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Record W3094442651 · doi:10.1016/j.ijrobp.2020.07.2583

Whole-Body Radiomics for Prediction of Treatment Failure in Cervical Cancer

2020· article· en· W3094442651 on OpenAlexaboutno aff
Tahir Yusufaly, Jingjing Zou, Tyler J. Nelson, Meenakshi Singhal, H. Wong, Casey W. Williamson, Cheryl Saenz, Jyoti Mayadev, Michael McHale, Catheryn M. Yashar, Ramez N. Eskander, Loren K. Mell

Bibliographic record

VenueInternational Journal of Radiation Oncology*Biology*Physics · 2020
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChemoradiotherapyConfidence intervalCervical cancerRadiomicsRadiation treatment planningNuclear medicineRadiologyRadiation therapyCancerInternal medicine

Abstract

fetched live from OpenAlex

Purpose/Objective(s): Over the past decade increasing access to high quality imaging modalities has allowed for improved conformality in cervical cancer (CC) brachytherapy (BT) planning, especially in the utilization of interstitial brachytherapy (ISBT).The purpose of this study was to document current ISBT practices across Canada, and compare them with practices from 4 years ago.Materials/Methods: All Canadian centers with gynecologic BT (n Z 38) were identified, and one gynecology radiation oncologist per center was sent a 64-item e-mail questionnaire regarding the center's practice for CC patients and ISBT.Aggregate responses regarding utilization of ISBT are reported and compared with practice patterns identified in our 2015 survey.Descriptive statistics were used to summarize the data and Fisher's exact test was used for comparison.Results: 37 of 38 center respondents completed the survey (RR: 97.4%).MRI has been incorporated into treatment of CC by 100% and 68% of respondents, prior to EBRT and BT, respectively.In 2019, a significantly higher proportion of respondents indicated they offered ISBT to radical CC patients compared to 2015, p Z 0.023.In the 2019, 22/37 (59.4%) respondents reported their center has ability to perform ISBT, compared to 14/28 (50%) in 2015 (p Z 0.121).For selecting patients for ISBT, 74% of respondents in 2019 indicated that they used pre-EBRT and pre-BT imaging compared 48% to 2015, p Z 0.05.There was an increase in the use of hybrid applicators: 5/14 (36%) centers with availability of ISBT and 5/ 28 (18%) of centers overall in 2015 versus 17/22 (77%) centers with availability of ISBT and 17/37 (46%) of centers overall in 2019, p Z 0.02 and p Z 0.03, respectively.Perineal template-based (which includes Venezia applicator) also increased slightly over this time: 71% of ISBT centers and 36% of overall centers in 2015 versus 73% of ISBT centers and 43% of centers overall in 2019, p>0.05.Of the respondents who use hybrid applicators, most (78%) utilized 3-4 interstitial needles, while those who used perineal template 61.5% implanted on average 10-15 needles.Most (56%) respondents used general anesthesia for performing ISBT, 31% used epidural and 14% used spinal anesthesia.For determining needle positioning, the majority (60%) of respondents used intra-operative transabdominal or trans-rectal ultrasound; pre-operative imaging was the second most common modality; laparoscopic guidance was used by 1 respondent.48% of respondents perform ISBT as an outpatient procedure, with an average of 3 (range 2-4) fractions delivered over multiple implants.Conclusion: In Canada, there is general trend towards increased utilization of interstitial brachytherapy for treatment of cervical patients.Increased utilization of imaging modalities such as MRI and availability of hybrid applicators are potential contributors for this upwards trend.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.354

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.344
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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