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Record W4378576107 · doi:10.1016/j.adro.2023.101275

Unanticipated Radiation Replanning for Stage III Non-small Cell Lung Cancer

2023· article· en· W4378576107 on OpenAlexaff
Melinda Mushonga, Yee Ung, Alexander V. Louie, Patrick Cheung, Ian Poon, Liying Zhang, May Tsao

Bibliographic record

VenueAdvances in Radiation Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineLung cancerOdds ratioStage (stratigraphy)Logistic regressionRadiation therapyInternal medicineComputed tomographySurgeryNuclear medicine

Abstract

fetched live from OpenAlex

PurposeThe purpose of this study was to identify factors associated with unanticipated radiation therapy (RT) replanning in stage III non-small cell lung cancer (NSCLC).Methods and MaterialsPatients from a single institution with newly diagnosed stage III NSCLC treated with radical RT from January 1, 2016, to December 31, 2019, were retrospectively analyzed. The frequency and reasons for replanning were determined. Logistic regression analysis was used to identify factors associated with replanning.ResultsOf 144 patients included in this study, 11% (n = 16) required replanning after the start of RT. The reason for replanning in these 16 patients was changes in the target detected by cone beam computed tomography (shift in 10 patients, shrinkage in 5 patients, and growth in 1 patient). Larger planning target volume (primary and nodal) was statistically predictive of replanning (odds ratio, 2.5; 95% CI, 1.2-5.4; P = .02). The actuarial median overall survival was 33.3 months (95% CI, 10.3-43.9) for the 16 patients who were replanned and 36.3 months (95% CI, 27.4-66.5) for the remaining 128 patients (P = .96). The median time to local recurrence was 25.0 months (95% CI, 10.3-41.3) for those patients who underwent replanning, which was similar to those patients who did not undergo replanning (19.5 months; 95% CI, 11.8-23.2; P = .28).ConclusionsIn this study, 11% of patients treated with radical RT for NSCLC required replanning due to changes in the target detected by cone beam computed tomography. A larger planning target volume predicts a higher likelihood of requiring adaptive RT. Overall survival and local control were similar between patients who were replanned compared with those who were not replanned. The purpose of this study was to identify factors associated with unanticipated radiation therapy (RT) replanning in stage III non-small cell lung cancer (NSCLC). Patients from a single institution with newly diagnosed stage III NSCLC treated with radical RT from January 1, 2016, to December 31, 2019, were retrospectively analyzed. The frequency and reasons for replanning were determined. Logistic regression analysis was used to identify factors associated with replanning. Of 144 patients included in this study, 11% (n = 16) required replanning after the start of RT. The reason for replanning in these 16 patients was changes in the target detected by cone beam computed tomography (shift in 10 patients, shrinkage in 5 patients, and growth in 1 patient). Larger planning target volume (primary and nodal) was statistically predictive of replanning (odds ratio, 2.5; 95% CI, 1.2-5.4; P = .02). The actuarial median overall survival was 33.3 months (95% CI, 10.3-43.9) for the 16 patients who were replanned and 36.3 months (95% CI, 27.4-66.5) for the remaining 128 patients (P = .96). The median time to local recurrence was 25.0 months (95% CI, 10.3-41.3) for those patients who underwent replanning, which was similar to those patients who did not undergo replanning (19.5 months; 95% CI, 11.8-23.2; P = .28). In this study, 11% of patients treated with radical RT for NSCLC required replanning due to changes in the target detected by cone beam computed tomography. A larger planning target volume predicts a higher likelihood of requiring adaptive RT. Overall survival and local control were similar between patients who were replanned compared with those who were not replanned.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

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

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 head0.402
Teacher spread0.380 · 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 teacher head, 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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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