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Head and neck cancer predictive risk estimator to determine control and therapeutic outcomes of radiotherapy (HNC-PREDICTOR): development, international multi-institutional validation, and web implementation of clinic-ready model-based risk stratification for head and neck cancer.
van Dijk, Lisanne V; Mohamed, Abdallah Sr; Ahmed, Sara; Nipu, Nafiul; Marai, G Elisabeta; Wahid, Kareem; Sijtsema, Nanna M; Gunn, Brandon; Garden, Adam S; Moreno, Amy; Hope, Andrew J; Langendijk, Johannes A; Fuller, Clifton D.
Afiliação
  • van Dijk LV; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands. Electronic address: l.v.van.dijk@umcg.nl.
  • Mohamed AS; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Ahmed S; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Nipu N; Department of Computer Science, The University of Illinois Chicago, Chicago, USA.
  • Marai GE; Department of Computer Science, The University of Illinois Chicago, Chicago, USA.
  • Wahid K; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Sijtsema NM; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
  • Gunn B; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Garden AS; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Moreno A; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; MD Anderson Stiefel Center for Oropharyngeal Cancer Research and Education (MDA-SCORE), Houston, TX, USA.
  • Hope AJ; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
  • Langendijk JA; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
  • Fuller CD; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; MD Anderson Stiefel Center for Oropharyngeal Cancer Research and Education (MDA-SCORE), Houston, TX, USA.
Eur J Cancer ; 178: 150-161, 2023 01.
Article em En | MEDLINE | ID: mdl-36442460
ABSTRACT

BACKGROUND:

Personalised radiotherapy can improve treatment outcomes of patients with head and neck cancer (HNC), where currently a 'one-dose-fits-all' approach is the standard. The aim was to establish individualised outcome prediction based on multi-institutional international 'big-data' to facilitate risk-based stratification of patients with HNC.

METHODS:

The data of 4611 HNC radiotherapy patients from three academic cancer centres were split into four cohorts a training (n = 2241), independent test (n = 786), and external validation cohorts 1 (n = 1087) and 2 (n = 497). Tumour- and patient-related clinical variables were considered in a machine learning pipeline to predict overall survival (primary end-point) and local and regional tumour control (secondary end-points); serially, imaging features were considered for optional model improvement. Finally, patients were stratified into high-, intermediate-, and low-risk groups.

RESULTS:

Performance score, AJCC8thstage, pack-years, and Age were identified as predictors for overall survival, demonstrating good performance in both the training cohort (c-index = 0.72 [95% CI, 0.66-0.77]) and in all three validation cohorts (c-indices 0.76 [0.69-0.83], 0.73 [0.68-0.77], and 0.75 [0.68-0.80]). Excellent stratification of patients with HNC into high, intermediate, and low mortality risk was achieved; with 5-year overall survival rates of 17-46% for the high-risk group compared to 92-98% for the low-risk group. The addition of morphological image feature further improved the performance (c-index = 0.73 [0.64-0.81]). These models are integrated in a clinic-ready interactive web interface https//uic-evl.github.io/hnc-predictor/

CONCLUSIONS:

Robust model-based prediction was able to stratify patients with HNC in distinct high, intermediate, and low mortality risk groups. This can effectively be capitalised for personalised radiotherapy, e.g., for tumour radiation dose escalation/de-escalation.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias de Cabeça e Pescoço Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias de Cabeça e Pescoço Idioma: En Ano de publicação: 2023 Tipo de documento: Article