Serum Raman spectroscopy combined with multiple algorithms for diagnosing thyroid dysfunction and chronic renal failure

Wang, Hang; Chen, Cheng; Tong, Dongni; Chen, Chen; Gao, Rui; Han, Huijie; Lv, Xiaoyi

doi:10.1016/j.pdpdt.2021.102241

Cited by 11 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…21 Additionally, malignant thyroid nodules may display extra spectral features due to increased blood vessel formation, characterized by distinct NIR absorption characteristics of hemoglobin. 22 On the other hand, benign nodules, especially colloid goiter, exhibit a unique NIR spectrum owing to their thyroglobulin-rich gelatinous colloid material within thyroid follicles. 23 Spectral features of the thyroid nodule sample in the present study are shown in Figure 2.…”

Section: Resultsmentioning

confidence: 99%

Near-Infrared Spectroscopy for Distinguishing Malignancy in Thyroid Nodules

Zufry,

Munawar

2024

Appl Spectrosc

View full text Add to dashboard Cite

Thyroid nodules are common clinical entities, with a significant proportion being malignant. Early, accurate, and non-invasive tools to differentiate benign and malignant nodules can optimize patient management and reduce unnecessary surgery. This study aimed to evaluate the efficacy and accuracy of near-infrared spectroscopy (NIRS) in distinguishing benign from malignant thyroid nodules. A diffuse reflectance spectrum for a total of 20 thyroid nodule samples (10 samples as colloid goiter and 10 samples as thyroid cancer), were acquired in the wavelength range from 1000 to 2500 nm. Spectral data from NIRS were analyzed by means of principal component analysis (PCA), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) to classify and differentiate thyroid nodule samples. The present study found that NIRS effectively distinguished colloid goiter and thyroid cancer using the first two principal components (PCs), explaining 90% and 10% of the variance, respectively. QDA discrimination plot displayed a clear separation between colloid goiter and thyroid cancer with minimal overlap, aligning with reported 95% accuracy. Additionally, applying LDA to seven PCs from PCA achieved a 100% accuracy rate in classifying colloid goiter and thyroid cancer from near-infrared spectral data. In conclusion, NIRS offers a promising, non-invasive complementing diagnostic tool for differentiating benign from malignant thyroid nodules with high accuracy. Future work should integrate these results into predictive model development, emphasizing external validation, alternative performance metrics, and protecting against potential overfitting translation of a machine learning model to a clinical setting.

show abstract

Section: Resultsmentioning

confidence: 99%

Near-Infrared Spectroscopy for Distinguishing Malignancy in Thyroid Nodules

Zufry,

Munawar

2024

Appl Spectrosc

View full text Add to dashboard Cite

show abstract

“…Membranous nephropathy, a relatively common glomerular disease, is a major cause of the high prevalence of clinical nephrotic syndrome 1 . Approximately 35–47% of patients with persistent nephrotic syndrome develop renal failure and uremia 2 – 4 . Xinjiang is a region with a high prevalence of primary glomerular diseases, among which the number of patients with membranous nephropathy (MN) has been increasing in recent years and is gradually becoming younger 5 .…”

Section: Introductionmentioning

confidence: 99%

Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods

Zhang

Xue

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Membranous nephropathy is the main cause of nephrotic syndrome, which has an insidious onset and may progress to end-stage renal disease with a high mortality rate, such as renal failure and uremia. At present, the diagnosis of membranous nephropathy mainly relies on the clinical manifestations of patients and pathological examination of kidney biopsy, which are expensive, time-consuming, and have certain chance and other disadvantages. Therefore, there is an urgent need to find a rapid, accurate and non-invasive diagnostic technique for the diagnosis of membranous nephropathy. In this study, Raman spectra of serum and urine were combined with deep learning methods to diagnose membranous nephropathy. After baseline correction and smoothing of the data, Gaussian white noise of different decibels was added to the training set for data amplification, and the amplified data were imported into ResNet, AlexNet and GoogleNet models to obtain the evaluation results of the models for membranous nephropathy. The experimental results showed that the three deep learning models achieved an accuracy of 1 for the classification of serum data of patients with membranous nephropathy and control group, and the discrimination of urine data was above 0.85, among which AlexNet was the best classification model for both samples. The above experimental results illustrate the great potential of serum- and urine-based Raman spectroscopy combined with deep learning methods for rapid and accurate identification of patients with membranous nephropathy.

show abstract