Artificial Intelligence Based High Resolution Melting Analysis for Comprehensive Calreticulin Mutation Screening and Novel Variant Detection in Myeloproliferative Neoplasms.

International journal of laboratory hematology Pub Date : 2026-10-01 Epub Date: 2026-05-18 DOI:10.1111/ijlh.70150
Qingyun Zhang, Xiaotong Ma, Quanli Wu, Yusi Duan, Can Yang, Xuemei Tang, Chun Xu, Ming Guan, Zhiyuan Wu
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Abstract

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.

基于人工智能的高分辨率熔融分析在骨髓增殖性肿瘤中钙调钙蛋白综合突变筛选和新变异检测。
目前骨髓增殖性肿瘤(mpn)的CALR突变筛查面临着覆盖率和成本之间的权衡:等位基因特异性qPCR和片段分析价格低廉,但只覆盖了部分变异,而测序成本为每个样品300-500美元,需要3-5天的周转时间。高分辨率熔化(HRM)提供了两者,但受到主观解释的影响。我们开发了自动CALR筛选平台HRM-CALR-AI。方法:在2667个样本上开发HRM-CALR-AI,将YOLOv8s-cls深度学习与CatBoost元分类器相结合,对未训练的突变进行开放集识别。验证包括三个独立实验室,188名连续的jak2阴性MPN患者,并与毛细管电泳片段分析进行头对头比较。结果:HRM- calr - ai在90分钟内处理了96个样本。结论:HRM- calr - ai将开放集识别与低成本人力资源管理相结合,以低50倍的成本和当日周转时间检测罕见和未报告的变异。针对片段分析的多中心可重复性和基准测试支持作为NGS访问受限的一线筛选工具的实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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