Evidence map›Paper›PMID 36469245›Full record

ArticlePhysical and engineering sciences in medicine2023

Recurrence prediction with local binary pattern-based dosiomics in patients with head and neck squamous cell carcinoma.

Hidemi Kamezawa, Hidetaka Arimura

Abstract read
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In one paragraph

Article in Physical and engineering sciences in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

2 authors.

Hidemi KamezawaDepartment of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, 6-22 Misaki-machi, Omuta-City, Fukuoka, 836-8505, Japan. kamezawa@fmt.teikyo-u.ac.jp.ORCID http://orcid.org/0000-0002-8163-9230
Hidetaka ArimuraDivision of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.

Funding

Japan Society for the Promotion of Science JP22K15808Teikyo University 2100007
6 · The paper itself

Abstract

We investigated an approach for predicting recurrence after radiation therapy using local binary pattern (LBP)-based dosiomics in patients with head and neck squamous cell carcinoma (HNSCC). Recurrence/non-recurrence data were collected from 131 patients after intensity-modulated radiation therapy. The cases were divided into training (80%) and test (20%) datasets. A total of 327 dosiomics features, including cold spot volume, first-order features, and texture features, were extracted from the original dose distribution (ODD) and LBP on gross tumor volume, clinical target volume, and planning target volume. The CoxNet algorithm was employed in the training dataset for feature selection and dosiomics signature construction. Based on a dosiomics score (DS)-based Cox proportional hazard model, two recurrence prediction models (DS

Indexed as

Head and Neck NeoplasmsRadiotherapy, Intensity-ModulatedHumansProportional Hazards ModelsROC CurveSquamous Cell Carcinoma of Head and NeckDosiomicsHead and neck carcinomaLocal binary patternRecurrence prediction

Identifiers

PMID36469245

What OpenQuestion holds

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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.