Evidence map›Paper›PMID 34200332›Full record

ArticleDiagnostics (Basel, Switzerland)2021

Predicting the Local Response of Esophageal Squamous Cell Carcinoma to Neoadjuvant Chemoradiotherapy by Radiomics with a Machine Learning Method Using

Yuji Murakami, Daisuke Kawahara, Shigeyuki Tani, Katsumaro Kubo, Tsuyoshi Katsuta, Nobuki Imano, Yuki Takeuchi, Ikuno Nishibuchi, Akito Saito, Yasushi Nagata

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
2.4field-weighted citation impact, top 11% of its field
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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.

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  7. Radiomics based onFrontiers in oncology · 2024
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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

10 authors at 1 institution in 1 country.

Yuji MurakamiDepartment of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Daisuke KawaharaDepartment of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Shigeyuki TaniSchool of Medicine, Hiroshima University, Hiroshima 734-8551, Japan.
Katsumaro KuboDepartment of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Tsuyoshi KatsutaDepartment of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Nobuki ImanoDepartment of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Yuki TakeuchiDepartment of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Ikuno NishibuchiDepartment of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Akito SaitoDepartment of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Yasushi NagataDepartment of Radiation Oncology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan.
Hiroshima University · JP

Funding

Japan Society for the Promotion of Science JP19K08153Varian Research Grant Not applicable
6 · The paper itself

Abstract

backgroundThis study aimed to propose a machine learning model to predict the local response of resectable locally advanced esophageal squamous cell carcinoma (LA-ESCC) treated by neoadjuvant chemoradiotherapy (NCRT) using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) images.

methodsThe local responses of 98 patients were categorized into two groups (complete response and noncomplete response). We performed a radiomics analysis using five segmentations created on FDG PET images, resulting in 4250 features per patient. To construct a machine learning model, we used the least absolute shrinkage and selection operator (LASSO) regression to extract radiomics features optimal for the prediction. Then, a prediction model was constructed by using a neural network classifier. The training model was evaluated with 5-fold cross-validation.

resultsBy the LASSO analysis of the training data, 22 radiomics features were extracted. In the testing data, the average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve score of the five prediction models were 89.6%, 92.7%, 89.5%, and 0.95, respectively.

conclusionsThe proposed machine learning model using radiomics showed promising predictive accuracy of the local response of LA-ESCC treated by NCRT.

Indexed as

esophageal cancermachine learningneoadjuvant chemoradiotherapypathological responseradiomicssquamous cell carcinoma

Identifiers

PMID34200332
PMCPMC8227132
OpenAlexW3169124713

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.