International journal of laboratory hematology · 2026
Artificial Intelligence Based High Resolution Melting Analysis for Comprehensive Calreticulin Mutation Screening and Novel Variant Detection in Myeloproliferative Neoplasms.
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Zhang Qingyun, Ma Xiaotong, Wu Quanli, Duan Yusi, Yang Can, Tang Xuemei, Xu Chun, Guan Ming, Wu Zhiyuan
Аннотация
INTRODUCTION: Current CALR mutation screening in myeloproliferative neoplasms (MPNs) faces a trade-off between coverage and cost: allele-specific qPCR and fragment analysis are inexpensive but cover only part of the variant landscape, while sequencing costs $300-500 per sample with 3-5 day turnaround. High-resolution melting (HRM) offers both but suffers from subjective interpretation. We developed HRM-CALR-AI, an automated CALR screening platform.
METHODS: HRM-CALR-AI was developed on 2667 samples and combines YOLOv8s-cls deep learning with CatBoost meta-classifier for open-set recognition of untrained mutations. Validation included three independent laboratories, 188 consecutive JAK2-negative MPN patients, and a head-to-head comparison with capillary electrophoresis fragment analysis.
RESULTS: HRM-CALR-AI processed 96 samples in 90-min at < $10 per test, with limits of detection of 6.25% (CALR-1) and 12.5% (CALR-2) variant allele frequency. On a 480-sample held-out set, it achieved 93.9% accuracy (95% CI: 91.7-95.8), AUC of 0.996 (95% CI: 0.993-0.998), and 89.1% (57/64) recognition of untrained rare variants. Clinical screening identified 160 wild-type (85.1%), 15 CALR-1 (8.0%), nine CALR-2 (4.8%), and four rare variants (2.1%), including a previously unreported variant (c.1145_1158delinsTCCT) classified as CALR-2-like. Multicenter accuracy was 98.1% (ICC 0.868). Against fragment analysis, HRM-CALR-AI showed equivalent detection of length-altering variants (kappa = 0.933) and additionally covered nonlength-altering variants that are invisible to size-based separation.
CONCLUSIONS: HRM-CALR-AI combines open-set recognition with low-cost HRM to detect rare and unreported variants at ~50-fold lower cost and same-day turnaround. Multicenter reproducibility and benchmarking against fragment analysis support implementation as a frontline screening tool where NGS access is limited.
METHODS: HRM-CALR-AI was developed on 2667 samples and combines YOLOv8s-cls deep learning with CatBoost meta-classifier for open-set recognition of untrained mutations. Validation included three independent laboratories, 188 consecutive JAK2-negative MPN patients, and a head-to-head comparison with capillary electrophoresis fragment analysis.
RESULTS: HRM-CALR-AI processed 96 samples in 90-min at < $10 per test, with limits of detection of 6.25% (CALR-1) and 12.5% (CALR-2) variant allele frequency. On a 480-sample held-out set, it achieved 93.9% accuracy (95% CI: 91.7-95.8), AUC of 0.996 (95% CI: 0.993-0.998), and 89.1% (57/64) recognition of untrained rare variants. Clinical screening identified 160 wild-type (85.1%), 15 CALR-1 (8.0%), nine CALR-2 (4.8%), and four rare variants (2.1%), including a previously unreported variant (c.1145_1158delinsTCCT) classified as CALR-2-like. Multicenter accuracy was 98.1% (ICC 0.868). Against fragment analysis, HRM-CALR-AI showed equivalent detection of length-altering variants (kappa = 0.933) and additionally covered nonlength-altering variants that are invisible to size-based separation.
CONCLUSIONS: HRM-CALR-AI combines open-set recognition with low-cost HRM to detect rare and unreported variants at ~50-fold lower cost and same-day turnaround. Multicenter reproducibility and benchmarking against fragment analysis support implementation as a frontline screening tool where NGS access is limited.