Dataset Information

The efficacy of ¹⁸F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

ABSTRACT:

Objective

To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (¹⁸F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumors (TETs).

Methods

This retrospective study included 79 TET [27 low-risk thymomas (types A, AB and B1), 31 high-risk thymomas (types B2 and B3) and 21 thymic carcinomas] patients who underwent pre-therapeutic ¹⁸F-FDG-PET/CT. High-risk TETs (high-risk thymomas and thymic carcinomas) were 52 patients. The 107 PET-based radiomic features, including SUV-related parameters [maximum SUV (SUV_max), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)] and 1024 deep-learning features extracted from the convolutional neural network were used to predict the pathological risk subtypes of TETs using six different machine-learning algorithms. The area under the curves (AUCs) were calculated to compare the predictive performances.

Results

SUV-related parameters yielded the following AUCs for predicting thymic carcinomas: SUVmax 0.713, MTV 0.442, and TLG 0.479 or high-risk TETs: SUVmax 0.673, MTV 0.533, and TLG 0.539. The best-performing algorithm was the logistic regression model for predicting thymic carcinomas (AUC 0.900, accuracy 81.0%), and the random forest (RF) model for high-risk TETs (AUC 0.744, accuracy 72.2%). The AUC was significantly higher in the logistic regression model than three SUV-related parameters for predicting thymic carcinomas, and in the RF model than MTV and TLG for predicting high-risk TETs (each; p < 0.05).

Conclusion

¹⁸F-FDG-PET-based radiomic analysis using a machine-learning approach may be useful for predicting the pathological risk subtypes of TETs.

Advances in knowledge

Machine-learning approach using ¹⁸F-FDG-PET-based radiomic features has the potential to predict the pathological risk subtypes of TETs.

SUBMITTER: Nakajo M

PROVIDER: S-EPMC10996420 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

The efficacy of <sup>18</sup>F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

Nakajo Masatoyo M Takeda Aya A Katsuki Akie A Jinguji Megumi M Ohmura Kazuyuki K Tani Atsushi A Sato Masami M Yoshiura Takashi T

The British journal of radiology 20220328 1134

<h4>Objective</h4>To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (<sup>18</sup>F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumors (TETs).<h4>Methods</h4>This retrospective study included 79 TET [27 low-risk thymomas (types A, AB and B1), 31 high-risk thymomas (types B2 and B3) and 21 thymic carcinomas] patients who underwent pre-therapeutic <sup>18</sup>F- ...[more]

PMID: 35312337

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Dataset Information

The efficacy of ¹⁸F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

Objective

Methods

Results

Conclusion

Advances in knowledge

Publications

The efficacy of <sup>18</sup>F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

The efficacy of 18F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

Objective

Methods

Results

Conclusion

Advances in knowledge

Publications

The efficacy of <sup>18</sup>F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

The efficacy of ¹⁸F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.