'd genuinely welcome feedback: use cases I haven't considered, edge cases in the DCA/calibration math, or places the API doesn't fit how people actually work with threshold-based models.