Evidence map›Paper›PMID 41726464›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Variogram Modeling of Spatially Variant Early Response to Therapy in Advanced Non-Small Cell Lung Cancer.

Faisal Yaseen, MHI, Daniel S Hippe, Parth V Soni, Shouyi Wang, Chunyan Duan, John H Gennari, Stephen R Bowen

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Faisal YaseenUniversity of Washington, Seattle, WA, USA.
MHI
Daniel S HippeFred Hutchinson Cancer Center, Seattle, WA, USA.
Parth V SoniUniversity of Texas, Arlington, TX, USA.
Shouyi WangUniversity of Texas, Arlington, TX, USA.
Chunyan DuanTongji University, Shanghai, China.
John H GennariUniversity of Washington, Seattle, WA, USA.
Stephen R BowenUniversity of Washington, Seattle, WA, USA.

Funding

Biomarkers of Response to Immuno-chemotherapy & oliGometastatic Hypofractionated radioTherapy (BRIGHT) for Lung Cancer: Synergy of PET/CT Imaging and Peripheral Blood AssaysR01CA258997 · NCI · UNIVERSITY OF WASHINGTON · PI Stephen R. Bowen, Jing Zeng · 2022 to 2026
$2.8M
Personalized radiation therapy through functional lung avoidance and response-adaptive dose escalation: utilizing multimodal molecular imaging to improve the therapeutic ratioR01CA204301 · NCI · UNIVERSITY OF WASHINGTON · PI BOWEN, STEPHEN R., ZENG, JING · 2016 to 2020
$2.3M
NCI NIH HHS R01 CA204301NCI NIH HHS R01 CA258997
6 · The paper itself

Abstract

Predicting heterogeneity in treatment response for non-small cell lung cancer (NSCLC) at multiple scales, both between patients and spatially within each patient, can support clinical decisions that personalize oncologic management. In this study, we evaluated different variogram models of voxel-level spatial correlation in tumor response in locally advanced NSCLC and metastatic NSCLC from two different clinical trials. The Stable model achieved the lowest root mean squared error (RMSE) on average (mean: 5.2-5.5%), followed by the Matérn model (mean: 5.8-7.4%), both of which performed better than most other models. In contrast, the Exponential model had the highest RMSE (mean: 9.4-15.6%). These results remained consistent across two different cohorts of NSCLC. Given the robust performance of the Stable model, it may generalize for modeling spatial response in other clinical settings beyond NSCLC and should be further studied.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsHumansTreatment Effect Heterogeneity

Identifiers

PMID41726464
PMCPMC12919420

What OpenQuestion holds

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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.