Background: Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve out-comes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard. Patient and methods: Data of 666 retrospective-and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivar-iable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radi-omics features. Patient risk stratification in three groups was assessed through Kaplan–Meier (KM) curves. A log-rank test was performed for significance (p-value < 0.05). The prognostic accuracy was reported through the concordance index (CI). Results: A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups (p < 0∙01, CI of 0.79 as validation). Conclusion: A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8.

A prospectively validated prognostic model for patients with locally advanced squamous cell carcinoma of the head and neck based on radiomics of computed tomography images / Keek, S. A.; Wesseling, F. W. R.; Woodruff, H. C.; van Timmeren, J. E.; Nauta, I. H.; Hoffmann, T. K.; Cavalieri, S.; Calareso, G.; Primakov, S.; Leijenaar, R. T. H.; Licitra, L.; Ravanelli, M.; Scheckenbach, K.; Poli, T.; Lanfranco, D.; Vergeer, M. R.; Leemans, C. R.; Brakenhoff, R. H.; Hoebers, F. J. P.; Lambin, P.. - In: CANCERS. - ISSN 2072-6694. - 13:13(2021), p. 3271.3271. [10.3390/cancers13133271]

A prospectively validated prognostic model for patients with locally advanced squamous cell carcinoma of the head and neck based on radiomics of computed tomography images

Poli T.
Investigation
;
Lanfranco D.
Investigation
;
2021-01-01

Abstract

Background: Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve out-comes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard. Patient and methods: Data of 666 retrospective-and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivar-iable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radi-omics features. Patient risk stratification in three groups was assessed through Kaplan–Meier (KM) curves. A log-rank test was performed for significance (p-value < 0.05). The prognostic accuracy was reported through the concordance index (CI). Results: A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups (p < 0∙01, CI of 0.79 as validation). Conclusion: A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8.
2021
A prospectively validated prognostic model for patients with locally advanced squamous cell carcinoma of the head and neck based on radiomics of computed tomography images / Keek, S. A.; Wesseling, F. W. R.; Woodruff, H. C.; van Timmeren, J. E.; Nauta, I. H.; Hoffmann, T. K.; Cavalieri, S.; Calareso, G.; Primakov, S.; Leijenaar, R. T. H.; Licitra, L.; Ravanelli, M.; Scheckenbach, K.; Poli, T.; Lanfranco, D.; Vergeer, M. R.; Leemans, C. R.; Brakenhoff, R. H.; Hoebers, F. J. P.; Lambin, P.. - In: CANCERS. - ISSN 2072-6694. - 13:13(2021), p. 3271.3271. [10.3390/cancers13133271]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2897446
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