Evidence map›Paper›PMID 39622922›Full record

ArticleScientific reports2024

Interpretable survival network for progression risk analysis of multimodality imaging biomarkers in poor-prognosis head and neck cancers.

Lise Wei, Madhava P Aryal, Choonik Lee, Jennifer L Shah, Michelle L Mierzwa, Yue Cao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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

6 authors.

Lise WeiDepartment of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA. liswei@med.umich.edu.
Madhava P AryalDepartment of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
Choonik LeeDepartment of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
Jennifer L ShahDepartment of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
Michelle L MierzwaDepartment of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
Yue CaoDepartment of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.

Funding

Functional imaging-directed adaptive therapy of head and neck cancerR01CA184153 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CAO, YUE · 2015 to 2019
$2.4M
Quantitative MRI models of HN Cancers for Physiological Adaption of RTU01CA183848 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CAO, YUE · 2014 to 2019
$2.0M
NCI NIH HHS R01 CA184153NCI NIH HHS U01 CA183848NIH/NCI RO1CA184153
6 · The paper itself

Abstract

This study explores the predictive utility of multi-time point, multi-modality quantitative imaging biomarkers (QIBs) and clinical factors in patients with poor-prognosis head and neck cancers (HNCs) using interpretable machine learning. We examined 93 patients with p16 + oropharyngeal squamous cell carcinoma or locally advanced p16- HNCs enrolled in a phase II adaptive radiation dose escalation trial. FDG-PET and multiparametric MRI scans were conducted before radiation therapy and at the 10th fraction (2 weeks). A survival network analyzed MRI and PET-derived biomarkers such as gross tumor volume (GTV), blood volume (BV), and metabolic tumor volume (MTV

Indexed as

Biomarkers, TumorHead and Neck NeoplasmsMultimodal ImagingAdultAgedCyclin-Dependent Kinase Inhibitor p16Disease ProgressionFemaleHumansMachine LearningMagnetic Resonance ImagingMaleMiddle AgedPositron-Emission TomographyPrognosisRisk AssessmentBiomarkers, TumorCyclin-Dependent Kinase Inhibitor p16Functional imaging biomarkerHNCsInterpretable machine learning

Identifiers

PMID39622922
PMCPMC11612283

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Registered trials

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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.