Rapid identification of papillary thyroid carcinoma and papillary microcarcinoma based on serum Raman spectroscopy combined with machine learning models

Song, Haitao; Dong, Chao; Zhang, Xudan; Wu, Wei; Chen, Cheng; Ma, Binlin; Chen, Fangfang

doi:10.1016/j.pdpdt.2021.102647

Cited by 15 publications

(10 citation statements)

References 35 publications

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“…Multi-model benchmarks and ensembles have been explored and used extensively across several industries. Such an approach has become commonplace in fields such as engineering, medicine, and agriculture [ 4 – 8 , 10 – 12 , 14 , 15 , 95 ]. Here, the application of a multi-model approach for optimizing spectroscopic prediction models in wildlife science is demonstrated.…”

Section: Discussionmentioning

confidence: 99%

“…Each model’s predictive performance is then compared to identify the candidate algorithm that best predicts the classification or regression task of interest. For example, model efficacy varied considerably when three supervised model procedures were utilized to detect papillary carcinoma from Raman spectra, such that the random forest (81.5%) and AdaBoost (84.6%) algorithms yielded markedly higher classification rates compared to the decision tree classifier (75.4%) [ 8 ]. While such multi-model approaches are routinely conducted to benchmark and evaluate candidate models in fields such as engineering, medicine, and agriculture [ 4 , 5 , 7 , 8 , 10 – 18 ], the majority of spectroscopic-based studies in wildlife sciences (61/65 studies; see Methods ) have applied a single-algorithm approach for model calibration and testing ( Table 1 ).…”

Section: Introductionmentioning

confidence: 99%

“…For example, model efficacy varied considerably when three supervised model procedures were utilized to detect papillary carcinoma from Raman spectra, such that the random forest (81.5%) and AdaBoost (84.6%) algorithms yielded markedly higher classification rates compared to the decision tree classifier (75.4%) [ 8 ]. While such multi-model approaches are routinely conducted to benchmark and evaluate candidate models in fields such as engineering, medicine, and agriculture [ 4 , 5 , 7 , 8 , 10 – 18 ], the majority of spectroscopic-based studies in wildlife sciences (61/65 studies; see Methods ) have applied a single-algorithm approach for model calibration and testing ( Table 1 ). PLS models have been applied to answer a diverse set of questions across many different sampling types, such as species discrimination from dried blood traces between Bengal tigers and Indian leopards, and quantification of dietary components from the feces of black bears [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians

Chen,

Caprio,

Chen

et al. 2024

PLoS Comput Biol

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Near infrared spectroscopy coupled with predictive modeling is a growing field of study for addressing questions in wildlife science aimed at improving management strategies and conservation outcomes for managed and threatened fauna. To date, the majority of spectroscopic studies in wildlife and fisheries applied chemometrics and predictive modeling with a single-algorithm approach. By contrast, multi-model approaches are used routinely for analyzing spectroscopic datasets across many major industries (e.g., medicine, agriculture) to maximize predictive outcomes for real-world applications. In this study, we conducted a benchmark modeling exercise to compare the performance of several machine learning algorithms in a multi-class problem utilizing a multivariate spectroscopic dataset obtained from live animals. Spectra obtained from live individuals representing eleven amphibian species were classified according to taxonomic designation. Seven modeling techniques were applied to generate prediction models, which varied significantly (p < 0.05) with regard to mean classification accuracy (e.g., support vector machine: 95.8 ± 0.8% vs. K-nearest neighbors: 89.3 ± 1.0%). Through the use of a multi-algorithm approach, candidate algorithms can be identified and applied to more effectively model complex spectroscopic data collected for wildlife sciences. Other key considerations in the predictive modeling workflow that serve to optimize spectroscopic model performance (e.g., variable selection and cross-validation procedures) are also discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians

