Evidence map›Paper›PMID 39371132›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Evaluating Observer Reliability and Diagnostic Accuracy of CT-LEFAT Criteria for Post-Treatment Head and Neck Lymphedema: A Prospective Blinded Comparative Analysis of Oncologist Human Inter-Rater Performance.

MD Anderson Head and Neck Cancer Symptom Working Group

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

1 author.

MD Anderson Head and Neck Cancer Symptom Working Group

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Paul Calabresi Clinical Oncology AwardK12CA088084 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI David S. Hong, Scott Kopetz · 2000 to 2026
$14.0M
Development of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac deviceR01DE028290 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI CHRISTODOULEAS, JOHN PAUL, FULLER, CLIFTON DAVID · 2019 to 2023
$4.3M
NRSA Training CoreTL1TR003169 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHANDRA, JOYA, FROST, JEFFREY A. · 2019 to 2023
$3.7M
Longitudinal Spatial-Nonspatial Decision Support for Competing Outcomes in Head and Neck Cancer TherapyR01CA258827 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI CANAHUATE, GUADALUPE, FULLER, CLIFTON DAVID · 2021 to 2025
$2.9M
Provider and Patient-generated Remote Oro-Dental Health Electronic Data Capture for Algorithmic Longitudinal Evaluation and Risk-Assessment (PROHEALER)K01DE030524 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI MORENO, AMY CATHERINE · 2022 to 2024
$479k
Radiation-specific Automated Dental Dose Distributions via Machine-learning based Mapping for Accurate Predictions of (Peri)odontal Problems (RADMAP)R21DE031082 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI MORENO, AMY CATHERINE · 2021 to 2022
$452k
NCATS NIH HHS TL1 TR003169NCI NIH HHS K12 CA088084NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA258827NIDCR NIH HHS K01 DE030524NIDCR NIH HHS R01 DE028290NIDCR NIH HHS R21 DE031082
6 · The paper itself

Abstract

Background: Radiation-associated lymphedema and fibrosis (LEF) is a significant toxicity following radiation therapy (RT) for head and neck cancer (HNC) patients. Recently, the CT Lymphedema and Fibrosis Assessment Tool (CT-LEFAT) was developed to standardize LEF diagnosis through fat stranding visualized on CT. This study aims to evaluate the inter-observer reliability and diagnostic accuracy of the CT-LEFAT criteria. Materials and Methods: This study retrospectively evaluated 26 HNC patients treated with RT that received a minimum of two contrast-enhanced CT scans. Qualitative review was conducted by five physician raters to assess the fat stranding observed on CT according to the CT-LEFAT criteria. Fleiss' kappa analysis was used to assess the inter- and intra-rater reliability, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) analysis was used to evaluate diagnostic accuracy. Results: The inter-rater reliability across the six CT-LEFAT regions generally indicated a slight to fair agreement across all raters (0.04 ≤ kappa ≤ 0.36). Intra-observer agreement was generally fair to moderate (overall kappa=0.44). The ROC AUC analysis varied based on aggregation method used (0.60 ≤ average AUC ≤ 0.70). Conclusion: This specific use-case evaluating CT-LEFAT criteria displays limited performance. This suggests that additional materials, such as further training, refinement of imaging methods, or other processes may be required before achieving clinically-ready diagnostic performance of LEF diagnosis.

Identifiers

PMID39371132
PMCPMC11451822

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