Ensemble learning approach for prediction of early complications after radiotherapy for head and neck cancer using CT and MRI radiomic features Article Swipe
Benyamin Khajetash
,
Seied Rabi Mahdavi
,
Alireza Nikoofar
,
Leigh Johnson
,
Meysam Tavakoli
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-93676-0
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-93676-0
There are different side effects in radiotherapy of head and neck cancer (HNC) including xerostomia. The present study utilizes the addition of [Formula: see text] and [Formula: see text]-weighted Magnetic Resonance (MR) radiomic image features to typical Computed Tomography (CT)-based features and radiation dose-based characteristics and incorporates the evaluation and validation of individual and ensemble classifiers for prediction of early-onset xerostomia in radiotherapy of HNC. A total of 80 patients diagnosed with HNC were evaluated prospectively. The dataset was divided into two subsets: 70% for training and validation and 30% for testing. Stratified random sampling was used to ensure that the proportion of xerostomia cases was consistent across both subsets. This approach preserved the class balance and ensured a representative distribution of demographic, dosimetric, and radiomic features in both sets. MR and CT imaging, dosimetric, and demographic features of patients were used as model input data. Bilateral parotid radiomic features were extracted from CT, [Formula: see text], and [Formula: see text] weighted MR images. Pearson statistical tests were used for selection of features and Random Tree (RT), Neural Network (NN), Linear Support Vector Machine (LSVM) and Bayesian Network (BN) classifiers were evaluated. To prevent overfitting and data leakage, preprocessing was conducted. All features were normalized using the z-score technique, with the mean and standard deviation calculated from the training set and then applied to the test set. For the training dataset, the Synthetic Minority Oversampling Technique (SMOTE) was used to balance the minority class. The results suggest the extracted features from [Formula: see text] weighted images have superior prediction ability compared to [Formula: see text] weighted acquisitions. The RT and BN models based on [Formula: see text] weighted images show better performance than those obtained with [Formula: see text] weighted images. [Formula: see text] weighted image-based analysis shows area under the curve (AUC) values for The RT and BN models of 0.90 and 0.84, respectively, while corresponding values obtained from [Formula: see text] weighted images are 0.79 and 0.78 for RT and BN models respectively. Combined [Formula: see text] weighted image-based models RT-BN, RT-LSVM-BN and RT-NN-LSVM-BN also show good performance having AUC values 0.97, 0.92, and 0.90, respectively. These results show that radiomic features from MR images obtained before radiotherapy can be used in addition to other metrics as personalized and unique biomarkers for prediction of early-onset xerostomia. Ensemble classifiers are more efficient than individual classifiers in prediction of early xerostomia.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-93676-0
- https://www.nature.com/articles/s41598-025-93676-0.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409778339
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409778339Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-025-93676-0Digital Object Identifier
- Title
-
Ensemble learning approach for prediction of early complications after radiotherapy for head and neck cancer using CT and MRI radiomic featuresWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-24Full publication date if available
- Authors
-
Benyamin Khajetash, Seied Rabi Mahdavi, Alireza Nikoofar, Leigh Johnson, Meysam TavakoliList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-93676-0Publisher landing page
- PDF URL
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https://www.nature.com/articles/s41598-025-93676-0.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-025-93676-0.pdfDirect OA link when available
- Concepts
-
Radiation therapy, Head and neck cancer, Head and neck, Radiology, Computer science, Ensemble learning, Radiomics, Medicine, Medical physics, Artificial intelligence, SurgeryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.including | 13 |
| abstract_inverted_index.preserved | 112 |
| abstract_inverted_index.radiation | 42 |
| abstract_inverted_index.selection | 170 |
| abstract_inverted_index.(CT)-based | 39 |
| abstract_inverted_index.RT-LSVM-BN | 343 |
| abstract_inverted_index.Stratified | 92 |
| abstract_inverted_index.Tomography | 38 |
| abstract_inverted_index.biomarkers | 382 |
| abstract_inverted_index.calculated | 215 |
| abstract_inverted_index.conducted. | 200 |
| abstract_inverted_index.consistent | 106 |
| abstract_inverted_index.dose-based | 43 |
| abstract_inverted_index.evaluated. | 191 |
| abstract_inverted_index.evaluation | 48 |
| abstract_inverted_index.individual | 52, 394 |
| abstract_inverted_index.normalized | 204 |
| abstract_inverted_index.prediction | 57, 258, 384, 397 |
| abstract_inverted_index.proportion | 101 |
| abstract_inverted_index.technique, | 208 |
| abstract_inverted_index.validation | 50, 87 |
| abstract_inverted_index.xerostomia | 60, 103 |
| abstract_inverted_index.classifiers | 55, 189, 389, 395 |
| abstract_inverted_index.demographic | 136 |
| abstract_inverted_index.dosimetric, | 123, 134 |
| abstract_inverted_index.early-onset | 59, 386 |
| abstract_inverted_index.image-based | 295, 340 |
| abstract_inverted_index.overfitting | 194 |
| abstract_inverted_index.performance | 281, 349 |
| abstract_inverted_index.statistical | 165 |
| abstract_inverted_index.xerostomia. | 14, 387, 400 |
| abstract_inverted_index.Oversampling | 234 |
| abstract_inverted_index.demographic, | 122 |
| abstract_inverted_index.distribution | 120 |
| abstract_inverted_index.incorporates | 46 |
| abstract_inverted_index.personalized | 379 |
| abstract_inverted_index.radiotherapy | 6, 62, 369 |
| abstract_inverted_index.RT-NN-LSVM-BN | 345 |
| abstract_inverted_index.acquisitions. | 266 |
| abstract_inverted_index.corresponding | 316 |
| abstract_inverted_index.preprocessing | 198 |
| abstract_inverted_index.respectively, | 314 |
| abstract_inverted_index.respectively. | 334, 357 |
| abstract_inverted_index.prospectively. | 75 |
| abstract_inverted_index.representative | 119 |
| abstract_inverted_index.text]-weighted | 28 |
| abstract_inverted_index.characteristics | 44 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.99071629 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |