ArticleJournal of biomedical informatics2020
A spatial neighborhood methodology for computing and analyzing lymph node carcinoma similarity in precision medicine.
Article in Journal of biomedical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 18 citations in OpenAlex.
- Neurosurgery Network Pattern Analysis with 2nd Generation Ensembles.Computer graphics forum : journal of the European Association for Computer Graphics · 2026Article
- Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator.... IEEE Workshop on Visual Analytics in Healthcare. IEEE Workshop on Visual Analytics in Healthcare · 2025Article
- A Generative Patient Digital Twin for Sequential Treatment of Oropharyngeal Squamous Carcinomas.medRxiv : the preprint server for health sciences · 2025Article
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer.IEEE transactions on visualization and computer graphics · 2025Article
- Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining.IEEE transactions on visualization and computer graphics · 2024Article
- Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas.Oral oncology · 2023Article
- DASS Good: Explainable Data Mining of Spatial Cohort Data.Computer graphics forum : journal of the European Association for Computer Graphics · 2023Article
- Optimal Treatment Selection in Sequential Systemic and Locoregional Therapy of Oropharyngeal Squamous Carcinomas: Deep Q-Learning With a Patient-Physician Digital Twin Dyad.Journal of medical Internet research · 2022Article
- Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2021Article
- Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features.Scientific reports · 2021Article
- Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes.Proceedings. International Database Engineering and Applications Symposium · 2021Article
- Identifying Symptom Clusters Through Association Rule Mining.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2021Article
- Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology.IEEE Visualization Conference : VIS. IEEE Conference on Visualization · 2020Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Precision medicine seeks to tailor therapy to the individual patient, based on statistical correlates from patients who are similar to the one under consideration. These correlates can and should go beyond genetics, and in general, beyond tabular or array data that can be easily represented computationally and compared. For example, in many types of cancer, cancer treatment and toxicity depend in large measure on the spatial disease spread-e.g., metastasizes to regional lymph nodes in head and neck cancer. However, there is currently a lack of methodology for integrating spatial information when considering patient similarity. We present a novel modeling methodology for the comparison of cancer patients within a cohort, based on the spatial spread of the lymph nodes affected in each patient. The method uses a topological map, bigrams, and hierarchical clustering to group patients based on their similarity. We compare this approach against a nonspatial (categorical) similarity approach where patients are binned solely by their affected nodes. We present similarity results on a 582 head and neck cancer patient cohort, along with two visual abstractions for analysis of the results, and we present clinician feedback. Our novel methodology partitions a patient cohort into clinically meaningful groups more susceptible to treatment side-effects. Such spatially-aware similarity approaches can help maximize the effectiveness of each patient's treatment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.