Evidence map›Paper›PMID 33235811›Full record

ArticleFuture science OA2020

Quantitative ultrasound delta-radiomics during radiotherapy for monitoring treatment responses in head and neck malignancies.

William T Tran, Harini Suraweera, Karina Quiaoit, Daniel DiCenzo, Kashuf Fatima, Deok Jang, Divya Bhardwaj, Christopher Kolios, Irene Karam, Ian Poon and 5 more

Abstract read
In one paragraph

Article in Future science OA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Delta radiomics: a systematic review.La Radiologia medica · 2021
    Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024
    Review
  6. Review
  7. Observational
  8. Article
  9. Review
  10. Review
  11. Current Role of Delta Radiomics in Head and Neck Oncology.International journal of molecular sciences · 2023
    Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Article
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

15 authors.

William T TranDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0002-0733-7234
Harini SuraweeraDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0001-5676-5203
Karina QuiaoitDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0001-5676-5203
Daniel DiCenzoDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0003-4721-9627
Kashuf FatimaDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0002-4466-9721
Deok JangDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0002-4282-2470
Divya BhardwajDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0002-9162-2213
Christopher KoliosDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0003-0590-890X
Irene KaramDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0002-6276-3535
Ian PoonDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0002-8508-9280
Lakshmanan SannachiDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0002-9775-0224
Mehrdad GangehDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0002-2907-3014
Ali Sadeghi-NainiDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0001-5055-339X
Archya DasguptaDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0001-8341-2622
Gregory J CzarnotaDepartment of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto M4N 3M5, Canada.ORCID https://orcid.org/0000-0002-9973-4586

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimWe investigated quantitative ultrasound (QUS) in patients with node-positive head and neck malignancies for monitoring responses to radical radiotherapy (RT). MATERIALS &

methodsQUS spectral and texture parameters were acquired from metastatic lymph nodes 24 h, 1 and 4 weeks after starting RT. K-nearest neighbor and naive-Bayes machine-learning classifiers were used to build prediction models for each time point. Response was detected after 3 months of RT, and patients were classified into complete and partial responders.

resultsSingle-feature naive-Bayes classification performed best with a prediction accuracy of 80, 86 and 85% at 24 h, week 1 and 4, respectively.

conclusionQUS-radiomics can predict RT response at 3 months as early as 24 h with reasonable accuracy, which further improves into 1 week of treatment.

Indexed as

biomarkerdelta-radiomicshead and neck cancerimagingmachine Learningquantitative ultrasoundradiomicsradiotherapyresponsetexture

Identifiers

PMID33235811
PMCPMC7668124

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.