A subregion-based prediction model for local–regional recurrence risk in head and neck squamous cell carcinoma

Pan, Zhengqiang; Men, Kuo; Liang, Bin; Song, Zhiyue; Wu, Runye; Dai, Jianrong

doi:10.1016/j.radonc.2023.109684

Cited by 10 publications

(3 citation statements)

References 48 publications

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“…Several studies have attempted to construct radiomics models to predict survival of patients with HNSCC. Pan et al 57 constructed a prognostic radiomics model for patients with HNSCC, Kaplan-Meier curves showed patients in radiomic high-risk group suffered significantly worse OS than those with in radiomic low-risk group. Our radiomics model consistently has discriminated performance in predicting OS in patients with HNSCC.…”

Section: G U R Ementioning

confidence: 99%

A radiomics model to predict γδ T‐cell abundance and overall survival in head and neck squamous cell carcinoma

Fan,

Sun

et al. 2024

The FASEB Journal

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Abstractγδ T cells are becoming increasingly popular because of their attractive potential for antitumor immunotherapy. However, the role and assessment of γδ T cells in head and neck squamous cell carcinoma (HNSCC) are not well understood. We aimed to explore the prognostic value of γδ T cell and predict its abundance using a radiomics model. Computer tomography images with corresponding gene expression data and clinicopathological data were obtained from online databases. After outlining the volumes of interest manually, the radiomic features were screened using maximum melevance minimum redundancy and recursive feature elimination algorithms. A radiomics model was developed to predict γδ T‐cell abundance using gradient boosting machine. Kaplan–Meier survival curves and univariate and multivariate Cox regression analyses were used for the survival analysis. In this study, we confirmed that γδ T‐cell abundance was an independent predictor of favorable overall survival (OS) in patients with HNSCC. Moreover, a radiomics model was built to predict the γδ T‐cell abundance level (the areas under the operating characteristic curves of 0.847 and 0.798 in the training and validation sets, respectively). The calibration and decision curves analysis demonstrated the fitness of the model. The high radiomic score was an independent protective factor for OS. Our results indicated that γδ T‐cell abundance was a promising prognostic predictor in HNSCC, and the radiomics model could discriminate its abundance levels and predict OS. The noninvasive radiomics model provided a potentially powerful prediction tool to aid clinical judgment and antitumor immunotherapy.

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Section: G U R Ementioning

confidence: 99%

A radiomics model to predict γδ T‐cell abundance and overall survival in head and neck squamous cell carcinoma

Fan,

Sun

et al. 2024

The FASEB Journal

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“…AI has also enabled the analysis of tumour sub-regions in a variety of clinical tasks, using several imaging modalities such as CT and MRI [54]. However, these analyses have been limited to only a few types of tumours, particularly brain tumours [55], head and neck tumours [56], and breast cancers [57]. To date, no study has attempted to analyse the effect of intra-tumoural heterogeneity on the diagnosis, treatment, and prognosis of renal masses and specifically ccRCCs.…”

Section: Introductionmentioning

confidence: 99%

Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction based on Intra-Tumoural Subregion Heterogeneity

Alhussaini,

Steele,

Jawli

et al. 2024

Preprint

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Background: Renal cancers are among the top ten causes of cancer specific mortality; of which the ccRCC subtype is responsible for most of the cases. Grading of ccRCC is important in determining the tumour aggressiveness and clinical management. Objectives: To predict the WHO/ISUP grade of ccRCC pre-operatively and characterise the heterogeneity of tumour subregions using radiomics and ML models including comparison with pre-operative biopsy determined grading in a subgroup. Methods: Data was obtained from multiple institutions across two countries from 391 patients with pathologically proven ccRCC. For analysis, the data were separated into 4 cohorts. Cohort 1 and 2 were data from the respective institutions from the two countries, cohort 3 was the combined data and cohort 4 data was a subset of cohort 1 where both biopsy and subsequent histology from resection (partial or total nephrectomy) was available. 3D image segmentation was done to derive a voxel of interest (VOI) mask. Radiomic features were then extracted from the contrast enhanced images and the data normalised. Correlation coefficients and XGBoost model were used to reduce the dimensionality of the features. Thereafter, 11 ML algorithms were implemented for the purpose of predicting the grade of ccRCC and characterising heterogeneity of subregions in the tumours; Results: For cohort 1, 50% tumor core and 25% tumor periphery exhibited best performance with an average AUC of 77.91% and 78.64% respectively. 50% tumor core had the highest performance in cohort 2 and cohort 3 with an average AUC of 87.64% and 76.91% respectively. Cohort 4 with 25% periphery showed an AUC of 95% and 80% for grade prediction using internal and external validation respectively while biopsy histology had an AUC of 31% for the prediction with final grade of resection histology as a reference standard; Conclusion: Radiomic signatures combined with ML have the potential to predict the WHO/ISUP grade of ccRCC with superior performance compared to pre-operative biopsy. Moreover, tumour subregions contain useful information that should be analysed independently when determining tumour grade. It is therefore possible to distinguish the grade of ccRCC pre-operatively to improve patient care and management.

