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Record W3196597636 · doi:10.1016/j.radonc.2021.08.012

Radiation therapy related cardiac disease risk in childhood cancer survivors: Updated dosimetry analysis from the Childhood Cancer Survivor Study

2021· article· en· W3196597636 on OpenAlexaff
Suman Shrestha, James E. Bates, Qi Liu, Susan A. Smith, Kevin C. Oeffinger, Eric J. Chow, Aashish C. Gupta, Constance A. Owens, Louis S. Constine, Bradford S. Hoppe, Wendy M. Leisenring, Ying Qiao, Rita E. Weathers, Laurence E. Court, Chelsea C. Pinnix, Stephen F. Kry, Daniel A. Mulrooney, Gregory T. Armstrong, Yutaka Yasui, Rebecca M. Howell

Bibliographic record

VenueRadiotherapy and Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of Alberta
FundersNational Cancer Institute
KeywordsMedicineCommon Terminology Criteria for Adverse EventsCoronary artery diseaseRadiation therapyCumulative doseInternal medicineCohortRelative riskCancerCardiologyNuclear medicineConfidence interval

Abstract

fetched live from OpenAlex

Background and purpose We previously evaluated late cardiac disease in long-term survivors in the Childhood Cancer Survivor Study (CCSS) based on heart radiation therapy (RT) doses estimated from an age-scaled phantom with a simple atlas-based heart model (H Atlas ). We enhanced our phantom with a high-resolution CT-based anatomically realistic and validated age-scalable cardiac model (H Hybrid ). We aimed to evaluate how this update would impact our prior estimates of RT-related late cardiac disease risk in the CCSS cohort. Methods We evaluated 24,214 survivors from the CCSS diagnosed from 1970 to 1999. RT fields were reconstructed on an age-scaled phantom with H Hybrid and mean heart dose (D m ), percent volume receiving ≥ 20 Gy (V 20 ) and ≥ 5 Gy with V 20 = 0 ( V 5 , V 20 = 0 % ) were calculated. We reevaluated cumulative incidences and adjusted relative rates of grade 3–5 Common Terminology Criteria for Adverse Events outcomes for any cardiac disease, coronary artery disease (CAD), and heart failure (HF) in association with D m , V 20 , and V 5 , V 20 = 0 % (as categorical variables). Dose-response relationships were evaluated using piecewise-exponential models, adjusting for attained age, sex, cancer diagnosis age, race/ethnicity, time-dependent smoking history, diagnosis year, and chemotherapy exposure and doses. For relative rates, D m was also considered as a continuous variable. Results Consistent with previous findings with H Atlas , reevaluation using H Hybrid dosimetry found that, D m ≥ 10 Gy, V 20 ≥ 0.1%, and V 5 , V 20 = 0 % ≥ 50% were all associated with increased cumulative incidences and relative rates for any cardiac disease, CAD, and HF. While updated risk estimates were consistent with previous estimates overall without statistically significant changes, there were some important and significant ( P < 0.05) increases in risk with updated dosimetry for D m in the category of 20 to 29.9 Gy and V 20 in the category of 30% to 79.9%. When changes in the linear dose–response relationship for D m were assessed, the slopes of the dose response were steeper ( P < 0.001) with updated dosimetry. Changes were primarily observed among individuals with chest-directed RT with prescribed doses ≥ 20 Gy. Conclusion These findings present a methodological advancement in heart RT dosimetry with improved estimates of RT-related late cardiac disease risk. While results are broadly consistent with our prior study, we report that, with updated cardiac dosimetry, risks of cardiac disease are significantly higher in two dose and volume categories and slopes of the Dm-specific RT-response relationships are steeper. These data support the use of contemporary RT to achieve lower heart doses for pediatric patients, particularly those requiring chest-directed RT.

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)0.001
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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.324
Teacher spread0.310 · 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".

Quick stats

Citations33
Published2021
Admission routes1
Has abstractno

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