Journal article
Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas
Oral oncology, Vol.144, 106460
09/2023
DOI: 10.1016/j.oraloncology.2023.106460
Abstract
•Retrospective analysis of 695 HPV+ oropharyngeal cancer patients.•Risk prediction using machine learning to predict toxicity and survival.•Pre-treatment anatomical information improves standard (AJCC-8) risk prediction.•Machine-learning derived patient stratifications improve outcome prediction.
Evaluate the effectiveness of machine learning tools that incorporate spatial information such as disease location and lymph node metastatic patterns-of-spread, for prediction of survival and toxicity in HPV+ oropharyngeal cancer (OPC).
675 HPV+ OPC patients that were treated at MD Anderson Cancer Center between 2005 and 2013 with curative intent IMRT were retrospectively collected under IRB approval. Risk stratifications incorporating patient radiometric data and lymph node metastasis patterns via an anatomically-adjacent representation with hierarchical clustering were identified. These clusterings were combined into a 3-level patient stratification and included along with other known clinical features in a Cox model for predicting survival outcomes, and logistic regression for toxicity, using independent subsets for training and validation.
Four groups were identified and combined into a 3-level stratification. The inclusion of patient stratifications in predictive models for 5-yr Overall survival (OS), 5-year recurrence free survival, (RFS) and Radiation-associated dysphagia (RAD) consistently improved model performance measured using the area under the curve (AUC). Test set AUC improvements over models with clinical covariates, was 9 % for predicting OS, and 18 % for predicting RFS, and 7 % for predicting RAD. For models with both clinical and AJCC covariates, AUC improvement was 7 %, 9 %, and 2 % for OS, RFS, and RAD, respectively.
Including data-driven patient stratifications considerably improve prognosis for survival and toxicity outcomes over the performance achieved by clinical staging and clinical covariates alone. These stratifications generalize well to across cohorts, and sufficient information for reproducing these clusters is included.
Details
- Title: Subtitle
- Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas
- Creators
- Guadalupe Canahuate - University of IowaAndrew Wentzel - University of Illinois ChicagoAbdallah S.R. Mohamed - The University of Texas MD Anderson Cancer CenterLisanne V. van Dijk - The University of Texas MD Anderson Cancer CenterDavid M. Vock - University of MinnesotaBaher Elgohari - The University of Texas MD Anderson Cancer CenterHesham Elhalawani - The University of Texas MD Anderson Cancer CenterClifton D. Fuller - The University of Texas MD Anderson Cancer CenterG. Elisabeta Marai - University of Illinois Chicago
- Resource Type
- Journal article
- Publication Details
- Oral oncology, Vol.144, 106460
- DOI
- 10.1016/j.oraloncology.2023.106460
- ISSN
- 1368-8375
- eISSN
- 1879-0593
- Publisher
- Elsevier Ltd
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health; DOI: 10.13039/100000054, name: National Cancer Institute, award: R01CA214825, R01CA225190
- Language
- English
- Date published
- 09/2023
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984438957802771
Metrics
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