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“…AI has also enabled the analysis of tumour sub-regions in a variety of clinical tasks, using several imaging modalities such as CT and MRI [25]. However, these analyses have been limited to only a few types of tumours, particularly brain tumours [26], head and neck tumours [27], and breast cancers [28]. To date, no study has attempted to analyse the effect of sub-region intra-tumoural heterogeneity on the diagnosis, treatment, and prognosis of renal masses and specifically ccRCCs.…”

Section: Introductionmentioning

confidence: 99%

Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction Based on Intra-Tumoural Sub-Region Heterogeneity

Alhussaini,

Steele,

Jawli

et al. 2024

Cancers

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Background: Renal cancers are among the top ten causes of cancer-specific mortality, of which the ccRCC subtype is responsible for most cases. The grading of ccRCC is important in determining tumour aggressiveness and clinical management. Objectives: The objectives of this research were to predict the WHO/ISUP grade of ccRCC pre-operatively and characterise the heterogeneity of tumour sub-regions using radiomics and ML models, including comparison with pre-operative biopsy-determined grading in a sub-group. Methods: Data were obtained from multiple institutions across two countries, including 391 patients with pathologically proven ccRCC. For analysis, the data were separated into four cohorts. Cohorts 1 and 2 included data from the respective institutions from the two countries, cohort 3 was the combined data from both cohort 1 and 2, and cohort 4 was a subset of cohort 1, for which both the biopsy and subsequent histology from resection (partial or total nephrectomy) were available. 3D image segmentation was carried out to derive a voxel of interest (VOI) mask. Radiomics features were then extracted from the contrast-enhanced images, and the data were normalised. The Pearson correlation coefficient and the XGBoost model were used to reduce the dimensionality of the features. Thereafter, 11 ML algorithms were implemented for the purpose of predicting the ccRCC grade and characterising the heterogeneity of sub-regions in the tumours. Results: For cohort 1, the 50% tumour core and 25% tumour periphery exhibited the best performance, with an average AUC of 77.9% and 78.6%, respectively. The 50% tumour core presented the highest performance in cohorts 2 and 3, with average AUC values of 87.6% and 76.9%, respectively. With the 25% periphery, cohort 4 showed AUC values of 95.0% and 80.0% for grade prediction when using internal and external validation, respectively, while biopsy histology had an AUC of 31.0% for the classification with the final grade of resection histology as a reference standard. The CatBoost classifier was the best for each of the four cohorts with an average AUC of 80.0%, 86.5%, 77.0% and 90.3% for cohorts 1, 2, 3 and 4 respectively. Conclusions: Radiomics signatures combined with ML have the potential to predict the WHO/ISUP grade of ccRCC with superior performance, when compared to pre-operative biopsy. Moreover, tumour sub-regions contain useful information that should be analysed independently when determining the tumour grade. Therefore, it is possible to distinguish the grade of ccRCC pre-operatively to improve patient care and management.

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A subregion-based prediction model for local–regional recurrence risk in head and neck squamous cell carcinoma

Cited by 10 publications

References 48 publications

A radiomics model to predict γδ T‐cell abundance and overall survival in head and neck squamous cell carcinoma

A radiomics model to predict γδ T‐cell abundance and overall survival in head and neck squamous cell carcinoma

Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction based on Intra-Tumoural Subregion Heterogeneity

Radiomics Machine Learning Analysis of Clear Cell Renal Cell Carcinoma for Tumour Grade Prediction Based on Intra-Tumoural Sub-Region Heterogeneity

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