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

T Luciani; A Wentzel; B Elgohari; H Elhalawani; A Mohamed; G Canahuate; D.M Vock; C.D Fuller; G.E Marai

doi:10.1016/j.yjbinx.2020.100067

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

Journal article

Open access

Peer reviewed

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

T Luciani, A Wentzel, B Elgohari, H Elhalawani, A Mohamed, G Canahuate, D.M Vock, C.D Fuller and G.E Marai

Journal of biomedical informatics, Vol.112, pp.100067-100067

2020

DOI: 10.1016/j.yjbinx.2020.100067

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Abstract

[Display omitted] •Precision medicine tailors therapy to a specific patient, using similar patient data.•In many cancer types, treatment and toxicity depend on the spatial disease spread.•However, there is a need for methodology to integrate spatial information similarity.•We present a method for patient comparison, based on disease spread to lymph nodes.•Our method partitions a 582 patient cohort into clinically meaningful groups. 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.

Oncology

Topology

Precision medicine

Spatial similarity

Visual interface

Details

Title: Subtitle: A spatial neighborhood methodology for computing and analyzing lymph node carcinoma similarity in precision medicine
Creators: T Luciani - University of Illinois Chicago
A Wentzel - University of Illinois Chicago
B Elgohari - University of Texas MD Anderson Cancer Center
H Elhalawani - University of Texas MD Anderson Cancer Center
A Mohamed - MD Anderson Cancer Center, United States
G Canahuate - University of Iowa
D.M Vock - University of Minnesota
C.D Fuller - University of Texas MD Anderson Cancer Center
G.E Marai - University of Illinois Chicago
Resource Type: Journal article
Publication Details: Journal of biomedical informatics, Vol.112, pp.100067-100067
DOI: 10.1016/j.yjbinx.2020.100067
ISSN: 1532-0464
eISSN: 1532-0480
Publisher: Elsevier Inc
Grant note: DOI: 10.13039/100000002, name: National Institutes of Health, award: NCI-R01CA214825, NCI-R01CA2251, NLM-R01LM012527
Language: English
Date published: 2020
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984197451402771

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