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Label-Space Mismatch Distorts Cross-Cohort Evaluation in Breast Cancer Molecular Subtyping

Suxing Liu, Byungwon Min, Zhijian Zheng, Jiahao Wang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.24962 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

基因分类对乳腺癌子类别分类器的分类器在来源和目标分类器使用不同的注释标准时是不可靠的:METABRIC标签六类,PAM50定义五个完整的面板,而一个 claudin-low预测不能在五类标准下正确。从1369 METABRIC患者(训练n=958)转移的分类器在TCGA-BRCA(n=987)和GSE21653(n=266)之间分离了在交叉场所比较中常规地混合的三项评估操作。(1)标签空间限制 - 评估规则的变化,而不是重新培训收益 - 必须是主导的治疗纠正(73.76%>79.74%在TCGA-BRCA上转移;63.16%>68.42%在

Machine translation (Meta M2M100); the English original follows.

Abstract

Cross-cohort evaluation of breast cancer subtype classifiers is unreliable when source and target cohorts use different annotation standards: METABRIC labels six classes, PAM50 defines five, and a claudin-low prediction cannot be correct under a five-class standard. A classifier transferred from 1,369 METABRIC patients (train n=958) to TCGA-BRCA (n=987) and GSE21653 (n=266) separates three evaluation operations routinely conflated in cross-site comparisons. (1) Label-space restriction - an evaluation-rule change, not a retraining gain - is the dominant correction (73.76%->79.74% on TCGA-BRCA; 63.16%->68.42% on GSE21653); (2) EM prior correction on the reconciled space is an optional calibration step (Brier fidelity up, macro F1 down; to 80.65% and 74.06%); and (3) the target label space is not recoverable from unlabelled predictions (EM retains prior 0.0751 on the absent class; calibrated detectors fail), so compliance must come from cohort documentation. The protocol exposes why internal validation overstates cross-cohort value: multimodal fusion gains do not transfer (no configuration beats mRNA alone, n=694; McNemar p=0.13-1.00), and the genomic panel adds nothing to pathological complete response prediction over clinical variables (DeltaAUC = +0.0045). Clinically, without label-space compliance, cross-site accuracy comparisons misattribute annotation-standard differences to model skill; Luminal A/B and claudin-low boundary errors map to distinct treatment decisions; and subtype accuracy is a weaker portable endpoint than survival discrimination (external C-index 0.577-0.584). Reporting the cohort's allowed label set and applying label-space compliance are therefore prerequisites before any cross-cohort performance claim.

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Topics: Artificial intelligence, Computer science, McNemar's test, Classifier (UML), Concordance, Subtyping

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