BMC cancer · 2026
Machine learning-powered audio-omics processing method as an auxiliary diagnostic approach for advanced nasopharyngeal carcinoma.
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You Ting, Huan Fangna, Luo Zhenzhen, Ma Yifan, Liu Zhihua, Wu Songjiang, Zhang Dan, Gong Liuyun
Аннотация
PURPOSE: Nasopharyngeal carcinoma (NPC) is located in the nasopharyngeal mucosa and is a malignant tumour of the head and neck, and approximately 70% of patients have intermediate to advanced disease at the time of initial diagnosis. Epstein-Barr virus (EBV) is closely correlated with etiology and pathogenesis of NPC, serological detection of EBV antibodies is a common screening method for NPC. However, only approximately 60% of NPC cases are associated with EBV infection. Herein, this work aimed to develop and internally evaluate a machine learning-based acoustic signal processing model as a preliminary non-invasive auxiliary diagnostic approach for advanced NPC.
MATERIALS AND METHODS: First, we collected the audio files from 359 advanced NPC patients and 304 healthy controls in our hospital from 2022 to 2025. The machine learning-powered Nasopharyngeal Carcinoma Screening (ML-NPCS) system for screening NPC. And the ML-NPCS system is composed of three steps: speech acquisition, acoustic features extraction, and classification decision-making.
RESULTS: In the independent test set, ML-NPCS achieved an accuracy of 84.2% (95% CI, 77.1%-89.4%), a sensitivity of 88.9% (95% CI, 79.6%-94.3%), and a specificity of 78.7% (95% CI, 66.9%-87.1%); the independent test set comprised 133 participants (72 patients with advanced NPC and 61 healthy controls).
CONCLUSION: The ML-NPCS model demonstrated preliminary potential for distinguishing advanced NPC patients from healthy controls using voice-derived acoustic features. Further prospective evaluation in early-stage disease, symptomatic controls, and external cohorts is required before population-level screening use can be considered.
MATERIALS AND METHODS: First, we collected the audio files from 359 advanced NPC patients and 304 healthy controls in our hospital from 2022 to 2025. The machine learning-powered Nasopharyngeal Carcinoma Screening (ML-NPCS) system for screening NPC. And the ML-NPCS system is composed of three steps: speech acquisition, acoustic features extraction, and classification decision-making.
RESULTS: In the independent test set, ML-NPCS achieved an accuracy of 84.2% (95% CI, 77.1%-89.4%), a sensitivity of 88.9% (95% CI, 79.6%-94.3%), and a specificity of 78.7% (95% CI, 66.9%-87.1%); the independent test set comprised 133 participants (72 patients with advanced NPC and 61 healthy controls).
CONCLUSION: The ML-NPCS model demonstrated preliminary potential for distinguishing advanced NPC patients from healthy controls using voice-derived acoustic features. Further prospective evaluation in early-stage disease, symptomatic controls, and external cohorts is required before population-level screening use can be considered.