Fungal species identification in FFPE tissues: A comparative evaluation of droplet digital PCR, ITS sequencing, and...

Accurate histological diagnosis of fungal infections is challenging due to morphological similarities among fungi, which can affect treatment outcomes. This study evaluated the performance of droplet digital PCR (ddPCR), internal transcribed spacer (ITS) sequencing, and metagenomic next-generation sequencing (mNGS) using 111 formalin-fixed, paraffin-embedded tissue samples with histologically confirmed fungal infections. All three methods showed comparable detection rates for filamentous fungi (84.2%-94.7%; P = .2). For yeast-like fungi, however, mNGS demonstrated significantly higher detection (66.7%) than ddPCR (46.3%) and ITS sequencing (35.2%) (P < .01). mNGS also achieved superior genus- and species-level identification (81.1% for both) compared to ddPCR (65.8% and 64.9%) and ITS sequencing (61.3% and 50.5%) (P < .01). Additionally, mNGS identified two unusual fungi (Scedosporium apiospermum and Schizophyllum commune) previously misdiagnosed as Aspergillus. These findings support the integration of mNGS into clinical diagnostic workflows for the accurate identification of yeast-like and rare fungal pathogens, thereby enabling targeted antifungal therapy.