Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study

Gu, Wenchao; Chen, Yingli; Zhu, Haibin; Chen, Haidi; Yang, Zongcheng; Mo, Shaocong; Zhao, Hongyue; Chen, Lei; Nakajima, Takahito; Yu, XianJun; Ji, Shunrong; Gu, YaJia; Chen, Jie; Tang, Wei

doi:10.1016/j.eclinm.2023.102269

Cited by 8 publications

(2 citation statements)

References 48 publications

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“…In particular, a number of studies about ML techniques for imaging data analysis have achieved gratifying results. Quantitative, high-throughput data can be extracted, processed, and analyzed using machine learning techniques to discover associations with meaningful and hidden information that is inaccessible when using traditional approaches ( 18 ). Radiomics represents a burgeoning technique for image analysis that leverages algorithms or statistical tools to discern unique phenotypic variations in diseases from diagnostic imaging data.…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls

Liao,

Wu,

Jiang

et al. 2024

Front. Neurol.

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BackgroundThis study aimed to investigate the clinical application of 18F-FDG PET radiomics features for temporal lobe epilepsy and to create PET radiomics-based machine learning models for differentiating temporal lobe epilepsy (TLE) patients from healthy controls.MethodsA total of 347 subjects who underwent 18F-FDG PET scans from March 2014 to January 2020 (234 TLE patients: 25.50 ± 8.89 years, 141 male patients and 93 female patients; and 113 controls: 27.59 ± 6.94 years, 48 male individuals and 65 female individuals) were allocated to the training (n = 248) and test (n = 99) sets. All 3D PET images were registered to the Montreal Neurological Institute template. PyRadiomics was used to extract radiomics features from the temporal regions segmented according to the Automated Anatomical Labeling (AAL) atlas. The least absolute shrinkage and selection operator (LASSO) and Boruta algorithms were applied to select the radiomics features significantly associated with TLE. Eleven machine-learning algorithms were used to establish models and to select the best model in the training set.ResultsThe final radiomics features (n = 7) used for model training were selected through the combinations of the LASSO and the Boruta algorithms with cross-validation. All data were randomly divided into a training set (n = 248) and a testing set (n = 99). Among 11 machine-learning algorithms, the logistic regression (AUC 0.984, F1-Score 0.959) model performed the best in the training set. Then, we deployed the corresponding online website version (https://wane199.shinyapps.io/TLE_Classification/), showing the details of the LR model for convenience. The AUCs of the tuned logistic regression model in the training and test sets were 0.981 and 0.957, respectively. Furthermore, the calibration curves demonstrated satisfactory alignment (visually assessed) for identifying the TLE patients.ConclusionThe radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool.

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Section: Introductionmentioning

confidence: 99%

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls

Liao,

Wu,

Jiang

et al. 2024

Front. Neurol.

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“…2 Apart from numerous applications in image classi cation, automatic speech recognition, and translation, machine learning has increasingly been an emerging trend in medicine. 3,4 A variety of medical ML applications includes disease prediction, diagnosis, and prognosis assessment, drug development, health management, analysis of clinical and imaging data, etc. In particular, several studies about machine learning techniques for imaging data analysis have achieved gratifying results.…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for classification of temporal lobe epilepsy patients from healthy controls

Liao,

Wu,

Jiang

et al. 2024

Preprint

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Background To investigate the clinical application of 18F-FDG PET radiomics features for temporal lobe epilepsy and create a radiomics-based model for differentiating TLE patients from healthy controls. Methods A total of 347 subjects that underwent 18F-FDG PET scans from March 2014 to January 2020 (234 TLE patients and 113 controls) were allocated to training (n = 242) and test (n = 105) sets. All PET images were registered to the Montreal Neurological Institute template. PyRadiomics was used to extract radiomics features from the temporal regions segmented according to the AAL atlas. The least absolute shrinkage and selection operator and Boruta algorithms were applied to select the radiomic features significantly associated with TLE. Eleven machine-learning algorithms were used to establish models. Results The final radiomics features (n = 22)used for model training were selected by the combinations of the least absolute shrinkage and selection operator and the Boruta algorithm with cross-validation. All data were randomly divided into a training set (n = 242) and a testing set (n = 105). Among eleven machine-learning algorithms, the logistic regression (AUC 0.984, F1-Score 0.959) performed the best of the 11 machine-learning models. The AUCs of the tuned logistic regression model in the training and test sets were 98.1 and 95.7. Conclusions The radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool.

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A PET-based radiomics nomogram for individualized predictions of seizure outcomes after temporal lobe epilepsy surgery

Wu,

Liao,

Tan

et al. 2024

Seizure: European Journal of Epilepsy

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Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study

Cited by 8 publications

References 48 publications

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for classification of temporal lobe epilepsy patients from healthy controls

A PET-based radiomics nomogram for individualized predictions of seizure outcomes after temporal lobe epilepsy surgery

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