THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy

Floricel, Carla; Nipu, Nafiul; Biggs, Mikayla; Wentzel, Andrew; Canahuate, Guadalupe; Dijk, Lisanne V. van; Mohamed, A.S.R.; Fuller, Clifton D.; Marai, G. Elisabeta

doi:10.1109/tvcg.2021.3114810

Cited by 22 publications

(5 citation statements)

References 81 publications

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“…Finally, we plan on incorporating additional information that may provide additional insight into patient risks, such as tumor location and bilaterality. Finally, other work may investigate correlating doses to more complicated patterns of symptom progression rather than simply considering late severe symptoms, such as those being investigated in other works such as ( 35 ).…”

Section: Discussionmentioning

confidence: 99%

Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool

et al. 2023

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PurposeIdentify Oropharyngeal cancer (OPC) patients at high-risk of developing long-term severe radiation-associated symptoms using dose volume histograms for organs-at-risk, via unsupervised clustering.Material and methodsAll patients were treated using radiation therapy for OPC. Dose-volume histograms of organs-at-risk were extracted from patients’ treatment plans. Symptom ratings were collected via the MD Anderson Symptom Inventory (MDASI) given weekly during, and 6 months post-treatment. Drymouth, trouble swallowing, mucus, and vocal dysfunction were selected for analysis in this study. Patient stratifications were obtained by applying Bayesian Mixture Models with three components to patient’s dose histograms for relevant organs. The clusters with the highest total mean doses were translated into dose thresholds using rule mining. Patient stratifications were compared against Tumor staging information using multivariate likelihood ratio tests. Model performance for prediction of moderate/severe symptoms at 6 months was compared against normal tissue complication probability (NTCP) models using cross-validation.ResultsA total of 349 patients were included for long-term symptom prediction. High-risk clusters were significantly correlated with outcomes for severe late drymouth (p <.0001, OR = 2.94), swallow (p = .002, OR = 5.13), mucus (p = .001, OR = 3.18), and voice (p = .009, OR = 8.99). Simplified clusters were also correlated with late severe symptoms for drymouth (p <.001, OR = 2.77), swallow (p = .01, OR = 3.63), mucus (p = .01, OR = 2.37), and voice (p <.001, OR = 19.75). Proposed cluster stratifications show better performance than NTCP models for severe drymouth (AUC.598 vs.559, MCC.143 vs.062), swallow (AUC.631 vs.561, MCC.20 vs -.030), mucus (AUC.596 vs.492, MCC.164 vs -.041), and voice (AUC.681 vs.555, MCC.181 vs -.019). Simplified dose thresholds also show better performance than baseline models for predicting late severe ratings for all symptoms.ConclusionOur results show that leveraging the 3-D dose histograms from radiation therapy plan improves stratification of patients according to their risk of experiencing long-term severe radiation associated symptoms, beyond existing NTPC models. Our rule-based method can approximate our stratifications with minimal loss of accuracy and can proactively identify risk factors for radiation-associated toxicity.

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Section: Discussionmentioning

confidence: 99%

Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool

et al. 2023

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“…Integrating test images in the system (e.g., Resonance, Xray, and Tomography tests) would greatly help in validating diagnostic hypotheses and comparing the development of abnormal findings (e.g., damage control of aspiration pneumonia). However, EHRs usually contain primarily information relevant for billing purposes that do not reflect the patient's well-being, whether the patient has agreed to specific treatments or not, and many other aspects that are relevant for treatment decisions, such as image data [6,15]. Because of that, this case was out of the scope of this work.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

ClinicalPath: a Visualization tool to Improve the Evaluation of Electronic Health Records in Clinical Decision-Making

Linhares,

Lima,

Ponciano

et al. 2022

Preprint

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Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily procedure, especially in developing countries. Electronic Health Records systems have been designed to keep the patients' history and reduce the time spent analyzing the patient's data. However, better tools to support decision-making are still needed. In this paper, we propose ClinicalPath, a visualization tool for users to track a patient's clinical path through a series of tests and data, which can aid in treatments and diagnoses. Our proposal is focused on patient's data analysis, presenting the test results and clinical history longitudinally. Both the visualization design and the system functionality were developed in close collaboration with experts in the medical domain to ensure a right fit of the technical solutions and the real needs of the professionals. We validated the proposed visualization based on case studies and user assessments through tasks based on the physician's daily activities. Our results show that our proposed system improves the physicians' experience in decision-making tasks, made with more confidence and better usage of the physicians' time, allowing them to take other needed care for the patients.

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“…Many systems use clustering [MV15] and dimensionality reduction [ODH * 07, ENBD08] on key features to guide explorations over high‐dimensionality data. Some tools have looked at visual analytics for creating clusters with unstructured health data [KEV * 17, CD18, GNDV * 17], while other systems incorporate temporal clustering methods [ZMP * 21, ZMW * 20a, FNB * 21, WMH * 21,GXZ * 17]. However, these systems do not attempt to incorporate spatial information in their clustering models, as we do.…”

Section: Related Workmentioning

confidence: 99%

DASS Good: Explainable Data Mining of Spatial Cohort Data

Wentzel

Floricel

Canahuate

et al. 2023

Computer Graphics Forum

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Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co‐design of a modeling system, DASS, to support the hybrid human‐machine development and validation of predictive models for estimating long‐term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human‐in‐the‐loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.

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THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy

Cited by 22 publications

References 81 publications

Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool

Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool

ClinicalPath: a Visualization tool to Improve the Evaluation of Electronic Health Records in Clinical Decision-Making

DASS Good: Explainable Data Mining of Spatial Cohort Data

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