Preliminary study on phonetic characteristics of patients with pulmonary nodules

Song, Xueyang; Wang, Sijing; Xu, Zhaoxia; Hao, Yiming; Feng, Lu; Ding, Xiaodong; Gao, Hui; Wang, Yiqin

doi:10.1016/j.joim.2020.09.004

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Yan et al [8] applied sample entropy for wavelet packet transform coe cients to quantify the signals from three patterns of tradition Chinese medicine, and achieved higher than 90% recognition accuracy rates with support vector machine. Song et al [9] explored the phonetic characteristics of patients with pulmonary nodules (PNs) and found that there were statistically signi cant differences in pitch, intensity and shimmer in patients with PNs compared with healthy people, and PNs with diameters ≥8 mm had a signi cantly higher third formant. Porter et al [10] developed an automatic cough detector and applied Time Delay Neural Network to identify asthma, pneumonia, and lower respiratory tract disease, croup and bronchiolitis in children.…”

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Development and validation of a nomogram for predicting lung cancer based on acoustic-clinical features

Lu,

Sha,

Zhu

et al. 2024

Preprint

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Objective Lung cancer has the highest incidence of all malignant tumors worldwide, and early diagnosis and treatment are crucial for improving patient survival rates. The aim of this study is to develop a nomogram based on acoustic and clinical features, providing clinical trial evidence for predicting lung cancer. Methods We reviewed the voice data and clinical data from 350 individuals: 189 pathologically confirmed lung cancer patients and 161 non lung cancer patients, which included 77 patients with benign pulmonary lesions and 84 healthy volunteers. First of all, acoustic features were extracted from all subjects, and optimal features were selected by least absolute shrinkage and selection operator (LASSO) regression. Subsequently, combining acoustic features and clinical features to build a nomogram for predicting lung cancer based on multivariate logistic regression model. The performance of the nomogram was evaluated by the area under the receiver operating characteristic curve (AUC) and the calibration curve, the clinical utility was estimated by decision curve analysis (DCA), and validation set was applied to confirm the predictive value of the nomogram. Results The acoustic-clinical nomogram model exhibited good diagnostic performance in the training set, achieving an AUC of 0.774, an accuracy of 0.701, a sensitivity of 0.693, and a specificity of 0.710. In the validation set, the nomogram attained AUC of 0.714, an accuracy of 0.642, a sensitivity of 0.673 and a specificity of 0.611. The DCA curve demonstrated the nomogram had good clinical usefulness. Conclusions The acoustic-clinical nomogram constructed in this study exhibited good discrimination, calibration, and clinical application value, providing a tool to predict lung cancer.

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confidence: 99%