A spatial neighborhood methodology for computing and analyzing lymph node carcinoma similarity in precision medicine

Luciani, Timothy; Wentzel, Andrew; Elgohari, Baher; Elhalawani, Hesham; Mohamed, A.S.R.; Canahuate, Guadalupe; Vock, David M.; Fuller, Clifton D.; Marai, G. Elisabeta

doi:10.1016/j.yjbinx.2020.100067

Cited by 14 publications

(11 citation statements)

References 32 publications

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“…Nevertheless, as can be seen in the model evaluation, the addition of the RFS clusters to other predictive clinical covariates including N-staging, HPV status, and Therapeutic combination improves model performance for both training and testing. Prior work has also effectively leveraged clustering to improve outcome prediction for OPC patients [39][40][41]49], however, none of these works have attempted to use the entire set of radiomic features or Random Survival Forest learning as we have done in this work.…”

Section: Discussionmentioning

confidence: 99%

Oropharyngeal Cancer Patient Stratification Using Random Forest Based-learning Over High-dimensional Radiomic Features

Patel

Vock

Marai

et al. 2021

Preprint

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OBJECTIVE: To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans.MATERIALS AND METHODS: OPC Patients were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence matrix obtained from a random survival forest (RSF) trained over 301 radiomic features. The cluster label was included together with other clinical data to train an ensemble model using five predictive models (Cox, random forest, RSF, logistic regression, and logistic-elastic net). Ensemble performance was evaluated over an independent test set for both recurrence free survival (RFS) and overall survival (OS). RESULTS: The Kaplan-Meier curves for OS stratified by cluster label show significant differences for both training (p-val<0.0001) and testing (p-val=0.005). Inclusion of the cluster label outperforms clinical data only improving AUC from .60 to .76 and from .63 to .75 for OS and RFS, respectively. CONCLUSION: The extraction of a single feature, namely a cluster label, to represent the high-dimensional radiomic feature space reduces the dimensionality and sparsity of the data. Moreover, inclusion of the cluster label improves model performance compared to clinical data only and compares to the raw radiomic features performance.

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Section: Discussionmentioning

confidence: 99%

Oropharyngeal Cancer Patient Stratification Using Random Forest Based-learning Over High-dimensional Radiomic Features

Patel

Vock

Marai

et al. 2021

Preprint

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“…Head and neck cancer, which includes cancers of the larynx, throat, lips, mouth, nose, and salivary glands, is now an epidemic with 65,000 new cases in the US annually [1], whose treatment is, as in many other types of cancers, a dynamic and complex process. This therapy process involves making multiple, patient-specific treatment decisions, to maximize efficacy---e.g., reduction in tumor size, time of local region control, and survival time, while minimizing side effects [2,3,4]. For example, a specific patient may undergo radiotherapy alone (RT), radiotherapy with concurrent chemotherapy (CC), or induction chemotherapy (IC) [5].…”

Section: Introductionmentioning

confidence: 99%

Optimal policy determination in sequential systemic and locoregional therapy of oropharyngeal squamous carcinomas: A patient-physician digital twin dyad with deep Q-learning for treatment selection

Tardini

Zhang

Canahuate

et al. 2021

Preprint

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Purpose: Currently, selection of patients for sequential vs. concurrent chemotherapy/radiation regimens lacks evidentiary support, and it is based on locally-optimal decisions for each step. We aim to optimize the multi-step treatment of head and neck cancer patients and to predict multiple patient survival and toxicity outcomes, and we develop, apply, and evaluate a first application of deep-Q-learning (DQL) and simulation to this problem. Patients and methods: The treatment decision DQL digital twin and the patient's digital twin were created, trained and evaluated on a dataset of 536 oropharyngeal squamous cell carcinoma (OPC) patients with the goal of, respectively, determining the optimal treatment decisions with respect to survival and toxicity metrics, and predicting the outcomes of the optimal treatment on the patient. The models were trained on a subset of 402 patients (split randomly) and evaluated on a separate set of 134 patients. Training and evaluation of the digital twin dyad was completed in August 2020. The dataset includes 3-step sequential treatment decisions and complete relevant history of the patients cohort treated at MD Anderson Cancer Center between 2005 and 2013, with radiomics analysis performed for the segmented primary tumor volumes. Results: On the validation set, 87.09% mean and 90.85% median accuracy in treatment outcome prediction, matching the clinicians' outcomes and improving (predicted) survival rate by +3.73% (95% CI: [-0.75%, +8.96%]), and dysphagia rate by +0.75% (CI: [-4.48%, +6.72%]) when following DQL treatment decisions. Conclusion: Given the prediction accuracy and predicted improvement on medically relevant outcomes yielded by this approach, this digital twin dyad of the patient-physician dynamic treatment problem has the potential of aiding physicians in determining the optimal course of treatment and in assessing its outcomes.

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“…Current predictive systems use a staging system based on the size and number of nodal tumors, but miss more nuanced predictions about how the different patterns of nodal spread may affect toxicity outcomes [14,35]. No prior machine learning methods correctly handle this type of spatial data, due to a lack of spatial similarity measures [12,18].…”

Section: Introductionmentioning

confidence: 99%

Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology

Wentzel

Canahuate

Dijk³

et al. 2020

2020 IEEE Visualization Conference (VIS)

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Advances in data collection in radiation therapy have led to an abundance of opportunities for applying data mining and machine learning techniques to promote new data-driven insights. In light of these advances, supporting collaboration between machine learning experts and clinicians is important for facilitating better development and adoption of these models. Although many medical use-cases rely on spatial data, where understanding and visualizing the underlying structure of the data is important, little is known about the interpretability of spatial clustering results by clinical audiences. In this work, we reflect on the design of visualizations for explaining novel approaches to clustering complex anatomical data from head and neck cancer patients. These visualizations were developed, through participatory design, for clinical audiences during a multiyear collaboration with radiation oncologists and statisticians. We distill this collaboration into a set of lessons learned for creating visual and explainable spatial clustering for clinical users.

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A spatial neighborhood methodology for computing and analyzing lymph node carcinoma similarity in precision medicine

Cited by 14 publications

References 32 publications

Oropharyngeal Cancer Patient Stratification Using Random Forest Based-learning Over High-dimensional Radiomic Features

Oropharyngeal Cancer Patient Stratification Using Random Forest Based-learning Over High-dimensional Radiomic Features

Optimal policy determination in sequential systemic and locoregional therapy of oropharyngeal squamous carcinomas: A patient-physician digital twin dyad with deep Q-learning for treatment selection

Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology

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