Chen,

Caprio,

Chen

et al. 2024

PLoS Comput Biol

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“…In QSAR, the correlation between the structure and activity of compounds such as pharmaceuticals is determined quantitively as numerical values. These values are handled via supervised learning that calculates feature values using chemical information for compounds and builds prediction models [37]. Molecular descriptors, which are the characteristic quantities that reflect the structure of a compound in QSAR, include fingerprints that determine the presence or absence of partial structures and the measured and estimated values of the physicochemical properties of compounds [38].…”

Section: Deepsnap: DL and Elmentioning

confidence: 99%

Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure–Activity Relationships

Matsuzaka

Uesawa

2023

Molecules

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A deep learning-based quantitative structure–activity relationship analysis, namely the molecular image-based DeepSNAP–deep learning method, can successfully and automatically capture the spatial and temporal features in an image generated from a three-dimensional (3D) structure of a chemical compound. It allows building high-performance prediction models without extracting and selecting features because of its powerful feature discrimination capability. Deep learning (DL) is based on a neural network with multiple intermediate layers that makes it possible to solve highly complex problems and improve the prediction accuracy by increasing the number of hidden layers. However, DL models are too complex when it comes to understanding the derivation of predictions. Instead, molecular descriptor-based machine learning has clear features owing to the selection and analysis of features. However, molecular descriptor-based machine learning has some limitations in terms of prediction performance, calculation cost, feature selection, etc., while the DeepSNAP–deep learning method outperforms molecular descriptor-based machine learning due to the utilization of 3D structure information and the advanced computer processing power of DL.

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“…In addition, since it is difficult to collect medical samples, and deep learning often requires the learning of a large number of sample features to obtain better prediction results, data expansion is carried out. [33][34][35] Therefore, in this experiment, after obtaining the Raman spectral data, we use the SMOTE (Synthetic Minority Oversampling Technique) method to amplify the data from the source domain spectral data and use the amplified data for the training of the model to obtain a more robust model.…”

Section: Data Augmentation and Preprocessingmentioning

confidence: 99%

A new method for Raman spectral analysis: Decision fusion‐based transfer learning model

Chen

Zhu

et al. 2022

J Raman Spectroscopy

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As an emerging technology for artificial intelligence‐aided medical diagnosis, deep learning combined with Raman spectroscopy has great potential. The technology still has some problems in the actual medical diagnosis research process. The differences in spectrometers, experimental conditions, and experimental operations can result in non‐uniform and universally applicable data standards, which in turn lead to low data utilization. At the same time, it is still necessary to retrain the models when building diagnostic models for different diseases, which is time‐consuming and laborious. In this paper, a more complete transfer learning model for multiple types of serum Raman spectra is established for the first time, and a decision fusion strategy is applied to this diagnostic model. The Raman spectral data of serum from hepatitis B patients/control group, serum from abnormal thyroid function patients/control group, and serum from glioma patients/control group were selected as the source domains, and the Raman spectral data of tissue from hepatitis C patients/control group, serum from esophageal cancer patients/control group, and tissue from cervical cancer and cervical inflammation (patients/control) group were selected as the target domains. Three deep neural network models, ResNet, GoogLeNet, and CNN‐LSTM were trained in the source domain data for disease diagnosis, and the trained models were transfer to the target domain. The model is fine‐tuned by freezing different layers and then combined with logistic regression algorithms to construct a decision fusion model, which further improves the model effect. The results show that the proposed method can effectively improve the accuracy of transfer learning models. At the same time, this experiment extends the application of transfer learning in Raman spectroscopy and demonstrates that unrelated and scale‐different Raman datasets are still intrinsically connected, which also lays the foundation for us to build more stable and data‐inclusive spectral transfer learning fusion models in the future.

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Rapid identification of papillary thyroid carcinoma and papillary microcarcinoma based on serum Raman spectroscopy combined with machine learning models

Cited by 15 publications

References 35 publications

Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians

Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians

Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure–Activity Relationships

A new method for Raman spectral analysis: Decision fusion‐based transfer learning model

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