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Record W4386725777 · doi:10.1001/jamaoncol.2023.3495

Individualized Stereotactic Ablative Radiotherapy for Lung Tumors

2023· article· en· W4386725777 on OpenAlexaff
Michael F. Gensheimer, Harriet E. Gee, Hiroki Shirato, Hiroshi Taguchi, John M. Snyder, Alexander L. Chin, Lucas K. Vitzthum, Peter G. Maxim, Heather A. Wakelee, Joel W. Neal, Millie Das, Daniel T. Chang, Elizabeth Kidd, Steven Hancock, David Shultz, Kathleen C. Horst, Quynh‐Thu Le, Samantha Wong, Eleanor Brown, Ngan Nguyen, Rachel Liang, Billy W. Loo, Maximilian Diehn

Bibliographic record

VenueJAMA Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
FundersNational Cancer Institute
KeywordsMedicineSABR volatility modelLung cancerRadiation therapyRadiosurgeryAblative caseLungInternal medicineOncologyNuclear medicine

Abstract

fetched live from OpenAlex

Importance: Stereotactic ablative radiotherapy (SABR) is used for treating lung tumors but can cause toxic effects, including life-threatening damage to central structures. Retrospective data suggested that small tumors up to 10 cm3 in volume can be well controlled with a biologically effective dose less than 100 Gy. Objective: To assess whether individualizing lung SABR dose and fractionation by tumor size, location, and histological characteristics may be associated with local tumor control. Design, Setting, and Participants: This nonrandomized controlled trial (the iSABR trial, so named for individualized SABR) was a phase 2 multicenter trial enrolling participants from November 15, 2011, to December 5, 2018, at academic medical centers in the US and Japan. Data were analyzed from December 9, 2020, to May 10, 2023. Patients were enrolled in 3 groups according to cancer type: initial diagnosis of non-small cell lung cancer (NSCLC) with an American Joint Committee on Cancer 7th edition T1-3N0M0 tumor (group 1), a T1-3N0M0 new primary NSCLC with a history of prior NSCLC or multiple NSCLCs (group 2), or lung metastases from NSCLC or another solid tumor (group 3). Intervention: Up to 4 tumors were treated with once-daily SABR. The dose ranged from 25 Gy in 1 fraction for peripheral tumors with a volume of 0 to 10 cm3 to 60 Gy in 8 fractions for central tumors with a volume greater than 30 cm3. Main outcome: Per-group freedom from local recurrence (same-lobe recurrence) at 1 year, with censoring at time of distant recurrence, death, or loss to follow-up. Results: In total, 217 unique patients (median [IQR] age, 72 [64-80] years; 129 [59%] male; 150 [69%] current or former smokers) were enrolled (some multiple times). There were 240 treatment courses: 79 in group 1, 82 in group 2, and 79 in group 3. A total of 285 tumors (211 [74%] peripheral and 74 [26%] central) were treated. The most common dose was 25 Gy in 1 fraction (158 tumors). The median (range) follow-up period was 33 (2-109) months, and the median overall survival was 59 (95% CI, 49-82) months. Freedom from local recurrence at 1 year was 97% (90% CI, 91%-99%) for group 1, 94% (90% CI, 87%-97%) for group 2, and 96% (90% CI, 89%-98%) for group 3. Freedom from local recurrence at 5 years ranged from 83% to 93% in the 3 groups. The proportion of patients with grade 3 to 5 toxic effects was low, at 5% (including a single patient [1%] with grade 5 toxic effects). Conclusions and Relevance: The results of this nonrandomized controlled trial suggest that individualized SABR (iSABR) used to treat lung tumors may allow minimization of treatment dose and is associated with excellent local control. Individualized dosing should be considered for use in future trials. Trial Registration: ClinicalTrials.gov Identifier: NCT01463423.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.402

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 head0.034
GPT teacher head0.387
Teacher spread0.353 · 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 designNot applicable
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

Citations26
Published2023
Admission routes1
Has abstractyes

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